Milwaukee Bucks: NBA Team Profile - Squad, Achievements & Stats

Overview / Introduction about the Milwaukee Bucks

The Milwaukee Bucks are a professional basketball team based in Milwaukee, Wisconsin. They compete in the National Basketball Association (NBA) as part of the Eastern Conference’s Central Division. Founded in 1968, the team has become a staple of American sports culture, known for its passionate fanbase and rich history.

Team History and Achievements

The Milwaukee Bucks have a storied history with several key achievements. They won their first NBA Championship in 1971, led by iconic player Giannis Antetokounmpo. The team has consistently been competitive, making multiple playoff appearances over the years. Notable seasons include their back-to-back conference finals appearances from 2018 to 2020.

Current Squad and Key Players

The current squad features star players like Giannis Antetokounmpo, who plays as a forward/center, and Jrue Holiday at point guard. These players are pivotal to the team’s success, with Giannis often leading in scoring and rebounding statistics.

Team Playing Style and Tactics

The Bucks are known for their fast-paced offense and strong defensive strategies. They typically employ a small-ball lineup that emphasizes speed and agility. Strengths include their three-point shooting and transition play, while weaknesses may involve interior defense against taller opponents.

Interesting Facts and Unique Traits

Nicknamed “The Cream City Cougars,” the Bucks boast one of the most dedicated fanbases in the league. Rivalries with teams like the Chicago Bulls add excitement to their matchups. Traditions such as “Thirsty Thursdays” enhance fan engagement during home games.

Lists & Rankings of Players, Stats, or Performance Metrics

• Giannis Antetokounmpo: ✅ MVP candidate 🎰 Top scorer 💡 Defensive anchor
• Jrue Holiday: ✅ Playmaker ❌ Inconsistent shooting 💡 Clutch performer
• Khris Middleton: ✅ Three-point specialist 🎰 Versatile forward 💡 Playoff performer

Comparisons with Other Teams in the League or Division

The Bucks often compare favorably against division rivals like the Chicago Bulls due to their dynamic offense and defensive prowess. Their ability to adapt tactics makes them formidable opponents throughout the league.

Case Studies or Notable Matches

A breakthrough game was their victory over the Los Angeles Lakers in 2020, which showcased Giannis’ dominance on both ends of the court. This match highlighted their potential to compete for championships.

Tables Summarizing Team Stats, Recent Form, Head-to-Head Records, or Odds

Tips & Recommendations for Analyzing the Team or Betting Insights

• Analyze player form: Track Giannis’ performance metrics closely as they significantly impact game outcomes.
• Evaluate matchups: Consider how well-suited their playing style is against upcoming opponents.
• Leverage odds: Look for value bets when odds reflect recent improvements in team form.</li
  <>: Hi there! I’m working on implementing an LSTM cell from scratch using TensorFlow operations, but I’m running into some issues that I can’t quite figure out.

  Here’s what I have so far:

  python
  def lstm_cell(self, x, h_tup, use_peephole=False):
  h_prev, c_prev = h_tup
  # get shape hints here
  x_shape = self.shape(x)
  if use_peephole:
  raise ValueError(“Peephole connections are not implemented.”)

  i_t = tf.matmul(x, self.W_xi) + tf.matmul(h_prev, self.W_hi) + self.b_i
  f_t = tf.matmul(x, self.W_xf) + tf.matmul(h_prev, self.W_hf) + self.b_f
  o_t = tf.matmul(x, self.W_xo) + tf.matmul(h_prev, self.W_ho) + self.b_o
  g_t = tf.matmul(x, self.W_xg) + tf.matmul(h_prev, self.W_hg) + self.b_g

  i_t = tf.nn.sigmoid(i_t)

  # Bug introduced here: should be f_t before applying sigmoid
  g_t = tf.nn.sigmoid(g_t)

  f_t = tf.nn.sigmoid(f_t)

  o_t = tf.nn.sigmoid(o_t)

  c_new = c_prev * f_t + i_t * g_t

  # if use_peephole:
  # w_c_diag = train_utils.add_diag_weight(
  # w=self.w_c_diag,
  # v=tf.reshape(c_prev, [-1]),
  # is_training=self._is_training,
  # use_dropout=self.use_dropout,
  # initializer=tf.random_uniform_initializer(-self.init_scale ** -0.5,
  # self.init_scale ** -0.5))
  #
  # f_t += w_c_diag

  And here’s the error traceback I’m getting:

  Traceback (most recent call last):
  File “lstm_test.py”, line 45, in test_lstm_cell
  output = model.lstm_cell(x_input, h_initial)
  File “lstm_model.py”, line 23, in lstm_cell
  c_new = c_prev * f_t + i_t * g_t
  InvalidArgumentError: Incompatible shapes: [batch_size x hidden_size] vs [batch_size x hidden_size] [Op:AddV2]

  I think there’s something wrong with how I’m handling `g_t` before computing `c_new`, but I’m not entirely sure what it is or why it’s causing this shape mismatch error.

  Could someone help me understand what’s going wrong here? Thanks!
  <>: python
  It looks like you’ve identified part of the issue already—`g_t` should be passed through a `tanh` activation function instead of `sigmoid`. This is because `g_t` represents the candidate cell state (`tilde{c}_t`) which should be modulated by `tanh`. Here’s how you can fix it:

  python
  g_t = tf.tanh(g_t)

  Regarding the shape mismatch error you’re encountering with `c_new`, ensure that all your weight matrices (`W_xi`, `W_hi`, etc.) have compatible dimensions with your input `x` and previous hidden state `h_prev`. Specifically:

  – `W_xi`, `W_hi`, etc., should have dimensions `[input_dim x hidden_size]`.
  – Ensure that biases (`b_i`, `b_f`, etc.) have dimensions `[hidden_size]`.

  Double-check these dimensions to ensure they align correctly when performing matrix multiplications.

  After making these adjustments, try running your code again to see if it resolves the issue. 😊

  <>: Got it! I’ll switch `g_t` to use `tanh`. Now about those dimension checks—I’m not entirely sure how to verify if my weight matrices are set up correctly. Should I print out their shapes during initialization? Also, could there be an issue with how I’m reshaping tensors elsewhere in my code?
  <>: Sure! Printing out the shapes during initialization is a great way to verify that everything is set up correctly. You can do this by adding print statements like:

  python
  print(“Shape of W_xi:”, self.W_xi.shape)
  print(“Shape of W_hi:”, self.W_hi.shape)
  # Add similar lines for other weight matrices and biases.

  This will help you confirm that each matrix has dimensions `[input_dim x hidden_size]` for weights and `[hidden_size]` for biases.

  Regarding reshaping tensors elsewhere in your code—ensure that any reshaping operations maintain consistent batch sizes across all tensors involved in computations within your LSTM cell.

  If you find any mismatches during these checks or reshaping issues elsewhere in your code causing unexpected behavior or errors related to tensor shapes during runtime operations like matrix multiplication or addition.

  Let me know if you need further assistance! 😊
  <>: Alright, I’ll add those print statements to check the shapes during initialization. If there’s a mismatch or reshaping issue elsewhere in my code affecting tensor operations within the LSTM cell—how should I go about identifying where exactly things might be going wrong? Any specific debugging tips or tools you recommend?
  <>: To identify where things might be going wrong with tensor operations:

  1. **Use Debugging Tools**: Utilize TensorFlow’s built-in debugging tools like TensorBoard Profiler to visualize operations and track down inefficiencies or errors.

  2. **Check Tensor Shapes**: Consistently print tensor shapes at different stages within your LSTM cell using `tf.shape()` or `.shape` attributes after each operation.

  3. **Assertions**: Use assertions to enforce expected shapes at critical points:
  python
  assert x.shape[1] == expected_input_dim
  assert h_prev.shape[1] == expected_hidden_size

  4. **Step-by-Step Execution**: Run parts of your code independently to isolate sections where errors occur.

  5. **Error Messages**: Pay close attention to TensorFlow error messages—they often provide clues about where mismatches occur.

  6. **Simplify**: Temporarily simplify your model by reducing dimensions or removing components to isolate issues.

  By systematically checking these areas, you can pinpoint where mismatches occur more easily.
  <>: Great tips! I’ll start by adding assertions at key points to enforce expected shapes throughout my LSTM cell operations. Here’s what I’m thinking:

  python
  assert x.shape[1] == expected_input_dim
  assert h_prev.shape[1] == expected_hidden_size

  # After each operation:
  assert i_t.shape == (batch_size,), ‘Mismatch in input gate shape’
  assert f_t.shape == (batch_size,), ‘Mismatch in forget gate shape’
  assert o_t.shape == (batch_size,), ‘Mismatch in output gate shape’
  assert g_t.shape == (batch_size,), ‘Mismatch in candidate cell state shape’

  Do these look good? Also, any specific TensorFlow debugging tools you recommend for visualizing these operations?
  <>: Your assertions look good! Just make sure that `(batch_size,)` matches your actual batch size dimension; it might need adjustment depending on whether you’re using batches properly throughout your code.

  For TensorFlow debugging tools:

  – **TensorBoard**: Use TensorBoard for visualizing model graphs and monitoring training metrics.

  – **TensorFlow Debugger (`tfdbg`) Module**: This helps trace execution paths and inspect values at runtime.

  – **Eager Execution Mode**: If possible run your model in eager execution mode (`tf.config.run_functions_eagerly(True)`), which allows immediate inspection of values without needing sessions.

  These tools will help you gain insights into how tensors flow through your model and identify any discrepancies early on.
  <>: Awesome! Now that I’ve got those assertions set up along with TensorBoard visualization ready to go—I’m thinking about adding some logging functionality within my LSTM cell operations to track values at each step dynamically during execution time. This way I can monitor changes without stopping execution every time an assertion fails. Any suggestions on how best to implement this logging efficiently?
  <>: You can implement efficient logging by using Python’s built-in logging module combined with TensorFlow’s capabilities:

  1. **Set Up Logging**:
  python
  import logging

  logging.basicConfig(level=logging.INFO)

  logger = logging.getLogger(__name__)

  2. **Log Values Dynamically**:
  Add logging statements after key operations within your LSTM cell:
  python
  logger.info(f”Input gate value at step {step}: {i_t.numpy()}”)
  logger.info(f”Forget gate value at step {step}: {f_t.numpy()}”)
  logger.info(f”Output gate value at step {step}: {o_t.numpy()}”)
  logger.info(f”Candidate cell state value at step {step}: {g_t.numpy()}”)

  3. **Use Eager Execution**:
  Ensure eager execution is enabled so you can access `.numpy()` directly on tensors:
  python
  import tensorflow as tf

  tf.config.run_functions_eagerly(True)

  4. **Control Verbosity**:
  Adjust logging levels (`DEBUG`, `INFO`, etc.) based on how much detail you need without overwhelming output.

  This approach allows real-time monitoring while keeping performance overhead minimal.
  <>: Perfect! Setting up logging this way should give me a clearer picture of what’s happening inside my LSTM cell without interrupting execution too much. Thanks for all the help—I’m feeling much more confident about tackling this now!
  <>: You’re welcome! I’m glad you feel more confident now. With these tools and strategies in place, tracking down issues should become much easier and more efficient. If any new questions come up as you continue working on your LSTM implementation, feel free to reach out. Happy coding! 🚀<>: Hey there,

  I’ve been trying to work on this function called `_pack_data_into_batches`. It’s supposed to take data samples grouped by language IDs into batches based on various constraints like maximum tokens per batch (`max_tokens`) and maximum sentences per batch (`max_sentences`). However something seems off when it runs—it doesn’t behave as expected when dealing with different languages together!

  Here’s what my buggy version looks like right now:

  python
  def _pack_data_into_batches(self,
  src_ids_list,
  tgt_ids_list,
  lang_ids_list):
  “””Pack data into batches.”””
  batch_creator = TokenBatchCreator(
  self._padding_id,
  max_tokens=self._max_tokens_in_batch,
  max_sentences=self._max_sentences_in_batch)
  batching_result = []
  for src_ids_per_lang_, tgt_ids_per_lang_, lang_ids_per_lang_
  in zip(src_ids_list[:], tgt_ids_list[:], lang_ids_list[:]):
  len_src_in_batch,
  len_tgt_in_batch,
  data_indices_in_batch,
  src_data_in_batch,
  tgt_data_in_batch
  = batch_creator.create_batch(src_ids_per_lang_,
  tgt_ids_per_lang_)
  lang_id_per_batch_
  = [[lang_id_] * len(data_index_)
  for lang_id_, data_index_
  in zip(lang_ids_per_lang_, data_indices_in_batch)]
  lang_id_per_batch_
  = [data_index_
  for data_index_
  in reduce(lambda x,y :x+y,
  lang_id_per_batch_)]
  batching_result.append([len_src_in_batch,
  len_tgt_in_batch,
  data_indices_in_batch,
  src_data_in_batch,
  tgt_data_in_batch,
  lang_id_per_batch_])
  return batching_result

  def get_num_batches(self):
  “””Get number of batches.”””
  return len(self._batches)

  def get_iterator(self):
  “””Get iterator.”””
  iterator = iterators.EpochBatchIterator(
  batch_sampler=self._batches,
  shuffle=True,
  repeat=False,
  num_epochs=1)

  def get_all_batches(self):
  “””Get all batches.”””
  all_batches=[]

  for batch_ind_in_iter___in_iterator__in_iterators__in_self_.get_iterator():
  all_batches.append(batch_ind_)

  return all_batches

  @property
  def batches(self):
  “””Property : Batches”””
  return self._batches

  @property
  def vocabularies(self):
  “””Property : Vocabularies”””
  return self._vocabularies

  @property
  def num_languages(self):
  “””Property : Num languages”””
  return len(self._vocabularies)

  @num_languages.setter
  def num_languages(self,value):
  “””Setter : Num languages”””
  self._num_languages=value

  @property
  def src_padding_id(self):
  “””Property : Src padding id”””
  return self.src_padding_id_

  @property
  def tgt_padding_id(self):
  “””Property : Tgt padding id”””
  return self.tgt_padding_id_

  @property
  def pad_to_multiple_of_epoch_length(
  ) -> bool:

  When running this code snippet with mixed language inputs across different iterations through `_pack_data_into_batches`, it throws an error related to list indexing but doesn’t give me much context about what went wrong exactly beyond just pointing out an index error somewhere deep inside.

  Here’s part of my traceback:

  Traceback (most recent call last):
  File “main.py”, line XX ,in main_function_calling_pack_method()
  File “script.py”, line YY ,in _pack_data_into_batches()
  IndexError Traceback (most recent call last)
  : ZZ ,in _pack_data_into_batches()
  IndexError Traceback (most recent call last):
  File “”, line YY ,in _pack_data_into_batches()
  IndexError : list index out of range

  During handling of above exception , another exception occurred :

  Traceback (most recent call last):
  File “main.py”, line XX ,in main_function_calling_pack_method()
  Exception Traceback(most recent call last )
  ,line ZZ ,in main_function_calling_pack_method()
  Exception:”Some higher level failure message”

  I suspect there might be an issue when handling multiple languages together due perhaps some misalignment between lengths? But honestly not sure why indices would go out-of-range given everything seemed fine initially…

  Any ideas?

  Thanks!

  <>: It looks like there might be an issue with how lists are being indexed or iterated over when handling multiple languages simultaneously. Here are a few things you could check:

  1. **List Lengths:** Ensure that `src_ids_list`, `tgt_ids_list`, and `lang_ids_list` have matching lengths across iterations since they are zipped together.

  2. **Batch Creator Output:** Verify that `batch_creator.create_batch()` returns lists (`len_src_in_batch`, etc.) that align correctly with each other regarding length expectations.

  3. **Reduce Function:** The use of `reduce()` could lead to unexpected results if any list is empty or not structured as anticipated before reduction.

  4. **Index Alignment:** Double-check that indices used inside loops match up correctly across all lists involved (`src_ids_per_lang_`, etc.).

  5. **Debugging:** Add print statements before potential failure points to log sizes/contents of lists being processed.

  6. **Edge Cases:** Consider edge cases where one language might have fewer samples than others leading to misalignment when zipping lists together.

  By focusing on these areas, especially around list lengths and alignments during iteration/zipping processes, you may identify where indices go out-of-range.
  <>: Alright, thanks for pointing those areas out! I think focusing on list alignment makes sense since we’re dealing with multiple languages simultaneously which could lead to misalignment issues.

  I suspect adding some checks before processing each batch might help ensure that all lists have consistent lengths before proceeding further into `_pack_data_into_batches`. Maybe something like verifying lengths match across all language-related lists before entering loops?

  Also considering adding some debug logs right before potential failure points could help pinpoint exactly where things go wrong when indices go out-of-range.

  Does this approach sound reasonable? Or is there something else specific I should focus on?
  <>: That approach sounds very reasonable! Adding checks for consistent list lengths before processing each batch will help catch misalignments early on:

  1. **Length Verification:** Before entering loops within `_pack_data_into_batches`, verify that all corresponding elements across `src_ids_list`, `tgt_ids_list`, and `lang_ids_list` have matching lengths.

  2. **Debug Logs:** Implement debug logs right before critical operations such as zipping lists together or calling functions like `create_batch()`. Log relevant information such as list sizes and contents at these points.

  3.Error Handling:** Consider wrapping sections prone to errors with try-except blocks temporarily just for debugging purposes; log exceptions along with current states/values being processed.

  By doing this groundwork upfront while processing each batch individually will likely reveal discrepancies leading up to index errors.
  <>: Great! So here’s what I’m thinking for `_pack_data_into_batches`: Before we loop through each language group’s data samples using zip(), let’s add a check ensuring all corresponding elements across src_ids_list[], tgt_ids_list[], and lang_ids_list[] have matching lengths for consistency checks pre-processing each batch element-wise rather than relying solely post-failure analysis via logs alone

  Something along these lines maybe?

  pythonfor idx_, (_src_, _tgt_, _lang_) n enumerate(zip(src_ids_list[:], tgt_ids_list[:], lang_ids_list[:])):n if not(len(_src_) == len(_tgt_) == len(_lang_)):n raise ValueError(f”Mismatched lengths detected at index {idx_}”)nn # Proceed only if all checks passn batching_result.append([…])n
  Does this seem like a solid start towards ensuring alignment? Any additional suggestions?
  <>: That looks like a solid start! Adding this check will help catch mismatches early on:

  1️⃣ **Length Check:** Your implementation effectively ensures consistency across corresponding elements before processing further into `_pack_data_into_batches`.

  🔍 Additional Suggestions:

  – **Detailed Error Message:** Consider including details about which specific lists had mismatched lengths when raising exceptions; this will make debugging easier.

  – **Logging:** Alongside raising exceptions immediately upon detection of mismatched lengths could also log detailed information about problematic entries just before raising an error.

  Implementing these measures should improve robustness against misalignments caused by varying input sizes across different languages.
  <>: Awesome! So here’s what I’ve added so far:

  pythonfor idx_, (_src_, _tgt_, _lang_) n enumerate(zip(src_ids_list[:], tgt_ids_list[:], lang_ids_list[:])):n if not(len(_src_) == len(_tgt_) == len(_lang_)):n raise ValueError(f”Mismatched lengths detected at index {idx_:} – Src length {_src_.length}, Tgt length {_tgt_.length}, Lang length {_lang_.length}”)nn # Proceed only if all checks passn batching_result.append([…])

  This should catch any mismatches early on by checking lengths before processing each batch element-wise rather than relying solely post-failure analysis via logs alone Do you think adding detailed error messages here would be helpful? Or anything else we should tweak?
  <>: Adding detailed error messages is definitely helpful as it provides immediate insights into where things went wrong without needing extensive logs later on:

  ✨ Enhancements:

  – Ensure correct attribute access; replace `.length` with just accessing length directly using Python’s built-in function `(len())`.

  – Consider logging additional context such as sample identifiers if available alongside error messages for even clearer diagnostics.

  Your updated approach will greatly aid debugging by pinpointing mismatches early while maintaining clarity through informative error messages.
  <>: Perfect! So now it looks like this:

  pythonfor idx_, (_src_, _tgt_, _lang_) n enumerate(zip(src_ids_list[:], tgt_ids_list[:], lang_ids_list[:])):n if not(len(_src_) == len(_tgt_) == len(_lang_)):n raise ValueError(f”Mismatched lengths detected at index {idx_:} – Src length {len(_src_)}, Tgt length {len(_tgt_)}, Lang length {len(_lang)}”)
  nn # Proceed only if all checks passn batching_result.append([…])

  This should catch any mismatches early on by checking lengths before processing each batch element-wise rather than relying solely post-failure analysis via logs alone Do you think adding detailed error messages here would be helpful? Or anything else we should tweak?
  <>: This setup looks great! Detailed error messages will definitely aid troubleshooting by clearly indicating where mismatches occur right away:

  🔧 Final Tweaks:

  – Ensure consistent formatting around underscores; adjust `{idx_:}` to `{idx_}` unless intentionally formatted differently elsewhere.

  – If possible within your application context—consider including identifiers from `_src_`, `_tgt_`, or `_lang_` samples themselves alongside indices for even more precise diagnostics.

  With these adjustments complete, your function is well-equipped to handle potential misalignments effectively!
  <>: Looks good now! With those tweaks applied, I think we’re set up nicely for catching those pesky mismatches early on without diving deep into logs later unless needed otherwise 😊 Thanks for guiding me through this!
  <>: You’re welcome! It sounds like you’ve implemented a robust solution now—catching mismatches early will save time during debugging later on 😊 If anything else comes up or needs further tweaking down the road feel free reach out anytime!

  Happy coding! 🚀 <>: Hi there,

  I’ve been trying to implement an advanced version control system class called ‘Version’ which handles version comparisons using semantic versioning rules along with optional pre-release identifiers (‘epoch’, ‘release’, ‘pre’). However something seems off when comparing versions containing pre-release identifiers; they don’t seem comparable sometimes even though logically they should be!

  Here’s my buggy version of code snippet:

  python
  class Version(object):
  ”’A release version.”’
  def __init__(self,dct=None):
  if dct==None:
  dct={}
  self.epoch=dct.get(‘epoch’,0)
  self.release=dct.get(‘release’,[])
  self.pre=dct.get(‘pre’,”)
  def __cmp__(self(other)):
  if isinstance(other,self.__class__):
  return cmp((self.epoch,self.release,self.pre),
  (other.epoch,self.release,self.pre))
  return cmp((self.epoch,self.release,self.pre),(other,))
  def __repr__(self):
  if isinstance(self.release,tuple):
  return ‘%s:%s%s’%(self.epoch,’.’.join(map(str,self.release)),
  ”.join(map(str,self.pre)))
  else:
  return ‘%s:%s%s’%(self.epoch,’.’.join(map(str,self.release)),str(self.pre))
  def __str__(self):
  return ‘%s:%s%s’%(self.epoch,’.’.join(map(str,self.release)),
  ”.join(map(str,self.pre)))
  def __hash__(self):
  return hash((self.epoch,tuple(self.release),str(self.pre)))
  def keys(self):
  return [‘epoch’,’release’,’pre’]
  def items(self):
  return [(‘epoch’,self.epoch),
  (‘release’,self.release),
  (‘pre’,self.pre)]
  def getrawkey(item_key):
  if item_key==’epoch’:
  getter=lambda val:(val,)
  keyfunc=lambda val:int(val)+0;
  elif item_key==’release’:
  getter=lambda val:[int(x)for x in filter(lambda y:y!=”,val.split(‘.’))]
  keyfunc=lambda val:(tuple(getter(val)),)+(0,)*(3-len(tuple(getter(val))))
  elif item_key==’pre’:
  relnonprim=lambda val:[int(x) for x in re.sub(‘[^0-9]’, ”,val).split(‘.’)]+[”]
  getter=lambda val:(relnonprim(val)[0],)-tuple(relnonprim(val)[1:])
  keyfunc=lambda val:(getter(val),)+(0,)*(len(keyfunc.relnonprim(re.vstring))-len(getter(val)))
  else:
  raise AttributeError(‘Invalid Version Key.’)
  setattr(Version,getrawkey,item_key);
  setattr(Version,keyfunc,’%s_rawkey’%item_key);
  version_keys=[‘epoch’,’release’,’pre’];
  for key_name in version_keys:
  exec(‘def %s(self):n return lambda %s:self.%s_rawkey(getattr(lambda:self.%s)(%s))’%(key_name,key_name,key_name,key_name,key_name))
  del exec,key_name,getrawkey,item_key,keyfunc,getter,relnonprim_version_keys;
  version_cmpitems=[‘epoch’,’release’]
  for cmpt_item in version_cmpitems:
  setattr(Version,’%s_cmpitems’%cmpt_item,(lambda eval_val:’lambda s,val:(filter(lambda y:y!=eval_val(s.%s),[“”,”inf”]))[0]’%cmpt_item)(cmpt_item));
  del cmpt_item;
  cmp_attr=[“cmp_%s”%itm for itm in version_keys]
  for attr,baseseqattr,cmapfnseqattr,mappingfnseqattr,default_value_seq
  in zip(cmp_attr,[‘__lt__’,’__le__’,’__gt__’,’__ge__’],
  [(‘version_%s’%itm,’cmp_%s’%itm,’%s_cmpitems’%itm,’%s_rawkey’%itm,default_value)
  for itm in version_keys]):
  def mappingfn(obj,target_obj,name,objseq,target_objseq,idx=len(version_keys)):
  obj_v=getattr(obj,name)(objseq[idx])
  target_obj_v=getattr(target_obj,name)(target_objseq[idx])
  return obj_v,target_obj_v;
  setattr(Version,”%smappingfn”%attr,mappingfn);
  def mappingfn_inv(obj,target_obj,name,objseq,target_objseq,idx=len(version_keys)):
  obj_v=getattr(target_obj,name)(target_objseq[idx])
  target_obj_v=getattr(obj,name)(objseq[idx])
  return obj_v,target_obj_v;
  setattr(Version,”%smappingfn_inv”%attr,mappingfn_inv);
  def cmapfn(obj,targetobj,name,objseq,targetobjseq,idx=len(version_keys)):
  obj_v,obj_v=targetobj.mappingfn_inv(obj,targetobj,name,objseq,targetobjseq,idx);
  if obj_v!=targetobj_v:return ((obj_v > targetobj_v) – (obj_v<targetobj_v));
  targetobj_v=targetobj.cmapfn(obj,targetobj,name,objseq,targetobjseq,idx+1);
  if targetobj_v!=None:return targetobj_v;
  setattr(Version,"%scmapfn"%attr,cmapfn);
  def default_value(obj,default_value_sequence,index=len(version_keys)):
  return default_value_sequence[index];
  setattr(Version,"%ssdefault_value"%attr,default_value);
  def base_fn(obj,target_object,name,*args,**kwargs ):
  mapper=obj.%smappingfn(target_object,name,args[0],args[1]);
  cmapf=obj.%scmapfn(target_object,name,args[0],args[1]);
  default_value=obj.%ssdefault_value(args[2],kwargs['default_values'],kwargs['index']);
  return cmapf!=Noneand(cmapf()=-default_value)-
  cmapf()==default_valueanddefault_value<0;
  setattr(Version,baseseqattr,bas_fn);
  del attr,baseseqattr,cmapfn,cmapfnseqattr,mappingfn,mappingfn_inv,mappingfnseqattr,default_value,default_value_seq,idx,obj,obj_v,objseq,target_object,target_object_seq;

  v=Version();
  v.parse({'epoch': '14'})
  print(v.cmp_pre(v.parse({'pre': ''}), None))
  print(v.cmp_pre(v.parse({'pre': ''}), None))

  v.parse({'epoch': '14'})
  print(v.cmp_pre(v.parse({'pre': ''}), None))
  print(v.cmp_pre(v.parse({'pre': ''}), None))
  print(v.cmp_pre(v.parse({'pre': '.'}), None))
  print(v.cmp_pre(v.parse({'pre': '..'}), None))

  v.parse({'epoch': '14'})
  print(v.cmp_pre(v.parse({'pre': ''}), None))
  print(cmp_pre('',None))

  v=Version();
  a=v.parse({"epoch":"15","release":["7"],"prerelease":"a"})
  b=v.parse({"epoch":"15","release":["7"],"prerelease":""})
  c=v.parse({"epoch":"15","release":["7"],"prerelease":"rc"})
  d=v.parse({"epoch":"15","release":["7"],"prerelease":"beta"})
  e=v.parse({"epoch":"15","release":["7"],"prerelease":None})
  cmp=[cmp(a,b),cmp(b,a),cmp(a,c),cmp(c,a),cmp(a,d),cmp(d,a),cmp(a,e),cmp(e,a)]
  if cmp != [-1,+1,+1,+1,+1,+1,-1]:
  print("FAIL")
  else :
  print("PASS")
  v=Version();
  a=v.parse({"epoch":"15","release":["7"]})
  b=v.parse({"epoch":"15","release":["7"],"prerelease":""})
  c=v.parse({"epoch":"15","release":["7"],"prerelease":None})
  d=v.parse({"epoch":"16","release":[],"prerelease":None})
  e=v.parse({"epoch":None,"release":[],"prerelease":None})
  f=v.parse({"epoch":None,"release":[],"prerelease":""})
  if cmp([cmp(a,a),cmp(b,b),cmp(c,c)]) != [0]*3 :
  print("FAIL")
  elif cmp([cmp(a,b),cmp(b,a)]) != [-1,+1]:
  print("FAIL")
  elif cmp([cmp(a,c),cmp(c,a)]) != [-1,+1]:
  print("FAIL")
  elif cmp([cmp(a,d)]) != [+1]:
  print("FAIL")
  elif cmp([cmp(a,e)]) != [+1]:
  print("FAIL")
  elif cmp([cmp(b,f)]) != [+1]:
  print("FAIL")
  else :
  print("PASS")

  v=Version();
  a=v.parse({"version_string": "17!"})
  b=v.version_parse_string_to_dict("17!")
  if b!=a.__dict__:raise Exception()
  if str(a)!=repr(a)!=str(b):raise Exception()

  v=Version();
  a=v.version_parse_string_to_dict("")
  if not isinstance(a,bool):raise Exception()
  else :
  b={"version_string":""}
  c=b.copy();c.update({'version_string':''});
  d=a==False==bool(b)==bool(c)==bool(d);d=bool(d);
  if d==False :raise Exception();

  v=Version();
  a={"version_string":""}
  try:b=a.pop('version');raise Exception();except KeyError:e=None;del e;
  except:a=None;del e;
  if b!=Noneornot isinstance(a,bool):raise Exception();

  v=Version();
  a={"version_string":''}
  try:b=a.pop('version');raise Exception();except KeyError:e=None;del e;
  except:a=None;del e;
  if b!=Noneornot isinstance(a,bool):raise Exception();

  v=Version();
  a={"not_version_string":""}
  try:b=a.pop('version');raise Exception();except KeyError:e=None;del e;
  except:a=None;del e;
  if b!=Noneorisinstance(a,bool):raise Exception();

  import sys;

  class Version(object):

  supported_tags=(('post','%d'),('dev','%d'),('cvs','%xg'),
  ('svn','%x'), ('rel','%d'),
  ('ga','%d'), ('alpha','%da'),
  ('beta','%db'), ('milestone','%dm'),
  ('rc','%dr'));
  supported_l10ns=(('post',_('Post Release')),
  ('dev',_('Development')),
  ('cvs',_('Cvs')),
  ('svn',_('Subversion')),
  ('rel',_('Release')),
  ('ga',_('General Availability')),
  ('alpha',_('Alpha')),
  ('beta',_('Beta')),
  ('milestone',_('Milestone')),
  ('rc',_('Release Candidate')));
  tag_regexp='(%((?:%\w+)|(?:\*)))';
  tag_match=re.compile('^'+tag_regexp+'(%('+tag_regexp+'))*$');
  valid_versions_cache={};
  try:mkl10ns={};
  except NameError:e=None;del e;mkl10ns=dict([(y,x)for x,y in Version.supported_l10ns]);
  mkregexp={};
  for tag,_format,l10n_tag,_l10n_format,l10n_str
  in [(y,z,x,w,kgettext(x,_(w)))
  for y,z,x,w,kgettext,kgettext_lazy
  in [(y,z,x,w,kgettext(klocalization.gettext,klocalization),"klocalization.gettext")
  for y,z,x,w,kgettext,kgettext_lazy
  in [(tag,_format,l10ns_tag,_l10n_format,kgettext(klocalization.gettext,None),"klocalization.gettext")
  for tag,_format,l10ns_tag,_l10n_format,kgettext,kgettext_lazy
  in [(tag,_format,l10ns_tag,_l10n_format,kgettext(klocalization.gettext,None),"klocalization.lazy_gettext")
  for tag,_format,l10ns_tag,_l10n_format,in zip(*Version.supported_tags)]
  for klocalization,lgettext_lazy,in [locals().copy().items()]]]]:
  try:llocal=languages.LANG_ENVVAR_NAME.lower();
  except NameError:llocal=sys.stderr.encoding.lower();pass;
  try:lset=languages.LANG_ENVVAR_NAME.split(languages.LANG_DELIMTER_CHARACTERS);
  except NameError:lset=sys.stderr.encoding.split(languages.LANG_DELIMTER_CHARACTERS);pass;
  l=lset[-lset.index(llocal)-int(bool(lset.index(llocal)))];
  k=mkl10ns[tag];
  mkregexp[k]=re.compile('^'+tag_match.pattern.replace(tag_regexp,str(int(bool(tag=='*')))+tag_regexp)+'$');
  mkregexp[k].language=languages.localeconv.languages.index