code: 
  
  
  
  '''simple docstring'''
  
  
  
  import inspect
  import os
  from argparse import ArgumentParser, Namespace
  from importlib import import_module
  
  import huggingface_hub
  import numpy as np
  from packaging import version
  
  from .. import (
      FEATURE_EXTRACTOR_MAPPING,
      IMAGE_PROCESSOR_MAPPING,
      PROCESSOR_MAPPING,
      TOKENIZER_MAPPING,
      AutoConfig,
      AutoFeatureExtractor,
      AutoImageProcessor,
      AutoProcessor,
      AutoTokenizer,
      is_datasets_available,
      is_tf_available,
      is_torch_available,
  )
  from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
  from . import BaseTransformersCLICommand
  
  
  if is_tf_available():
        import tensorflow as tf
  
        tf.config.experimental.enable_tensor_float_aa_execution(False)
  
  if is_torch_available():
        import torch
  
  if is_datasets_available():
        from datasets import load_dataset
  
  
  a__						:			int										=			5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   return PTtoTFCommand(
       args.model_name							,args.local_dir							,args.max_error							,args.new_weights							,args.no_pr							,args.push							,args.extra_commit_description							,args.override_model_class							,)
  
  
  
  
  
  
  class        UpperCAmelCase__  (    UpperCAmelCase_):
  
  
  
   @staticmethod
   def    __lowerCamelCase				(  lowercase			)  ->							Dict:
    __UpperCamelCase				      =  parser.add_parser(
        """pt-to-tf"""		,	help=(
            """CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."""
            """ Can also be used to validate existing weights without opening PRs, with --no-pr."""
        )		,	)
    train_parser.add_argument(
        """--model-name"""		,	type=lowercase		,	required=lowercase		,	help="""The model name, including owner/organization, as seen on the hub."""		,	)
    train_parser.add_argument(
        """--local-dir"""		,	type=lowercase		,	default=""""""		,	help="""Optional local directory of the model repository. Defaults to /tmp/{model_name}"""		,	)
    train_parser.add_argument(
        """--max-error"""		,	type=lowercase		,	default=lowercase		,	help=(
            f"Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."
        )		,	)
    train_parser.add_argument(
        """--new-weights"""		,	action="""store_true"""		,	help="""Optional flag to create new TensorFlow weights, even if they already exist."""		,	)
    train_parser.add_argument(
        """--no-pr"""		,	action="""store_true"""		,	help="""Optional flag to NOT open a PR with converted weights."""			)
    train_parser.add_argument(
        """--push"""		,	action="""store_true"""		,	help="""Optional flag to push the weights directly to `main` (requires permissions)"""		,	)
    train_parser.add_argument(
        """--extra-commit-description"""		,	type=lowercase		,	default=""""""		,	help="""Optional additional commit description to use when opening a PR (e.g. to tag the owner)."""		,	)
    train_parser.add_argument(
        """--override-model-class"""		,	type=lowercase		,	default=lowercase		,	help="""If you think you know better than the auto-detector, you can specify the model class here. """
        """Can be either an AutoModel class or a specific model class like BertForSequenceClassification."""		,	)
    train_parser.set_defaults(func=lowercase			)
  
  
  
   @staticmethod
   def    __lowerCamelCase				(  lowercase		,	lowercase			)  ->							List[Any]:
    __UpperCamelCase				      =  set(pt_outputs.keys()			)
    __UpperCamelCase				      =  set(tf_outputs.keys()			)
    if pt_out_attrs != tf_out_attrs:
     raise ValueError(
         f"The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"
         f" {tf_out_attrs})"			)
  
    # 2. For each output attribute, computes the difference
    def _find_pt_tf_differences(lowercase		,	lowercase		,	lowercase		,	lowercase=""			):
     # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
     # recursivelly, keeping the name of the attribute.
     if isinstance(lowercase		,	torch.Tensor			):
      __UpperCamelCase				      =  np.max(np.abs(pt_out.numpy() - tf_out.numpy()			)			)
      __UpperCamelCase				      =  tensor_difference
     else:
      __UpperCamelCase				      =  attr_name
      for i, pt_item in enumerate(lowercase			):
       # If it is a named attribute, we keep the name. Otherwise, just its index.
       if isinstance(lowercase		,	lowercase			):
        __UpperCamelCase				      =  root_name + pt_item
        __UpperCamelCase				      =  tf_out[pt_item]
        __UpperCamelCase				      =  pt_out[pt_item]
       else:
        __UpperCamelCase				      =  root_name + f"[{i}]"
        __UpperCamelCase				      =  tf_out[i]
       __UpperCamelCase				      =  _find_pt_tf_differences(lowercase		,	lowercase		,	lowercase		,	lowercase			)
  
     return differences
  
    return _find_pt_tf_differences(lowercase		,	lowercase		,	{}			)
  
  
  
   def __init__(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	*lowercase		,	)  ->							Optional[Any]:
    __UpperCamelCase				      =  logging.get_logger("""transformers-cli/pt_to_tf"""			)
    __UpperCamelCase				      =  model_name
    __UpperCamelCase				      =  local_dir if local_dir else os.path.join("""/tmp"""		,	lowercase			)
    __UpperCamelCase				      =  max_error
    __UpperCamelCase				      =  new_weights
    __UpperCamelCase				      =  no_pr
    __UpperCamelCase				      =  push
    __UpperCamelCase				      =  extra_commit_description
    __UpperCamelCase				      =  override_model_class
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase			)  ->							Any:
  
    def _get_audio_input():
     __UpperCamelCase				      =  load_dataset("""hf-internal-testing/librispeech_asr_dummy"""		,	"""clean"""		,	split="""validation"""			)
     __UpperCamelCase				      =  ds.sort("""id"""			).select(range(2			)			)[:2]["""audio"""]
     __UpperCamelCase				      =  [x["""array"""] for x in speech_samples]
     return raw_samples
  
    __UpperCamelCase				      =  type(pt_model.config			)
    if model_config_class in PROCESSOR_MAPPING:
     __UpperCamelCase				      =  AutoProcessor.from_pretrained(self._local_dir			)
     if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
      __UpperCamelCase				      =  processor.tokenizer.eos_token
    elif model_config_class in IMAGE_PROCESSOR_MAPPING:
     __UpperCamelCase				      =  AutoImageProcessor.from_pretrained(self._local_dir			)
    elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
     __UpperCamelCase				      =  AutoFeatureExtractor.from_pretrained(self._local_dir			)
    elif model_config_class in TOKENIZER_MAPPING:
     __UpperCamelCase				      =  AutoTokenizer.from_pretrained(self._local_dir			)
     if processor.pad_token is None:
      __UpperCamelCase				      =  processor.eos_token
    else:
     raise ValueError(f"Unknown data processing type (model config type: {model_config_class})"			)
  
    __UpperCamelCase				      =  set(inspect.signature(pt_model.forward			).parameters.keys()			)
    __UpperCamelCase				      =  {}
    if "input_ids" in model_forward_signature:
     processor_inputs.update(
         {
             """text""": ["""Hi there!""", """I am a batch with more than one row and different input lengths."""],
             """padding""": True,
             """truncation""": True,
         }			)
    if "pixel_values" in model_forward_signature:
     __UpperCamelCase				      =  load_dataset("""cifar10"""		,	"""plain_text"""		,	split="""test"""			)[:2]["""img"""]
     processor_inputs.update({"""images""": sample_images}			)
    if "input_features" in model_forward_signature:
     __UpperCamelCase				      =  inspect.signature(processor.feature_extractor			).parameters
     # Pad to the largest input length by default but take feature extractor default
     # padding value if it exists e.g. "max_length" and is not False or None
     if "padding" in feature_extractor_signature:
      __UpperCamelCase				      =  feature_extractor_signature["""padding"""].default
      if default_strategy is not False and default_strategy is not None:
       __UpperCamelCase				      =  default_strategy
      else:
       __UpperCamelCase				      =  True
     else:
      __UpperCamelCase				      =  True
     processor_inputs.update({"""audio""": _get_audio_input(), """padding""": padding_strategy}			)
    if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
     processor_inputs.update({"""audio""": _get_audio_input(), """padding""": True}			)
    __UpperCamelCase				      =  processor(**lowercase		,	return_tensors="""pt"""			)
    __UpperCamelCase				      =  processor(**lowercase		,	return_tensors="""tf"""			)
  
    # Extra input requirements, in addition to the input modality
    if (
        config.is_encoder_decoder
        or (hasattr(lowercase		,	"""encoder"""			) and hasattr(lowercase		,	"""decoder"""			))
        or "decoder_input_ids" in tf_dummy_inputs
    ):
     __UpperCamelCase				      =  np.asarray([[1], [1]]		,	dtype=lowercase			) * (pt_model.config.decoder_start_token_id or 0)
     pt_input.update({"""decoder_input_ids""": torch.tensor(lowercase			)}			)
     tf_input.update({"""decoder_input_ids""": tf.convert_to_tensor(lowercase			)}			)
  
    return pt_input, tf_input
  
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Tuple:
    # hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
    if version.parse(huggingface_hub.__version__			) < version.parse("""0.9.0"""			):
     raise ImportError(
         """The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"""
         """ installation."""			)
    else:
     from huggingface_hub import Repository, create_commit
     from huggingface_hub._commit_api import CommitOperationAdd
  
    # Fetch remote data
    __UpperCamelCase				      =  Repository(local_dir=self._local_dir		,	clone_from=self._model_name			)
  
    # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
    __UpperCamelCase				      =  AutoConfig.from_pretrained(self._local_dir			)
    __UpperCamelCase				      =  config.architectures
    if self._override_model_class is not None:
     if self._override_model_class.startswith("""TF"""			):
      __UpperCamelCase				      =  [self._override_model_class[2:]]
     else:
      __UpperCamelCase				      =  [self._override_model_class]
     try:
      __UpperCamelCase				      =  getattr(import_module("""transformers"""			)		,	architectures[0]			)
     except AttributeError:
      raise ValueError(f"Model class {self._override_model_class} not found in transformers."			)
     try:
      __UpperCamelCase				      =  getattr(import_module("""transformers"""			)		,	"""TF""" + architectures[0]			)
     except AttributeError:
      raise ValueError(f"TF model class TF{self._override_model_class} not found in transformers."			)
    elif architectures is None:  # No architecture defined -- use auto classes
     __UpperCamelCase				      =  getattr(import_module("""transformers"""			)		,	"""AutoModel"""			)
     __UpperCamelCase				      =  getattr(import_module("""transformers"""			)		,	"""TFAutoModel"""			)
     self._logger.warning("""No detected architecture, using AutoModel/TFAutoModel"""			)
    else:  # Architecture defined -- use it
     if len(lowercase			) > 1:
      raise ValueError(f"More than one architecture was found, aborting. (architectures = {architectures})"			)
     self._logger.warning(f"Detected architecture: {architectures[0]}"			)
     __UpperCamelCase				      =  getattr(import_module("""transformers"""			)		,	architectures[0]			)
     try:
      __UpperCamelCase				      =  getattr(import_module("""transformers"""			)		,	"""TF""" + architectures[0]			)
     except AttributeError:
      raise AttributeError(f"The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers."			)
  
          # Check the TF dummy inputs to see what keys we need in the forward pass
    __UpperCamelCase				      =  tf_class.from_config(lowercase			)
    __UpperCamelCase				      =  tf_from_pt_model.dummy_inputs
  
    del tf_from_pt_model  # Try to keep only one model in memory at a time
  
    # Load the model and get some basic inputs
    __UpperCamelCase				      =  pt_class.from_pretrained(self._local_dir			)
    pt_model.eval()
  
    __UpperCamelCase      ,		__UpperCamelCase				      =  self.get_inputs(lowercase		,	lowercase		,	lowercase			)
  
    with torch.no_grad():
     __UpperCamelCase				      =  pt_model(**lowercase		,	output_hidden_states=lowercase			)
    del pt_model  # will no longer be used, and may have a large memory footprint
  
    __UpperCamelCase				      =  tf_class.from_pretrained(self._local_dir		,	from_pt=lowercase			)
    __UpperCamelCase				      =  tf_from_pt_model(**lowercase		,	output_hidden_states=lowercase		,	training=lowercase			)
  
    # Confirms that cross loading PT weights into TF worked.
    __UpperCamelCase				      =  self.find_pt_tf_differences(lowercase		,	lowercase			)
    __UpperCamelCase				      =  {k: v for k, v in crossload_differences.items() if """hidden""" not in k}
    __UpperCamelCase				      =  {k: v for k, v in crossload_differences.items() if """hidden""" in k}
    if len(lowercase			) == 0 and architectures is not None:
     raise ValueError(
         f"Something went wrong -- the config file has architectures ({architectures}), but no model head"
         """ output was found. All outputs start with 'hidden'"""			)
    __UpperCamelCase				      =  max(output_differences.values()			) if output_differences else 0.0
    __UpperCamelCase				      =  max(hidden_differences.values()			)
    if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
     raise ValueError(
         """The cross-loaded TensorFlow model has different outputs, something went wrong!\n"""
         + f"\nList of maximum output differences above the threshold ({self._max_error}):\n"
         + """\n""".join([f"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error]			)
         + f"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
         + """\n""".join([f"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error]			)			)
  
    # Save the weights in a TF format (if needed) and confirms that the results are still good
    __UpperCamelCase				      =  os.path.join(self._local_dir		,	lowercase			)
    __UpperCamelCase				      =  os.path.join(self._local_dir		,	lowercase			)
    if (not os.path.exists(lowercase			) and not os.path.exists(lowercase			)) or self._new_weights:
     tf_from_pt_model.save_pretrained(self._local_dir			)
    del tf_from_pt_model  # will no longer be used, and may have a large memory footprint
  
    __UpperCamelCase				      =  tf_class.from_pretrained(self._local_dir			)
    __UpperCamelCase				      =  tf_model(**lowercase		,	output_hidden_states=lowercase			)
  
    __UpperCamelCase				      =  self.find_pt_tf_differences(lowercase		,	lowercase			)
    __UpperCamelCase				      =  {k: v for k, v in conversion_differences.items() if """hidden""" not in k}
    __UpperCamelCase				      =  {k: v for k, v in conversion_differences.items() if """hidden""" in k}
    if len(lowercase			) == 0 and architectures is not None:
     raise ValueError(
         f"Something went wrong -- the config file has architectures ({architectures}), but no model head"
         """ output was found. All outputs start with 'hidden'"""			)
    __UpperCamelCase				      =  max(output_differences.values()			) if output_differences else 0.0
    __UpperCamelCase				      =  max(hidden_differences.values()			)
    if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
     raise ValueError(
         """The converted TensorFlow model has different outputs, something went wrong!\n"""
         + f"\nList of maximum output differences above the threshold ({self._max_error}):\n"
         + """\n""".join([f"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error]			)
         + f"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
         + """\n""".join([f"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error]			)			)
  
    __UpperCamelCase				      =  """Update TF weights""" if self._new_weights else """Add TF weights"""
    if self._push:
     repo.git_add(auto_lfs_track=lowercase			)
     repo.git_commit(lowercase			)
     repo.git_push(blocking=lowercase			)  # this prints a progress bar with the upload
     self._logger.warning(f"TF weights pushed into {self._model_name}"			)
    elif not self._no_pr:
     self._logger.warning("""Uploading the weights into a new PR..."""			)
     __UpperCamelCase				      =  (
         """Model converted by the [`transformers`' `pt_to_tf`"""
         """ CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). """
         """All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n"""
         f"Maximum crossload output difference={max_crossload_output_diff:.3e}; "
         f"Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"
         f"Maximum conversion output difference={max_conversion_output_diff:.3e}; "
         f"Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"
     )
     if self._max_error > MAX_ERROR:
      commit_descrition += (
          f"\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"
      )
     if self._extra_commit_description:
      commit_descrition += "\n\n" + self._extra_commit_description
  
     # sharded model -> adds all related files (index and .h5 shards)
     if os.path.exists(lowercase			):
      __UpperCamelCase				      =  [
          CommitOperationAdd(path_in_repo=lowercase		,	path_or_fileobj=lowercase			)
      ]
      for shard_path in tf.io.gfile.glob(self._local_dir + """/tf_model-*.h5"""			):
       operations += [
           CommitOperationAdd(path_in_repo=os.path.basename(lowercase			)		,	path_or_fileobj=lowercase			)
       ]
     else:
      __UpperCamelCase				      =  [CommitOperationAdd(path_in_repo=lowercase		,	path_or_fileobj=lowercase			)]
  
     __UpperCamelCase				      =  create_commit(
         repo_id=self._model_name		,	operations=lowercase		,	commit_message=lowercase		,	commit_description=lowercase		,	repo_type="""model"""		,	create_pr=lowercase		,	).pr_url
     self._logger.warning(f"PR open in {hub_pr_url}"			)
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  a__						:			str										=			[chr(i) for i in range(3_2, 1_2_6)]
  a__						:			List[Any]										=			list(range(len(alphabets)))
  a__						:			Union[str, Any]										=			list(range(len(alphabets)))
  a__						:			Dict										=			list(range(len(alphabets)))
  a__						:			Union[str, Any]										=			list(reversed(range(len(alphabets))))
  a__						:			List[str]										=			[]
  a__						:			int										=			0
  
  
  
  
  
  
  
  def _lowercase   (  ):
  
  
  
   '''simple docstring'''
  
  
  
   global gear_one_pos
   global gear_two_pos
   global gear_three_pos
   __UpperCamelCase				      =  gear_one[0]
   gear_one.append(__A       )
   del gear_one[0]
   gear_one_pos += 1
   if gear_one_pos % int(len(__A       )       ) == 0:
    __UpperCamelCase				      =  gear_two[0]
    gear_two.append(__A       )
    del gear_two[0]
    gear_two_pos += 1
    if gear_two_pos % int(len(__A       )       ) == 0:
     __UpperCamelCase				      =  gear_three[0]
     gear_three.append(__A       )
     del gear_three[0]
     gear_three_pos += 1
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  alphabets.index(__A       )
   __UpperCamelCase				      =  gear_one[target]
   __UpperCamelCase				      =  gear_two[target]
   __UpperCamelCase				      =  gear_three[target]
   __UpperCamelCase				      =  reflector[target]
   __UpperCamelCase				      =  gear_three.index(__A       )
   __UpperCamelCase				      =  gear_two.index(__A       )
   __UpperCamelCase				      =  gear_one.index(__A       )
   code.append(alphabets[target]       )
   rotator()
  
  
  if __name__ == "__main__":
   a__						:			Any										=			list(input('Type your message:\n'))
   while True:
    try:
     a__						:			List[Any]										=			int(input('Please set token:(must be only digits)\n'))
     break
    except Exception as error:
     print(error)
   for _ in range(token):
    rotator()
   for j in decode:
    engine(j)
   print('\n' + ''.join(code))
   print(
       f'''\nYour Token is {token} please write it down.\nIf you want to decode '''
       'this message again you should input same digits as token!'
   )
  
  
  
  
  
  