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
  
  
  SCREAMING_SNAKE_CASE       = 5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	int:
   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_						(		A_							):
  
  
  
  
      @staticmethod
      def 							__magic_name__							(					snake_case_		: ArgumentParser   )   ->							Optional[Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       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=snake_case_			,				required=snake_case_			,				help="The model name, including owner/organization, as seen on the hub."			,				)
       train_parser.add_argument(
           "--local-dir"			,				type=snake_case_			,				default=""			,				help="Optional local directory of the model repository. Defaults to /tmp/{model_name}"			,				)
       train_parser.add_argument(
           "--max-error"			,				type=snake_case_			,				default=snake_case_			,				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=snake_case_			,				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=snake_case_			,				default=snake_case_			,				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=snake_case_   )
  
  
  
  
      @staticmethod
      def 							__magic_name__							(					snake_case_		: Optional[Any]			,				snake_case_		: Union[str, Any]   )   ->							Optional[Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       set(pt_outputs.keys()   )
       A__ =       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(snake_case_		: Optional[Any]			,				snake_case_		: Dict			,				snake_case_		: int			,				snake_case_		: Dict=""   ):
        # 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(snake_case_			,				torch.Tensor   ):
         A__ =       np.max(np.abs(pt_out.numpy() - tf_out.numpy()   )   )
         A__ =       tensor_difference
        else:
         A__ =       attr_name
         for i, pt_item in enumerate(snake_case_   ):
          # If it is a named attribute, we keep the name. Otherwise, just its index.
          if isinstance(snake_case_			,				snake_case_   ):
           A__ =       root_name + pt_item
           A__ =       tf_out[pt_item]
           A__ =       pt_out[pt_item]
          else:
           A__ =       root_name + F"""[{i}]"""
           A__ =       tf_out[i]
          A__ =       _find_pt_tf_differences(snake_case_			,				snake_case_			,				snake_case_			,				snake_case_   )
  
        return differences
  
       return _find_pt_tf_differences(snake_case_			,				snake_case_			,				{}   )
  
  
  
  
      def __init__(					self		: Any			,				snake_case_		: str			,				snake_case_		: str			,				snake_case_		: float			,				snake_case_		: bool			,				snake_case_		: bool			,				snake_case_		: bool			,				snake_case_		: str			,				snake_case_		: str			,				*snake_case_		: Tuple			,				)   ->							Optional[Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       logging.get_logger("transformers-cli/pt_to_tf"   )
       A__ =       model_name
       A__ =       local_dir if local_dir else os.path.join("/tmp"			,				snake_case_   )
       A__ =       max_error
       A__ =       new_weights
       A__ =       no_pr
       A__ =       push
       A__ =       extra_commit_description
       A__ =       override_model_class
  
  
  
  
      def 							__magic_name__							(					self		: int			,				snake_case_		: Optional[Any]			,				snake_case_		: Optional[Any]			,				snake_case_		: Optional[int]   )   ->							List[str]:
  
  
  
  
       '''simple docstring'''
  
       def _get_audio_input():
        A__ =       load_dataset("hf-internal-testing/librispeech_asr_dummy"			,				"clean"			,				split="validation"   )
        A__ =       ds.sort("id"   ).select(range(2   )   )[:2]["audio"]
        A__ =       [x["array"] for x in speech_samples]
        return raw_samples
  
       A__ =       type(pt_model.config   )
       if model_config_class in PROCESSOR_MAPPING:
        A__ =       AutoProcessor.from_pretrained(self._local_dir   )
        if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
         A__ =       processor.tokenizer.eos_token
       elif model_config_class in IMAGE_PROCESSOR_MAPPING:
        A__ =       AutoImageProcessor.from_pretrained(self._local_dir   )
       elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
        A__ =       AutoFeatureExtractor.from_pretrained(self._local_dir   )
       elif model_config_class in TOKENIZER_MAPPING:
        A__ =       AutoTokenizer.from_pretrained(self._local_dir   )
        if processor.pad_token is None:
         A__ =       processor.eos_token
       else:
        raise ValueError(F"""Unknown data processing type (model config type: {model_config_class})"""   )
  
       A__ =       set(inspect.signature(pt_model.forward   ).parameters.keys()   )
       A__ =       {}
       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:
        A__ =       load_dataset("cifar10"			,				"plain_text"			,				split="test"   )[:2]["img"]
        processor_inputs.update({"images": sample_images}   )
       if "input_features" in model_forward_signature:
        A__ =       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:
         A__ =       feature_extractor_signature["padding"].default
         if default_strategy is not False and default_strategy is not None:
          A__ =       default_strategy
         else:
          A__ =       True
        else:
         A__ =       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}   )
       A__ =       processor(**snake_case_			,				return_tensors="pt"   )
       A__ =       processor(**snake_case_			,				return_tensors="tf"   )
  
       # Extra input requirements, in addition to the input modality
       if (
           config.is_encoder_decoder
           or (hasattr(snake_case_			,				"encoder"   ) and hasattr(snake_case_			,				"decoder"   ))
           or "decoder_input_ids" in tf_dummy_inputs
       ):
        A__ =       np.asarray([[1], [1]]			,				dtype=snake_case_   ) * (pt_model.config.decoder_start_token_id or 0)
        pt_input.update({"decoder_input_ids": torch.tensor(snake_case_   )}   )
        tf_input.update({"decoder_input_ids": tf.convert_to_tensor(snake_case_   )}   )
  
       return pt_input, tf_input
  
  
  
  
      def 							__magic_name__							(					self		: List[Any]   )   ->							Dict:
  
  
  
  
       '''simple docstring'''
  
       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
       A__ =       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
       A__ =       AutoConfig.from_pretrained(self._local_dir   )
       A__ =       config.architectures
       if self._override_model_class is not None:
        if self._override_model_class.startswith("TF"   ):
         A__ =       [self._override_model_class[2:]]
        else:
         A__ =       [self._override_model_class]
        try:
         A__ =       getattr(import_module("transformers"   )			,				architectures[0]   )
        except AttributeError:
         raise ValueError(F"""Model class {self._override_model_class} not found in transformers."""   )
        try:
         A__ =       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
        A__ =       getattr(import_module("transformers"   )			,				"AutoModel"   )
        A__ =       getattr(import_module("transformers"   )			,				"TFAutoModel"   )
        self._logger.warning("No detected architecture, using AutoModel/TFAutoModel"   )
       else:  # Architecture defined -- use it
        if len(snake_case_   ) > 1:
         raise ValueError(F"""More than one architecture was found, aborting. (architectures = {architectures})"""   )
        self._logger.warning(F"""Detected architecture: {architectures[0]}"""   )
        A__ =       getattr(import_module("transformers"   )			,				architectures[0]   )
        try:
         A__ =       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
       A__ =       tf_class.from_config(snake_case_   )
       A__ =       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
       A__ =       pt_class.from_pretrained(self._local_dir   )
       pt_model.eval()
  
       A__,   A__ =       self.get_inputs(snake_case_			,				snake_case_			,				snake_case_   )
  
       with torch.no_grad():
        A__ =       pt_model(**snake_case_			,				output_hidden_states=snake_case_   )
       del pt_model  # will no longer be used, and may have a large memory footprint
  
       A__ =       tf_class.from_pretrained(self._local_dir			,				from_pt=snake_case_   )
       A__ =       tf_from_pt_model(**snake_case_			,				output_hidden_states=snake_case_			,				training=snake_case_   )
  
       # Confirms that cross loading PT weights into TF worked.
       A__ =       self.find_pt_tf_differences(snake_case_			,				snake_case_   )
       A__ =       {k: v for k, v in crossload_differences.items() if "hidden" not in k}
       A__ =       {k: v for k, v in crossload_differences.items() if "hidden" in k}
       if len(snake_case_   ) == 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'"   )
       A__ =       max(output_differences.values()   ) if output_differences else 0.0
       A__ =       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
       A__ =       os.path.join(self._local_dir			,				snake_case_   )
       A__ =       os.path.join(self._local_dir			,				snake_case_   )
       if (not os.path.exists(snake_case_   ) and not os.path.exists(snake_case_   )) 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
  
       A__ =       tf_class.from_pretrained(self._local_dir   )
       A__ =       tf_model(**snake_case_			,				output_hidden_states=snake_case_   )
  
       A__ =       self.find_pt_tf_differences(snake_case_			,				snake_case_   )
       A__ =       {k: v for k, v in conversion_differences.items() if "hidden" not in k}
       A__ =       {k: v for k, v in conversion_differences.items() if "hidden" in k}
       if len(snake_case_   ) == 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'"   )
       A__ =       max(output_differences.values()   ) if output_differences else 0.0
       A__ =       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]   )   )
  
       A__ =       "Update TF weights" if self._new_weights else "Add TF weights"
       if self._push:
        repo.git_add(auto_lfs_track=snake_case_   )
        repo.git_commit(snake_case_   )
        repo.git_push(blocking=snake_case_   )  # 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..."   )
        A__ =       (
            "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(snake_case_   ):
         A__ =       [
             CommitOperationAdd(path_in_repo=snake_case_			,				path_or_fileobj=snake_case_   )
         ]
         for shard_path in tf.io.gfile.glob(self._local_dir + "/tf_model-*.h5"   ):
          operations += [
              CommitOperationAdd(path_in_repo=os.path.basename(snake_case_   )			,				path_or_fileobj=snake_case_   )
          ]
        else:
         A__ =       [CommitOperationAdd(path_in_repo=snake_case_			,				path_or_fileobj=snake_case_   )]
  
        A__ =       create_commit(
            repo_id=self._model_name			,				operations=snake_case_			,				commit_message=snake_case_			,				commit_description=snake_case_			,				repo_type="model"			,				create_pr=snake_case_			,				).pr_url
        self._logger.warning(F"""PR open in {hub_pr_url}"""   )
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  from transformers import PretrainedConfig
  
  
  
  
  class 			UpperCAmelCase_						(		A_							):
      lowercase__		      =   '''custom'''
  
  
  
  
      def __init__(					self		: Any			,				snake_case_		: List[Any]=1			,				**snake_case_		: Any   )   ->							str:
  
  
  
  
       '''simple docstring'''
  
       A__ =       attribute
       super().__init__(**snake_case_   )
  
  
  
  
  
  
  class 			UpperCAmelCase_						(		A_							):
      lowercase__		      =   '''custom'''
  
  
  
  
      def __init__(					self		: Any			,				snake_case_		: Tuple=1			,				**snake_case_		: Dict   )   ->							Any:
  
  
  
  
       '''simple docstring'''
  
       A__ =       attribute
  
  
  
  
  
  