


"""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


__lowercase          = 5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors


def 	lowercase     (							A_			)-> int:
  '''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 		_A    (    SCREAMING_SNAKE_CASE_    ):







    """simple docstring"""




    @staticmethod
    def       __snake_case				(   __UpperCAmelCase							:       Union[str, Any]):
      a		:						Dict 				=     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=UpperCamelCase__							,					required=UpperCamelCase__							,					help="The model name, including owner/organization, as seen on the hub."							,					)
      train_parser.add_argument(
          "--local-dir"							,					type=UpperCamelCase__							,					default=""							,					help="Optional local directory of the model repository. Defaults to /tmp/{model_name}"							,					)
      train_parser.add_argument(
          "--max-error"							,					type=UpperCamelCase__							,					default=UpperCamelCase__							,					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=UpperCamelCase__							,					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=UpperCamelCase__							,					default=UpperCamelCase__							,					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=UpperCamelCase__)






    @staticmethod
    def       __snake_case				(   __UpperCAmelCase							:       List[str]							,					__UpperCAmelCase							:       List[str]):
      a		:						Optional[Any] 				=     set(pt_outputs.keys())
      a		:						List[Any] 				=     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(__UpperCAmelCase							:       int							,					__UpperCAmelCase							:       Optional[int]							,					__UpperCAmelCase							:       Dict							,					__UpperCAmelCase							:       Tuple=""):
        # 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(UpperCamelCase__							,					torch.Tensor):
          a		:						Union[str, Any] 				=     np.max(np.abs(pt_out.numpy() - tf_out.numpy()))
          a		:						str 				=     tensor_difference
        else:
          a		:						Union[str, Any] 				=     attr_name
          for i, pt_item in enumerate(UpperCamelCase__):
            # If it is a named attribute, we keep the name. Otherwise, just its index.
            if isinstance(UpperCamelCase__							,					UpperCamelCase__):
              a		:						str 				=     root_name + pt_item
              a		:						Any 				=     tf_out[pt_item]
              a		:						Tuple 				=     pt_out[pt_item]
            else:
              a		:						int 				=     root_name + f'''[{i}]'''
              a		:						int 				=     tf_out[i]
            a		:						Union[str, Any] 				=     _find_pt_tf_differences(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__)

        return differences

      return _find_pt_tf_differences(UpperCamelCase__							,					UpperCamelCase__							,					{})






    def __init__(   self							:       int							,					__UpperCAmelCase							:       Optional[Any]							,					__UpperCAmelCase							:       List[str]							,					__UpperCAmelCase							:       int							,					__UpperCAmelCase							:       str							,					__UpperCAmelCase							:       str							,					__UpperCAmelCase							:       str							,					__UpperCAmelCase							:       Optional[Any]							,					__UpperCAmelCase							:       List[str]							,					*__UpperCAmelCase							:       int							,					):
      a		:						Optional[int] 				=     logging.get_logger("transformers-cli/pt_to_tf")
      a		:						Tuple 				=     model_name
      a		:						Any 				=     local_dir if local_dir else os.path.join("/tmp"							,					UpperCamelCase__)
      a		:						int 				=     max_error
      a		:						str 				=     new_weights
      a		:						Any 				=     no_pr
      a		:						Optional[Any] 				=     push
      a		:						List[str] 				=     extra_commit_description
      a		:						List[Any] 				=     override_model_class






    def       __snake_case				(   self							:       int							,					__UpperCAmelCase							:       Any							,					__UpperCAmelCase							:       Any							,					__UpperCAmelCase							:       List[Any]):

      def _get_audio_input():
        a		:						Optional[Any] 				=     load_dataset("hf-internal-testing/librispeech_asr_dummy"							,					"clean"							,					split="validation")
        a		:						List[Any] 				=     ds.sort("id").select(range(2))[:2]['''audio''']
        a		:						str 				=     [x['''array'''] for x in speech_samples]
        return raw_samples

      a		:						List[Any] 				=     type(pt_model.config)
      if model_config_class in PROCESSOR_MAPPING:
        a		:						Optional[int] 				=     AutoProcessor.from_pretrained(self._local_dir)
        if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
          a		:						str 				=     processor.tokenizer.eos_token
      elif model_config_class in IMAGE_PROCESSOR_MAPPING:
        a		:						int 				=     AutoImageProcessor.from_pretrained(self._local_dir)
      elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
        a		:						int 				=     AutoFeatureExtractor.from_pretrained(self._local_dir)
      elif model_config_class in TOKENIZER_MAPPING:
        a		:						str 				=     AutoTokenizer.from_pretrained(self._local_dir)
        if processor.pad_token is None:
          a		:						Dict 				=     processor.eos_token
      else:
        raise ValueError(f'''Unknown data processing type (model config type: {model_config_class})''')

      a		:						Tuple 				=     set(inspect.signature(pt_model.forward).parameters.keys())
      a		:						Optional[Any] 				=     {}
      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		:						Any 				=     load_dataset("cifar10"							,					"plain_text"							,					split="test")[:2]['''img''']
        processor_inputs.update({"images": sample_images})
      if "input_features" in model_forward_signature:
        a		:						Optional[int] 				=     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		:						Optional[Any] 				=     feature_extractor_signature['''padding'''].default
          if default_strategy is not False and default_strategy is not None:
            a		:						Any 				=     default_strategy
          else:
            a		:						Dict 				=     True
        else:
          a		:						List[Any] 				=     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		:						int 				=     processor(**UpperCamelCase__							,					return_tensors="pt")
      a		:						List[str] 				=     processor(**UpperCamelCase__							,					return_tensors="tf")

      # Extra input requirements, in addition to the input modality
      if (
          config.is_encoder_decoder
          or (hasattr(UpperCamelCase__							,					"encoder") and hasattr(UpperCamelCase__							,					"decoder"))
          or "decoder_input_ids" in tf_dummy_inputs
      ):
        a		:						str 				=     np.asarray([[1], [1]]							,					dtype=UpperCamelCase__) * (pt_model.config.decoder_start_token_id or 0)
        pt_input.update({"decoder_input_ids": torch.tensor(UpperCamelCase__)})
        tf_input.update({"decoder_input_ids": tf.convert_to_tensor(UpperCamelCase__)})

      return pt_input, tf_input






    def       __snake_case				(   self							:       str):
      # 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
      a		:						Union[str, Any] 				=     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		:						List[Any] 				=     AutoConfig.from_pretrained(self._local_dir)
      a		:						Dict 				=     config.architectures
      if self._override_model_class is not None:
        if self._override_model_class.startswith("TF"):
          a		:						Optional[int] 				=     [self._override_model_class[2:]]
        else:
          a		:						str 				=     [self._override_model_class]
        try:
          a		:						Tuple 				=     getattr(import_module("transformers")							,					architectures[0])
        except AttributeError:
          raise ValueError(f'''Model class {self._override_model_class} not found in transformers.''')
        try:
          a		:						str 				=     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		:						Union[str, Any] 				=     getattr(import_module("transformers")							,					"AutoModel")
        a		:						List[str] 				=     getattr(import_module("transformers")							,					"TFAutoModel")
        self._logger.warning("No detected architecture, using AutoModel/TFAutoModel")
      else:  # Architecture defined -- use it
        if len(UpperCamelCase__) > 1:
          raise ValueError(f'''More than one architecture was found, aborting. (architectures = {architectures})''')
        self._logger.warning(f'''Detected architecture: {architectures[0]}''')
        a		:						Union[str, Any] 				=     getattr(import_module("transformers")							,					architectures[0])
        try:
          a		:						int 				=     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		:						Any 				=     tf_class.from_config(UpperCamelCase__)
      a		:						List[str] 				=     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		:						List[str] 				=     pt_class.from_pretrained(self._local_dir)
      pt_model.eval()

      a		:						Optional[Any] 				=     self.get_inputs(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__)

      with torch.no_grad():
        a		:						Optional[Any] 				=     pt_model(**UpperCamelCase__							,					output_hidden_states=UpperCamelCase__)
      del pt_model  # will no longer be used, and may have a large memory footprint

      a		:						List[Any] 				=     tf_class.from_pretrained(self._local_dir							,					from_pt=UpperCamelCase__)
      a		:						str 				=     tf_from_pt_model(**UpperCamelCase__							,					output_hidden_states=UpperCamelCase__							,					training=UpperCamelCase__)

      # Confirms that cross loading PT weights into TF worked.
      a		:						Optional[Any] 				=     self.find_pt_tf_differences(UpperCamelCase__							,					UpperCamelCase__)
      a		:						str 				=     {k: v for k, v in crossload_differences.items() if '''hidden''' not in k}
      a		:						str 				=     {k: v for k, v in crossload_differences.items() if '''hidden''' in k}
      if len(UpperCamelCase__) == 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		:						List[str] 				=     max(output_differences.values()) if output_differences else 0.0
      a		:						Dict 				=     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		:						int 				=     os.path.join(self._local_dir							,					UpperCamelCase__)
      a		:						List[Any] 				=     os.path.join(self._local_dir							,					UpperCamelCase__)
      if (not os.path.exists(UpperCamelCase__) and not os.path.exists(UpperCamelCase__)) 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		:						str 				=     tf_class.from_pretrained(self._local_dir)
      a		:						Optional[int] 				=     tf_model(**UpperCamelCase__							,					output_hidden_states=UpperCamelCase__)

      a		:						Optional[Any] 				=     self.find_pt_tf_differences(UpperCamelCase__							,					UpperCamelCase__)
      a		:						Optional[int] 				=     {k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
      a		:						Dict 				=     {k: v for k, v in conversion_differences.items() if '''hidden''' in k}
      if len(UpperCamelCase__) == 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		:						str 				=     max(output_differences.values()) if output_differences else 0.0
      a		:						Optional[Any] 				=     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		:						Optional[int] 				=     '''Update TF weights''' if self._new_weights else '''Add TF weights'''
      if self._push:
        repo.git_add(auto_lfs_track=UpperCamelCase__)
        repo.git_commit(UpperCamelCase__)
        repo.git_push(blocking=UpperCamelCase__)  # 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		:						Dict 				=     (
            '''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(UpperCamelCase__):
          a		:						Optional[Any] 				=     [
              CommitOperationAdd(path_in_repo=UpperCamelCase__							,					path_or_fileobj=UpperCamelCase__)
          ]
          for shard_path in tf.io.gfile.glob(self._local_dir + "/tf_model-*.h5"):
            operations += [
                CommitOperationAdd(path_in_repo=os.path.basename(UpperCamelCase__)							,					path_or_fileobj=UpperCamelCase__)
            ]
        else:
          a		:						Optional[Any] 				=     [CommitOperationAdd(path_in_repo=UpperCamelCase__							,					path_or_fileobj=UpperCamelCase__)]

        a		:						Optional[Any] 				=     create_commit(
            repo_id=self._model_name							,					operations=UpperCamelCase__							,					commit_message=UpperCamelCase__							,					commit_description=UpperCamelCase__							,					repo_type="model"							,					create_pr=UpperCamelCase__							,					).pr_url
        self._logger.warning(f'''PR open in {hub_pr_url}''')








_SCREAMING_SNAKE_CASE     :   dict[str, float]							 =      {
    "kilogram": 1,
    "gram": pow(1_0, 3),
    "milligram": pow(1_0, 6),
    "metric-ton": pow(1_0, -3),
    "long-ton": 0.00_0984_2073,
    "short-ton": 0.00_1102_3122,
    "pound": 2.20_4624_4202,
    "stone": 0.15_7473_1728,
    "ounce": 35.2_7399_0723,
    "carrat": 5_0_0_0,
    "atomic-mass-unit": 6.022136652E26,
}

_SCREAMING_SNAKE_CASE     :   dict[str, float]							 =      {
    "kilogram": 1,
    "gram": pow(1_0, -3),
    "milligram": pow(1_0, -6),
    "metric-ton": pow(1_0, 3),
    "long-ton": 1016.0_4608,
    "short-ton": 907.184,
    "pound": 0.45_3592,
    "stone": 6.3_5029,
    "ounce": 0.028_3495,
    "carrat": 0.0002,
    "atomic-mass-unit": 1.660540199E-27,
}

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    str				,    __UpperCamelCase			:    str				,    __UpperCamelCase			:    float							)			->							float:

				"""simple docstring"""







				if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
								A__	:						int								=				(
								    F"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
								    F"Supported values are: {', '.join(__UpperCamelCase							)}"
								)
								raise ValueError(__UpperCamelCase							)
				return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
					import doctest

					doctest.testmod()