




from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('3.8'):
							import importlib_metadata
else:
							import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
UpperCamelCase__              =						'\\n'

UpperCamelCase__              =						'\\n    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.\n    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.\n'

UpperCamelCase__              =						'\nCompute XTREME-S evaluation metric associated to each XTREME-S dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    bleu_kwargs: optional dict of keywords to be passed when computing \'bleu\'.\n        Keywords include Dict can be one of \'smooth_method\', \'smooth_value\', \'force\', \'lowercase\',\n        \'tokenize\', \'use_effective_order\'.\n    wer_kwargs: optional dict of keywords to be passed when computing \'wer\' and \'cer\'.\n        Keywords include \'concatenate_texts\'.\nReturns: depending on the XTREME-S task, one or several of:\n    "accuracy": Accuracy - for \'fleurs-lang_id\', \'minds14\'\n    "f1": F1 score - for \'minds14\'\n    "wer": Word error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "cer": Character error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "bleu": BLEU score according to the `sacrebleu` metric - for \'covost2\'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'mls\')  # \'mls\', \'voxpopuli\', \'fleurs-asr\' or \'babel\'\n    >>> references = ["it is sunny here", "paper and pen are essentials"]\n    >>> predictions = ["it\'s sunny", "paper pen are essential"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'wer\': 0.56, \'cer\': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'covost2\')\n    >>> references = ["bonjour paris", "il est necessaire de faire du sport de temps en temp"]\n    >>> predictions = ["bonjour paris", "il est important de faire du sport souvent"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'bleu\': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'fleurs-lang_id\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'accuracy\': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'minds14\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'f1\': 0.58, \'accuracy\': 0.6}\n'

UpperCamelCase__              =						['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
UpperCamelCase__              =						''

try:
							from jiwer import transforms as tr

							UpperCamelCase__              =						True
except ImportError:
							UpperCamelCase__              =						False

if _jiwer_available and version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):


							class 				__SCREAMING_SNAKE_CASE							(		tr.AbstractTransform  ):

												def __init__(				self ,       __lowercase = " "					):







													'''simple docstring'''




													lowerCamelCase_							:		str							   =							sentence_delimiter

												def 	lowercase_							(				self ,       __lowercase					):







													'''simple docstring'''




													return list(__lowercase					)





												def 	lowercase_							(				self ,       __lowercase					):







													'''simple docstring'''




													lowerCamelCase_							:		Optional[int]							   =							[]
													for sent_idx, sentence in enumerate(__lowercase					):
														chars.extend(self.process_string(__lowercase					)					)
														if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__lowercase					) - 1:
															chars.append(self.sentence_delimiter					)
													return chars
							UpperCamelCase__              =						tr.Compose(
							    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
							)
elif _jiwer_available:
							UpperCamelCase__              =						tr.Compose(
							    [
							        tr.RemoveMultipleSpaces(),
							        tr.Strip(),
							        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
							        tr.ReduceToListOfListOfChars(),
							    ]
							)
else:
							UpperCamelCase__              =						None






def 					snake_case					(							A						,   A			)							->							Optional[Any]:




	'''simple docstring'''


	return float((preds == labels).mean()			)






def 					snake_case					(							A						,   A			)							->							Any:




	'''simple docstring'''


	return {
	    "f1": float(fa_score(y_true=A						,   y_pred=A						,   average='macro'			)			),
	    "accuracy": simple_accuracy(A						,   A			),
	}






def 					snake_case					(							A						,   A						,   A="exp"						,   A=None						,   A=False						,   A=False						,   A=None						,   A=False						,   )							->							int:




	'''simple docstring'''


	lowerCamelCase_							:		Optional[int]							   =							[[label] for label in labels]
	lowerCamelCase_							:		Any							   =							list(A			)
	try:
		import sacrebleu as scb
	except ImportError:
		raise ValueError(
		    'sacrebleu has to be installed in order to apply the bleu metric for covost2.'
		    'You can install it via `pip install sacrebleu`.'			)

	if version.parse(scb.__version__			) < version.parse('1.4.12'			):
		raise ImportWarning(
		    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
		    'You can install it with `pip install "sacrebleu>=1.4.12"`.'			)

	lowerCamelCase_							:		str							   =							len(labels[0]			)
	if any(len(A			) != references_per_prediction for refs in labels			):
		raise ValueError('Sacrebleu requires the same number of references for each prediction'			)
	lowerCamelCase_							:		Tuple							   =							[[refs[i] for refs in labels] for i in range(A			)]
	lowerCamelCase_							:		Optional[Any]							   =							scb.corpus_bleu(
	    A						,   A						,   smooth_method=A						,   smooth_value=A						,   force=A						,   lowercase=A						,   use_effective_order=A						,   **({'tokenize': tokenize} if tokenize else {})						,   )
	return {"bleu": output.score}






def 					snake_case					(							A						,   A						,   A						,   A			)							->							Optional[int]:




	'''simple docstring'''


	try:
		from jiwer import compute_measures
	except ImportError:
		raise ValueError(
		    f"jiwer has to be installed in order to apply the wer metric for {config_name}."
		    'You can install it via `pip install jiwer`.'			)

	if concatenate_texts:
		lowerCamelCase_							:		Optional[int]							   =							compute_measures(A						,   A			)['wer']

		lowerCamelCase_							:		Tuple							   =							compute_measures(A						,   A						,   truth_transform=A						,   hypothesis_transform=A			)['wer']
		return {"wer": wer, "cer": cer}
	else:

		def compute_score(A						,   A						,   A="wer"			):
			lowerCamelCase_							:		Union[str, Any]							   =							0
			lowerCamelCase_							:		Optional[int]							   =							0
			for prediction, reference in zip(A						,   A			):
				if score_type == "wer":
					lowerCamelCase_							:		List[str]							   =							compute_measures(A						,   A			)
				elif score_type == "cer":
					lowerCamelCase_							:		Union[str, Any]							   =							compute_measures(
					    A						,   A						,   truth_transform=A						,   hypothesis_transform=A			)
				incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
				total += measures["substitutions"] + measures["deletions"] + measures["hits"]
			return incorrect / total

		return {"wer": compute_score(A						,   A						,   'wer'			), "cer": compute_score(A						,   A						,   'cer'			)}



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,     _KWARGS_DESCRIPTION  )
class 				__SCREAMING_SNAKE_CASE							(		datasets.Metric  ):

					def 	lowercase_							(				self					):







						'''simple docstring'''




						if self.config_name not in _CONFIG_NAMES:
							raise KeyError(f"You should supply a configuration name selected in {_CONFIG_NAMES}"					)

						lowerCamelCase_							:		List[str]							   =							'int64' if self.config_name in ['fleurs-lang_id', 'minds14'] else 'string'

						return datasets.MetricInfo(
						    description=_DESCRIPTION ,       citation=_CITATION ,       inputs_description=_KWARGS_DESCRIPTION ,       features=datasets.Features(
						        {'predictions': datasets.Value(__lowercase					), 'references': datasets.Value(__lowercase					)}					) ,       codebase_urls=[] ,       reference_urls=[] ,       format='numpy' ,       )





					def 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       __lowercase=None ,       __lowercase=None					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							   =							bleu_kwargs if bleu_kwargs is not None else {}
						lowerCamelCase_							:		List[Any]							   =							wer_kwargs if wer_kwargs is not None else {}

						if self.config_name == "fleurs-lang_id":
							return {"accuracy": simple_accuracy(__lowercase ,       __lowercase					)}
						elif self.config_name == "minds14":
							return fa_and_simple_accuracy(__lowercase ,       __lowercase					)
						elif self.config_name == "covost2":
							lowerCamelCase_							:		Tuple							   =							bleu_kwargs.pop('smooth_method' ,       'exp'					)
							lowerCamelCase_							:		Optional[Any]							   =							bleu_kwargs.pop('smooth_value' ,       __lowercase					)
							lowerCamelCase_							:		Tuple							   =							bleu_kwargs.pop('force' ,       __lowercase					)
							lowerCamelCase_							:		Optional[int]							   =							bleu_kwargs.pop('lowercase' ,       __lowercase					)
							lowerCamelCase_							:		Dict							   =							bleu_kwargs.pop('tokenize' ,       __lowercase					)
							lowerCamelCase_							:		List[str]							   =							bleu_kwargs.pop('use_effective_order' ,       __lowercase					)
							return bleu(
							    preds=__lowercase ,       labels=__lowercase ,       smooth_method=__lowercase ,       smooth_value=__lowercase ,       force=__lowercase ,       lowercase=__lowercase ,       tokenize=__lowercase ,       use_effective_order=__lowercase ,       )
						elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
							lowerCamelCase_							:		Optional[Any]							   =							wer_kwargs.pop('concatenate_texts' ,       __lowercase					)
							return wer_and_cer(__lowercase ,       __lowercase ,       __lowercase ,       self.config_name					)
						else:
							raise KeyError(f"You should supply a configuration name selected in {_CONFIG_NAMES}"					)



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


UpperCamelCase__     :					str								=			5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						Namespace				):





    """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 					__snake_case     (			lowerCAmelCase__				):

  @staticmethod
  def   lowerCAmelCase__   (    _A):
      SCREAMING_SNAKE_CASE_													=  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=_A  ,						required=_A  ,						help='The model name, including owner/organization, as seen on the hub.'  ,						)
      train_parser.add_argument(
          '--local-dir'  ,						type=_A  ,						default=''  ,						help='Optional local directory of the model repository. Defaults to /tmp/{model_name}'  ,						)
      train_parser.add_argument(
          '--max-error'  ,						type=_A  ,						default=_A  ,						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=_A  ,						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=_A  ,						default=_A  ,						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=_A)

  @staticmethod
  def   lowerCAmelCase__   (    _A  ,						_A):
      SCREAMING_SNAKE_CASE_													=  set(pt_outputs.keys())
      SCREAMING_SNAKE_CASE_													=  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(_A  ,						_A  ,						_A  ,						_A=""):
          # 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(_A  ,						torch.Tensor):
              SCREAMING_SNAKE_CASE_													=  np.max(np.abs(pt_out.numpy() - tf_out.numpy()))
              SCREAMING_SNAKE_CASE_													=  tensor_difference
          else:
              SCREAMING_SNAKE_CASE_													=  attr_name
              for i, pt_item in enumerate(_A):
                  # If it is a named attribute, we keep the name. Otherwise, just its index.
                  if isinstance(_A  ,						_A):
                      SCREAMING_SNAKE_CASE_													=  root_name + pt_item
                      SCREAMING_SNAKE_CASE_													=  tf_out[pt_item]
                      SCREAMING_SNAKE_CASE_													=  pt_out[pt_item]
                  else:
                      SCREAMING_SNAKE_CASE_													=  root_name + f"""[{i}]"""
                      SCREAMING_SNAKE_CASE_													=  tf_out[i]
                  SCREAMING_SNAKE_CASE_													=  _find_pt_tf_differences(_A  ,						_A  ,						_A  ,						_A)

          return differences

      return _find_pt_tf_differences(_A  ,						_A  ,						{})

  def __init__(    self  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						*_A  ,						):
      SCREAMING_SNAKE_CASE_													=  logging.get_logger('transformers-cli/pt_to_tf')
      SCREAMING_SNAKE_CASE_													=  model_name
      SCREAMING_SNAKE_CASE_													=  local_dir if local_dir else os.path.join('/tmp'  ,						_A)
      SCREAMING_SNAKE_CASE_													=  max_error
      SCREAMING_SNAKE_CASE_													=  new_weights
      SCREAMING_SNAKE_CASE_													=  no_pr
      SCREAMING_SNAKE_CASE_													=  push
      SCREAMING_SNAKE_CASE_													=  extra_commit_description
      SCREAMING_SNAKE_CASE_													=  override_model_class

  def   lowerCAmelCase__   (    self  ,						_A  ,						_A  ,						_A):

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

      SCREAMING_SNAKE_CASE_													=  type(pt_model.config)
      if model_config_class in PROCESSOR_MAPPING:
          SCREAMING_SNAKE_CASE_													=  AutoProcessor.from_pretrained(self._local_dir)
          if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
              SCREAMING_SNAKE_CASE_													=  processor.tokenizer.eos_token
      elif model_config_class in IMAGE_PROCESSOR_MAPPING:
          SCREAMING_SNAKE_CASE_													=  AutoImageProcessor.from_pretrained(self._local_dir)
      elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
          SCREAMING_SNAKE_CASE_													=  AutoFeatureExtractor.from_pretrained(self._local_dir)
      elif model_config_class in TOKENIZER_MAPPING:
          SCREAMING_SNAKE_CASE_													=  AutoTokenizer.from_pretrained(self._local_dir)
          if processor.pad_token is None:
              SCREAMING_SNAKE_CASE_													=  processor.eos_token
      else:
          raise ValueError(f"""Unknown data processing type (model config type: {model_config_class})""")

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

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

      return pt_input, tf_input


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

      SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  self.get_inputs(_A  ,						_A  ,						_A)

      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  pt_model(**_A  ,						output_hidden_states=_A)
      del pt_model  # will no longer be used, and may have a large memory footprint

      SCREAMING_SNAKE_CASE_													=  tf_class.from_pretrained(self._local_dir  ,						from_pt=_A)
      SCREAMING_SNAKE_CASE_													=  tf_from_pt_model(**_A  ,						output_hidden_states=_A  ,						training=_A)

      # Confirms that cross loading PT weights into TF worked.
      SCREAMING_SNAKE_CASE_													=  self.find_pt_tf_differences(_A  ,						_A)
      SCREAMING_SNAKE_CASE_													=  {k: v for k, v in crossload_differences.items() if 'hidden' not in k}
      SCREAMING_SNAKE_CASE_													=  {k: v for k, v in crossload_differences.items() if 'hidden' in k}
      if len(_A) == 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\'')
      SCREAMING_SNAKE_CASE_													=  max(output_differences.values()) if output_differences else 0.0
      SCREAMING_SNAKE_CASE_													=  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
      SCREAMING_SNAKE_CASE_													=  os.path.join(self._local_dir  ,						_A)
      SCREAMING_SNAKE_CASE_													=  os.path.join(self._local_dir  ,						_A)
      if (not os.path.exists(_A) and not os.path.exists(_A)) 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

      SCREAMING_SNAKE_CASE_													=  tf_class.from_pretrained(self._local_dir)
      SCREAMING_SNAKE_CASE_													=  tf_model(**_A  ,						output_hidden_states=_A)

      SCREAMING_SNAKE_CASE_													=  self.find_pt_tf_differences(_A  ,						_A)
      SCREAMING_SNAKE_CASE_													=  {k: v for k, v in conversion_differences.items() if 'hidden' not in k}
      SCREAMING_SNAKE_CASE_													=  {k: v for k, v in conversion_differences.items() if 'hidden' in k}
      if len(_A) == 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\'')
      SCREAMING_SNAKE_CASE_													=  max(output_differences.values()) if output_differences else 0.0
      SCREAMING_SNAKE_CASE_													=  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]))

      SCREAMING_SNAKE_CASE_													=  'Update TF weights' if self._new_weights else 'Add TF weights'
      if self._push:
          repo.git_add(auto_lfs_track=_A)
          repo.git_commit(_A)
          repo.git_push(blocking=_A)  # 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...')
          SCREAMING_SNAKE_CASE_													=  (
              '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(_A):
              SCREAMING_SNAKE_CASE_													=  [
                  CommitOperationAdd(path_in_repo=_A  ,						path_or_fileobj=_A)
              ]
              for shard_path in tf.io.gfile.glob(self._local_dir + '/tf_model-*.h5'):
                  operations += [
                      CommitOperationAdd(path_in_repo=os.path.basename(_A)  ,						path_or_fileobj=_A)
                  ]
          else:
              SCREAMING_SNAKE_CASE_													=  [CommitOperationAdd(path_in_repo=_A  ,						path_or_fileobj=_A)]

          SCREAMING_SNAKE_CASE_													=  create_commit(
              repo_id=self._model_name  ,						operations=_A  ,						commit_message=_A  ,						commit_description=_A  ,						repo_type='model'  ,						create_pr=_A  ,						).pr_url
          self._logger.warning(f"""PR open in {hub_pr_url}""")


