




from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


lowercase_		=							{
    '''id''': '''5733be284776f41900661182''',
    '''title''': '''University_of_Notre_Dame''',
    '''context''': '''Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.''',
    '''question''': '''To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?''',
    '''answers''': {'''text''': ['''Saint Bernadette Soubirous'''], '''answer_start''': [515]},
}







@pytest.mark.parametrize(
    '''task_cls''',     [
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ],     )
def 			lowerCAmelCase  (     UpperCAmelCase	)  ->Any:






					"""simple docstring"""






					__magic_name__ :							Tuple		       =							task_cls()
					__magic_name__ :							int		       =							asdict(__UpperCamelCase	)
					__magic_name__ :							str		       =							task_template_from_dict(__UpperCamelCase	)
					assert task == reloaded





class 		A__    :






	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						__magic_name__ :							Optional[Any]		       =							LanguageModeling(text_column='''input_text'''						)
						assert {"input_text": "text"} == task.column_mapping






	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							Features({'''text''': Value('''string'''						)}						)
						__magic_name__ :							Optional[Any]		       =							{'''text_column''': '''input_text'''}
						__magic_name__ :							Any		       =							LanguageModeling.from_dict(_UpperCamelCase						)
						assert "language-modeling" == task.task
						assert input_schema == task.input_schema





class 		A__    (       __lowerCamelCase			):






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							List[str]		       =							sorted(['''pos''', '''neg''']						)






	def 			lowercase				(     self						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							str		       =							TextClassification(text_column='''input_text'''    ,					label_column='''input_label'''						)
						self.assertDictEqual({'''input_text''': '''text''', '''input_label''': '''labels'''}    ,					task.column_mapping						)






	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							int		       =							Features({'''text''': Value('''string'''						)}						)
						# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
						__magic_name__ :							List[Any]		       =							Features({'''labels''': ClassLabel}						)
						__magic_name__ :							int		       =							{'''text_column''': '''input_text''', '''label_column''': '''input_labels'''}
						__magic_name__ :							Optional[int]		       =							TextClassification.from_dict(_UpperCamelCase						)
						self.assertEqual('''text-classification'''    ,					task.task						)
						self.assertEqual(_UpperCamelCase    ,					task.input_schema						)
						self.assertEqual(_UpperCamelCase    ,					task.label_schema						)






	def 			lowercase				(     self						)				->       List[str]:




						"""simple docstring"""






						__magic_name__ :							Optional[Any]		       =							TextClassification(text_column='''input_text'''    ,					label_column='''input_label'''						)
						self.assertEqual(task.label_schema['''labels''']    ,					_UpperCamelCase						)
						__magic_name__ :							Dict		       =							task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels						)}						)						)
						self.assertEqual(task.label_schema['''labels''']    ,					ClassLabel(names=self.labels						)						)





class 		A__    (       __lowerCamelCase			):






	def 			lowercase				(     self						)				->       str:




						"""simple docstring"""






						__magic_name__ :							Optional[Any]		       =							QuestionAnsweringExtractive(
						    context_column='''input_context'''    ,					question_column='''input_question'''    ,					answers_column='''input_answers'''						)
						self.assertDictEqual(
						    {'''input_context''': '''context''', '''input_question''': '''question''', '''input_answers''': '''answers'''}    ,					task.column_mapping						)






	def 			lowercase				(     self						)				->       Optional[Any]:




						"""simple docstring"""






						__magic_name__ :							int		       =							Features({'''question''': Value('''string'''						), '''context''': Value('''string'''						)}						)
						__magic_name__ :							Union[str, Any]		       =							Features(
						    {
						        '''answers''': Sequence(
						            {
						                '''text''': Value('''string'''						),
						                '''answer_start''': Value('''int32'''						),
						            }						)
						    }						)
						__magic_name__ :							Dict		       =							{
						    '''context_column''': '''input_input_context''',
						    '''question_column''': '''input_question''',
						    '''answers_column''': '''input_answers''',
						}
						__magic_name__ :							Tuple		       =							QuestionAnsweringExtractive.from_dict(_UpperCamelCase						)
						self.assertEqual('''question-answering-extractive'''    ,					task.task						)
						self.assertEqual(_UpperCamelCase    ,					task.input_schema						)
						self.assertEqual(_UpperCamelCase    ,					task.label_schema						)





class 		A__    (       __lowerCamelCase			):






	def 			lowercase				(     self						)				->       Optional[Any]:




						"""simple docstring"""






						__magic_name__ :							Optional[Any]		       =							Summarization(text_column='''input_text'''    ,					summary_column='''input_summary'''						)
						self.assertDictEqual({'''input_text''': '''text''', '''input_summary''': '''summary'''}    ,					task.column_mapping						)






	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						__magic_name__ :							Any		       =							Features({'''text''': Value('''string'''						)}						)
						__magic_name__ :							Any		       =							Features({'''summary''': Value('''string'''						)}						)
						__magic_name__ :							Tuple		       =							{'''text_column''': '''input_text''', '''summary_column''': '''input_summary'''}
						__magic_name__ :							List[str]		       =							Summarization.from_dict(_UpperCamelCase						)
						self.assertEqual('''summarization'''    ,					task.task						)
						self.assertEqual(_UpperCamelCase    ,					task.input_schema						)
						self.assertEqual(_UpperCamelCase    ,					task.label_schema						)





class 		A__    (       __lowerCamelCase			):






	def 			lowercase				(     self						)				->       Optional[Any]:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							AutomaticSpeechRecognition(audio_column='''input_audio'''    ,					transcription_column='''input_transcription'''						)
						self.assertDictEqual({'''input_audio''': '''audio''', '''input_transcription''': '''transcription'''}    ,					task.column_mapping						)






	def 			lowercase				(     self						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							int		       =							Features({'''audio''': Audio()}						)
						__magic_name__ :							Dict		       =							Features({'''transcription''': Value('''string'''						)}						)
						__magic_name__ :							Union[str, Any]		       =							{
						    '''audio_column''': '''input_audio''',
						    '''transcription_column''': '''input_transcription''',
						}
						__magic_name__ :							Union[str, Any]		       =							AutomaticSpeechRecognition.from_dict(_UpperCamelCase						)
						self.assertEqual('''automatic-speech-recognition'''    ,					task.task						)
						self.assertEqual(_UpperCamelCase    ,					task.input_schema						)
						self.assertEqual(_UpperCamelCase    ,					task.label_schema						)





class 		A__    (       __lowerCamelCase			):






	def 			lowercase				(     self						)				->       Union[str, Any]:




						"""simple docstring"""






						__magic_name__ :							Any		       =							sorted(['''pos''', '''neg''']						)






	def 			lowercase				(     self						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							AudioClassification(audio_column='''input_audio'''    ,					label_column='''input_label'''						)
						self.assertDictEqual({'''input_audio''': '''audio''', '''input_label''': '''labels'''}    ,					task.column_mapping						)






	def 			lowercase				(     self						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							Features({'''audio''': Audio()}						)
						__magic_name__ :							List[Any]		       =							Features({'''labels''': ClassLabel}						)
						__magic_name__ :							List[str]		       =							{
						    '''audio_column''': '''input_image''',
						    '''label_column''': '''input_label''',
						}
						__magic_name__ :							int		       =							AudioClassification.from_dict(_UpperCamelCase						)
						self.assertEqual('''audio-classification'''    ,					task.task						)
						self.assertEqual(_UpperCamelCase    ,					task.input_schema						)
						self.assertEqual(_UpperCamelCase    ,					task.label_schema						)






	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							AudioClassification(audio_column='''input_audio'''    ,					label_column='''input_label'''						)
						self.assertEqual(task.label_schema['''labels''']    ,					_UpperCamelCase						)
						__magic_name__ :							List[str]		       =							task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels						)}						)						)
						self.assertEqual(task.label_schema['''labels''']    ,					ClassLabel(names=self.labels						)						)





class 		A__    (       __lowerCamelCase			):






	def 			lowercase				(     self						)				->       List[str]:




						"""simple docstring"""






						__magic_name__ :							List[str]		       =							sorted(['''pos''', '''neg''']						)






	def 			lowercase				(     self						)				->       List[str]:




						"""simple docstring"""






						__magic_name__ :							List[Any]		       =							ImageClassification(image_column='''input_image'''    ,					label_column='''input_label'''						)
						self.assertDictEqual({'''input_image''': '''image''', '''input_label''': '''labels'''}    ,					task.column_mapping						)






	def 			lowercase				(     self						)				->       List[str]:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							Features({'''image''': Image()}						)
						__magic_name__ :							str		       =							Features({'''labels''': ClassLabel}						)
						__magic_name__ :							Union[str, Any]		       =							{
						    '''image_column''': '''input_image''',
						    '''label_column''': '''input_label''',
						}
						__magic_name__ :							str		       =							ImageClassification.from_dict(_UpperCamelCase						)
						self.assertEqual('''image-classification'''    ,					task.task						)
						self.assertEqual(_UpperCamelCase    ,					task.input_schema						)
						self.assertEqual(_UpperCamelCase    ,					task.label_schema						)






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							ImageClassification(image_column='''input_image'''    ,					label_column='''input_label'''						)
						self.assertEqual(task.label_schema['''labels''']    ,					_UpperCamelCase						)
						__magic_name__ :							Any		       =							task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels						)}						)						)
						self.assertEqual(task.label_schema['''labels''']    ,					ClassLabel(names=self.labels						)						)





class 		A__    (       __lowerCamelCase			):






	def 			lowercase				(     self						)				->       Union[str, Any]:




						"""simple docstring"""






						__magic_name__ :							Optional[Any]		       =							DatasetInfo(task_templates=QuestionAnsweringExtractive()						)
						__magic_name__ :							Optional[Any]		       =							Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}    ,					info=_UpperCamelCase						)
						assert isinstance(dataset.info.task_templates    ,					_UpperCamelCase						)
						assert len(dataset.info.task_templates						) == 1

						def keep_task(lowerCamelCase						):
											return x

						def dont_keep_task(lowerCamelCase						):
											__magic_name__ :							List[str]		       =							deepcopy(_UpperCamelCase						)
											__magic_name__ :							Tuple		       =							0
											return out

						__magic_name__ :							Dict		       =							dataset.map(_UpperCamelCase						)
						assert mapped_dataset.info.task_templates == dataset.info.task_templates
						# reload from cache
						__magic_name__ :							Optional[int]		       =							dataset.map(_UpperCamelCase						)
						assert mapped_dataset.info.task_templates == dataset.info.task_templates

						__magic_name__ :							str		       =							dataset.map(_UpperCamelCase						)
						assert mapped_dataset.info.task_templates == []
						# reload from cache
						__magic_name__ :							Any		       =							dataset.map(_UpperCamelCase						)
						assert mapped_dataset.info.task_templates == []






	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						__magic_name__ :							str		       =							Features({'''text''': Value('''string'''						), '''label''': ClassLabel(names=('''pos''', '''neg''')						)}						)
						__magic_name__ :							List[str]		       =							TextClassification(text_column='''text'''    ,					label_column='''label'''						)
						__magic_name__ :							Any		       =							DatasetInfo(features=_UpperCamelCase    ,					task_templates=_UpperCamelCase						)
						__magic_name__ :							Union[str, Any]		       =							Dataset.from_dict({'''text''': ['''A sentence.'''], '''label''': ['''pos''']}    ,					info=_UpperCamelCase						)

						def process(lowerCamelCase						):
											return example

						__magic_name__ :							Optional[Any]		       =							dataset.remove_columns('''label'''						)
						__magic_name__ :							Optional[int]		       =							modified_dataset.map(_UpperCamelCase						)
						assert mapped_dataset.info.task_templates == []









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 			lowerCAmelCase  (     UpperCAmelCase	)  ->Dict:






					"""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			):






	@staticmethod
	def 			lowercase				(     lowerCamelCase						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							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=lowerCamelCase    ,					required=lowerCamelCase    ,					help='''The model name, including owner/organization, as seen on the hub.'''    ,					)
						train_parser.add_argument(
						    '''--local-dir'''    ,					type=lowerCamelCase    ,					default=''''''    ,					help='''Optional local directory of the model repository. Defaults to /tmp/{model_name}'''    ,					)
						train_parser.add_argument(
						    '''--max-error'''    ,					type=lowerCamelCase    ,					default=lowerCamelCase    ,					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=lowerCamelCase    ,					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=lowerCamelCase    ,					default=lowerCamelCase    ,					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=lowerCamelCase						)






	@staticmethod
	def 			lowercase				(     lowerCamelCase    ,					lowerCamelCase						)				->       str:




						"""simple docstring"""






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

											return differences

						return _find_pt_tf_differences(lowerCamelCase    ,					lowerCamelCase    ,					{}						)






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					*lowerCamelCase    ,					)				->       str:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							logging.get_logger('''transformers-cli/pt_to_tf'''						)
						__magic_name__ :							Dict		       =							model_name
						__magic_name__ :							Optional[Any]		       =							local_dir if local_dir else os.path.join('''/tmp'''    ,					lowerCamelCase						)
						__magic_name__ :							str		       =							max_error
						__magic_name__ :							Any		       =							new_weights
						__magic_name__ :							Optional[Any]		       =							no_pr
						__magic_name__ :							Union[str, Any]		       =							push
						__magic_name__ :							Optional[Any]		       =							extra_commit_description
						__magic_name__ :							Optional[int]		       =							override_model_class






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Tuple:




						"""simple docstring"""






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

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

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

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

						return pt_input, tf_input






	def 			lowercase				(     self						)				->       List[Any]:




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

						__magic_name__       ,			__magic_name__ :							Any		       =							self.get_inputs(lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)

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

						__magic_name__ :							str		       =							tf_class.from_pretrained(self._local_dir    ,					from_pt=lowerCamelCase						)
						__magic_name__ :							Optional[Any]		       =							tf_from_pt_model(**lowerCamelCase    ,					output_hidden_states=lowerCamelCase    ,					training=lowerCamelCase						)

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

						__magic_name__ :							Any		       =							tf_class.from_pretrained(self._local_dir						)
						__magic_name__ :							Optional[int]		       =							tf_model(**lowerCamelCase    ,					output_hidden_states=lowerCamelCase						)

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

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

											__magic_name__ :							Dict		       =							create_commit(
											    repo_id=self._model_name    ,					operations=lowerCamelCase    ,					commit_message=lowerCamelCase    ,					commit_description=lowerCamelCase    ,					repo_type='''model'''    ,					create_pr=lowerCamelCase    ,					).pr_url
											self._logger.warning(F'''PR open in {hub_pr_url}'''						)



