code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import inspect
  import os
  from argparse import ArgumentParser, Namespace
  from importlib import import_module
  
  import huggingface_hub
  import numpy as np
  from packaging import version
  
  from .. import (
      FEATURE_EXTRACTOR_MAPPING,
      IMAGE_PROCESSOR_MAPPING,
      PROCESSOR_MAPPING,
      TOKENIZER_MAPPING,
      AutoConfig,
      AutoFeatureExtractor,
      AutoImageProcessor,
      AutoProcessor,
      AutoTokenizer,
      is_datasets_available,
      is_tf_available,
      is_torch_available,
  )
  from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
  from . import BaseTransformersCLICommand
  
  
  if is_tf_available():
  			import tensorflow as tf
  
  			tf.config.experimental.enable_tensor_float_aa_execution(False)
  
  if is_torch_available():
  			import torch
  
  if is_datasets_available():
  			from datasets import load_dataset
  
  
  a__				: int									=    5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_       )      ->int:
  	return PTtoTFCommand(
  	    args.model_name				,							args.local_dir				,							args.max_error				,							args.new_weights				,							args.no_pr				,							args.push				,							args.extra_commit_description				,							args.override_model_class				,							)
  
  
  
  
  
  
  
  class 							__snake_case    (				UpperCAmelCase_       ):
  
  
  
  
  
  	@staticmethod
  	def      _snake_case      (		UpperCamelCase_		)			->		Dict:
  		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=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_			,					UpperCamelCase_		)			->		List[Any]:
  		snake_case__  					=      set(pt_outputs.keys()		)
  		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(UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_=""		):
  			# 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		):
  				snake_case__  					=      np.max(np.abs(pt_out.numpy() - tf_out.numpy()		)		)
  				snake_case__  					=      tensor_difference
  			else:
  				snake_case__  					=      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_		):
  						snake_case__  					=      root_name + pt_item
  						snake_case__  					=      tf_out[pt_item]
  						snake_case__  					=      pt_out[pt_item]
  					else:
  						snake_case__  					=      root_name + F'''[{i}]'''
  						snake_case__  					=      tf_out[i]
  					snake_case__  					=      _find_pt_tf_differences(UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		)
  
  			return differences
  
  		return _find_pt_tf_differences(UpperCamelCase_			,					UpperCamelCase_			,					{}		)
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					*UpperCamelCase_			,					)			->		Optional[Any]:
  		snake_case__  					=      logging.get_logger('transformers-cli/pt_to_tf'		)
  		snake_case__  					=      model_name
  		snake_case__  					=      local_dir if local_dir else os.path.join('/tmp'			,					UpperCamelCase_		)
  		snake_case__  					=      max_error
  		snake_case__  					=      new_weights
  		snake_case__  					=      no_pr
  		snake_case__  					=      push
  		snake_case__  					=      extra_commit_description
  		snake_case__  					=      override_model_class
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		)			->		Any:
  
  		def _get_audio_input():
  			snake_case__  					=      load_dataset('hf-internal-testing/librispeech_asr_dummy'			,					'clean'			,					split='validation'		)
  			snake_case__  					=      ds.sort('id'		).select(range(2		)		)[:2]['audio']
  			snake_case__  					=      [x['array'] for x in speech_samples]
  			return raw_samples
  
  		snake_case__  					=      type(pt_model.config		)
  		if model_config_class in PROCESSOR_MAPPING:
  			snake_case__  					=      AutoProcessor.from_pretrained(self._local_dir		)
  			if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
  				snake_case__  					=      processor.tokenizer.eos_token
  		elif model_config_class in IMAGE_PROCESSOR_MAPPING:
  			snake_case__  					=      AutoImageProcessor.from_pretrained(self._local_dir		)
  		elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
  			snake_case__  					=      AutoFeatureExtractor.from_pretrained(self._local_dir		)
  		elif model_config_class in TOKENIZER_MAPPING:
  			snake_case__  					=      AutoTokenizer.from_pretrained(self._local_dir		)
  			if processor.pad_token is None:
  				snake_case__  					=      processor.eos_token
  		else:
  			raise ValueError(F'''Unknown data processing type (model config type: {model_config_class})'''		)
  
  		snake_case__  					=      set(inspect.signature(pt_model.forward		).parameters.keys()		)
  		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:
  			snake_case__  					=      load_dataset('cifar10'			,					'plain_text'			,					split='test'		)[:2]['img']
  			processor_inputs.update({'images': sample_images}		)
  		if "input_features" in model_forward_signature:
  			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:
  				snake_case__  					=      feature_extractor_signature['padding'].default
  				if default_strategy is not False and default_strategy is not None:
  					snake_case__  					=      default_strategy
  				else:
  					snake_case__  					=      True
  			else:
  				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}		)
  		snake_case__  					=      processor(**UpperCamelCase_			,					return_tensors='pt'		)
  		snake_case__  					=      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
  		):
  			snake_case__  					=      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		)			->		Tuple:
  		# hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
  		if version.parse(huggingface_hub.__version__		) < version.parse('0.9.0'		):
  			raise ImportError(
  			    'The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub'
  			    ' installation.'		)
  		else:
  			from huggingface_hub import Repository, create_commit
  			from huggingface_hub._commit_api import CommitOperationAdd
  
  		# Fetch remote data
  		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
  		snake_case__  					=      AutoConfig.from_pretrained(self._local_dir		)
  		snake_case__  					=      config.architectures
  		if self._override_model_class is not None:
  			if self._override_model_class.startswith('TF'		):
  				snake_case__  					=      [self._override_model_class[2:]]
  			else:
  				snake_case__  					=      [self._override_model_class]
  			try:
  				snake_case__  					=      getattr(import_module('transformers'		)			,					architectures[0]		)
  			except AttributeError:
  				raise ValueError(F'''Model class {self._override_model_class} not found in transformers.'''		)
  			try:
  				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
  			snake_case__  					=      getattr(import_module('transformers'		)			,					'AutoModel'		)
  			snake_case__  					=      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]}'''		)
  			snake_case__  					=      getattr(import_module('transformers'		)			,					architectures[0]		)
  			try:
  				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
  		snake_case__  					=      tf_class.from_config(UpperCamelCase_		)
  		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
  		snake_case__  					=      pt_class.from_pretrained(self._local_dir		)
  		pt_model.eval()
  
  		snake_case__					,     snake_case__  					=      self.get_inputs(UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		)
  
  		with torch.no_grad():
  			snake_case__  					=      pt_model(**UpperCamelCase_			,					output_hidden_states=UpperCamelCase_		)
  		del pt_model  # will no longer be used, and may have a large memory footprint
  
  		snake_case__  					=      tf_class.from_pretrained(self._local_dir			,					from_pt=UpperCamelCase_		)
  		snake_case__  					=      tf_from_pt_model(**UpperCamelCase_			,					output_hidden_states=UpperCamelCase_			,					training=UpperCamelCase_		)
  
  		# Confirms that cross loading PT weights into TF worked.
  		snake_case__  					=      self.find_pt_tf_differences(UpperCamelCase_			,					UpperCamelCase_		)
  		snake_case__  					=      {k: v for k, v in crossload_differences.items() if 'hidden' not in k}
  		snake_case__  					=      {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\''		)
  		snake_case__  					=      max(output_differences.values()		) if output_differences else 0.0
  		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
  		snake_case__  					=      os.path.join(self._local_dir			,					UpperCamelCase_		)
  		snake_case__  					=      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
  
  		snake_case__  					=      tf_class.from_pretrained(self._local_dir		)
  		snake_case__  					=      tf_model(**UpperCamelCase_			,					output_hidden_states=UpperCamelCase_		)
  
  		snake_case__  					=      self.find_pt_tf_differences(UpperCamelCase_			,					UpperCamelCase_		)
  		snake_case__  					=      {k: v for k, v in conversion_differences.items() if 'hidden' not in k}
  		snake_case__  					=      {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\''		)
  		snake_case__  					=      max(output_differences.values()		) if output_differences else 0.0
  		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]		)		)
  
  		snake_case__  					=      '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...'		)
  			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(UpperCamelCase_		):
  				snake_case__  					=      [
  				    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:
  				snake_case__  					=      [CommitOperationAdd(path_in_repo=UpperCamelCase_			,					path_or_fileobj=UpperCamelCase_		)]
  
  			snake_case__  					=      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}'''		)
  
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from packaging import version
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  a__				: Dict									=    logging.get_logger(__name__)
  
  a__				: Tuple									=    {
      '''apple/mobilevitv2-1.0''': '''https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json''',
  }
  
  
  
  
  
  
  class 							__snake_case    (				__magic_name__       ):
  	__lowerCAmelCase						=     '''mobilevitv2'''
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_=3			,					UpperCamelCase_=256			,					UpperCamelCase_=2			,					UpperCamelCase_=2.0			,					UpperCamelCase_="swish"			,					UpperCamelCase_=3			,					UpperCamelCase_=32			,					UpperCamelCase_=0.1			,					UpperCamelCase_=0.0_2			,					UpperCamelCase_=1E-5			,					UpperCamelCase_=512			,					UpperCamelCase_=[6, 12, 18]			,					UpperCamelCase_=0.1			,					UpperCamelCase_=255			,					UpperCamelCase_=[2, 4, 3]			,					UpperCamelCase_=[128, 192, 256]			,					UpperCamelCase_=1.0			,					UpperCamelCase_=2			,					UpperCamelCase_=0.0			,					UpperCamelCase_=0.0			,					**UpperCamelCase_			,					)			->		Union[str, Any]:
  		super().__init__(**UpperCamelCase_		)
  
  		snake_case__  					=      num_channels
  		snake_case__  					=      image_size
  		snake_case__  					=      patch_size
  		snake_case__  					=      expand_ratio
  		snake_case__  					=      hidden_act
  		snake_case__  					=      conv_kernel_size
  		snake_case__  					=      output_stride
  		snake_case__  					=      initializer_range
  		snake_case__  					=      layer_norm_eps
  		snake_case__  					=      n_attn_blocks
  		snake_case__  					=      base_attn_unit_dims
  		snake_case__  					=      width_multiplier
  		snake_case__  					=      ffn_multiplier
  		snake_case__  					=      ffn_dropout
  		snake_case__  					=      attn_dropout
  		snake_case__  					=      classifier_dropout_prob
  
  		# decode head attributes for semantic segmentation
  		snake_case__  					=      aspp_out_channels
  		snake_case__  					=      atrous_rates
  		snake_case__  					=      aspp_dropout_prob
  		snake_case__  					=      semantic_loss_ignore_index
  
  
  
  
  
  
  class 							__snake_case    (				__magic_name__       ):
  	__lowerCAmelCase						=     version.parse('''1.11'''       )
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		Mapping[str, Mapping[int, str]]:
  		return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]		)
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		Mapping[str, Mapping[int, str]]:
  		if self.task == "image-classification":
  			return OrderedDict([('logits', {0: 'batch'})]		)
  		else:
  			return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]		)
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		float:
  		return 1E-4
  
  
  
  