





'''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}'''		)











'''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



