
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


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

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    int							)			->							int:

				"""simple docstring"""







				return PTtoTFCommand(
				    args.model_name				,    args.local_dir				,    args.max_error				,    args.new_weights				,    args.no_pr				,    args.push				,    args.extra_commit_description				,    args.override_model_class				,    )







class 			UpperCamelCase__		(     snake_case_ ):







	'''simple docstring'''





	@staticmethod
	def 	__snake_case					(      UpperCamelCase__		):
					A__	:						str								=				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 	__snake_case					(      UpperCamelCase__					,							UpperCamelCase__		):
					A__	:						Tuple								=				set(pt_outputs.keys()		)
					A__	:						int								=				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(_A					,							torch.Tensor		):
													A__	:						List[str]								=				np.max(np.abs(pt_out.numpy() - tf_out.numpy()		)		)
													A__	:						int								=				tensor_difference
									else:
													A__	:						List[str]								=				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		):
																					A__	:						int								=				root_name + pt_item
																					A__	:						Any								=				tf_out[pt_item]
																					A__	:						Any								=				pt_out[pt_item]
																	else:
																					A__	:						Optional[int]								=				root_name + F"[{i}]"
																					A__	:						str								=				tf_out[i]
																	A__	:						str								=				_find_pt_tf_differences(_A					,							_A					,							_A					,							_A		)

									return differences

					return _find_pt_tf_differences(_A					,							_A					,							{}		)





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





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__		):


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

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

					A__	:						int								=				set(inspect.signature(pt_model.forward		).parameters.keys()		)
					A__	:						Optional[int]								=				{}
					if "input_ids" in model_forward_signature:
									processor_inputs.update(
									    {
									        '''text''': ['''Hi there!''', '''I am a batch with more than one row and different input lengths.'''],
									        '''padding''': True,
									        '''truncation''': True,
									    }		)
					if "pixel_values" in model_forward_signature:
									A__	:						Union[str, Any]								=				load_dataset('''cifar10'''					,							'''plain_text'''					,							split='''test'''		)[:2]['img']
									processor_inputs.update({'''images''': sample_images}		)
					if "input_features" in model_forward_signature:
									A__	:						Tuple								=				inspect.signature(processor.feature_extractor		).parameters
									# Pad to the largest input length by default but take feature extractor default
									# padding value if it exists e.g. "max_length" and is not False or None
									if "padding" in feature_extractor_signature:
													A__	:						Union[str, Any]								=				feature_extractor_signature['padding'].default
													if default_strategy is not False and default_strategy is not None:
																	A__	:						Tuple								=				default_strategy
													else:
																	A__	:						Union[str, Any]								=				True
									else:
													A__	:						Union[str, Any]								=				True
									processor_inputs.update({'''audio''': _get_audio_input(), '''padding''': padding_strategy}		)
					if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
									processor_inputs.update({'''audio''': _get_audio_input(), '''padding''': True}		)
					A__	:						Optional[Any]								=				processor(**_A					,							return_tensors='''pt'''		)
					A__	:						int								=				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
					):
									A__	:						List[Any]								=				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 	__snake_case					(      self		):


					if version.parse(huggingface_hub.__version__		) < version.parse('''0.9.0'''		):
									raise ImportError(
									    '''The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub'''
									    ''' installation.'''		)
					else:
									from huggingface_hub import Repository, create_commit
									from huggingface_hub._commit_api import CommitOperationAdd

					# Fetch remote data
					A__	:						Union[str, Any]								=				Repository(local_dir=self._local_dir					,							clone_from=self._model_name		)

					# Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
					A__	:						Optional[int]								=				AutoConfig.from_pretrained(self._local_dir		)
					A__	:						Union[str, Any]								=				config.architectures
					if self._override_model_class is not None:
									if self._override_model_class.startswith('''TF'''		):
													A__	:						Dict								=				[self._override_model_class[2:]]
									else:
													A__	:						int								=				[self._override_model_class]
									try:
													A__	:						Dict								=				getattr(import_module('''transformers'''		)					,							architectures[0]		)
									except AttributeError:
													raise ValueError(F"Model class {self._override_model_class} not found in transformers."		)
									try:
													A__	:						str								=				getattr(import_module('''transformers'''		)					,							'''TF''' + architectures[0]		)
									except AttributeError:
													raise ValueError(F"TF model class TF{self._override_model_class} not found in transformers."		)
					elif architectures is None:  # No architecture defined -- use auto classes
									A__	:						Optional[Any]								=				getattr(import_module('''transformers'''		)					,							'''AutoModel'''		)
									A__	:						Dict								=				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]}"		)
									A__	:						Optional[Any]								=				getattr(import_module('''transformers'''		)					,							architectures[0]		)
									try:
													A__	:						List[str]								=				getattr(import_module('''transformers'''		)					,							'''TF''' + architectures[0]		)
									except AttributeError:
													raise AttributeError(F"The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers."		)

        # Check the TF dummy inputs to see what keys we need in the forward pass
					A__	:						Optional[int]								=				tf_class.from_config(_A		)
					A__	:						Union[str, Any]								=				tf_from_pt_model.dummy_inputs

					del tf_from_pt_model  # Try to keep only one model in memory at a time

					# Load the model and get some basic inputs
					A__	:						List[Any]								=				pt_class.from_pretrained(self._local_dir		)
					pt_model.eval()

					A__	:						Any								=				self.get_inputs(_A					,							_A					,							_A		)

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

					A__	:						List[str]								=				tf_class.from_pretrained(self._local_dir					,							from_pt=_A		)
					A__	:						List[str]								=				tf_from_pt_model(**_A					,							output_hidden_states=_A					,							training=_A		)

					# Confirms that cross loading PT weights into TF worked.
					A__	:						Tuple								=				self.find_pt_tf_differences(_A					,							_A		)
					A__	:						Optional[int]								=				{k: v for k, v in crossload_differences.items() if 'hidden' not in k}
					A__	:						Any								=				{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\''''		)
					A__	:						List[str]								=				max(output_differences.values()		) if output_differences else 0.0
					A__	:						List[str]								=				max(hidden_differences.values()		)
					if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
									raise ValueError(
									    '''The cross-loaded TensorFlow model has different outputs, something went wrong!\n'''
									    + F"\nList of maximum output differences above the threshold ({self._max_error}):\n"
									    + '''\n'''.join([F"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error]		)
									    + F"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
									    + '''\n'''.join([F"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error]		)		)

					# Save the weights in a TF format (if needed) and confirms that the results are still good
					A__	:						List[Any]								=				os.path.join(self._local_dir					,							_A		)
					A__	:						Dict								=				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

					A__	:						Optional[int]								=				tf_class.from_pretrained(self._local_dir		)
					A__	:						List[str]								=				tf_model(**_A					,							output_hidden_states=_A		)

					A__	:						Dict								=				self.find_pt_tf_differences(_A					,							_A		)
					A__	:						List[str]								=				{k: v for k, v in conversion_differences.items() if 'hidden' not in k}
					A__	:						Tuple								=				{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\''''		)
					A__	:						Dict								=				max(output_differences.values()		) if output_differences else 0.0
					A__	:						Any								=				max(hidden_differences.values()		)
					if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
									raise ValueError(
									    '''The converted TensorFlow model has different outputs, something went wrong!\n'''
									    + F"\nList of maximum output differences above the threshold ({self._max_error}):\n"
									    + '''\n'''.join([F"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error]		)
									    + F"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
									    + '''\n'''.join([F"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error]		)		)

					A__	:						Tuple								=				'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...'''		)
									A__	:						Union[str, Any]								=				(
									    '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		):
													A__	:						Optional[int]								=				[
													    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:
													A__	:						int								=				[CommitOperationAdd(path_in_repo=_A					,							path_or_fileobj=_A		)]

									A__	:						Any								=				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}"		)






def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	if len(str(__a ) ) == 1 or len(str(__a ) ) == 1:
		return a * b

	snake_case_		:      Dict           =						max(len(str(__a ) ) ,			len(str(__a ) ) )
	snake_case_		:      Tuple           =						ma // 2

	snake_case_    ,snake_case_		:      Tuple           =						divmod(__a ,			10**ma )
	snake_case_    ,snake_case_		:      int           =						divmod(__a ,			10**ma )

	snake_case_		:      str           =						karatsuba(__a ,			__a )
	snake_case_		:      List[str]           =						karatsuba((aa + aa) ,			(ba + ba) )
	snake_case_		:      Optional[Any]           =						karatsuba(__a ,			__a )

	return (z * 10 ** (2 * ma)) + ((y - z - x) * 10 ** (ma)) + (x)


def 				SCREAMING_SNAKE_CASE__    (     ):
	print(karatsuba(1_54_63 ,			2_34_89 ) )


if __name__ == "__main__":
						main()

