'''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      :				Dict 				=    5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors







def 					UpperCamelCase_						( snake_case_	:	Namespace				) ->					Any:






	'''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     _lowercase					(    UpperCAmelCase__      ):


	'''simple docstring'''






	@staticmethod
	def a (				SCREAMING_SNAKE_CASE__       :     ArgumentParser							)  ->   List[str]:
		__lowerCAmelCase    							=						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=SCREAMING_SNAKE_CASE__				,	required=SCREAMING_SNAKE_CASE__				,	help="""The model name, including owner/organization, as seen on the hub."""				,	)
		train_parser.add_argument(
		    """--local-dir"""				,	type=SCREAMING_SNAKE_CASE__				,	default=""""""				,	help="""Optional local directory of the model repository. Defaults to /tmp/{model_name}"""				,	)
		train_parser.add_argument(
		    """--max-error"""				,	type=SCREAMING_SNAKE_CASE__				,	default=SCREAMING_SNAKE_CASE__				,	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=SCREAMING_SNAKE_CASE__				,	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=SCREAMING_SNAKE_CASE__				,	default=SCREAMING_SNAKE_CASE__				,	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=SCREAMING_SNAKE_CASE__							)
	@staticmethod
	def a (				SCREAMING_SNAKE_CASE__       :     Any				,	SCREAMING_SNAKE_CASE__       :     Any							)  ->   str:
		__lowerCAmelCase    							=						set(pt_outputs.keys()							)
		__lowerCAmelCase    							=						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(SCREAMING_SNAKE_CASE__       :     Dict				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     Optional[int]				,	SCREAMING_SNAKE_CASE__       :     str=""							):
			# 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(SCREAMING_SNAKE_CASE__				,	torch.Tensor							):
				__lowerCAmelCase    							=						np.max(np.abs(pt_out.numpy() - tf_out.numpy()							)							)
				__lowerCAmelCase    							=						tensor_difference
			else:
				__lowerCAmelCase    							=						attr_name
				for i, pt_item in enumerate(SCREAMING_SNAKE_CASE__							):
					# If it is a named attribute, we keep the name. Otherwise, just its index.
					if isinstance(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							):
						__lowerCAmelCase    							=						root_name + pt_item
						__lowerCAmelCase    							=						tf_out[pt_item]
						__lowerCAmelCase    							=						pt_out[pt_item]
					else:
						__lowerCAmelCase    							=						root_name + f"""[{i}]"""
						__lowerCAmelCase    							=						tf_out[i]
					__lowerCAmelCase    							=						_find_pt_tf_differences(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)

			return differences

		return _find_pt_tf_differences(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	{}							)
	def __init__(				self       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     str				,	SCREAMING_SNAKE_CASE__       :     str				,	SCREAMING_SNAKE_CASE__       :     float				,	SCREAMING_SNAKE_CASE__       :     bool				,	SCREAMING_SNAKE_CASE__       :     bool				,	SCREAMING_SNAKE_CASE__       :     bool				,	SCREAMING_SNAKE_CASE__       :     str				,	SCREAMING_SNAKE_CASE__       :     str				,	*SCREAMING_SNAKE_CASE__       :     Union[str, Any]				,	)  ->   Union[str, Any]:
		__lowerCAmelCase    							=						logging.get_logger("""transformers-cli/pt_to_tf"""							)
		__lowerCAmelCase    							=						model_name
		__lowerCAmelCase    							=						local_dir if local_dir else os.path.join("""/tmp"""				,	SCREAMING_SNAKE_CASE__							)
		__lowerCAmelCase    							=						max_error
		__lowerCAmelCase    							=						new_weights
		__lowerCAmelCase    							=						no_pr
		__lowerCAmelCase    							=						push
		__lowerCAmelCase    							=						extra_commit_description
		__lowerCAmelCase    							=						override_model_class
	def a (				self       :     int				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]				,	SCREAMING_SNAKE_CASE__       :     Tuple				,	SCREAMING_SNAKE_CASE__       :     Dict							)  ->   Union[str, Any]:

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

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

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

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

		return pt_input, tf_input





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

		__lowerCAmelCase							,	__lowerCAmelCase    							=						self.get_inputs(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)

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

		__lowerCAmelCase    							=						tf_class.from_pretrained(self._local_dir				,	from_pt=SCREAMING_SNAKE_CASE__							)
		__lowerCAmelCase    							=						tf_from_pt_model(**SCREAMING_SNAKE_CASE__				,	output_hidden_states=SCREAMING_SNAKE_CASE__				,	training=SCREAMING_SNAKE_CASE__							)

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

		__lowerCAmelCase    							=						tf_class.from_pretrained(self._local_dir							)
		__lowerCAmelCase    							=						tf_model(**SCREAMING_SNAKE_CASE__				,	output_hidden_states=SCREAMING_SNAKE_CASE__							)

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

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

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







'''simple docstring'''

from unittest.mock import patch

import datasets
from datasets import Dataset







def 					UpperCamelCase_						( ) ->					Dict:






 '''simple docstring'''

 __lowerCAmelCase    							=						Dataset.from_dict({"""col_1""": [3, 2, 0, 1]}				)

 with patch("""tqdm.auto.tqdm"""				) as mock_tqdm:
  datasets.disable_progress_bar()
  dset.map(lambda snake_case_				: {"col_2": x["col_1"] + 1}				)
  mock_tqdm.assert_not_called()

  mock_tqdm.reset_mock()

  datasets.enable_progress_bar()
  dset.map(lambda snake_case_				: {"col_2": x["col_1"] + 1}				)
  mock_tqdm.assert_called()






