
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


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



def       _lowercase					(      __SCREAMING_SNAKE_CASE )	->     str:
			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_			(						__UpperCamelCase   ):




							'''simple docstring'''

							@staticmethod
							def        lowerCAmelCase__ (				UpperCamelCase) ->	Union[str, Any]:
										UpperCamelCase__    :		Dict     =				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        lowerCAmelCase__ (				UpperCamelCase     ,						UpperCamelCase) ->	int:
										UpperCamelCase__    :		Any     =				set(pt_outputs.keys())
										UpperCamelCase__    :		Union[str, Any]     =				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):
																UpperCamelCase__    :		Optional[int]     =				np.max(np.abs(pt_out.numpy() - tf_out.numpy()))
																UpperCamelCase__    :		Tuple     =				tensor_difference
													else:
																UpperCamelCase__    :		str     =				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):
																						UpperCamelCase__    :		Dict     =				root_name + pt_item
																						UpperCamelCase__    :		Union[str, Any]     =				tf_out[pt_item]
																						UpperCamelCase__    :		List[str]     =				pt_out[pt_item]
																			else:
																						UpperCamelCase__    :		int     =				root_name + F"""[{i}]"""
																						UpperCamelCase__    :		int     =				tf_out[i]
																			UpperCamelCase__    :		int     =				_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]:
										UpperCamelCase__    :		str     =				logging.get_logger('transformers-cli/pt_to_tf')
										UpperCamelCase__    :		Optional[int]     =				model_name
										UpperCamelCase__    :		Tuple     =				local_dir if local_dir else os.path.join('/tmp'     ,						UpperCamelCase)
										UpperCamelCase__    :		str     =				max_error
										UpperCamelCase__    :		Dict     =				new_weights
										UpperCamelCase__    :		Tuple     =				no_pr
										UpperCamelCase__    :		Any     =				push
										UpperCamelCase__    :		Tuple     =				extra_commit_description
										UpperCamelCase__    :		Optional[Any]     =				override_model_class
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) ->	Dict:

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

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

										UpperCamelCase__    :		Tuple     =				set(inspect.signature(pt_model.forward).parameters.keys())
										UpperCamelCase__    :		Union[str, Any]     =				{}
										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:
													UpperCamelCase__    :		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:
													UpperCamelCase__    :		List[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:
																UpperCamelCase__    :		Any     =				feature_extractor_signature['padding'].default
																if default_strategy is not False and default_strategy is not None:
																			UpperCamelCase__    :		Dict     =				default_strategy
																else:
																			UpperCamelCase__    :		Dict     =				True
													else:
																UpperCamelCase__    :		List[str]     =				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})
										UpperCamelCase__    :		List[Any]     =				processor(**UpperCamelCase     ,						return_tensors='pt')
										UpperCamelCase__    :		List[str]     =				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
										):
													UpperCamelCase__    :		str     =				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        lowerCAmelCase__ (				self) ->	Dict:
										# 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
										UpperCamelCase__    :		List[str]     =				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
										UpperCamelCase__    :		Any     =				AutoConfig.from_pretrained(self._local_dir)
										UpperCamelCase__    :		str     =				config.architectures
										if self._override_model_class is not None:
													if self._override_model_class.startswith('TF'):
																UpperCamelCase__    :		int     =				[self._override_model_class[2:]]
													else:
																UpperCamelCase__    :		Optional[Any]     =				[self._override_model_class]
													try:
																UpperCamelCase__    :		Tuple     =				getattr(import_module('transformers')     ,						architectures[0])
													except AttributeError:
																raise ValueError(F"""Model class {self._override_model_class} not found in transformers.""")
													try:
																UpperCamelCase__    :		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
													UpperCamelCase__    :		List[str]     =				getattr(import_module('transformers')     ,						'AutoModel')
													UpperCamelCase__    :		Tuple     =				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]}""")
													UpperCamelCase__    :		List[Any]     =				getattr(import_module('transformers')     ,						architectures[0])
													try:
																UpperCamelCase__    :		Union[str, Any]     =				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
										UpperCamelCase__    :		int     =				tf_class.from_config(UpperCamelCase)
										UpperCamelCase__    :		Optional[int]     =				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
										UpperCamelCase__    :		str     =				pt_class.from_pretrained(self._local_dir)
										pt_model.eval()

										UpperCamelCase__   ,			UpperCamelCase__    :		int     =				self.get_inputs(UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase)

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

										UpperCamelCase__    :		List[str]     =				tf_class.from_pretrained(self._local_dir     ,						from_pt=UpperCamelCase)
										UpperCamelCase__    :		Optional[int]     =				tf_from_pt_model(**UpperCamelCase     ,						output_hidden_states=UpperCamelCase     ,						training=UpperCamelCase)

										# Confirms that cross loading PT weights into TF worked.
										UpperCamelCase__    :		int     =				self.find_pt_tf_differences(UpperCamelCase     ,						UpperCamelCase)
										UpperCamelCase__    :		int     =				{k: v for k, v in crossload_differences.items() if 'hidden' not in k}
										UpperCamelCase__    :		List[Any]     =				{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\'')
										UpperCamelCase__    :		int     =				max(output_differences.values()) if output_differences else 0.0
										UpperCamelCase__    :		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
										UpperCamelCase__    :		Optional[Any]     =				os.path.join(self._local_dir     ,						UpperCamelCase)
										UpperCamelCase__    :		Any     =				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

										UpperCamelCase__    :		Optional[Any]     =				tf_class.from_pretrained(self._local_dir)
										UpperCamelCase__    :		str     =				tf_model(**UpperCamelCase     ,						output_hidden_states=UpperCamelCase)

										UpperCamelCase__    :		List[str]     =				self.find_pt_tf_differences(UpperCamelCase     ,						UpperCamelCase)
										UpperCamelCase__    :		Optional[int]     =				{k: v for k, v in conversion_differences.items() if 'hidden' not in k}
										UpperCamelCase__    :		Any     =				{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\'')
										UpperCamelCase__    :		Any     =				max(output_differences.values()) if output_differences else 0.0
										UpperCamelCase__    :		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]))

										UpperCamelCase__    :		Optional[int]     =				'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...')
													UpperCamelCase__    :		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(UpperCamelCase):
																UpperCamelCase__    :		Union[str, Any]     =				[
																    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:
																UpperCamelCase__    :		Any     =				[CommitOperationAdd(path_in_repo=UpperCamelCase     ,						path_or_fileobj=UpperCamelCase)]

													UpperCamelCase__    :		Any     =				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}""")





from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


UpperCAmelCase__				:  Tuple       					=				[
    '''AutomaticSpeechRecognition''',
    '''AudioClassification''',
    '''ImageClassification''',
    '''LanguageModeling''',
    '''QuestionAnsweringExtractive''',
    '''Summarization''',
    '''TaskTemplate''',
    '''TextClassification''',
]

UpperCAmelCase__				:  Dict       					=				get_logger(__name__)


UpperCAmelCase__				:  Dict       					=				{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}



def       _lowercase					(      __SCREAMING_SNAKE_CASE )	->     Optional[TaskTemplate]:
			UpperCamelCase__    :		Any     =				task_template_dict.get('task' )
			if task_name is None:
						logger.warning(F"""Couldn't find template for task '{task_name}'. Available templates: {list(__SCREAMING_SNAKE_CASE )}""" )
						return None
			UpperCamelCase__    :		Any     =				NAMEaTEMPLATE.get(__SCREAMING_SNAKE_CASE )
			return template.from_dict(__SCREAMING_SNAKE_CASE )



