

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


lowerCAmelCase_	     =     5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						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_       (		__A ):







			'''simple docstring'''

			@staticmethod
			def     snake_case__(					_UpperCamelCase			:    ArgumentParser							)				->Optional[Any]:
									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			:    List[Any]							,       _UpperCamelCase			:    List[Any]							)				->Optional[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			:    Optional[int]							,       _UpperCamelCase			:    Dict							,       _UpperCamelCase			:    Optional[int]							,       _UpperCamelCase			:    Any=""							):
															# 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			:    Tuple							,       _UpperCamelCase			:    str							,       _UpperCamelCase			:    str							,       _UpperCamelCase			:    float							,       _UpperCamelCase			:    bool							,       _UpperCamelCase			:    bool							,       _UpperCamelCase			:    bool							,       _UpperCamelCase			:    str							,       _UpperCamelCase			:    str							,       *_UpperCamelCase			:    Optional[int]							,       )				->int:
									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			:    List[str]							,       _UpperCamelCase			:    Any							,       _UpperCamelCase			:    Tuple							,       _UpperCamelCase			:    Optional[int]							)				->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			:    str							)				->str:
									# 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}'''							)


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class      snake_case_       (		__A ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       List[str]						      =    ["image_processor", "tokenizer"]
			SCREAMING_SNAKE_CASE					:       List[Any]						      =    "AutoImageProcessor"
			SCREAMING_SNAKE_CASE					:       Optional[int]						      =    "AutoTokenizer"







			def __init__(					self			:    List[str]							,       _UpperCamelCase			:    List[str]=None							,       _UpperCamelCase			:    Any=None							,       **_UpperCamelCase			:    Tuple							)				->str:
									snake_case_       =     None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
															    ''' instead.'''							,       _UpperCamelCase							,       )
															snake_case_       =     kwargs.pop('''feature_extractor'''							)

									snake_case_       =     image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError('''You need to specify an `image_processor`.'''							)
									if tokenizer is None:
															raise ValueError('''You need to specify a `tokenizer`.'''							)

									super().__init__(_UpperCamelCase							,       _UpperCamelCase							)
									snake_case_       =     self.image_processor
									snake_case_       =     False







			def __call__(					self			:    int							,       *_UpperCamelCase			:    Tuple							,       **_UpperCamelCase			:    Any							)				->Tuple:
									# For backward compatibility
									if self._in_target_context_manager:
															return self.current_processor(*_UpperCamelCase							,       **_UpperCamelCase							)

									snake_case_       =     kwargs.pop('''images'''							,       _UpperCamelCase							)
									snake_case_       =     kwargs.pop('''text'''							,       _UpperCamelCase							)
									if len(_UpperCamelCase							) > 0:
															snake_case_       =     args[0]
															snake_case_       =     args[1:]

									if images is None and text is None:
															raise ValueError('''You need to specify either an `images` or `text` input to process.'''							)

									if images is not None:
															snake_case_       =     self.image_processor(_UpperCamelCase							,       *_UpperCamelCase							,       **_UpperCamelCase							)
									if text is not None:
															snake_case_       =     self.tokenizer(_UpperCamelCase							,       **_UpperCamelCase							)

									if text is None:
															return inputs
									elif images is None:
															return encodings
									else:
															snake_case_       =     encodings['''input_ids''']
															return inputs







			def     snake_case__(					self			:    List[Any]							,       *_UpperCamelCase			:    Union[str, Any]							,       **_UpperCamelCase			:    Tuple							)				->Any:
									return self.tokenizer.batch_decode(*_UpperCamelCase							,       **_UpperCamelCase							)







			def     snake_case__(					self			:    Optional[Any]							,       *_UpperCamelCase			:    List[Any]							,       **_UpperCamelCase			:    Union[str, Any]							)				->List[str]:
									return self.tokenizer.decode(*_UpperCamelCase							,       **_UpperCamelCase							)







			@contextmanager
			def     snake_case__(					self			:    Any							)				->Any:
									warnings.warn(
									    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
									    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
									    '''your images inputs, or in a separate call.'''							)
									snake_case_       =     True
									snake_case_       =     self.tokenizer
									yield
									snake_case_       =     self.image_processor
									snake_case_       =     False







			@property
			def     snake_case__(					self			:    Optional[int]							)				->int:
									warnings.warn(
									    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,       _UpperCamelCase							,       )
									return self.image_processor_class







			@property
			def     snake_case__(					self			:    Union[str, Any]							)				->List[Any]:
									warnings.warn(
									    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,       _UpperCamelCase							,       )
									return self.image_processor