




import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


lowercase_		=							get_logger(__name__)

# Datasets
lowercase_		=							'''https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets'''
lowercase_		=							'''https://cdn-datasets.huggingface.co/datasets/datasets'''
lowercase_		=							'''https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}'''

# Metrics
lowercase_		=							'''https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics'''
lowercase_		=							'''https://cdn-datasets.huggingface.co/datasets/metric'''
lowercase_		=							'''https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}'''

# Hub
lowercase_		=							os.environ.get('''HF_ENDPOINT''', '''https://huggingface.co''')
lowercase_		=							HF_ENDPOINT + '''/datasets/{repo_id}/resolve/{revision}/{path}'''
lowercase_		=							'''main'''

lowercase_		=							version.parse(platform.python_version())

if PY_VERSION < version.parse('''3.8'''):
				import importlib_metadata
else:
				import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
lowercase_		=							{'''1''', '''ON''', '''YES''', '''TRUE'''}
lowercase_		=							ENV_VARS_TRUE_VALUES.union({'''AUTO'''})


# Imports
lowercase_		=							version.parse(importlib_metadata.version('''dill'''))
lowercase_		=							version.parse(importlib_metadata.version('''pandas'''))
lowercase_		=							version.parse(importlib_metadata.version('''pyarrow'''))

lowercase_		=							os.environ.get('''USE_TF''', '''AUTO''').upper()
lowercase_		=							os.environ.get('''USE_TORCH''', '''AUTO''').upper()
lowercase_		=							os.environ.get('''USE_JAX''', '''AUTO''').upper()

lowercase_		=							'''N/A'''
lowercase_		=							False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
				lowercase_		=							importlib.util.find_spec('''torch''') is not None
				if TORCH_AVAILABLE:
								try:
												lowercase_		=							version.parse(importlib_metadata.version('''torch'''))
												logger.info(f"PyTorch version {TORCH_VERSION} available.")
								except importlib_metadata.PackageNotFoundError:
												pass
else:
				logger.info('''Disabling PyTorch because USE_TF is set''')

lowercase_		=							'''N/A'''
lowercase_		=							False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
				lowercase_		=							importlib.util.find_spec('''tensorflow''') is not None
				if TF_AVAILABLE:
								# For the metadata, we have to look for both tensorflow and tensorflow-cpu
								for package in [
								    "tensorflow",
								    "tensorflow-cpu",
								    "tensorflow-gpu",
								    "tf-nightly",
								    "tf-nightly-cpu",
								    "tf-nightly-gpu",
								    "intel-tensorflow",
								    "tensorflow-rocm",
								    "tensorflow-macos",
								]:
												try:
																lowercase_		=							version.parse(importlib_metadata.version(package))
												except importlib_metadata.PackageNotFoundError:
																continue
												else:
																break
								else:
												lowercase_		=							False
				if TF_AVAILABLE:
								if TF_VERSION.major < 2:
												logger.info(f"TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.")
												lowercase_		=							False
								else:
												logger.info(f"TensorFlow version {TF_VERSION} available.")
else:
				logger.info('''Disabling Tensorflow because USE_TORCH is set''')


lowercase_		=							'''N/A'''
lowercase_		=							False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
				lowercase_		=							importlib.util.find_spec('''jax''') is not None and importlib.util.find_spec('''jaxlib''') is not None
				if JAX_AVAILABLE:
								try:
												lowercase_		=							version.parse(importlib_metadata.version('''jax'''))
												logger.info(f"JAX version {JAX_VERSION} available.")
								except importlib_metadata.PackageNotFoundError:
												pass
else:
				logger.info('''Disabling JAX because USE_JAX is set to False''')


lowercase_		=							os.environ.get('''USE_BEAM''', '''AUTO''').upper()
lowercase_		=							'''N/A'''
lowercase_		=							False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
				try:
								lowercase_		=							version.parse(importlib_metadata.version('''apache_beam'''))
								lowercase_		=							True
								logger.info(f"Apache Beam version {BEAM_VERSION} available.")
				except importlib_metadata.PackageNotFoundError:
								pass
else:
				logger.info('''Disabling Apache Beam because USE_BEAM is set to False''')


# Optional tools for data loading
lowercase_		=							importlib.util.find_spec('''sqlalchemy''') is not None

# Optional tools for feature decoding
lowercase_		=							importlib.util.find_spec('''PIL''') is not None
lowercase_		=							importlib.util.find_spec('''soundfile''') is not None and version.parse(
    importlib.import_module('''soundfile''').__libsndfile_version__
) >= version.parse('''1.0.31''')
lowercase_		=							importlib.util.find_spec('''soundfile''') is not None and version.parse(
    importlib.import_module('''soundfile''').__libsndfile_version__
) >= version.parse('''1.1.0''')

# Optional compression tools
lowercase_		=							importlib.util.find_spec('''rarfile''') is not None
lowercase_		=							importlib.util.find_spec('''zstandard''') is not None
lowercase_		=							importlib.util.find_spec('''lz4''') is not None
lowercase_		=							importlib.util.find_spec('''py7zr''') is not None


# Cache location
lowercase_		=							'''~/.cache'''
lowercase_		=							os.getenv('''XDG_CACHE_HOME''', DEFAULT_XDG_CACHE_HOME)
lowercase_		=							os.path.join(XDG_CACHE_HOME, '''huggingface''')
lowercase_		=							os.path.expanduser(os.getenv('''HF_HOME''', DEFAULT_HF_CACHE_HOME))

lowercase_		=							os.path.join(HF_CACHE_HOME, '''datasets''')
lowercase_		=							Path(os.getenv('''HF_DATASETS_CACHE''', DEFAULT_HF_DATASETS_CACHE))

lowercase_		=							os.path.join(HF_CACHE_HOME, '''metrics''')
lowercase_		=							Path(os.getenv('''HF_METRICS_CACHE''', DEFAULT_HF_METRICS_CACHE))

lowercase_		=							os.path.join(HF_CACHE_HOME, '''modules''')
lowercase_		=							Path(os.getenv('''HF_MODULES_CACHE''', DEFAULT_HF_MODULES_CACHE))

lowercase_		=							'''downloads'''
lowercase_		=							os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
lowercase_		=							Path(os.getenv('''HF_DATASETS_DOWNLOADED_DATASETS_PATH''', DEFAULT_DOWNLOADED_DATASETS_PATH))

lowercase_		=							'''extracted'''
lowercase_		=							os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
lowercase_		=							Path(os.getenv('''HF_DATASETS_EXTRACTED_DATASETS_PATH''', DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
lowercase_		=							(
    os.environ.get('''HF_UPDATE_DOWNLOAD_COUNTS''', '''AUTO''').upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
lowercase_		=							1000

# Size of the preloaded record batch in `Dataset.__iter__`
lowercase_		=							10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
lowercase_		=							4 << 30

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
lowercase_		=							'''500MB'''

# Parquet configuration
lowercase_		=							100
lowercase_		=							100
lowercase_		=							100

# Offline mode
lowercase_		=							os.environ.get('''HF_DATASETS_OFFLINE''', '''AUTO''').upper() in ENV_VARS_TRUE_VALUES

# In-memory
lowercase_		=							0  # Disabled
lowercase_		=							float(os.environ.get('''HF_DATASETS_IN_MEMORY_MAX_SIZE''', DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
lowercase_		=							'''dataset.arrow'''
lowercase_		=							'''indices.arrow'''
lowercase_		=							'''state.json'''
lowercase_		=							'''dataset_info.json'''
lowercase_		=							'''dataset_infos.json'''
lowercase_		=							'''LICENSE'''
lowercase_		=							'''metric_info.json'''
lowercase_		=							'''dataset_dict.json'''

lowercase_		=							'''datasets_modules'''

lowercase_		=							255

# Streaming
lowercase_		=							20
lowercase_		=							5

# Datasets without script
lowercase_		=							200
lowercase_		=							10
lowercase_		=							200

# Progress bars
lowercase_		=							0.0_5  # 20 progress updates per sec









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


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







def 			lowerCAmelCase  (     UpperCAmelCase	)  ->Dict:






					"""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 		A__    (       __SCREAMING_SNAKE_CASE			):






	@staticmethod
	def 			lowercase				(     lowerCamelCase						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							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=lowerCamelCase    ,					required=lowerCamelCase    ,					help='''The model name, including owner/organization, as seen on the hub.'''    ,					)
						train_parser.add_argument(
						    '''--local-dir'''    ,					type=lowerCamelCase    ,					default=''''''    ,					help='''Optional local directory of the model repository. Defaults to /tmp/{model_name}'''    ,					)
						train_parser.add_argument(
						    '''--max-error'''    ,					type=lowerCamelCase    ,					default=lowerCamelCase    ,					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=lowerCamelCase    ,					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=lowerCamelCase    ,					default=lowerCamelCase    ,					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=lowerCamelCase						)






	@staticmethod
	def 			lowercase				(     lowerCamelCase    ,					lowerCamelCase						)				->       str:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							set(pt_outputs.keys()						)
						__magic_name__ :							Tuple		       =							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(lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase=""						):
											# 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(lowerCamelCase    ,					torch.Tensor						):
																__magic_name__ :							int		       =							np.max(np.abs(pt_out.numpy() - tf_out.numpy()						)						)
																__magic_name__ :							int		       =							tensor_difference
											else:
																__magic_name__ :							Union[str, Any]		       =							attr_name
																for i, pt_item in enumerate(lowerCamelCase						):
																					# If it is a named attribute, we keep the name. Otherwise, just its index.
																					if isinstance(lowerCamelCase    ,					lowerCamelCase						):
																										__magic_name__ :							Tuple		       =							root_name + pt_item
																										__magic_name__ :							Any		       =							tf_out[pt_item]
																										__magic_name__ :							Optional[Any]		       =							pt_out[pt_item]
																					else:
																										__magic_name__ :							Union[str, Any]		       =							root_name + F'''[{i}]'''
																										__magic_name__ :							Any		       =							tf_out[i]
																					__magic_name__ :							str		       =							_find_pt_tf_differences(lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)

											return differences

						return _find_pt_tf_differences(lowerCamelCase    ,					lowerCamelCase    ,					{}						)






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					*lowerCamelCase    ,					)				->       str:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							logging.get_logger('''transformers-cli/pt_to_tf'''						)
						__magic_name__ :							Dict		       =							model_name
						__magic_name__ :							Optional[Any]		       =							local_dir if local_dir else os.path.join('''/tmp'''    ,					lowerCamelCase						)
						__magic_name__ :							str		       =							max_error
						__magic_name__ :							Any		       =							new_weights
						__magic_name__ :							Optional[Any]		       =							no_pr
						__magic_name__ :							Union[str, Any]		       =							push
						__magic_name__ :							Optional[Any]		       =							extra_commit_description
						__magic_name__ :							Optional[int]		       =							override_model_class






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Tuple:




						"""simple docstring"""






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

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

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

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

						return pt_input, tf_input






	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






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

						__magic_name__       ,			__magic_name__ :							Any		       =							self.get_inputs(lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)

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

						__magic_name__ :							str		       =							tf_class.from_pretrained(self._local_dir    ,					from_pt=lowerCamelCase						)
						__magic_name__ :							Optional[Any]		       =							tf_from_pt_model(**lowerCamelCase    ,					output_hidden_states=lowerCamelCase    ,					training=lowerCamelCase						)

						# Confirms that cross loading PT weights into TF worked.
						__magic_name__ :							Any		       =							self.find_pt_tf_differences(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							Any		       =							{k: v for k, v in crossload_differences.items() if '''hidden''' not in k}
						__magic_name__ :							Union[str, Any]		       =							{k: v for k, v in crossload_differences.items() if '''hidden''' in k}
						if len(lowerCamelCase						) == 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\''''						)
						__magic_name__ :							str		       =							max(output_differences.values()						) if output_differences else 0.0
						__magic_name__ :							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
						__magic_name__ :							Any		       =							os.path.join(self._local_dir    ,					lowerCamelCase						)
						__magic_name__ :							Tuple		       =							os.path.join(self._local_dir    ,					lowerCamelCase						)
						if (not os.path.exists(lowerCamelCase						) and not os.path.exists(lowerCamelCase						)) 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

						__magic_name__ :							Any		       =							tf_class.from_pretrained(self._local_dir						)
						__magic_name__ :							Optional[int]		       =							tf_model(**lowerCamelCase    ,					output_hidden_states=lowerCamelCase						)

						__magic_name__ :							Any		       =							self.find_pt_tf_differences(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							Any		       =							{k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
						__magic_name__ :							int		       =							{k: v for k, v in conversion_differences.items() if '''hidden''' in k}
						if len(lowerCamelCase						) == 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\''''						)
						__magic_name__ :							str		       =							max(output_differences.values()						) if output_differences else 0.0
						__magic_name__ :							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]						)						)

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

											__magic_name__ :							Dict		       =							create_commit(
											    repo_id=self._model_name    ,					operations=lowerCamelCase    ,					commit_message=lowerCamelCase    ,					commit_description=lowerCamelCase    ,					repo_type='''model'''    ,					create_pr=lowerCamelCase    ,					).pr_url
											self._logger.warning(F'''PR open in {hub_pr_url}'''						)



