






"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


_snake_case					   =     logging.get_logger(__name__)






class  UpperCamelCase    (			snake_case_   ):
 UpperCamelCase      : str    =      ['''pixel_values''']


 def __init__(  self	:		str							,		UpperCAmelCase__	:		bool = True							,		UpperCAmelCase__	:		Dict[str, int] = None							,		UpperCAmelCase__	:		bool = True							,		UpperCAmelCase__	:		Dict[str, int] = None							,		UpperCAmelCase__	:		PILImageResampling = PILImageResampling.BICUBIC							,		UpperCAmelCase__	:		bool = True							,		UpperCAmelCase__	:		Union[int, float] = 1 / 255							,		UpperCAmelCase__	:		bool = True							,		UpperCAmelCase__	:		Optional[Union[float, List[float]]] = None							,		UpperCAmelCase__	:		Optional[Union[float, List[float]]] = None							,		**UpperCAmelCase__	:		Optional[Any]							,		)      -> None:
   super().__init__(**UpperCAmelCase__							)
   _a						: Union[str, Any]    =			crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
   _a						: int    =			get_size_dict(UpperCAmelCase__							,		param_name="""crop_size"""							)
   _a						: str    =			size if size is not None else {"""height""": 224, """width""": 224}
   _a						: Optional[int]    =			get_size_dict(UpperCAmelCase__							)

   _a						: Dict    =			do_center_crop
   _a						: int    =			crop_size
   _a						: Union[str, Any]    =			do_resize
   _a						: Dict    =			size
   _a						: Tuple    =			resample
   _a						: List[str]    =			do_rescale
   _a						: Optional[Any]    =			rescale_factor
   _a						: List[Any]    =			do_normalize
   _a						: List[Any]    =			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
   _a						: List[Any]    =			image_std if image_std is not None else IMAGENET_DEFAULT_STD


 def _lowercase			(  self	:		str							,		UpperCAmelCase__	:		np.ndarray							,		UpperCAmelCase__	:		Dict[str, int]							,		UpperCAmelCase__	:		Optional[int] = None							,		UpperCAmelCase__	:		Optional[Union[str, ChannelDimension]] = None							,		**UpperCAmelCase__	:		Dict							,		)      -> np.ndarray:
   _a						: int    =			self.size if size is None else size
   _a						: Union[str, Any]    =			get_size_dict(UpperCAmelCase__							)
   _a						: Tuple    =			get_size_dict(UpperCAmelCase__							,		param_name="""crop_size"""							)

   _a   ,						_a						: Dict    =			get_image_size(UpperCAmelCase__							)
   _a						: Dict    =			min(UpperCAmelCase__							,		UpperCAmelCase__							)
   _a						: Tuple    =			(size["""height"""] / crop_size["""height"""]) * min_dim
   _a						: Optional[int]    =			(size["""width"""] / crop_size["""width"""]) * min_dim
   return center_crop(UpperCAmelCase__							,		size=(cropped_height, cropped_width)							,		data_format=UpperCAmelCase__							,		**UpperCAmelCase__							)


 def _lowercase			(  self	:		Dict							,		UpperCAmelCase__	:		np.ndarray							,		UpperCAmelCase__	:		Dict[str, int]							,		UpperCAmelCase__	:		PILImageResampling = PIL.Image.BICUBIC							,		UpperCAmelCase__	:		Optional[Union[str, ChannelDimension]] = None							,		**UpperCAmelCase__	:		Dict							,		)      -> np.ndarray:
   _a						: Optional[Any]    =			get_size_dict(UpperCAmelCase__							)
   if "height" not in size or "width" not in size:
     raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""							)
   return resize(
       UpperCAmelCase__							,		size=(size["""height"""], size["""width"""])							,		resample=UpperCAmelCase__							,		data_format=UpperCAmelCase__							,		**UpperCAmelCase__							)


 def _lowercase			(  self	:		List[Any]							,		UpperCAmelCase__	:		np.ndarray							,		UpperCAmelCase__	:		Union[int, float]							,		UpperCAmelCase__	:		Optional[Union[str, ChannelDimension]] = None							,		**UpperCAmelCase__	:		Optional[Any]							,		)      -> int:
   return rescale(UpperCAmelCase__							,		scale=UpperCAmelCase__							,		data_format=UpperCAmelCase__							,		**UpperCAmelCase__							)


 def _lowercase			(  self	:		List[Any]							,		UpperCAmelCase__	:		np.ndarray							,		UpperCAmelCase__	:		Union[float, List[float]]							,		UpperCAmelCase__	:		Union[float, List[float]]							,		UpperCAmelCase__	:		Optional[Union[str, ChannelDimension]] = None							,		**UpperCAmelCase__	:		str							,		)      -> np.ndarray:
   return normalize(UpperCAmelCase__							,		mean=UpperCAmelCase__							,		std=UpperCAmelCase__							,		data_format=UpperCAmelCase__							,		**UpperCAmelCase__							)







 def _lowercase			(  self	:		Tuple							,		UpperCAmelCase__	:		ImageInput							,		UpperCAmelCase__	:		Optional[bool] = None							,		UpperCAmelCase__	:		Optional[Dict[str, int]] = None							,		UpperCAmelCase__	:		Optional[bool] = None							,		UpperCAmelCase__	:		Optional[Dict[str, int]] = None							,		UpperCAmelCase__	:		PILImageResampling = None							,		UpperCAmelCase__	:		Optional[bool] = None							,		UpperCAmelCase__	:		Optional[float] = None							,		UpperCAmelCase__	:		Optional[bool] = None							,		UpperCAmelCase__	:		Optional[Union[float, List[float]]] = None							,		UpperCAmelCase__	:		Optional[Union[float, List[float]]] = None							,		UpperCAmelCase__	:		Optional[Union[str, TensorType]] = None							,		UpperCAmelCase__	:		ChannelDimension = ChannelDimension.FIRST							,		**UpperCAmelCase__	:		List[str]							,		)      -> PIL.Image.Image:
   _a						: Optional[Any]    =			do_center_crop if do_center_crop is not None else self.do_center_crop
   _a						: int    =			crop_size if crop_size is not None else self.crop_size
   _a						: Union[str, Any]    =			get_size_dict(UpperCAmelCase__							,		param_name="""crop_size"""							)
   _a						: Dict    =			do_resize if do_resize is not None else self.do_resize
   _a						: List[str]    =			size if size is not None else self.size
   _a						: Tuple    =			get_size_dict(UpperCAmelCase__							)
   _a						: Any    =			resample if resample is not None else self.resample
   _a						: Optional[Any]    =			do_rescale if do_rescale is not None else self.do_rescale
   _a						: Tuple    =			rescale_factor if rescale_factor is not None else self.rescale_factor
   _a						: Tuple    =			do_normalize if do_normalize is not None else self.do_normalize
   _a						: int    =			image_mean if image_mean is not None else self.image_mean
   _a						: Optional[int]    =			image_std if image_std is not None else self.image_std

   _a						: Optional[Any]    =			make_list_of_images(UpperCAmelCase__							)

   if not valid_images(UpperCAmelCase__							):
     raise ValueError(
         """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
         """torch.Tensor, tf.Tensor or jax.ndarray."""							)

   if do_center_crop and crop_size is None:
     raise ValueError("""If `do_center_crop` is set to `True`, `crop_size` must be provided."""							)

   if do_resize and size is None:
     raise ValueError("""Size must be specified if do_resize is True."""							)

   if do_rescale and rescale_factor is None:
     raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

   if do_normalize and (image_mean is None or image_std is None):
     raise ValueError("""Image mean and image standard deviation must be specified if do_normalize is True."""							)

   # All transformations expect numpy arrays.
   _a						: Optional[Any]    =			[to_numpy_array(UpperCAmelCase__							) for image in images]

   if do_center_crop:
     _a						: str    =			[self.center_crop(UpperCAmelCase__							,		UpperCAmelCase__							,		size=UpperCAmelCase__							) for image in images]

   if do_resize:
     _a						: Tuple    =			[self.resize(image=UpperCAmelCase__							,		size=UpperCAmelCase__							,		resample=UpperCAmelCase__							) for image in images]

   if do_rescale:
     _a						: Optional[int]    =			[self.rescale(image=UpperCAmelCase__							,		scale=UpperCAmelCase__							) for image in images]

   if do_normalize:
     _a						: List[Any]    =			[self.normalize(image=UpperCAmelCase__							,		mean=UpperCAmelCase__							,		std=UpperCAmelCase__							) for image in images]

   _a						: Tuple    =			[to_channel_dimension_format(UpperCAmelCase__							,		UpperCAmelCase__							) for image in images]

   _a						: Optional[Any]    =			{"""pixel_values""": images}
   return BatchFeature(data=UpperCAmelCase__							,		tensor_type=UpperCAmelCase__							)









"""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


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

def     lowerCAmelCase__  (  UpperCamelCase__       ):



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






class  UpperCamelCase    (			snake_case_   ):


 @staticmethod
 def _lowercase			(  UpperCAmelCase__	:		ArgumentParser							)      -> Dict:
   _a						: Optional[int]    =			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 _lowercase			(  UpperCAmelCase__	:		Optional[int]							,		UpperCAmelCase__	:		Any							)      -> List[Any]:
   _a						: Optional[Any]    =			set(pt_outputs.keys()							)
   _a						: List[str]    =			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__	:		int							,		UpperCAmelCase__	:		Optional[int]							,		UpperCAmelCase__	:		Tuple							,		UpperCAmelCase__	:		int=""							):
     # 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							):
       _a						: List[str]    =			np.max(np.abs(pt_out.numpy() - tf_out.numpy()							)							)
       _a						: Optional[Any]    =			tensor_difference
     else:
       _a						: Dict    =			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__							):
           _a						: int    =			root_name + pt_item
           _a						: List[str]    =			tf_out[pt_item]
           _a						: int    =			pt_out[pt_item]
         else:
           _a						: int    =			root_name + f"""[{i}]"""
           _a						: Optional[int]    =			tf_out[i]
         _a						: Optional[Any]    =			_find_pt_tf_differences(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							)

     return differences

   return _find_pt_tf_differences(UpperCAmelCase__							,		UpperCAmelCase__							,		{}							)


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


 def _lowercase			(  self	:		Any							,		UpperCAmelCase__	:		Tuple							,		UpperCAmelCase__	:		str							,		UpperCAmelCase__	:		Union[str, Any]							)      -> str:

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

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

   _a						: int    =			set(inspect.signature(pt_model.forward							).parameters.keys()							)
   _a						: List[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:
     _a						: Optional[int]    =			load_dataset("""cifar10"""							,		"""plain_text"""							,		split="""test"""							)[:2]["""img"""]
     processor_inputs.update({"""images""": sample_images}							)
   if "input_features" in model_forward_signature:
     _a						: List[Any]    =			inspect.signature(processor.feature_extractor							).parameters
     # Pad to the largest input length by default but take feature extractor default
     # padding value if it exists e.g. "max_length" and is not False or None
     if "padding" in feature_extractor_signature:
       _a						: Any    =			feature_extractor_signature["""padding"""].default
       if default_strategy is not False and default_strategy is not None:
         _a						: Dict    =			default_strategy
       else:
         _a						: str    =			True
     else:
       _a						: List[Any]    =			True
     processor_inputs.update({"""audio""": _get_audio_input(), """padding""": padding_strategy}							)
   if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
     processor_inputs.update({"""audio""": _get_audio_input(), """padding""": True}							)
   _a						: Optional[int]    =			processor(**UpperCAmelCase__							,		return_tensors="""pt"""							)
   _a						: Optional[Any]    =			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
   ):
     _a						: 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 _lowercase			(  self	:		Optional[int]							)      -> Union[str, Any]:
   # 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
   _a						: Any    =			Repository(local_dir=self._local_dir							,		clone_from=self._model_name							)

   # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
   _a						: Optional[Any]    =			AutoConfig.from_pretrained(self._local_dir							)
   _a						: Optional[int]    =			config.architectures
   if self._override_model_class is not None:
     if self._override_model_class.startswith("""TF"""							):
       _a						: List[str]    =			[self._override_model_class[2:]]
     else:
       _a						: Optional[int]    =			[self._override_model_class]
     try:
       _a						: str    =			getattr(import_module("""transformers"""							)							,		architectures[0]							)
     except AttributeError:
       raise ValueError(f"""Model class {self._override_model_class} not found in transformers."""							)
     try:
       _a						: int    =			getattr(import_module("""transformers"""							)							,		"""TF""" + architectures[0]							)
     except AttributeError:
       raise ValueError(f"""TF model class TF{self._override_model_class} not found in transformers."""							)
   elif architectures is None:  # No architecture defined -- use auto classes
     _a						: List[Any]    =			getattr(import_module("""transformers"""							)							,		"""AutoModel"""							)
     _a						: Union[str, Any]    =			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]}"""							)
     _a						: List[Any]    =			getattr(import_module("""transformers"""							)							,		architectures[0]							)
     try:
       _a						: 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
   _a						: Optional[int]    =			tf_class.from_config(UpperCAmelCase__							)
   _a						: 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
   _a						: List[str]    =			pt_class.from_pretrained(self._local_dir							)
   pt_model.eval()

   _a   ,						_a						: int    =			self.get_inputs(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							)

   with torch.no_grad():
     _a						: List[str]    =			pt_model(**UpperCAmelCase__							,		output_hidden_states=UpperCAmelCase__							)
   del pt_model  # will no longer be used, and may have a large memory footprint

   _a						: int    =			tf_class.from_pretrained(self._local_dir							,		from_pt=UpperCAmelCase__							)
   _a						: Optional[Any]    =			tf_from_pt_model(**UpperCAmelCase__							,		output_hidden_states=UpperCAmelCase__							,		training=UpperCAmelCase__							)

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

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

   _a						: Any    =			tf_class.from_pretrained(self._local_dir							)
   _a						: Union[str, Any]    =			tf_model(**UpperCAmelCase__							,		output_hidden_states=UpperCAmelCase__							)

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

   _a						: Optional[Any]    =			"""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..."""							)
     _a						: 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(UpperCAmelCase__							):
       _a						: str    =			[
           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:
       _a						: Dict    =			[CommitOperationAdd(path_in_repo=UpperCAmelCase__							,		path_or_fileobj=UpperCAmelCase__							)]

     _a						: Tuple    =			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}"""							)

