




from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


lowercase_		=							logging.get_logger(__name__)  # pylint: disable=invalid-name





class 		A__    (       __SCREAMING_SNAKE_CASE			):
	lowerCamelCase__		:			Tuple       =["safety_checker", "feature_extractor"]






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase = True    ,					)				->       Tuple:




						"""simple docstring"""






						super().__init__()
						if hasattr(scheduler.config    ,					'''steps_offset'''						) and scheduler.config.steps_offset != 1:
											__magic_name__ :							Dict		       =							(
											    F'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
											    F''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
											    '''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
											    ''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
											    ''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
											    ''' file'''
											)
											deprecate('''steps_offset!=1'''    ,					'''1.0.0'''    ,					lowerCamelCase    ,					standard_warn=lowerCamelCase						)
											__magic_name__ :							Optional[int]		       =							dict(scheduler.config						)
											__magic_name__ :							Any		       =							1
											__magic_name__ :							Any		       =							FrozenDict(lowerCamelCase						)

						self.register_modules(
						    vae=lowerCamelCase    ,					text_encoder=lowerCamelCase    ,					tokenizer=lowerCamelCase    ,					unet=lowerCamelCase    ,					scheduler=lowerCamelCase    ,					safety_checker=lowerCamelCase    ,					feature_extractor=lowerCamelCase    ,					)
						self.register_to_config(requires_safety_checker=lowerCamelCase						)






	@property
	def 			lowercase				(     self						)				->       Dict[str, Any]:




						"""simple docstring"""






						return {k: getattr(self    ,					lowerCamelCase						) for k in self.config.keys() if not k.startswith('''_'''						)}






	def 			lowercase				(     self    ,					lowerCamelCase = "auto"						)				->       Union[str, Any]:




						"""simple docstring"""






						if slice_size == "auto":
											# half the attention head size is usually a good trade-off between
											# speed and memory
											__magic_name__ :							str		       =							self.unet.config.attention_head_dim // 2
						self.unet.set_attention_slice(lowerCamelCase						)






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						self.enable_attention_slicing(lowerCamelCase						)






	@torch.no_grad()
	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase = 0.8    ,					lowerCamelCase = 50    ,					lowerCamelCase = 7.5    ,					lowerCamelCase = None    ,					lowerCamelCase = 1    ,					lowerCamelCase = 0.0    ,					lowerCamelCase = None    ,					lowerCamelCase = "pil"    ,					lowerCamelCase = True    ,					lowerCamelCase = None    ,					lowerCamelCase = 1    ,					)				->       Any:




						"""simple docstring"""






						return StableDiffusionInpaintPipelineLegacy(**self.components						)(
						    prompt=lowerCamelCase    ,					image=lowerCamelCase    ,					mask_image=lowerCamelCase    ,					strength=lowerCamelCase    ,					num_inference_steps=lowerCamelCase    ,					guidance_scale=lowerCamelCase    ,					negative_prompt=lowerCamelCase    ,					num_images_per_prompt=lowerCamelCase    ,					eta=lowerCamelCase    ,					generator=lowerCamelCase    ,					output_type=lowerCamelCase    ,					return_dict=lowerCamelCase    ,					callback=lowerCamelCase    ,					)






	@torch.no_grad()
	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase = 0.8    ,					lowerCamelCase = 50    ,					lowerCamelCase = 7.5    ,					lowerCamelCase = None    ,					lowerCamelCase = 1    ,					lowerCamelCase = 0.0    ,					lowerCamelCase = None    ,					lowerCamelCase = "pil"    ,					lowerCamelCase = True    ,					lowerCamelCase = None    ,					lowerCamelCase = 1    ,					**lowerCamelCase    ,					)				->       Dict:




						"""simple docstring"""






						return StableDiffusionImgaImgPipeline(**self.components						)(
						    prompt=lowerCamelCase    ,					image=lowerCamelCase    ,					strength=lowerCamelCase    ,					num_inference_steps=lowerCamelCase    ,					guidance_scale=lowerCamelCase    ,					negative_prompt=lowerCamelCase    ,					num_images_per_prompt=lowerCamelCase    ,					eta=lowerCamelCase    ,					generator=lowerCamelCase    ,					output_type=lowerCamelCase    ,					return_dict=lowerCamelCase    ,					callback=lowerCamelCase    ,					callback_steps=lowerCamelCase    ,					)






	@torch.no_grad()
	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase = 512    ,					lowerCamelCase = 512    ,					lowerCamelCase = 50    ,					lowerCamelCase = 7.5    ,					lowerCamelCase = None    ,					lowerCamelCase = 1    ,					lowerCamelCase = 0.0    ,					lowerCamelCase = None    ,					lowerCamelCase = None    ,					lowerCamelCase = "pil"    ,					lowerCamelCase = True    ,					lowerCamelCase = None    ,					lowerCamelCase = 1    ,					)				->       Optional[int]:




						"""simple docstring"""






						return StableDiffusionPipeline(**self.components						)(
						    prompt=lowerCamelCase    ,					height=lowerCamelCase    ,					width=lowerCamelCase    ,					num_inference_steps=lowerCamelCase    ,					guidance_scale=lowerCamelCase    ,					negative_prompt=lowerCamelCase    ,					num_images_per_prompt=lowerCamelCase    ,					eta=lowerCamelCase    ,					generator=lowerCamelCase    ,					latents=lowerCamelCase    ,					output_type=lowerCamelCase    ,					return_dict=lowerCamelCase    ,					callback=lowerCamelCase    ,					callback_steps=lowerCamelCase    ,					)









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}'''						)



