


import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


_lowerCAmelCase:	Union[str, Any]      							=					logging.getLogger(__name__)






def      _lowercase(				__a							:	int    ):
							random.seed(__a    )
							np.random.seed(__a    )
							torch.manual_seed(__a    )
							torch.cuda.manual_seed_all(__a    )






def      _lowercase(				__a							:	Tuple						,    __a							:	List[str]						,    __a							:	Optional[Any]    ):
							model.eval()
							a__     						=next(model.parameters()    ).device
							a__     						=1
							a__     						=torch.zeros((eval_batch_size, context_len)						,    dtype=torch.long						,    device=__a    )
							a__     						=DataLoader(__a						,    shuffle=__a						,    batch_size=__a    )
							a__     						=torch.zeros(1						,    device=__a    )
							a__     						=0
							for batch in eval_dataloader:
														batch.to(__a    )
														# pad
														context.zero_()
														for i in range(__a    ):
																					a__     						=batch[i]
														a__     						=model(__a						,    labels=__a    )
														eval_loss += outputs[0].sum().item()
														nb_eval_examples += batch.size(0    )
							a__     						=eval_loss / nb_eval_examples
							a__     						=torch.exp(__a    )
							model.train()
							return perplexity






def      _lowercase(				__a							:	Any="gpt2"    ):
							a__     						=GPTaLMHeadModel.from_pretrained(__a						,    output_hidden_states=__a    )
							torch.save(model.state_dict()						,    model_name + 'local.pt'    )
							return model






def      _lowercase(				__a							:	Union[str, Any]						,    __a							:	int						,    __a							:	Dict    ):
							a__     						=copy.deepcopy(__a    )
							model.to(__a    )

							a__     						=['bias', 'LayerNorm.weight']
							a__     						=[
							    {
							        'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay    )],
							        'weight_decay': 0.0,
							    },
							    {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay    )], 'weight_decay': 0.0},
							]
							a__     						=AdamW(__a						,    lr=5e-5						,    eps=1e-8    )
							a__     						=get_linear_schedule_with_warmup(__a						,    0						,    __a    )
							torch.cuda.empty_cache()
							return model, lm_optimizer, lm_scheduler






def      _lowercase(				__a							:	Optional[Any]						,    __a							:	Union[str, Any]						,    __a							:	Union[str, Any]						,    __a							:	Optional[int]    ):
							a__     						=np.array(__a    ).mean()
							a__     						=np.array(__a    ).std()
							a__     						=(real_perps - avg) / std
							a__     						=list(zip(__a						,    __a						,    __a    )    )
							joblib.dump(__a						,    __a    )






def      _lowercase(				__a							:	Union[str, Any]						,    __a							:	int						,    __a							:	Dict						,    __a							:	Optional[Any]						,    __a							:	str						,    __a							:	Any						,    __a							:	List[str]						,    __a							:	Optional[Any]="dev.jbl"						,    __a							:	List[str]=recopy_gpta						,    ):
							a__     						=[]
							a__     						=[]
							a__     						=[]

							# Initialize the transformer model
							a__     						=copy.deepcopy(__a    )
							orig_model.to(device='cpu'    )
							torch.cuda.empty_cache()

							# Compute perplexity of initial transformer model for comparison
							model.train()
							a__						,    a__						,    a__     						=recopy_model(__a						,    __a						,    __a    )

							for step in tqdm(range(__a    )    ):
														a__     						=torch.zeros((1, context_len)						,    dtype=torch.long						,    device=__a    )
														a__     						=random.choice(__a    )
														a__     						=random.randint(0						,    len(story[0]    ) - context_len - 1    )
														a__     						=story[0][start : start + context_len]
														lm_optimizer.zero_grad()
														a__     						=model(__a						,    labels=__a    )
														a__     						=outputs[0]
														a__     						=compute_perplexity(__a						,    __a						,    __a    )
														model.train()
														lm_loss.backward()
														# Do LM backprop
														torch.nn.utils.clip_grad_norm_(model.parameters()						,    3.0    )
														lm_optimizer.step()
														lm_scheduler.step()  # Update learning rate schedule

														# Compute perplexity after back-propagating on the selected context
														a__     						=compute_perplexity(__a						,    __a						,    __a    )

														# Periodically save the stored (X, IG(X)) pairs
														if step % 1000 == 0 and step > 1:
																					intermittent_save(__a						,    __a						,    __a						,    __a    )

														# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
														a__						,    a__						,    a__     						=recopy_model(__a						,    __a						,    __a    )

														past_perps.append(past_perp.item()    )
														real_perps.append(orig_perp - real_perp.item()    )
														contexts.append(np.array(context.cpu()    )    )

							intermittent_save(__a						,    __a						,    __a						,    __a    )






def      _lowercase(				__a							:	Dict						,    __a							:	List[str]="data/tokenized_stories_train_wikitext103.jbl"						,    __a							:	Union[str, Any]=100						,    __a							:	List[str]=1026						,    __a							:	str=True    ):
							a__     						=joblib.load(__a    )
							print('data loaded'    )
							a__     						=[]
							if trim:
														for i, example in enumerate(__a    ):
																					if len(example[0]    ) > min_len:
																												a__     						=random.randint(0						,    len(example[0]    ) - context_len - 1    )
																												objective_set.append(example[0, start : start + context_len]    )
																					if len(__a    ) >= number:
																												break
														a__     						=[]
														for j in range(i + 1						,    len(__a    )    ):
																					if len(data[j][0]    ) > min_len:
																												train_data.append(data[j]    )
							else:
														a__     						=data[0:number]
														a__     						=data[number:]

							joblib.dump(__a						,    'objective_set.jbl'    )
							print('objective set saved'    )
							return train_data, objective_set






def      _lowercase(				__a							:	int						,    __a							:	str						,    __a							:	List[Any]						,    __a							:	Tuple						,    __a							:	Optional[int]=50						,    __a							:	List[str]="secondary_learner.pt"    ):
							a__     						=torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'    )
							# We will use the first 512 pairs from our dataset as a test set for
							# our secondary learner and the rest to train
							a__     						=train_dataset[:512]
							a__     						=train_dataset[512:]
							a__     						=DataLoader(__a						,    shuffle=__a						,    batch_size=__a    )
							a__     						=DataLoader(__a						,    shuffle=__a						,    batch_size=__a    )

							# secondary learner model set up
							a__     						=nn.MSELoss()
							a__     						=nn.MSELoss(reduction='sum'    )
							secondary_learner.to(__a    )
							a__     						=torch.optim.Adam(secondary_learner.parameters()						,    lr=0.0_00_01    )
							secondary_learner.train()

							# TODO in original code this is written as number of actual batches seen
							# not number of items seen but other places it is number of items instead.
							# improve consistency! changed this to epochs for clarity
							a__     						=float('inf'    )
							# Iterate through batches until we've used max_steps batches
							for epoch in range(int(__a    )    ):
														a__     						=0.0
														secondary_learner.train()
														for step, batch in enumerate(__a    ):
																					a__     						=batch[0].to(__a    )
																					a__     						=batch[1].to(__a    )
																					a__     						=secondary_learner(__a    )
																					q_optimizer.zero_grad()
																					a__     						=loss(__a						,    real_q.float()    )
																					q_loss.backward()
																					q_optimizer.step()
																					tr_q_loss += q_loss.item()

																					# model trains fairly quickly so we won't wait for a full epoch
																					# eval is triggered at eval_freq and end of epochs
																					if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(__a    )):
																												a__     						=tr_q_loss / (step + 1)

																												secondary_learner.eval()
																												a__     						=0.0
																												a__     						=0.0
																												a__     						=[]
																												a__     						=[]
																												# Compute performance of the secondary learner after this batch
																												for stepa, batcha in enumerate(__a    ):
																																			a__     						=batcha[0].to(__a    )
																																			a__     						=batcha[1].to(__a    )
																																			a__     						=secondary_learner(__a    )
																																			q_lossa += test_loss(__a						,    __a    ).item()
																																			sum_qa += torch.sum(__a    ).item()
																																			for ei, i in enumerate(predicted_qa.cpu().detach().numpy()    ):
																																										predicted.append(i.item()    )
																																			for ei, i in enumerate(real_qa.cpu().detach().numpy()    ):
																																										actual.append(i.item()    )

																												q_lossa /= len(__a    )
																												print(
																												    'Epoch: '						,    __a						,    'step: '						,    __a						,    'Avg. q:'						,    sum_qa / len(__a    )						,    'Train Loss: '						,    __a						,    'Test Loss: '						,    __a						,    )
																												if q_lossa < best_test_loss:
																																			joblib.dump((predicted, actual)						,    'pred_vs_actual.jbl'    )
																																			torch.save(secondary_learner.state_dict()						,    __a    )
																																			a__     						=q_lossa

																					secondary_learner.train()
							return secondary_learner







class 			lowercase_				(nn.Module      ):





				def __init__(							self					,     lowercase_)     ->   Tuple:
											super(lowercase_					,     self).__init__()
											a__     						=model.transformer.wte
											a__     						=copy.deepcopy(model.transformer.wte.weight)
											a__     						=nn.Convad(self.embeddings.weight.size(1)					,     256					,     3					,     padding=1)
											a__     						=nn.Sequential(nn.Linear(256					,     32)					,     nn.Dropout(p=0.1)					,     nn.Linear(32					,     32)					,     nn.Linear(32					,     1))





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   Optional[int]:
											a__     						=torch.max(self.conv(self.embeddings(lowercase_).squeeze(1).transpose(1					,     2))					,     2)[0]
											a__     						=self.fc(lowercase_)
											return qs.squeeze(1)





				@classmethod
				def 	__UpperCamelCase						(							cls					,     lowercase_					,     lowercase_)     ->   Dict:
											a__     						=cls(lowercase_)  # this calls __init__
											a__     						=torch.load(lowercase_)
											secondary_learner.load_state_dict(lowercase_)
											a__     						=model.transformer.wte
											a__     						=copy.deepcopy(model.transformer.wte.weight)
											return secondary_learner





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






def      _lowercase(				__a							:	Namespace    ):
							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 			lowercase_				(lowercase__      ):





				@staticmethod
				def 	__UpperCamelCase						(							lowercase_)     ->   int:
											a__     						=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=lowercase_					,     required=lowercase_					,     help='The model name, including owner/organization, as seen on the hub.'					,     )
											train_parser.add_argument(
											    '--local-dir'					,     type=lowercase_					,     default=''					,     help='Optional local directory of the model repository. Defaults to /tmp/{model_name}'					,     )
											train_parser.add_argument(
											    '--max-error'					,     type=lowercase_					,     default=lowercase_					,     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=lowercase_					,     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=lowercase_					,     default=lowercase_					,     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=lowercase_)





				@staticmethod
				def 	__UpperCamelCase						(							lowercase_					,     lowercase_)     ->   Optional[int]:
											a__     						=set(pt_outputs.keys())
											a__     						=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(lowercase_					,     lowercase_					,     lowercase_					,     lowercase_=""):
																		# 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(lowercase_					,     torch.Tensor):
																									a__     						=np.max(np.abs(pt_out.numpy() - tf_out.numpy()))
																									a__     						=tensor_difference
																		else:
																									a__     						=attr_name
																									for i, pt_item in enumerate(lowercase_):
																																# If it is a named attribute, we keep the name. Otherwise, just its index.
																																if isinstance(lowercase_					,     lowercase_):
																																							a__     						=root_name + pt_item
																																							a__     						=tf_out[pt_item]
																																							a__     						=pt_out[pt_item]
																																else:
																																							a__     						=root_name + F"""[{i}]"""
																																							a__     						=tf_out[i]
																																a__     						=_find_pt_tf_differences(lowercase_					,     lowercase_					,     lowercase_					,     lowercase_)

																		return differences

											return _find_pt_tf_differences(lowercase_					,     lowercase_					,     {})





				def __init__(							self					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     *lowercase_					,     )     ->   str:
											a__     						=logging.get_logger('transformers-cli/pt_to_tf')
											a__     						=model_name
											a__     						=local_dir if local_dir else os.path.join('/tmp'					,     lowercase_)
											a__     						=max_error
											a__     						=new_weights
											a__     						=no_pr
											a__     						=push
											a__     						=extra_commit_description
											a__     						=override_model_class





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_)     ->   List[Any]:

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

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

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

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

											return pt_input, tf_input





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

											a__						,    a__     						=self.get_inputs(lowercase_					,     lowercase_					,     lowercase_)

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

											a__     						=tf_class.from_pretrained(self._local_dir					,     from_pt=lowercase_)
											a__     						=tf_from_pt_model(**lowercase_					,     output_hidden_states=lowercase_					,     training=lowercase_)

											# Confirms that cross loading PT weights into TF worked.
											a__     						=self.find_pt_tf_differences(lowercase_					,     lowercase_)
											a__     						={k: v for k, v in crossload_differences.items() if 'hidden' not in k}
											a__     						={k: v for k, v in crossload_differences.items() if 'hidden' in k}
											if len(lowercase_) == 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__     						=max(output_differences.values()) if output_differences else 0.0
											a__     						=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__     						=os.path.join(self._local_dir					,     lowercase_)
											a__     						=os.path.join(self._local_dir					,     lowercase_)
											if (not os.path.exists(lowercase_) and not os.path.exists(lowercase_)) 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__     						=tf_class.from_pretrained(self._local_dir)
											a__     						=tf_model(**lowercase_					,     output_hidden_states=lowercase_)

											a__     						=self.find_pt_tf_differences(lowercase_					,     lowercase_)
											a__     						={k: v for k, v in conversion_differences.items() if 'hidden' not in k}
											a__     						={k: v for k, v in conversion_differences.items() if 'hidden' in k}
											if len(lowercase_) == 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__     						=max(output_differences.values()) if output_differences else 0.0
											a__     						=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__     						='Update TF weights' if self._new_weights else 'Add TF weights'
											if self._push:
																		repo.git_add(auto_lfs_track=lowercase_)
																		repo.git_commit(lowercase_)
																		repo.git_push(blocking=lowercase_)  # 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__     						=(
																		    '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(lowercase_):
																									a__     						=[
																									    CommitOperationAdd(path_in_repo=lowercase_					,     path_or_fileobj=lowercase_)
																									]
																									for shard_path in tf.io.gfile.glob(self._local_dir + '/tf_model-*.h5'):
																																operations += [
																																    CommitOperationAdd(path_in_repo=os.path.basename(lowercase_)					,     path_or_fileobj=lowercase_)
																																]
																		else:
																									a__     						=[CommitOperationAdd(path_in_repo=lowercase_					,     path_or_fileobj=lowercase_)]

																		a__     						=create_commit(
																		    repo_id=self._model_name					,     operations=lowercase_					,     commit_message=lowercase_					,     commit_description=lowercase_					,     repo_type='model'					,     create_pr=lowercase_					,     ).pr_url
																		self._logger.warning(F"""PR open in {hub_pr_url}""")

