
"""simple docstring"""


import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


lowerCAmelCase			=		"""\
@article{eddine2021frugalscore,
  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2110.08559},
  year={2021}
}
"""

lowerCAmelCase			=		"""\
FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
"""


lowerCAmelCase			=		"""
Calculates how good are predictions given some references, using certain scores.
Args:
    predictions (list of str): list of predictions to score. Each predictions
        should be a string.
    references (list of str): list of reference for each prediction. Each
        reference should be a string.
    batch_size (int): the batch size for predictions.
    max_length (int): maximum sequence length.
    device (str): either gpu or cpu
Returns:
    scores (list of int): list of scores.
Examples:
    >>> frugalscore = datasets.load_metric(\"frugalscore\")
    >>> results = frugalscore.compute(predictions=['hello there', 'huggingface'], references=['hello world', 'hugging face'])
    >>> print([round(s, 3) for s in results[\"scores\"]])
    [0.631, 0.645]
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,      _KWARGS_DESCRIPTION     )
class 					A_			(  datasets.Metric     ):



							"""simple docstring"""






							def    UpperCAmelCase__							(						self						:Union[str, Any] ):





										"""simple docstring"""

										return datasets.MetricInfo(
										    description=_DESCRIPTION  ,							citation=_CITATION  ,							inputs_description=_KWARGS_DESCRIPTION  ,							features=datasets.Features(
										        {
										            'predictions': datasets.Value('string' ),
										            'references': datasets.Value('string' ),
										        } )  ,							homepage='https://github.com/moussaKam/FrugalScore'  ,							)





							def    UpperCAmelCase__							(						self						:Any  ,							lowerCamelCase_						:Union[str, Any] ):





										"""simple docstring"""

										if self.config_name == "default":
													lowerCamelCase__			:  List[str]  ='moussaKam/frugalscore_tiny_bert-base_bert-score'
										else:
													lowerCamelCase__			:  int  =self.config_name
										lowerCamelCase__			:  Union[str, Any]  =AutoModelForSequenceClassification.from_pretrained(lowerCamelCase_ )
										lowerCamelCase__			:  Dict  =AutoTokenizer.from_pretrained(lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:Optional[int]  ,							lowerCamelCase_						:Optional[Any]  ,							lowerCamelCase_						:List[Any]  ,							lowerCamelCase_						:Optional[Any]=32  ,							lowerCamelCase_						:Union[str, Any]=128  ,							lowerCamelCase_						:Dict=None  ,							):





										"""simple docstring"""

										assert len(lowerCamelCase_ ) == len(
										    lowerCamelCase_ ), "predictions and references should have the same number of sentences."
										if device is not None:
													assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
										else:
													lowerCamelCase__			:  Union[str, Any]  ='gpu' if torch.cuda.is_available() else 'cpu'
										lowerCamelCase__			:  List[str]  =TrainingArguments(
										    'trainer'  ,							fpaa=(device == 'gpu')  ,							per_device_eval_batch_size=lowerCamelCase_  ,							report_to='all'  ,							no_cuda=(device == 'cpu')  ,							log_level='warning'  ,							)
										lowerCamelCase__			:  Optional[int]  ={'sentence1': predictions, 'sentence2': references}
										lowerCamelCase__			:  Tuple  =datasets.Dataset.from_dict(lowerCamelCase_ )

										def tokenize_function(lowerCamelCase_						:str ):
													return self.tokenizer(
													    data['sentence1']  ,							data['sentence2']  ,							max_length=lowerCamelCase_  ,							truncation=lowerCamelCase_  ,							padding=lowerCamelCase_ )

										lowerCamelCase__			:  Any  =raw_datasets.map(lowerCamelCase_  ,							batched=lowerCamelCase_ )
										tokenized_datasets.remove_columns(['sentence1', 'sentence2'] )
										lowerCamelCase__			:  List[Any]  =Trainer(self.model  ,							lowerCamelCase_  ,							tokenizer=self.tokenizer )
										lowerCamelCase__			:  List[str]  =trainer.predict(lowerCamelCase_ )
										return {"scores": list(predictions.predictions.squeeze(-1 ) )}

"""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


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






def      lowerCAmelCase_     (	snake_case_	:    Namespace    )     ->Optional[int]:
			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_			(  A__     ):



							"""simple docstring"""






							@staticmethod
							def    UpperCAmelCase__							(						lowerCamelCase_						:ArgumentParser ):





										"""simple docstring"""

										lowerCamelCase__			:  Tuple  =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    UpperCAmelCase__							(						lowerCamelCase_						:int  ,							lowerCamelCase_						:Optional[Any] ):





										"""simple docstring"""

										lowerCamelCase__			:  str  =set(pt_outputs.keys() )
										lowerCamelCase__			:  Union[str, Any]  =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_						:Dict  ,							lowerCamelCase_						:Any  ,							lowerCamelCase_						:Dict  ,							lowerCamelCase_						:Tuple="" ):
													# 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 ):
																lowerCamelCase__			:  List[str]  =np.max(np.abs(pt_out.numpy() - tf_out.numpy() ) )
																lowerCamelCase__			:  Dict  =tensor_difference
													else:
																lowerCamelCase__			:  List[str]  =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_ ):
																						lowerCamelCase__			:  str  =root_name + pt_item
																						lowerCamelCase__			:  List[Any]  =tf_out[pt_item]
																						lowerCamelCase__			:  str  =pt_out[pt_item]
																			else:
																						lowerCamelCase__			:  Union[str, Any]  =root_name + f"""[{i}]"""
																						lowerCamelCase__			:  Tuple  =tf_out[i]
																			lowerCamelCase__			:  Any  =_find_pt_tf_differences(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_ )

													return differences

										return _find_pt_tf_differences(lowerCamelCase_  ,							lowerCamelCase_  ,							{} )





							def __init__(						self						:Union[str, Any]  ,							lowerCamelCase_						:str  ,							lowerCamelCase_						:str  ,							lowerCamelCase_						:float  ,							lowerCamelCase_						:bool  ,							lowerCamelCase_						:bool  ,							lowerCamelCase_						:bool  ,							lowerCamelCase_						:str  ,							lowerCamelCase_						:str  ,							*lowerCamelCase_						:Union[str, Any]  ,							):





										"""simple docstring"""

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





							def    UpperCAmelCase__							(						self						:Optional[int]  ,							lowerCamelCase_						:Dict  ,							lowerCamelCase_						:Optional[int]  ,							lowerCamelCase_						:Any ):





										"""simple docstring"""

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

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

										lowerCamelCase__			:  Tuple  =set(inspect.signature(pt_model.forward ).parameters.keys() )
										lowerCamelCase__			:  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:
													lowerCamelCase__			:  List[str]  =load_dataset('cifar10'  ,							'plain_text'  ,							split='test' )[:2]['img']
													processor_inputs.update({'images': sample_images} )
										if "input_features" in model_forward_signature:
													lowerCamelCase__			:  Optional[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:
																lowerCamelCase__			:  str  =feature_extractor_signature['padding'].default
																if default_strategy is not False and default_strategy is not None:
																			lowerCamelCase__			:  Tuple  =default_strategy
																else:
																			lowerCamelCase__			:  List[str]  =True
													else:
																lowerCamelCase__			:  Optional[int]  =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} )
										lowerCamelCase__			:  str  =processor(**lowerCamelCase_  ,							return_tensors='pt' )
										lowerCamelCase__			:  Optional[int]  =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
										):
													lowerCamelCase__			:  Union[str, 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    UpperCAmelCase__							(						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
										lowerCamelCase__			:  Dict  =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
										lowerCamelCase__			:  Union[str, Any]  =AutoConfig.from_pretrained(self._local_dir )
										lowerCamelCase__			:  Optional[int]  =config.architectures
										if self._override_model_class is not None:
													if self._override_model_class.startswith('TF' ):
																lowerCamelCase__			:  List[Any]  =[self._override_model_class[2:]]
													else:
																lowerCamelCase__			:  str  =[self._override_model_class]
													try:
																lowerCamelCase__			:  str  =getattr(import_module('transformers' )  ,							architectures[0] )
													except AttributeError:
																raise ValueError(f"""Model class {self._override_model_class} not found in transformers.""" )
													try:
																lowerCamelCase__			:  Tuple  =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
													lowerCamelCase__			:  List[str]  =getattr(import_module('transformers' )  ,							'AutoModel' )
													lowerCamelCase__			:  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]}""" )
													lowerCamelCase__			:  str  =getattr(import_module('transformers' )  ,							architectures[0] )
													try:
																lowerCamelCase__			:  Tuple  =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
										lowerCamelCase__			:  Any  =tf_class.from_config(lowerCamelCase_ )
										lowerCamelCase__			:  List[Any]  =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
										lowerCamelCase__			:  List[str]  =pt_class.from_pretrained(self._local_dir )
										pt_model.eval()

										lowerCamelCase__ ,       lowerCamelCase__			:  Dict  =self.get_inputs(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_ )

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

										lowerCamelCase__			:  Tuple  =tf_class.from_pretrained(self._local_dir  ,							from_pt=lowerCamelCase_ )
										lowerCamelCase__			:  int  =tf_from_pt_model(**lowerCamelCase_  ,							output_hidden_states=lowerCamelCase_  ,							training=lowerCamelCase_ )

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

										lowerCamelCase__			:  Optional[int]  =tf_class.from_pretrained(self._local_dir )
										lowerCamelCase__			:  List[Any]  =tf_model(**lowerCamelCase_  ,							output_hidden_states=lowerCamelCase_ )

										lowerCamelCase__			:  Any  =self.find_pt_tf_differences(lowerCamelCase_  ,							lowerCamelCase_ )
										lowerCamelCase__			:  Tuple  ={k: v for k, v in conversion_differences.items() if 'hidden' not in k}
										lowerCamelCase__			:  Tuple  ={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\'' )
										lowerCamelCase__			:  Any  =max(output_differences.values() ) if output_differences else 0.0
										lowerCamelCase__			:  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] ) )

										lowerCamelCase__			:  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...' )
													lowerCamelCase__			:  List[Any]  =(
													    '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_ ):
																lowerCamelCase__			:  Dict  =[
																    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:
																lowerCamelCase__			:  Optional[Any]  =[CommitOperationAdd(path_in_repo=lowerCamelCase_  ,							path_or_fileobj=lowerCamelCase_ )]

													lowerCamelCase__			:  Tuple  =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}""" )