
from sklearn.metrics import mean_absolute_error

import datasets


lowerCAmelCase			=		"""\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
"""

lowerCAmelCase			=		"""\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


lowerCAmelCase			=		"""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 \"raw_values\" : Returns a full set of errors in case of multioutput input.

                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""

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



							"""simple docstring"""






							def    UpperCAmelCase__							(						self						:Dict ):





										"""simple docstring"""

										return datasets.MetricInfo(
										    description=_DESCRIPTION  ,							citation=_CITATION  ,							inputs_description=_KWARGS_DESCRIPTION  ,							features=datasets.Features(self._get_feature_types() )  ,							reference_urls=[
										        'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html'
										    ]  ,							)





							def    UpperCAmelCase__							(						self						:Optional[Any] ):





										"""simple docstring"""

										if self.config_name == "multilist":
													return {
													    "predictions": datasets.Sequence(datasets.Value('float' ) ),
													    "references": datasets.Sequence(datasets.Value('float' ) ),
													}
										else:
													return {
													    "predictions": datasets.Value('float' ),
													    "references": datasets.Value('float' ),
													}





							def    UpperCAmelCase__							(						self						:Any  ,							lowerCamelCase_						:Optional[int]  ,							lowerCamelCase_						:List[str]  ,							lowerCamelCase_						:int=None  ,							lowerCamelCase_						:Optional[Any]="uniform_average" ):





										"""simple docstring"""

										lowerCamelCase__			:  Dict  =mean_absolute_error(lowerCamelCase_  ,							lowerCamelCase_  ,							sample_weight=lowerCamelCase_  ,							multioutput=lowerCamelCase_ )

										return {"mae": mae_score}

"""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}""" )