






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


SCREAMING_SNAKE_CASE								=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE								=  {"vocab_file": "vocab.txt"}

SCREAMING_SNAKE_CASE								=  {
    "vocab_file": {
        "tau/splinter-base": "https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt",
        "tau/splinter-base-qass": "https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt",
        "tau/splinter-large": "https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt",
        "tau/splinter-large-qass": "https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt",
    }
}

SCREAMING_SNAKE_CASE								=  {
    "tau/splinter-base": 512,
    "tau/splinter-base-qass": 512,
    "tau/splinter-large": 512,
    "tau/splinter-large-qass": 512,
}

SCREAMING_SNAKE_CASE								=  {
    "tau/splinter-base": {"do_lower_case": False},
    "tau/splinter-base-qass": {"do_lower_case": False},
    "tau/splinter-large": {"do_lower_case": False},
    "tau/splinter-large-qass": {"do_lower_case": False},
}
class 							A_  (  SCREAMING_SNAKE_CASE_     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     str	=   VOCAB_FILES_NAMES
 _SCREAMING_SNAKE_CASE							:     Optional[Any]	=   PRETRAINED_VOCAB_FILES_MAP
 _SCREAMING_SNAKE_CASE							:     List[Any]	=   PRETRAINED_INIT_CONFIGURATION
 _SCREAMING_SNAKE_CASE							:     Optional[Any]	=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _SCREAMING_SNAKE_CASE							:     str	=   SplinterTokenizer


 def __init__(		self							,					_A=None							,					_A=None							,					_A=True							,					_A="[UNK]"							,					_A="[SEP]"							,					_A="[PAD]"							,					_A="[CLS]"							,					_A="[MASK]"							,					_A="[QUESTION]"							,					_A=True							,					_A=None							,					**_A							,					)	->						List[str]:

     """simple docstring"""



     super().__init__(
         SCREAMING_SNAKE_CASE_							,					tokenizer_file=SCREAMING_SNAKE_CASE_							,					do_lower_case=SCREAMING_SNAKE_CASE_							,					unk_token=SCREAMING_SNAKE_CASE_							,					sep_token=SCREAMING_SNAKE_CASE_							,					pad_token=SCREAMING_SNAKE_CASE_							,					cls_token=SCREAMING_SNAKE_CASE_							,					mask_token=SCREAMING_SNAKE_CASE_							,					tokenize_chinese_chars=SCREAMING_SNAKE_CASE_							,					strip_accents=SCREAMING_SNAKE_CASE_							,					additional_special_tokens=(question_token,)							,					**SCREAMING_SNAKE_CASE_							,					)

     _UpperCAmelCase			: Optional[int]     					=			json.loads(self.backend_tokenizer.normalizer.__getstate__())
     if (
         pre_tok_state.get('''lowercase'''							,					SCREAMING_SNAKE_CASE_) != do_lower_case
         or pre_tok_state.get('''strip_accents'''							,					SCREAMING_SNAKE_CASE_) != strip_accents
     ):
         _UpperCAmelCase			: Optional[int]     					=			getattr(SCREAMING_SNAKE_CASE_							,					pre_tok_state.pop('''type'''))
         _UpperCAmelCase			: Dict     					=			do_lower_case
         _UpperCAmelCase			: Dict     					=			strip_accents
         _UpperCAmelCase			: Any     					=			pre_tok_class(**SCREAMING_SNAKE_CASE_)

     _UpperCAmelCase			: Tuple     					=			do_lower_case


 @property
 def 	snake_case__			(		self)	->						Tuple:

     """simple docstring"""



     return self.convert_tokens_to_ids(self.question_token)


 def 	snake_case__			(		self							,					_A							,					_A = None)	->						List[int]:

     """simple docstring"""



     if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

     _UpperCAmelCase			: List[Any]     					=			[self.cls_token_id]
     _UpperCAmelCase			: Dict     					=			[self.sep_token_id]
     _UpperCAmelCase			: Optional[int]     					=			[self.question_token_id] + [self.convert_tokens_to_ids('''.''')]
     if self.padding_side == "right":
         # Input is question-then-context
         return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
     else:
         # Input is context-then-question
         return cls + token_ids_a + sep + token_ids_a + question_suffix + sep


 def 	snake_case__			(		self							,					_A							,					_A = None)	->						List[int]:

     """simple docstring"""



     _UpperCAmelCase			: str     					=			[self.sep_token_id]
     _UpperCAmelCase			: Optional[Any]     					=			[self.cls_token_id]
     _UpperCAmelCase			: List[Any]     					=			[self.question_token_id] + [self.convert_tokens_to_ids('''.''')]
     if token_ids_a is None:
         return len(cls + token_ids_a + sep) * [0]

     if self.padding_side == "right":
         # Input is question-then-context
         return len(cls + token_ids_a + question_suffix + sep) * [0] + len(token_ids_a + sep) * [1]
     else:
         # Input is context-then-question
         return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + question_suffix + sep) * [1]


 def 	snake_case__			(		self							,					_A							,					_A = None)	->						Tuple[str]:

     """simple docstring"""



     _UpperCAmelCase			: Tuple     					=			self._tokenizer.model.save(SCREAMING_SNAKE_CASE_							,					name=SCREAMING_SNAKE_CASE_)
     return tuple(SCREAMING_SNAKE_CASE_)












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


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

def        _lowerCamelCase		(  __A      :							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_      (  __lowercase     ):






 '''simple docstring'''




 @staticmethod
 def 	snake_case__			(		_A)	->						Optional[Any]:

     """simple docstring"""



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


 @staticmethod
 def 	snake_case__			(		_A							,					_A)	->						Dict:

     """simple docstring"""



     _UpperCAmelCase			: Tuple     					=			set(pt_outputs.keys())
     _UpperCAmelCase			: Dict     					=			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(_A							,					_A							,					_A							,					_A=""):
         # 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(_A							,					torch.Tensor):
             _UpperCAmelCase			: Tuple     					=			np.max(np.abs(pt_out.numpy() - tf_out.numpy()))
             _UpperCAmelCase			: int     					=			tensor_difference
         else:
             _UpperCAmelCase			: int     					=			attr_name
             for i, pt_item in enumerate(_A):
                 # If it is a named attribute, we keep the name. Otherwise, just its index.
                 if isinstance(_A							,					_A):
                     _UpperCAmelCase			: Union[str, Any]     					=			root_name + pt_item
                     _UpperCAmelCase			: int     					=			tf_out[pt_item]
                     _UpperCAmelCase			: List[Any]     					=			pt_out[pt_item]
                 else:
                     _UpperCAmelCase			: Tuple     					=			root_name + f'''[{i}]'''
                     _UpperCAmelCase			: Optional[Any]     					=			tf_out[i]
                 _UpperCAmelCase			: List[str]     					=			_find_pt_tf_differences(_A							,					_A							,					_A							,					_A)

         return differences

     return _find_pt_tf_differences(_A							,					_A							,					{})


 def __init__(		self							,					_A							,					_A							,					_A							,					_A							,					_A							,					_A							,					_A							,					_A							,					*_A							,					)	->						Optional[int]:

     """simple docstring"""



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


 def 	snake_case__			(		self							,					_A							,					_A							,					_A)	->						Tuple:

     """simple docstring"""



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

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

     _UpperCAmelCase			: str     					=			set(inspect.signature(pt_model.forward).parameters.keys())
     _UpperCAmelCase			: List[Any]     					=			{}
     if "input_ids" in model_forward_signature:
         processor_inputs.update(
             {
                 '''text''': ['''Hi there!''', '''I am a batch with more than one row and different input lengths.'''],
                 '''padding''': True,
                 '''truncation''': True,
             })
     if "pixel_values" in model_forward_signature:
         _UpperCAmelCase			: Optional[int]     					=			load_dataset('''cifar10'''							,					'''plain_text'''							,					split='''test''')[:2]['''img''']
         processor_inputs.update({'''images''': sample_images})
     if "input_features" in model_forward_signature:
         _UpperCAmelCase			: 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:
             _UpperCAmelCase			: str     					=			feature_extractor_signature['''padding'''].default
             if default_strategy is not False and default_strategy is not None:
                 _UpperCAmelCase			: Union[str, Any]     					=			default_strategy
             else:
                 _UpperCAmelCase			: List[str]     					=			True
         else:
             _UpperCAmelCase			: Optional[Any]     					=			True
         processor_inputs.update({'''audio''': _get_audio_input(), '''padding''': padding_strategy})
     if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
         processor_inputs.update({'''audio''': _get_audio_input(), '''padding''': True})
     _UpperCAmelCase			: str     					=			processor(**_A							,					return_tensors='''pt''')
     _UpperCAmelCase			: int     					=			processor(**_A							,					return_tensors='''tf''')

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

     return pt_input, tf_input


 def 	snake_case__			(		self)	->						int:

     """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
     _UpperCAmelCase			: Any     					=			Repository(local_dir=self._local_dir							,					clone_from=self._model_name)

     # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
     _UpperCAmelCase			: Any     					=			AutoConfig.from_pretrained(self._local_dir)
     _UpperCAmelCase			: List[Any]     					=			config.architectures
     if self._override_model_class is not None:
         if self._override_model_class.startswith('''TF'''):
             _UpperCAmelCase			: str     					=			[self._override_model_class[2:]]
         else:
             _UpperCAmelCase			: str     					=			[self._override_model_class]
         try:
             _UpperCAmelCase			: str     					=			getattr(import_module('''transformers''')							,					architectures[0])
         except AttributeError:
             raise ValueError(f'''Model class {self._override_model_class} not found in transformers.''')
         try:
             _UpperCAmelCase			: List[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
         _UpperCAmelCase			: Optional[Any]     					=			getattr(import_module('''transformers''')							,					'''AutoModel''')
         _UpperCAmelCase			: Optional[Any]     					=			getattr(import_module('''transformers''')							,					'''TFAutoModel''')
         self._logger.warning('''No detected architecture, using AutoModel/TFAutoModel''')
     else:  # Architecture defined -- use it
         if len(_A) > 1:
             raise ValueError(f'''More than one architecture was found, aborting. (architectures = {architectures})''')
         self._logger.warning(f'''Detected architecture: {architectures[0]}''')
         _UpperCAmelCase			: Tuple     					=			getattr(import_module('''transformers''')							,					architectures[0])
         try:
             _UpperCAmelCase			: 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
     _UpperCAmelCase			: Union[str, Any]     					=			tf_class.from_config(_A)
     _UpperCAmelCase			: List[str]     					=			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
     _UpperCAmelCase			: int     					=			pt_class.from_pretrained(self._local_dir)
     pt_model.eval()

     _UpperCAmelCase					,	_UpperCAmelCase			: Dict     					=			self.get_inputs(_A							,					_A							,					_A)

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

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

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

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

     _UpperCAmelCase			: Any     					=			self.find_pt_tf_differences(_A							,					_A)
     _UpperCAmelCase			: str     					=			{k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
     _UpperCAmelCase			: Any     					=			{k: v for k, v in conversion_differences.items() if '''hidden''' in k}
     if len(_A) == 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\'''')
     _UpperCAmelCase			: List[str]     					=			max(output_differences.values()) if output_differences else 0.0
     _UpperCAmelCase			: List[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]))

     _UpperCAmelCase			: List[Any]     					=			'''Update TF weights''' if self._new_weights else '''Add TF weights'''
     if self._push:
         repo.git_add(auto_lfs_track=_A)
         repo.git_commit(_A)
         repo.git_push(blocking=_A)  # 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...''')
         _UpperCAmelCase			: Dict     					=			(
             '''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(_A):
             _UpperCAmelCase			: int     					=			[
                 CommitOperationAdd(path_in_repo=_A							,					path_or_fileobj=_A)
             ]
             for shard_path in tf.io.gfile.glob(self._local_dir + '''/tf_model-*.h5'''):
                 operations += [
                     CommitOperationAdd(path_in_repo=os.path.basename(_A)							,					path_or_fileobj=_A)
                 ]
         else:
             _UpperCAmelCase			: List[str]     					=			[CommitOperationAdd(path_in_repo=_A							,					path_or_fileobj=_A)]

         _UpperCAmelCase			: Any     					=			create_commit(
             repo_id=self._model_name							,					operations=_A							,					commit_message=_A							,					commit_description=_A							,					repo_type='''model'''							,					create_pr=_A							,					).pr_url
         self._logger.warning(f'''PR open in {hub_pr_url}''')




