






'''simple docstring'''
import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()





def 	_a(					UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  List[Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Tuple													=AlbertConfig.from_json_file(UpperCamelCase__							)
      print(f"Building PyTorch model from configuration: {config}"							)
      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=AlbertForPreTraining(UpperCamelCase__							)

      # Load weights from tf checkpoint
      load_tf_weights_in_albert(UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__							)

      # Save pytorch-model
      print(f"Save PyTorch model to {pytorch_dump_path}"							)
      torch.save(model.state_dict(),		UpperCamelCase__							)


if __name__ == "__main__":
  a_					=	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--albert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained ALBERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  a_					=	parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)







'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							"""facebook/bart-large-mnli"""
 snake_case_												=							(
     """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
     """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
     """It returns the most likely label in the list of provided `labels` for the input text."""
 )
 snake_case_												=							"""text_classifier"""
 snake_case_												=							AutoTokenizer
 snake_case_												=							AutoModelForSequenceClassification

 snake_case_												=							["""text""", ["""text"""]]
 snake_case_												=							["""text"""]




 def        __magic_name__		( self		:							Any )		->  Any:
       super().setup()
       SCREAMING_SNAKE_CASE__       :			int													=self.model.config
       SCREAMING_SNAKE_CASE__       :			int													=-1
       for idx, label in config.idalabel.items():
             if label.lower().startswith('''entail''' ):
                   SCREAMING_SNAKE_CASE__       :			Any													=int(__lowercase )
       if self.entailment_id == -1:
             raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.''' )




 def        __magic_name__		( self		:							str   , __lowercase		:							Any   , __lowercase		:							Optional[Any] )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Dict													=labels
       return self.pre_processor(
           [text] * len(__lowercase )   , [F"This example is {label}" for label in labels]   , return_tensors='''pt'''   , padding='''max_length'''   , )




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							str )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			int													=outputs.logits
       SCREAMING_SNAKE_CASE__       :			Any													=torch.argmax(logits[:, 2] ).item()
       return self._labels[label_id]