






# Copyright 2021 The HuggingFace 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 argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


lowerCamelCase	:							Tuple				='''Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'''







def 		SCREAMING_SNAKE_CASE							(					) ->	int:
     UpperCamelCase__			:   Dict       				=  _ask_options(
         "In which compute environment are you running?"					,    ["This machine", "AWS (Amazon SageMaker)"]					,    _convert_compute_environment					,    )
     if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
          UpperCamelCase__			:   Optional[int]       				=  get_sagemaker_input()
     else:
          UpperCamelCase__			:   Dict       				=  get_cluster_input()
     return config







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase=None			) ->	List[str]:
     if subparsers is not None:
          UpperCamelCase__			:   List[Any]       				=  subparsers.add_parser("config"					,    description=__lowerCAmelCase			)
     else:
          UpperCamelCase__			:   Any       				=  argparse.ArgumentParser("Accelerate config command"					,    description=__lowerCAmelCase			)

     parser.add_argument(
         "--config_file"					,    default=__lowerCAmelCase					,    help=(
             "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
             "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
             "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
             "with 'huggingface'."
         )					,    )

     if subparsers is not None:
          parser.set_defaults(func=__lowerCAmelCase			)
     return parser







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Optional[Any]:
     UpperCamelCase__			:   Union[str, Any]       				=  get_user_input()
     if args.config_file is not None:
          UpperCamelCase__			:   Tuple       				=  args.config_file
     else:
          if not os.path.isdir(__lowerCAmelCase			):
               os.makedirs(__lowerCAmelCase			)
          UpperCamelCase__			:   Union[str, Any]       				=  default_yaml_config_file

     if config_file.endswith(".json"			):
          config.to_json_file(__lowerCAmelCase			)
     else:
          config.to_yaml_file(__lowerCAmelCase			)
     print(f'accelerate configuration saved at {config_file}'			)







def 		SCREAMING_SNAKE_CASE							(					) ->	int:
     UpperCamelCase__			:   List[str]       				=  config_command_parser()
     UpperCamelCase__			:   Dict       				=  parser.parse_args()
     config_command(__lowerCAmelCase			)


if __name__ == "__main__":
 main()







# 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 			__a  (    A__       ):
 _lowerCAmelCase       :       str        = '''facebook/bart-large-mnli'''
 _lowerCAmelCase       :       Tuple        = (
     '''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.'''
 )
 _lowerCAmelCase       :       Any        = '''text_classifier'''
 _lowerCAmelCase       :       int        = AutoTokenizer
 _lowerCAmelCase       :       Union[str, Any]        = AutoModelForSequenceClassification

 _lowerCAmelCase       :       Union[str, Any]        = ['''text''', ['''text''']]
 _lowerCAmelCase       :       Dict        = ['''text''']







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      super().setup()
      UpperCamelCase__			:   Dict       				=  self.model.config
      UpperCamelCase__			:   Union[str, Any]       				=  -1
      for idx, label in config.idalabel.items():
           if label.lower().startswith("entail"						):
                UpperCamelCase__			:   List[str]       				=  int(SCREAMING_SNAKE_CASE						)
      if self.entailment_id == -1:
           raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."						)







 def 					__lowercase					(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  labels
      return self.pre_processor(
          [text] * len(SCREAMING_SNAKE_CASE						)							,   [F'This example is {label}' for label in labels]							,   return_tensors="pt"							,   padding="max_length"							,   )







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  outputs.logits
      UpperCamelCase__			:   Any       				=  torch.argmax(logits[:, 2]						).item()
      return self._labels[label_id]