import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
      import torch



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase__			):
  '''simple docstring'''


  lowercase		:     Union[str, Any]            =				"char"
  lowercase		:     Union[str, Any]            =				"bpe"
  lowercase		:     List[Any]            =				"wp"







_lowercase							:      Optional[Any]      							=(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase__			):
  '''simple docstring'''


  lowercase		:     Optional[Any]            =				["image_processor", "char_tokenizer"]
  lowercase		:     str            =				"ViTImageProcessor"
  lowercase		:     List[Any]            =				"MgpstrTokenizer"







  def __init__(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=None      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=None      ,	**SCREAMING_SNAKE_CASE__    :					Union[str, Any]     )    ->				Dict:
         A						:     Tuple         =None
         if "feature_extractor" in kwargs:
                warnings.warn(
                    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                    ' instead.'      ,	_SCREAMING_SNAKE_CASE      ,	)
                A						:     List[Any]         =kwargs.pop('feature_extractor'     )

         A						:     int         =image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
                raise ValueError('You need to specify an `image_processor`.'     )
         if tokenizer is None:
                raise ValueError('You need to specify a `tokenizer`.'     )

         A						:     Tuple         =tokenizer
         A						:     List[Any]         =AutoTokenizer.from_pretrained('gpt2'     )
         A						:     Any         =AutoTokenizer.from_pretrained('bert-base-uncased'     )

         super().__init__(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE     )







  def __call__(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					List[str]=None      ,	SCREAMING_SNAKE_CASE__    :					Any=None      ,	SCREAMING_SNAKE_CASE__    :					int=None      ,	**SCREAMING_SNAKE_CASE__    :					Any     )    ->				Optional[int]:
         if images is None and text is None:
                raise ValueError('You need to specify either an `images` or `text` input to process.'     )

         if images is not None:
                A						:     Any         =self.image_processor(_SCREAMING_SNAKE_CASE      ,	return_tensors=_SCREAMING_SNAKE_CASE      ,	**_SCREAMING_SNAKE_CASE     )
         if text is not None:
                A						:     Union[str, Any]         =self.char_tokenizer(_SCREAMING_SNAKE_CASE      ,	return_tensors=_SCREAMING_SNAKE_CASE      ,	**_SCREAMING_SNAKE_CASE     )

         if text is None:
                return inputs
         elif images is None:
                return encodings
         else:
                A						:     List[Any]         =encodings['input_ids']
                return inputs







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Any     )    ->				Optional[Any]:
         A  ,  A  ,  A						:     Optional[int]         =sequences
         A						:     Optional[int]         =char_preds.size(0     )

         A  ,  A						:     Any         =self._decode_helper(_SCREAMING_SNAKE_CASE      ,	'char'     )
         A  ,  A						:     Any         =self._decode_helper(_SCREAMING_SNAKE_CASE      ,	'bpe'     )
         A  ,  A						:     Any         =self._decode_helper(_SCREAMING_SNAKE_CASE      ,	'wp'     )

         A						:     Dict         =[]
         A						:     str         =[]
         for i in range(_SCREAMING_SNAKE_CASE     ):
                A						:     Optional[Any]         =[char_scores[i], bpe_scores[i], wp_scores[i]]
                A						:     Tuple         =[char_strs[i], bpe_strs[i], wp_strs[i]]
                A						:     Any         =scores.index(max(_SCREAMING_SNAKE_CASE     )     )
                final_strs.append(strs[max_score_index]     )
                final_scores.append(scores[max_score_index]     )

         A						:     Dict         ={}
         A						:     List[str]         =final_strs
         A						:     str         =final_scores
         A						:     int         =char_strs
         A						:     Tuple         =bpe_strs
         A						:     str         =wp_strs
         return out







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				List[Any]:
         if format == DecodeType.CHARACTER:
                A						:     int         =self.char_decode
                A						:     int         =1
                A						:     Union[str, Any]         ='[s]'
         elif format == DecodeType.BPE:
                A						:     Optional[int]         =self.bpe_decode
                A						:     List[str]         =2
                A						:     Any         ='#'
         elif format == DecodeType.WORDPIECE:
                A						:     List[Any]         =self.wp_decode
                A						:     Optional[int]         =1_02
                A						:     Optional[int]         ='[SEP]'
         else:
                raise ValueError(f'Format {format} is not supported.'     )

         A  ,  A						:     int         =[], []
         A						:     Tuple         =pred_logits.size(0     )
         A						:     List[str]         =pred_logits.size(1     )
         A  ,  A						:     Dict         =pred_logits.topk(1      ,	dim=-1      ,	largest=_SCREAMING_SNAKE_CASE      ,	sorted=_SCREAMING_SNAKE_CASE     )
         A						:     str         =preds_index.view(-1      ,	_SCREAMING_SNAKE_CASE     )[:, 1:]
         A						:     List[str]         =decoder(_SCREAMING_SNAKE_CASE     )
         A  ,  A						:     Optional[Any]         =torch.nn.functional.softmax(_SCREAMING_SNAKE_CASE      ,	dim=2     ).max(dim=2     )
         A						:     int         =preds_max_prob[:, 1:]

         for index in range(_SCREAMING_SNAKE_CASE     ):
                A						:     Dict         =preds_str[index].find(_SCREAMING_SNAKE_CASE     )
                A						:     Union[str, Any]         =preds_str[index][:pred_eos]
                A						:     List[Any]         =preds_index[index].cpu().tolist()
                A						:     Dict         =pred_index.index(_SCREAMING_SNAKE_CASE     ) if eos_token in pred_index else -1
                A						:     Dict         =preds_max_prob[index][: pred_eos_index + 1]
                A						:     Tuple         =pred_max_prob.cumprod(dim=0     )[-1] if pred_max_prob.nelement() != 0 else 0.0
                dec_strs.append(_SCREAMING_SNAKE_CASE     )
                conf_scores.append(_SCREAMING_SNAKE_CASE     )

         return dec_strs, conf_scores







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]     )    ->				Optional[int]:
         A						:     int         =[seq.replace(' '      ,	''     ) for seq in self.char_tokenizer.batch_decode(_SCREAMING_SNAKE_CASE     )]
         return decode_strs







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					List[str]     )    ->				Any:
         return self.bpe_tokenizer.batch_decode(_SCREAMING_SNAKE_CASE     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Any     )    ->				Optional[int]:
         A						:     List[str]         =[seq.replace(' '      ,	''     ) for seq in self.wp_tokenizer.batch_decode(_SCREAMING_SNAKE_CASE     )]
         return decode_strs







# 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


_lowercase							:      List[str]      							='''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 A__  (			)				->		List[Any]:
       A						:     Any         =_ask_options(
           'In which compute environment are you running?',		['This machine', 'AWS (Amazon SageMaker)'],		_convert_compute_environment,		)
       if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
              A						:     Tuple         =get_sagemaker_input()
       else:
              A						:     str         =get_cluster_input()
       return config



def A__  (			lowercase:     int=None					)				->		str:
       if subparsers is not None:
              A						:     List[str]         =subparsers.add_parser('config',		description=lowercase					)
       else:
              A						:     Union[str, Any]         =argparse.ArgumentParser('Accelerate config command',		description=lowercase					)

       parser.add_argument(
           '--config_file',		default=lowercase,		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=lowercase					)
       return parser



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

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



def A__  (			)				->		Optional[int]:
       A						:     Any         =config_command_parser()
       A						:     int         =parser.parse_args()
       config_command(lowercase					)


if __name__ == "__main__":
    main()






