




import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase=True					,		_lowercase="pt" ):

     '''simple docstring'''


     UpperCAmelCase_     :				Dict  =     {'''add_prefix_space''': True} if isinstance(_lowercase					,		_lowercase ) and not line.startswith(''' ''' ) else {}
     UpperCAmelCase_     :				List[str]  =     padding_side
     return tokenizer(
         [line]					,		max_length=_lowercase					,		padding='''max_length''' if pad_to_max_length else None					,		truncation=_lowercase					,		return_tensors=_lowercase					,		add_special_tokens=_lowercase					,		**_lowercase					,		)







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase=None					,		):

     '''simple docstring'''


     UpperCAmelCase_     :				str  =     input_ids.ne(_lowercase ).any(dim=0 )
     if attention_mask is None:
          return input_ids[:, keep_column_mask]
     else:
          return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])

class     __a(	_a  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE="train"  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=""  ,)					->						Tuple:
         super().__init__()
         UpperCAmelCase_     :				Optional[Any]  =     Path(_SCREAMING_SNAKE_CASE    ).joinpath(type_path + '''.source'''    )
         UpperCAmelCase_     :				List[Any]  =     Path(_SCREAMING_SNAKE_CASE    ).joinpath(type_path + '''.target'''    )
         UpperCAmelCase_     :				Tuple  =     self.get_char_lens(self.src_file    )
         UpperCAmelCase_     :				Any  =     max_source_length
         UpperCAmelCase_     :				str  =     max_target_length
         assert min(self.src_lens    ) > 0, f'''found empty line in {self.src_file}'''
         UpperCAmelCase_     :				str  =     tokenizer
         UpperCAmelCase_     :				Union[str, Any]  =     prefix
         if n_obs is not None:
              UpperCAmelCase_     :				Dict  =     self.src_lens[:n_obs]
         UpperCAmelCase_     :				Union[str, Any]  =     src_lang
         UpperCAmelCase_     :				Any  =     tgt_lang





    def __len__( self    )					->						Any:
         return len(self.src_lens    )





    def __getitem__( self  ,_SCREAMING_SNAKE_CASE    )					->						Dict[str, torch.Tensor]:
         UpperCAmelCase_     :				Any  =     index + 1  # linecache starts at 1
         UpperCAmelCase_     :				int  =     self.prefix + linecache.getline(str(self.src_file    )  ,_SCREAMING_SNAKE_CASE    ).rstrip('''\n'''    )
         UpperCAmelCase_     :				int  =     linecache.getline(str(self.tgt_file    )  ,_SCREAMING_SNAKE_CASE    ).rstrip('''\n'''    )
         assert source_line, f'''empty source line for index {index}'''
         assert tgt_line, f'''empty tgt line for index {index}'''

         # Need to add eos token manually for T5
         if isinstance(self.tokenizer  ,_SCREAMING_SNAKE_CASE    ):
              source_line += self.tokenizer.eos_token
              tgt_line += self.tokenizer.eos_token

         # Pad source and target to the right
         UpperCAmelCase_     :				Tuple  =     (
             self.tokenizer.question_encoder if isinstance(self.tokenizer  ,_SCREAMING_SNAKE_CASE    ) else self.tokenizer
         )
         UpperCAmelCase_     :				Dict  =     self.tokenizer.generator if isinstance(self.tokenizer  ,_SCREAMING_SNAKE_CASE    ) else self.tokenizer

         UpperCAmelCase_     :				Tuple  =     encode_line(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,self.max_source_length  ,'''right'''    )
         UpperCAmelCase_     :				Tuple  =     encode_line(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,self.max_target_length  ,'''right'''    )

         UpperCAmelCase_     :				Optional[Any]  =     source_inputs['''input_ids'''].squeeze()
         UpperCAmelCase_     :				Optional[Any]  =     target_inputs['''input_ids'''].squeeze()
         UpperCAmelCase_     :				Tuple  =     source_inputs['''attention_mask'''].squeeze()
         return {
             "input_ids": source_ids,
             "attention_mask": src_mask,
             "decoder_input_ids": target_ids,
         }





    @staticmethod
    def 				a__ ( _SCREAMING_SNAKE_CASE    )					->						Optional[int]:
         return [len(_SCREAMING_SNAKE_CASE    ) for x in Path(_SCREAMING_SNAKE_CASE    ).open().readlines()]





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						Dict[str, torch.Tensor]:
         UpperCAmelCase_     :				int  =     torch.stack([x['''input_ids'''] for x in batch]    )
         UpperCAmelCase_     :				Dict  =     torch.stack([x['''attention_mask'''] for x in batch]    )
         UpperCAmelCase_     :				Tuple  =     torch.stack([x['''decoder_input_ids'''] for x in batch]    )
         UpperCAmelCase_     :				int  =     (
             self.tokenizer.generator.pad_token_id
             if isinstance(self.tokenizer  ,_SCREAMING_SNAKE_CASE    )
             else self.tokenizer.pad_token_id
         )
         UpperCAmelCase_     :				Tuple  =     (
             self.tokenizer.question_encoder.pad_token_id
             if isinstance(self.tokenizer  ,_SCREAMING_SNAKE_CASE    )
             else self.tokenizer.pad_token_id
         )
         UpperCAmelCase_     :				str  =     trim_batch(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_,       UpperCAmelCase_     :				Union[str, Any]  =     trim_batch(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Tuple  =     {
             '''input_ids''': source_ids,
             '''attention_mask''': source_mask,
             '''decoder_input_ids''': y,
         }
         return batch


__a							     =					getLogger(__name__)







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     return list(itertools.chain.from_iterable(_lowercase ) )







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				str  =     get_git_info()
     save_json(_lowercase					,		os.path.join(_lowercase					,		'''git_log.json''' ) )







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase=4					,		**_lowercase ):

     '''simple docstring'''


     with open(_lowercase					,		'''w''' ) as f:
          json.dump(_lowercase					,		_lowercase					,		indent=_lowercase					,		**_lowercase )







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     with open(_lowercase ) as f:
          return json.load(_lowercase )







def lowerCamelCase__  (     ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[Any]  =     git.Repo(search_parent_directories=_lowercase )
     UpperCAmelCase_     :				Union[str, Any]  =     {
         '''repo_id''': str(_lowercase ),
         '''repo_sha''': str(repo.head.object.hexsha ),
         '''repo_branch''': str(repo.active_branch ),
         '''hostname''': str(socket.gethostname() ),
     }
     return repo_infos







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     return list(map(_lowercase					,		_lowercase ) )







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     with open(_lowercase					,		'''wb''' ) as f:
          return pickle.dump(_lowercase					,		_lowercase )







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     def remove_articles(_lowercase ):
          return re.sub(r'''\b(a|an|the)\b'''					,		''' '''					,		_lowercase )

     def white_space_fix(_lowercase ):
          return " ".join(text.split() )

     def remove_punc(_lowercase ):
          UpperCAmelCase_     :				Any  =     set(string.punctuation )
          return "".join(ch for ch in text if ch not in exclude )

     def lower(_lowercase ):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(_lowercase ) ) ) )







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Any  =     normalize_answer(_lowercase ).split()
     UpperCAmelCase_     :				List[Any]  =     normalize_answer(_lowercase ).split()
     UpperCAmelCase_     :				Any  =     Counter(_lowercase ) & Counter(_lowercase )
     UpperCAmelCase_     :				Tuple  =     sum(common.values() )
     if num_same == 0:
          return 0
     UpperCAmelCase_     :				str  =     1.0 * num_same / len(_lowercase )
     UpperCAmelCase_     :				Union[str, Any]  =     1.0 * num_same / len(_lowercase )
     UpperCAmelCase_     :				Any  =     (2 * precision * recall) / (precision + recall)
     return fa







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     return normalize_answer(_lowercase ) == normalize_answer(_lowercase )







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     assert len(_lowercase ) == len(_lowercase )
     UpperCAmelCase_     :				Optional[int]  =     0
     for hypo, pred in zip(_lowercase					,		_lowercase ):
          em += exact_match_score(_lowercase					,		_lowercase )
     if len(_lowercase ) > 0:
          em /= len(_lowercase )
     return {"em": em}







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     return model_prefix.startswith('''rag''' )







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[int]  =     {p: p for p in extra_params}
     # T5 models don't have `dropout` param, they have `dropout_rate` instead
     UpperCAmelCase_     :				Dict  =     '''dropout_rate'''
     for p in extra_params:
          if getattr(_lowercase					,		_lowercase					,		_lowercase ):
               if not hasattr(_lowercase					,		_lowercase ) and not hasattr(_lowercase					,		equivalent_param[p] ):
                    logger.info('''config doesn\'t have a `{}` attribute'''.format(_lowercase ) )
                    delattr(_lowercase					,		_lowercase )
                    continue
               UpperCAmelCase_     :				Union[str, Any]  =     p if hasattr(_lowercase					,		_lowercase ) else equivalent_param[p]
               setattr(_lowercase					,		_lowercase					,		getattr(_lowercase					,		_lowercase ) )
               delattr(_lowercase					,		_lowercase )
     return hparams, config

"""simple docstring"""


# 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				 =					'''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    _snake_case					(		):
	A     = _ask_options(
	    'In which compute environment are you running?'						,   ['This machine', 'AWS (Amazon SageMaker)']						,   _convert_compute_environment						,   )
	if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
		A     = get_sagemaker_input()
	else:
		A     = get_cluster_input()
	return config






def    _snake_case					(		snake_case__ :  Any=None  ):
	if subparsers is not None:
		A     = subparsers.add_parser('config'						,   description=snake_case__  )
	else:
		A     = argparse.ArgumentParser('Accelerate config command'						,   description=snake_case__  )

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






def    _snake_case					(		snake_case__ :  Tuple  ):
	A     = get_user_input()
	if args.config_file is not None:
		A     = args.config_file
	else:
		if not os.path.isdir(snake_case__  ):
			os.makedirs(snake_case__  )
		A     = default_yaml_config_file

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






def    _snake_case					(		):
	A     = config_command_parser()
	A     = parser.parse_args()
	config_command(snake_case__  )


if __name__ == "__main__":
		main()