



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 						UpperCamelCase      (  snake_case__			:       Dict			,	snake_case__			:       List[str]			,	snake_case__			:       str			,	snake_case__			:       Any			,	snake_case__			:       Tuple=True			,	snake_case__			:       Union[str, Any]="pt"      )      ->	int:
   UpperCamelCase       :    List[Any]		      =      {'add_prefix_space': True} if isinstance(_A			,	_A      ) and not line.startswith(' '      ) else {}
   UpperCamelCase       :    Any		      =      padding_side
   return tokenizer(
       [line]			,	max_length=_A			,	padding='max_length' if pad_to_max_length else None			,	truncation=_A			,	return_tensors=_A			,	add_special_tokens=_A			,	**_A			,	)
def 						UpperCamelCase      (  snake_case__			:       List[str]			,	snake_case__			:       List[Any]			,	snake_case__			:       Optional[int]=None			,	)      ->	Optional[Any]:
   UpperCamelCase       :    str		      =      input_ids.ne(_A      ).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 							lowerCAmelCase_						(    lowerCAmelCase__				):
       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_="",     )	->						Union[str, Any]:
          super().__init__()
          UpperCamelCase       :    int		      =      Path(__lowerCAmelCase			).joinpath(type_path + '.source'			)
          UpperCamelCase       :    Dict		      =      Path(__lowerCAmelCase			).joinpath(type_path + '.target'			)
          UpperCamelCase       :    Tuple		      =      self.get_char_lens(self.src_file			)
          UpperCamelCase       :    Dict		      =      max_source_length
          UpperCamelCase       :    int		      =      max_target_length
          assert min(self.src_lens			) > 0, F"""found empty line in {self.src_file}"""
          UpperCamelCase       :    List[str]		      =      tokenizer
          UpperCamelCase       :    Dict		      =      prefix
          if n_obs is not None:
             UpperCamelCase       :    Optional[int]		      =      self.src_lens[:n_obs]
          UpperCamelCase       :    Tuple		      =      src_lang
          UpperCamelCase       :    str		      =      tgt_lang
       def __len__(     self			)	->						List[str]:
          return len(self.src_lens			)
       def __getitem__(     self,     SCREAMING_SNAKE_CASE_			)	->						Dict:
          UpperCamelCase       :    List[Any]		      =      index + 1  # linecache starts at 1
          UpperCamelCase       :    Union[str, Any]		      =      self.prefix + linecache.getline(str(self.src_file			),     __lowerCAmelCase			).rstrip('\n'			)
          UpperCamelCase       :    int		      =      linecache.getline(str(self.tgt_file			),     __lowerCAmelCase			).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,     __lowerCAmelCase			):
             source_line += self.tokenizer.eos_token
             tgt_line += self.tokenizer.eos_token

          # Pad source and target to the right
          UpperCamelCase       :    Optional[int]		      =      (
              self.tokenizer.question_encoder if isinstance(self.tokenizer,     __lowerCAmelCase			) else self.tokenizer
          )
          UpperCamelCase       :    List[Any]		      =      self.tokenizer.generator if isinstance(self.tokenizer,     __lowerCAmelCase			) else self.tokenizer

          UpperCamelCase       :    Union[str, Any]		      =      encode_line(__lowerCAmelCase,     __lowerCAmelCase,     self.max_source_length,     'right'			)
          UpperCamelCase       :    Optional[Any]		      =      encode_line(__lowerCAmelCase,     __lowerCAmelCase,     self.max_target_length,     'right'			)

          UpperCamelCase       :    Optional[int]		      =      source_inputs['input_ids'].squeeze()
          UpperCamelCase       :    Any		      =      target_inputs['input_ids'].squeeze()
          UpperCamelCase       :    str		      =      source_inputs['attention_mask'].squeeze()
          return {
              "input_ids": source_ids,
              "attention_mask": src_mask,
              "decoder_input_ids": target_ids,
          }
       @staticmethod
       def    snake_case_			(     SCREAMING_SNAKE_CASE_			)	->						Optional[Any]:
          return [len(__lowerCAmelCase			) for x in Path(__lowerCAmelCase			).open().readlines()]






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






__UpperCAmelCase			  =			getLogger(__name__)
def 						UpperCamelCase      (  snake_case__			:       List[List]      )      ->	List[str]:
   return list(itertools.chain.from_iterable(_A      )      )
def 						UpperCamelCase      (  snake_case__			:       str      )      ->	None:
   UpperCamelCase       :    Optional[Any]		      =      get_git_info()
   save_json(_A			,	os.path.join(_A			,	'git_log.json'      )      )
def 						UpperCamelCase      (  snake_case__			:       Tuple			,	snake_case__			:       Optional[Any]			,	snake_case__			:       Optional[Any]=4			,	**snake_case__			:       List[Any]      )      ->	Tuple:


   with open(_A			,	'w'      ) as f:
      json.dump(_A			,	_A			,	indent=_A			,	**_A      )
def 						UpperCamelCase      (  snake_case__			:       Dict      )      ->	Tuple:


   with open(_A      ) as f:
      return json.load(_A      )
def 						UpperCamelCase      (  )      ->	Union[str, Any]:
   UpperCamelCase       :    Any		      =      git.Repo(search_parent_directories=_A      )
   UpperCamelCase       :    Union[str, Any]		      =      {
       'repo_id': str(_A      ),
       'repo_sha': str(repo.head.object.hexsha      ),
       'repo_branch': str(repo.active_branch      ),
       'hostname': str(socket.gethostname()      ),
   }
   return repo_infos
def 						UpperCamelCase      (  snake_case__			:       Callable			,	snake_case__			:       Iterable      )      ->	List:
   return list(map(_A			,	_A      )      )
def 						UpperCamelCase      (  snake_case__			:       Optional[int]			,	snake_case__			:       Tuple      )      ->	Optional[int]:


   with open(_A			,	'wb'      ) as f:
      return pickle.dump(_A			,	_A      )
def 						UpperCamelCase      (  snake_case__			:       int      )      ->	List[str]:


   def remove_articles(snake_case__			:       Any      ):
      return re.sub(R'\b(a|an|the)\b'			,	' '			,	_A      )

   def white_space_fix(snake_case__			:       List[str]      ):
      return " ".join(text.split()      )

   def remove_punc(snake_case__			:       List[Any]      ):
      UpperCamelCase       :    str		      =      set(string.punctuation      )
      return "".join(ch for ch in text if ch not in exclude      )

   def lower(snake_case__			:       List[Any]      ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(_A      )      )      )      )
def 						UpperCamelCase      (  snake_case__			:       Optional[Any]			,	snake_case__			:       List[Any]      )      ->	Optional[int]:
   UpperCamelCase       :    Optional[int]		      =      normalize_answer(_A      ).split()
   UpperCamelCase       :    Tuple		      =      normalize_answer(_A      ).split()
   UpperCamelCase       :    Any		      =      Counter(_A      ) & Counter(_A      )
   UpperCamelCase       :    Union[str, Any]		      =      sum(common.values()      )
   if num_same == 0:
      return 0
   UpperCamelCase       :    Union[str, Any]		      =      1.0 * num_same / len(_A      )
   UpperCamelCase       :    int		      =      1.0 * num_same / len(_A      )
   UpperCamelCase       :    List[str]		      =      (2 * precision * recall) / (precision + recall)
   return fa
def 						UpperCamelCase      (  snake_case__			:       Tuple			,	snake_case__			:       int      )      ->	int:
   return normalize_answer(_A      ) == normalize_answer(_A      )
def 						UpperCamelCase      (  snake_case__			:       List[str]			,	snake_case__			:       List[str]      )      ->	Dict:
   assert len(_A      ) == len(_A      )
   UpperCamelCase       :    Optional[Any]		      =      0
   for hypo, pred in zip(_A			,	_A      ):
      em += exact_match_score(_A			,	_A      )
   if len(_A      ) > 0:
      em /= len(_A      )
   return {"em": em}
def 						UpperCamelCase      (  snake_case__			:       str      )      ->	Optional[Any]:
   return model_prefix.startswith('rag'      )


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

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


__lowercase        =    '''Create a default config file for Accelerate with only a few flags set.'''






def 							snake_case__				(     _A:   Dict="no" ,  _A:   str = default_json_config_file ,  _A:   bool = False					)       ->      List[str]:

		'''simple docstring'''


		lowerCAmelCase     =    Path(_A					)
		path.parent.mkdir(parents=_A ,  exist_ok=_A					)
		if path.exists():
				print(
				    f"Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."					)
				return False
		lowerCAmelCase     =    mixed_precision.lower()
		if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
				raise ValueError(
				    f"`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"					)
		lowerCAmelCase     =    {
		    """compute_environment""": """LOCAL_MACHINE""",
		    """mixed_precision""": mixed_precision,
		}
		if torch.cuda.is_available():
				lowerCAmelCase     =    torch.cuda.device_count()
				lowerCAmelCase     =    num_gpus
				lowerCAmelCase     =    False
				if num_gpus > 1:
						lowerCAmelCase     =    """MULTI_GPU"""
				else:
						lowerCAmelCase     =    """NO"""
		elif is_xpu_available() and use_xpu:
				lowerCAmelCase     =    torch.xpu.device_count()
				lowerCAmelCase     =    num_xpus
				lowerCAmelCase     =    False
				if num_xpus > 1:
						lowerCAmelCase     =    """MULTI_XPU"""
				else:
						lowerCAmelCase     =    """NO"""
		elif is_npu_available():
				lowerCAmelCase     =    torch.npu.device_count()
				lowerCAmelCase     =    num_npus
				lowerCAmelCase     =    False
				if num_npus > 1:
						lowerCAmelCase     =    """MULTI_NPU"""
				else:
						lowerCAmelCase     =    """NO"""
		else:
				lowerCAmelCase     =    0
				lowerCAmelCase     =    True
				lowerCAmelCase     =    1
				lowerCAmelCase     =    """NO"""
		lowerCAmelCase     =    ClusterConfig(**_A					)
		config.to_json_file(_A					)
		return path






def 							snake_case__				(     _A:   str ,  _A:   Any					)       ->      Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase     =    parser.add_parser("""default""" ,  parents=_A ,  help=_A ,  formatter_class=_A					)
		parser.add_argument(
		    """--config_file""" ,  default=_A ,  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'."""
		    ) ,  dest="""save_location""" ,  )

		parser.add_argument(
		    """--mixed_precision""" ,  choices=["""no""", """fp16""", """bf16"""] ,  type=_A ,  help="""Whether or not to use mixed precision training. """
		    """Choose between FP16 and BF16 (bfloat16) training. """
		    """BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.""" ,  default="""no""" ,  )
		parser.set_defaults(func=_A					)
		return parser






def 							snake_case__				(     _A:   Optional[int]					)       ->      Optional[int]:

		'''simple docstring'''


		lowerCAmelCase     =    write_basic_config(args.mixed_precision ,  args.save_location					)
		if config_file:
				print(f"accelerate configuration saved at {config_file}"					)




