



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      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase=True   , _UpperCAmelCase="pt"				):
    __a						       =				{'''add_prefix_space''': True} if isinstance(_UpperCAmelCase   , _UpperCAmelCase				) and not line.startswith(''' '''				) else {}
    __a						       =				padding_side
    return tokenizer(
        [line]   , max_length=_UpperCAmelCase   , padding='''max_length''' if pad_to_max_length else None   , truncation=_UpperCAmelCase   , return_tensors=_UpperCAmelCase   , add_special_tokens=_UpperCAmelCase   , **_UpperCAmelCase   , )





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase=None   , ):
    __a						       =				input_ids.ne(_UpperCAmelCase				).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       (	__UpperCAmelCase    ):



       def __init__(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Any="train"     ,		__SCREAMING_SNAKE_CASE							:     Any=None     ,		__SCREAMING_SNAKE_CASE							:     int=None     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=None     ,		__SCREAMING_SNAKE_CASE							:     Any=""     ,		):


           '''simple docstring'''

           super().__init__()
           __a						       =				Path(__SCREAMING_SNAKE_CASE).joinpath(type_path + '''.source''')
           __a						       =				Path(__SCREAMING_SNAKE_CASE).joinpath(type_path + '''.target''')
           __a						       =				self.get_char_lens(self.src_file)
           __a						       =				max_source_length
           __a						       =				max_target_length
           assert min(self.src_lens) > 0, F'found empty line in {self.src_file}'
           __a						       =				tokenizer
           __a						       =				prefix
           if n_obs is not None:
               __a						       =				self.src_lens[:n_obs]
           __a						       =				src_lang
           __a						       =				tgt_lang



       def __len__(		self							:     Any):


           '''simple docstring'''

           return len(self.src_lens)



       def __getitem__(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

           __a						       =				index + 1  # linecache starts at 1
           __a						       =				self.prefix + linecache.getline(str(self.src_file)     ,		__SCREAMING_SNAKE_CASE).rstrip('''\n''')
           __a						       =				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
           __a						       =				(
               self.tokenizer.question_encoder if isinstance(self.tokenizer     ,		__SCREAMING_SNAKE_CASE) else self.tokenizer
           )
           __a						       =				self.tokenizer.generator if isinstance(self.tokenizer     ,		__SCREAMING_SNAKE_CASE) else self.tokenizer

           __a						       =				encode_line(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		self.max_source_length     ,		'''right''')
           __a						       =				encode_line(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		self.max_target_length     ,		'''right''')

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



       @staticmethod
       def 		_lowerCamelCase			(		__SCREAMING_SNAKE_CASE							:     Tuple):


           '''simple docstring'''

           return [len(__SCREAMING_SNAKE_CASE) for x in Path(__SCREAMING_SNAKE_CASE).open().readlines()]





       def 		_lowerCamelCase			(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           __a						       =				torch.stack([x['''input_ids'''] for x in batch])
           __a						       =				torch.stack([x['''attention_mask'''] for x in batch])
           __a						       =				torch.stack([x['''decoder_input_ids'''] for x in batch])
           __a						       =				(
               self.tokenizer.generator.pad_token_id
               if isinstance(self.tokenizer     ,		__SCREAMING_SNAKE_CASE)
               else self.tokenizer.pad_token_id
           )
           __a						       =				(
               self.tokenizer.question_encoder.pad_token_id
               if isinstance(self.tokenizer     ,		__SCREAMING_SNAKE_CASE)
               else self.tokenizer.pad_token_id
           )
           __a						       =				trim_batch(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a       ,   __a						       =				trim_batch(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE)
           __a						       =				{
               '''input_ids''': source_ids,
               '''attention_mask''': source_mask,
               '''decoder_input_ids''': y,
           }
           return batch


__snake_case						:Optional[int]      		=     getLogger(__name__)





def      __snake_case	(			_UpperCAmelCase				):
    return list(itertools.chain.from_iterable(_UpperCAmelCase				)				)





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				get_git_info()
    save_json(_UpperCAmelCase   , os.path.join(_UpperCAmelCase   , '''git_log.json'''				)				)





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase=4   , **_UpperCAmelCase				):
    with open(_UpperCAmelCase   , '''w'''				) as f:
        json.dump(_UpperCAmelCase   , _UpperCAmelCase   , indent=_UpperCAmelCase   , **_UpperCAmelCase				)





def      __snake_case	(			_UpperCAmelCase				):
    with open(_UpperCAmelCase				) as f:
        return json.load(_UpperCAmelCase				)





def      __snake_case	(			):
    __a						       =				git.Repo(search_parent_directories=_UpperCAmelCase				)
    __a						       =				{
        '''repo_id''': str(_UpperCAmelCase				),
        '''repo_sha''': str(repo.head.object.hexsha				),
        '''repo_branch''': str(repo.active_branch				),
        '''hostname''': str(socket.gethostname()				),
    }
    return repo_infos





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    return list(map(_UpperCAmelCase   , _UpperCAmelCase				)				)





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    with open(_UpperCAmelCase   , '''wb'''				) as f:
        return pickle.dump(_UpperCAmelCase   , _UpperCAmelCase				)





def      __snake_case	(			_UpperCAmelCase				):

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

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

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

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

    return white_space_fix(remove_articles(remove_punc(lower(_UpperCAmelCase				)				)				)				)





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				normalize_answer(_UpperCAmelCase				).split()
    __a						       =				normalize_answer(_UpperCAmelCase				).split()
    __a						       =				Counter(_UpperCAmelCase				) & Counter(_UpperCAmelCase				)
    __a						       =				sum(common.values()				)
    if num_same == 0:
        return 0
    __a						       =				1.0 * num_same / len(_UpperCAmelCase				)
    __a						       =				1.0 * num_same / len(_UpperCAmelCase				)
    __a						       =				(2 * precision * recall) / (precision + recall)
    return fa





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    return normalize_answer(_UpperCAmelCase				) == normalize_answer(_UpperCAmelCase				)





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    assert len(_UpperCAmelCase				) == len(_UpperCAmelCase				)
    __a						       =				0
    for hypo, pred in zip(_UpperCAmelCase   , _UpperCAmelCase				):
        em += exact_match_score(_UpperCAmelCase   , _UpperCAmelCase				)
    if len(_UpperCAmelCase				) > 0:
        em /= len(_UpperCAmelCase				)
    return {"em": em}





def      __snake_case	(			_UpperCAmelCase				):
    return model_prefix.startswith('''rag'''				)





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				{p: p for p in extra_params}
    # T5 models don't have `dropout` param, they have `dropout_rate` instead
    __a						       =				'''dropout_rate'''
    for p in extra_params:
        if getattr(_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
            if not hasattr(_UpperCAmelCase   , _UpperCAmelCase				) and not hasattr(_UpperCAmelCase   , equivalent_param[p]				):
                logger.info('''config doesn\'t have a `{}` attribute'''.format(_UpperCAmelCase				)				)
                delattr(_UpperCAmelCase   , _UpperCAmelCase				)
                continue
            __a						       =				p if hasattr(_UpperCAmelCase   , _UpperCAmelCase				) else equivalent_param[p]
            setattr(_UpperCAmelCase   , _UpperCAmelCase   , getattr(_UpperCAmelCase   , _UpperCAmelCase				)				)
            delattr(_UpperCAmelCase   , _UpperCAmelCase				)
    return hparams, config








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_									=   {
    'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   ['BloomTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   [
      'BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BloomForCausalLM',
      'BloomModel',
      'BloomPreTrainedModel',
      'BloomForSequenceClassification',
      'BloomForTokenClassification',
      'BloomForQuestionAnswering',
  ]

if TYPE_CHECKING:
  from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bloom_fast import BloomTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bloom import (
        BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
        BloomForCausalLM,
        BloomForQuestionAnswering,
        BloomForSequenceClassification,
        BloomForTokenClassification,
        BloomModel,
        BloomPreTrainedModel,
    )

else:
  import sys

  lowercase_									=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


