





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        _a						(    UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase=True			,  UpperCAmelCase="pt"     )				->				Union[str, Any]:







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



def        _a						(    UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase=None			,  )				->				List[Any]:







   """simple docstring"""
   lowerCamelCase__   :						str					   =							input_ids.ne(__snake_case     ).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    __SCREAMING_SNAKE_CASE			( _snake_case					):







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







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







       def __getitem__(      self     :	List[Any]		,					A     :	List[str]			)   ->Dict[str, torch.Tensor]:
          lowerCamelCase__   :						Union[str, Any]					   =							index + 1  # linecache starts at 1
          lowerCamelCase__   :						Any					   =							self.prefix + linecache.getline(str(self.src_file			)		,					_UpperCamelCase			).rstrip('''\n'''			)
          lowerCamelCase__   :						Optional[int]					   =							linecache.getline(str(self.tgt_file			)		,					_UpperCamelCase			).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		,					_UpperCamelCase			):
             source_line += self.tokenizer.eos_token
             tgt_line += self.tokenizer.eos_token

          # Pad source and target to the right
          lowerCamelCase__   :						str					   =							(
              self.tokenizer.question_encoder if isinstance(self.tokenizer		,					_UpperCamelCase			) else self.tokenizer
          )
          lowerCamelCase__   :						Optional[Any]					   =							self.tokenizer.generator if isinstance(self.tokenizer		,					_UpperCamelCase			) else self.tokenizer

          lowerCamelCase__   :						str					   =							encode_line(_UpperCamelCase		,					_UpperCamelCase		,					self.max_source_length		,					'''right'''			)
          lowerCamelCase__   :						List[Any]					   =							encode_line(_UpperCamelCase		,					_UpperCamelCase		,					self.max_target_length		,					'''right'''			)

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







       @staticmethod
       def 						__lowerCamelCase    (      A     :	Any			)   ->Optional[int]:
          return [len(_UpperCamelCase			) for x in Path(_UpperCamelCase			).open().readlines()]







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


_A					:    List[str]   =  getLogger(__name__)



def        _a						(    UpperCAmelCase     )				->				List[str]:







   """simple docstring"""
   return list(itertools.chain.from_iterable(__snake_case     )     )



def        _a						(    UpperCAmelCase     )				->				Dict:







   """simple docstring"""
   lowerCamelCase__   :						Union[str, Any]					   =							get_git_info()
   save_json(__snake_case			,  os.path.join(__snake_case			,  '''git_log.json'''     )     )



def        _a						(    UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase=4			,  **UpperCAmelCase     )				->				str:







   """simple docstring"""


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



def        _a						(    UpperCAmelCase     )				->				str:







   """simple docstring"""


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



def        _a						(    )				->				Optional[Any]:







   """simple docstring"""
   lowerCamelCase__   :						int					   =							git.Repo(search_parent_directories=__snake_case     )
   lowerCamelCase__   :						Tuple					   =							{
       'repo_id': str(__snake_case     ),
       'repo_sha': str(repo.head.object.hexsha     ),
       'repo_branch': str(repo.active_branch     ),
       'hostname': str(socket.gethostname()     ),
   }
   return repo_infos



def        _a						(    UpperCAmelCase			,  UpperCAmelCase     )				->				List[str]:







   """simple docstring"""
   return list(map(__snake_case			,  __snake_case     )     )



def        _a						(    UpperCAmelCase			,  UpperCAmelCase     )				->				List[Any]:







   """simple docstring"""


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



def        _a						(    UpperCAmelCase     )				->				Dict:







   """simple docstring"""


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

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

   def remove_punc(UpperCAmelCase     ):
      lowerCamelCase__   :						Optional[int]					   =							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(__snake_case     )     )     )     )



def        _a						(    UpperCAmelCase			,  UpperCAmelCase     )				->				List[str]:







   """simple docstring"""
   lowerCamelCase__   :						Any					   =							normalize_answer(__snake_case     ).split()
   lowerCamelCase__   :						str					   =							normalize_answer(__snake_case     ).split()
   lowerCamelCase__   :						Tuple					   =							Counter(__snake_case     ) & Counter(__snake_case     )
   lowerCamelCase__   :						Optional[Any]					   =							sum(common.values()     )
   if num_same == 0:
      return 0
   lowerCamelCase__   :						List[Any]					   =							1.0 * num_same / len(__snake_case     )
   lowerCamelCase__   :						Tuple					   =							1.0 * num_same / len(__snake_case     )
   lowerCamelCase__   :						List[Any]					   =							(2 * precision * recall) / (precision + recall)
   return fa



def        _a						(    UpperCAmelCase			,  UpperCAmelCase     )				->				Optional[int]:







   """simple docstring"""
   return normalize_answer(__snake_case     ) == normalize_answer(__snake_case     )



def        _a						(    UpperCAmelCase			,  UpperCAmelCase     )				->				List[str]:







   """simple docstring"""
   assert len(__snake_case     ) == len(__snake_case     )
   lowerCamelCase__   :						Any					   =							0
   for hypo, pred in zip(__snake_case			,  __snake_case     ):
      em += exact_match_score(__snake_case			,  __snake_case     )
   if len(__snake_case     ) > 0:
      em /= len(__snake_case     )
   return {"em": em}



def        _a						(    UpperCAmelCase     )				->				str:







   """simple docstring"""
   return model_prefix.startswith('''rag'''     )




def        _a						(    UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase     )				->				Any:







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











__UpperCAmelCase								=						{
    'Pillow': 'Pillow<10.0.0',
    'accelerate': 'accelerate>=0.20.3',
    'av': 'av==9.2.0',
    'beautifulsoup4': 'beautifulsoup4',
    'black': 'black~=23.1',
    'codecarbon': 'codecarbon==1.2.0',
    'cookiecutter': 'cookiecutter==1.7.3',
    'dataclasses': 'dataclasses',
    'datasets': 'datasets!=2.5.0',
    'decord': 'decord==0.6.0',
    'deepspeed': 'deepspeed>=0.9.3',
    'diffusers': 'diffusers',
    'dill': 'dill<0.3.5',
    'evaluate': 'evaluate>=0.2.0',
    'fairscale': 'fairscale>0.3',
    'faiss-cpu': 'faiss-cpu',
    'fastapi': 'fastapi',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1,<=0.7.0',
    'ftfy': 'ftfy',
    'fugashi': 'fugashi>=1.0',
    'GitPython': 'GitPython<3.1.19',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.14.1,<1.0',
    'importlib_metadata': 'importlib_metadata',
    'ipadic': 'ipadic>=1.0.0,<2.0',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2,<=0.4.13',
    'jaxlib': 'jaxlib>=0.1.65,<=0.4.13',
    'jieba': 'jieba',
    'kenlm': 'kenlm',
    'keras-nlp': 'keras-nlp>=0.3.1',
    'librosa': 'librosa',
    'nltk': 'nltk',
    'natten': 'natten>=0.14.6',
    'numpy': 'numpy>=1.17',
    'onnxconverter-common': 'onnxconverter-common',
    'onnxruntime-tools': 'onnxruntime-tools>=1.4.2',
    'onnxruntime': 'onnxruntime>=1.4.0',
    'opencv-python': 'opencv-python',
    'optuna': 'optuna',
    'optax': 'optax>=0.0.8,<=0.1.4',
    'packaging': 'packaging>=20.0',
    'parameterized': 'parameterized',
    'phonemizer': 'phonemizer',
    'protobuf': 'protobuf',
    'psutil': 'psutil',
    'pyyaml': 'pyyaml>=5.1',
    'pydantic': 'pydantic<2',
    'pytest': 'pytest>=7.2.0',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'python': 'python>=3.8.0',
    'ray[tune]': 'ray[tune]',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'rhoknp': 'rhoknp>=1.1.0,<1.3.1',
    'rjieba': 'rjieba',
    'rouge-score': 'rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1',
    'ruff': 'ruff>=0.0.241,<=0.0.259',
    'sacrebleu': 'sacrebleu>=1.4.12,<2.0.0',
    'sacremoses': 'sacremoses',
    'safetensors': 'safetensors>=0.3.1',
    'sagemaker': 'sagemaker>=2.31.0',
    'scikit-learn': 'scikit-learn',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'sigopt': 'sigopt',
    'starlette': 'starlette',
    'sudachipy': 'sudachipy>=0.6.6',
    'sudachidict_core': 'sudachidict_core>=20220729',
    'tensorflow-cpu': 'tensorflow-cpu>=2.6,<2.14',
    'tensorflow': 'tensorflow>=2.6,<2.14',
    'tensorflow-text': 'tensorflow-text<2.14',
    'tf2onnx': 'tf2onnx',
    'timeout-decorator': 'timeout-decorator',
    'timm': 'timm',
    'tokenizers': 'tokenizers>=0.11.1,!=0.11.3,<0.14',
    'torch': 'torch>=1.9,!=1.12.0',
    'torchaudio': 'torchaudio',
    'torchvision': 'torchvision',
    'pyctcdecode': 'pyctcdecode>=0.4.0',
    'tqdm': 'tqdm>=4.27',
    'unidic': 'unidic>=1.0.2',
    'unidic_lite': 'unidic_lite>=1.0.7',
    'urllib3': 'urllib3<2.0.0',
    'uvicorn': 'uvicorn',
}






