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 (	__lowerCamelCase			:						Optional[Any] ,		__lowerCamelCase			:						str ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						Tuple ,		__lowerCamelCase			:						List[str]=True ,		__lowerCamelCase			:						List[Any]="pt"				)	->							Optional[Any]:
      '''simple docstring'''


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


def __a (	__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						str=None ,		)	->							List[Any]:
      '''simple docstring'''


      lowercase_     =      input_ids.ne(__lowerCamelCase				).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 							lowercase						(						__lowerCamelCase ):


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


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


   def __getitem__(   self				:	Any   ,		__lowerCAmelCase				:	Optional[int])      ->					Dict[str, torch.Tensor]:
         lowercase_     =      index + 1  # linecache starts at 1
         lowercase_     =      self.prefix + linecache.getline(str(self.src_file)   ,		__lowerCAmelCase).rstrip("\n")
         lowercase_     =      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
         lowercase_     =      (
             self.tokenizer.question_encoder if isinstance(self.tokenizer   ,		__lowerCAmelCase) else self.tokenizer
         )
         lowercase_     =      self.tokenizer.generator if isinstance(self.tokenizer   ,		__lowerCAmelCase) else self.tokenizer

         lowercase_     =      encode_line(__lowerCAmelCase   ,		__lowerCAmelCase   ,		self.max_source_length   ,		"right")
         lowercase_     =      encode_line(__lowerCAmelCase   ,		__lowerCAmelCase   ,		self.max_target_length   ,		"right")

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


   @staticmethod
   def __UpperCAmelCase       (   __lowerCAmelCase				:	int)      ->					Dict:
         return [len(__lowerCAmelCase) for x in Path(__lowerCAmelCase).open().readlines()]


   def __UpperCAmelCase       (   self				:	Dict   ,		__lowerCAmelCase				:	List[str])      ->					Dict[str, torch.Tensor]:
         lowercase_     =      torch.stack([x["input_ids"] for x in batch])
         lowercase_     =      torch.stack([x["attention_mask"] for x in batch])
         lowercase_     =      torch.stack([x["decoder_input_ids"] for x in batch])
         lowercase_     =      (
             self.tokenizer.generator.pad_token_id
             if isinstance(self.tokenizer   ,		__lowerCAmelCase)
             else self.tokenizer.pad_token_id
         )
         lowercase_     =      (
             self.tokenizer.question_encoder.pad_token_id
             if isinstance(self.tokenizer   ,		__lowerCAmelCase)
             else self.tokenizer.pad_token_id
         )
         lowercase_     =      trim_batch(__lowerCAmelCase   ,		__lowerCAmelCase)
         lowercase_ ,							lowercase_     =      trim_batch(__lowerCAmelCase   ,		__lowerCAmelCase   ,		attention_mask=__lowerCAmelCase)
         lowercase_     =      {
             "input_ids": source_ids,
             "attention_mask": source_mask,
             "decoder_input_ids": y,
         }
         return batch




lowerCAmelCase_ : Union[str, Any]						  =      getLogger(__name__)


def __a (	__lowerCamelCase			:						List[List]				)	->							Optional[int]:
      '''simple docstring'''


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


def __a (	__lowerCamelCase			:						str				)	->							None:
      '''simple docstring'''


      lowercase_     =      get_git_info()
      save_json(__lowerCamelCase ,		os.path.join(__lowerCamelCase ,		"git_log.json"				)				)


def __a (	__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						Union[str, Any]=4 ,		**__lowerCamelCase			:						Dict				)	->							Dict:
      '''simple docstring'''


      with open(__lowerCamelCase ,		"w"				) as f:
            json.dump(__lowerCamelCase ,		__lowerCamelCase ,		indent=__lowerCamelCase ,		**__lowerCamelCase				)


def __a (	__lowerCamelCase			:						int				)	->							Tuple:
      '''simple docstring'''


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


def __a (	)	->							Any:
      '''simple docstring'''


      lowercase_     =      git.Repo(search_parent_directories=__lowerCamelCase				)
      lowercase_     =      {
          "repo_id": str(__lowerCamelCase				),
          "repo_sha": str(repo.head.object.hexsha				),
          "repo_branch": str(repo.active_branch				),
          "hostname": str(socket.gethostname()				),
      }
      return repo_infos


def __a (	__lowerCamelCase			:						Callable ,		__lowerCamelCase			:						Iterable				)	->							List:
      '''simple docstring'''


      return list(map(__lowerCamelCase ,		__lowerCamelCase				)				)


def __a (	__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						Optional[Any]				)	->							Union[str, Any]:
      '''simple docstring'''


      with open(__lowerCamelCase ,		"wb"				) as f:
            return pickle.dump(__lowerCamelCase ,		__lowerCamelCase				)


def __a (	__lowerCamelCase			:						Optional[Any]				)	->							Optional[Any]:
      '''simple docstring'''


      def remove_articles(__lowerCamelCase			:						Tuple				):
            return re.sub(R"\b(a|an|the)\b" ,		" " ,		__lowerCamelCase				)

      def white_space_fix(__lowerCamelCase			:						Optional[Any]				):
            return " ".join(text.split()				)

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

      def lower(__lowerCamelCase			:						Dict				):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(__lowerCamelCase				)				)				)				)


def __a (	__lowerCamelCase			:						Tuple ,		__lowerCamelCase			:						Tuple				)	->							Dict:
      '''simple docstring'''


      lowercase_     =      normalize_answer(__lowerCamelCase				).split()
      lowercase_     =      normalize_answer(__lowerCamelCase				).split()
      lowercase_     =      Counter(__lowerCamelCase				) & Counter(__lowerCamelCase				)
      lowercase_     =      sum(common.values()				)
      if num_same == 0:
            return 0
      lowercase_     =      1.0 * num_same / len(__lowerCamelCase				)
      lowercase_     =      1.0 * num_same / len(__lowerCamelCase				)
      lowercase_     =      (2 * precision * recall) / (precision + recall)
      return fa


def __a (	__lowerCamelCase			:						Optional[Any] ,		__lowerCamelCase			:						List[Any]				)	->							Dict:
      '''simple docstring'''


      return normalize_answer(__lowerCamelCase				) == normalize_answer(__lowerCamelCase				)


def __a (	__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						List[str]				)	->							Dict:
      '''simple docstring'''


      assert len(__lowerCamelCase				) == len(__lowerCamelCase				)
      lowercase_     =      0
      for hypo, pred in zip(__lowerCamelCase ,		__lowerCamelCase				):
            em += exact_match_score(__lowerCamelCase ,		__lowerCamelCase				)
      if len(__lowerCamelCase				) > 0:
            em /= len(__lowerCamelCase				)
      return {"em": em}


def __a (	__lowerCamelCase			:						Optional[Any]				)	->							Tuple:
      '''simple docstring'''


      return model_prefix.startswith("rag"				)






def __a (	__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						str				)	->							Union[str, Any]:
      '''simple docstring'''


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







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase_ : str						  =      {
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : Union[str, Any]						  =      [
           "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
           "Wav2Vec2ForAudioFrameClassification",
           "Wav2Vec2ForCTC",
           "Wav2Vec2ForMaskedLM",
           "Wav2Vec2ForPreTraining",
           "Wav2Vec2ForSequenceClassification",
           "Wav2Vec2ForXVector",
           "Wav2Vec2Model",
           "Wav2Vec2PreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : int						  =      [
           "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFWav2Vec2ForCTC",
           "TFWav2Vec2Model",
           "TFWav2Vec2PreTrainedModel",
           "TFWav2Vec2ForSequenceClassification",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : Dict						  =      [
           "FlaxWav2Vec2ForCTC",
           "FlaxWav2Vec2ForPreTraining",
           "FlaxWav2Vec2Model",
           "FlaxWav2Vec2PreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
       from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
       from .processing_wavaveca import WavaVecaProcessor
       from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_wavaveca import (
                  WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  WavaVecaForAudioFrameClassification,
                  WavaVecaForCTC,
                  WavaVecaForMaskedLM,
                  WavaVecaForPreTraining,
                  WavaVecaForSequenceClassification,
                  WavaVecaForXVector,
                  WavaVecaModel,
                  WavaVecaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_wavaveca import (
                  TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFWavaVecaForCTC,
                  TFWavaVecaForSequenceClassification,
                  TFWavaVecaModel,
                  TFWavaVecaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_wavaveca import (
                  FlaxWavaVecaForCTC,
                  FlaxWavaVecaForPreTraining,
                  FlaxWavaVecaModel,
                  FlaxWavaVecaPreTrainedModel,
              )


else:
       import sys

       lowerCAmelCase_ : Optional[int]						  =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






