

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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=True  ,		SCREAMING_SNAKE_CASE__="pt"       ):
						snake_case_   =	{'''add_prefix_space''': True} if isinstance(__A  ,		__A       ) and not line.startswith(''' '''       ) else {}
						snake_case_   =	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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=None  ,		):
						snake_case_   =	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 	snake_case_   (		__A					):







			'''simple docstring'''

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







			def __len__(     self :				Union[str, Any]							)      ->Optional[Any]:
									return len(self.src_lens							)







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

									snake_case_   =	encode_line(_UpperCamelCase							,   _UpperCamelCase							,   self.max_source_length							,   '''right'''							)
									snake_case_   =	encode_line(_UpperCamelCase							,   _UpperCamelCase							,   self.max_target_length							,   '''right'''							)

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







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







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






lowerCAmelCase_       	=	getLogger(__name__)


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						return list(itertools.chain.from_iterable(__A       )       )


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	get_git_info()
						save_json(__A  ,		os.path.join(__A  ,		'''git_log.json'''       )       )


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=4  ,		**SCREAMING_SNAKE_CASE__       ):



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


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):



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


def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	git.Repo(search_parent_directories=__A       )
						snake_case_   =	{
						    '''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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						return list(map(__A  ,		__A       )       )


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):



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


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):



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

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

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

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

						return white_space_fix(remove_articles(remove_punc(lower(__A       )       )       )       )


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	normalize_answer(__A       ).split()
						snake_case_   =	normalize_answer(__A       ).split()
						snake_case_   =	Counter(__A       ) & Counter(__A       )
						snake_case_   =	sum(common.values()       )
						if num_same == 0:
												return 0
						snake_case_   =	1.0 * num_same / len(__A       )
						snake_case_   =	1.0 * num_same / len(__A       )
						snake_case_   =	(2 * precision * recall) / (precision + recall)
						return fa


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						return normalize_answer(__A       ) == normalize_answer(__A       )


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						assert len(__A       ) == len(__A       )
						snake_case_   =	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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						return model_prefix.startswith('''rag'''       )


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	{p: p for p in extra_params}
						# T5 models don't have `dropout` param, they have `dropout_rate` instead
						snake_case_   =	'''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
																		snake_case_   =	p if hasattr(__A  ,		__A       ) else equivalent_param[p]
																		setattr(__A  ,		__A  ,		getattr(__A  ,		__A       )       )
																		delattr(__A  ,		__A       )
						return hparams, config




'''simple docstring'''





import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Optional[Any]          =       'MCTCTFeatureExtractor'
 __lowerCamelCase    :       Optional[Any]          =       'AutoTokenizer'



 def __init__(self ,   A ,   A       )    ->     Dict:


      """simple docstring"""



      super().__init__(A ,   A       )
      _a      =  self.feature_extractor
      _a      =  False



 def __call__(self ,   *A ,   **A       )    ->     Optional[int]:


      """simple docstring"""



      if self._in_target_context_manager:
           return self.current_processor(*A ,   **A       )

      if "raw_speech" in kwargs:
           warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''       )
           _a      =  kwargs.pop('''raw_speech'''       )
      else:
           _a      =  kwargs.pop('''audio''' ,   A       )
      _a      =  kwargs.pop('''sampling_rate''' ,   A       )
      _a      =  kwargs.pop('''text''' ,   A       )
      if len(A       ) > 0:
           _a      =  args[0]
           _a      =  args[1:]

      if audio is None and text is None:
           raise ValueError('''You need to specify either an `audio` or `text` input to process.'''       )

      if audio is not None:
           _a      =  self.feature_extractor(A ,   *A ,   sampling_rate=A ,   **A       )
      if text is not None:
           _a      =  self.tokenizer(A ,   **A       )

      if text is None:
           return inputs
      elif audio is None:
           return encodings
      else:
           _a      =  encodings['''input_ids''']
           return inputs



 def 					a__  (self ,   *A ,   **A       )    ->     int:


      """simple docstring"""



      return self.tokenizer.batch_decode(*A ,   **A       )



 def 					a__  (self ,   *A ,   **A       )    ->     Tuple:


      """simple docstring"""



      if self._in_target_context_manager:
           return self.current_processor.pad(*A ,   **A       )

      _a      =  kwargs.pop('''input_features''' ,   A       )
      _a      =  kwargs.pop('''labels''' ,   A       )
      if len(A       ) > 0:
           _a      =  args[0]
           _a      =  args[1:]

      if input_features is not None:
           _a      =  self.feature_extractor.pad(A ,   *A ,   **A       )
      if labels is not None:
           _a      =  self.tokenizer.pad(A ,   **A       )

      if labels is None:
           return input_features
      elif input_features is None:
           return labels
      else:
           _a      =  labels['''input_ids''']
           return input_features



 def 					a__  (self ,   *A ,   **A       )    ->     List[str]:


      """simple docstring"""



      return self.tokenizer.decode(*A ,   **A       )



 @contextmanager
 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      warnings.warn(
          '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
          '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
          '''your audio inputs, or in a separate call.'''       )
      _a      =  True
      _a      =  self.tokenizer
      yield
      _a      =  self.feature_extractor
      _a      =  False



