
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       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase=True       ,      lowerCamelCase="pt"  )					->		List[str]:
				UpperCamelCase_:							Optional[int]		=  {"""add_prefix_space""": True} if isinstance(lowerCamelCase       ,      lowerCamelCase  ) and not line.startswith(""" """  ) else {}
				UpperCamelCase_:							int		=  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       ,      lowerCamelCase       ,      lowerCamelCase=None       ,      )					->		Optional[int]:
				UpperCamelCase_:							Any		=  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     _UpperCamelCase			(   _A					):






			'''simple docstring'''

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

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

			def __getitem__(    self	:    Tuple  ,		snake_case_	:    List[str] ):
							UpperCamelCase_:							int		=  index + 1  # linecache starts at 1
							UpperCamelCase_:							List[str]		=  self.prefix + linecache.getline(str(self.src_file )  ,		snake_case_ ).rstrip("""\n""" )
							UpperCamelCase_:							Tuple		=  linecache.getline(str(self.tgt_file )  ,		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  ,		snake_case_ ):
											source_line += self.tokenizer.eos_token
											tgt_line += self.tokenizer.eos_token

							# Pad source and target to the right
							UpperCamelCase_:							List[str]		=  (
							    self.tokenizer.question_encoder if isinstance(self.tokenizer  ,		snake_case_ ) else self.tokenizer
							)
							UpperCamelCase_:							int		=  self.tokenizer.generator if isinstance(self.tokenizer  ,		snake_case_ ) else self.tokenizer

							UpperCamelCase_:							Any		=  encode_line(snake_case_  ,		snake_case_  ,		self.max_source_length  ,		"""right""" )
							UpperCamelCase_:							Tuple		=  encode_line(snake_case_  ,		snake_case_  ,		self.max_target_length  ,		"""right""" )

							UpperCamelCase_:							List[Any]		=  source_inputs["""input_ids"""].squeeze()
							UpperCamelCase_:							Optional[Any]		=  target_inputs["""input_ids"""].squeeze()
							UpperCamelCase_:							Tuple		=  source_inputs["""attention_mask"""].squeeze()
							return {
							    "input_ids": source_ids,
							    "attention_mask": src_mask,
							    "decoder_input_ids": target_ids,
							}

			@staticmethod
			def  lowerCAmelCase__		(    snake_case_	:    List[str] ):
							return [len(snake_case_ ) for x in Path(snake_case_ ).open().readlines()]




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




lowerCamelCase_  :	List[str] 				=					getLogger(__name__)







def     A__   (  lowerCamelCase  )					->		Union[str, Any]:
				return list(itertools.chain.from_iterable(lowerCamelCase  )  )







def     A__   (  lowerCamelCase  )					->		None:
				UpperCamelCase_:							int		=  get_git_info()
				save_json(lowerCamelCase       ,      os.path.join(lowerCamelCase       ,      """git_log.json"""  )  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase=4       ,      **lowerCamelCase  )					->		Optional[int]:
				with open(lowerCamelCase       ,      """w"""  ) as f:
								json.dump(lowerCamelCase       ,      lowerCamelCase       ,      indent=lowerCamelCase       ,      **lowerCamelCase  )







def     A__   (  lowerCamelCase  )					->		str:
				with open(lowerCamelCase  ) as f:
								return json.load(lowerCamelCase  )







def     A__   (  )					->		Dict:
				UpperCamelCase_:							List[str]		=  git.Repo(search_parent_directories=lowerCamelCase  )
				UpperCamelCase_:							List[str]		=  {
				    """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       ,      lowerCamelCase  )					->		List:
				return list(map(lowerCamelCase       ,      lowerCamelCase  )  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		Dict:
				with open(lowerCamelCase       ,      """wb"""  ) as f:
								return pickle.dump(lowerCamelCase       ,      lowerCamelCase  )







def     A__   (  lowerCamelCase  )					->		Optional[int]:

				def remove_articles(lowerCamelCase  ):
								return re.sub(r"""\b(a|an|the)\b"""       ,      """ """       ,      lowerCamelCase  )

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

				def remove_punc(lowerCamelCase  ):
								UpperCamelCase_:							Optional[Any]		=  set(string.punctuation  )
								return "".join(ch for ch in text if ch not in exclude  )

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

				return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase  )  )  )  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		List[str]:
				UpperCamelCase_:							str		=  normalize_answer(lowerCamelCase  ).split()
				UpperCamelCase_:							List[str]		=  normalize_answer(lowerCamelCase  ).split()
				UpperCamelCase_:							Optional[int]		=  Counter(lowerCamelCase  ) & Counter(lowerCamelCase  )
				UpperCamelCase_:							Any		=  sum(common.values()  )
				if num_same == 0:
								return 0
				UpperCamelCase_:							Tuple		=  1.0 * num_same / len(lowerCamelCase  )
				UpperCamelCase_:							Dict		=  1.0 * num_same / len(lowerCamelCase  )
				UpperCamelCase_:							Optional[int]		=  (2 * precision * recall) / (precision + recall)
				return fa







def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		List[Any]:
				return normalize_answer(lowerCamelCase  ) == normalize_answer(lowerCamelCase  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		Dict:
				assert len(lowerCamelCase  ) == len(lowerCamelCase  )
				UpperCamelCase_:							Tuple		=  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  )					->		Dict:
				return model_prefix.startswith("""rag"""  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Optional[Any]:
				UpperCamelCase_:							List[Any]		=  {p: p for p in extra_params}
				# T5 models don't have `dropout` param, they have `dropout_rate` instead
				UpperCamelCase_:							str		=  """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
												UpperCamelCase_:							Union[str, Any]		=  p if hasattr(lowerCamelCase       ,      lowerCamelCase  ) else equivalent_param[p]
												setattr(lowerCamelCase       ,      lowerCamelCase       ,      getattr(lowerCamelCase       ,      lowerCamelCase  )  )
												delattr(lowerCamelCase       ,      lowerCamelCase  )
				return hparams, config








from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Union[str, Any]    =	["""image_processor""", """tokenizer"""]
			__UpperCamelCase :  List[Any]    =	"""AutoImageProcessor"""
			__UpperCamelCase :  Tuple    =	"""AutoTokenizer"""

			def __init__(    self	:    Any  ,		snake_case_	:    Optional[Any]  ,		snake_case_	:    Any ):
							super().__init__(snake_case_  ,		snake_case_ )
							UpperCamelCase_:							int		=  self.image_processor

			def __call__(    self	:    str  ,		snake_case_	:    Optional[int]=None  ,		snake_case_	:    int=None  ,		snake_case_	:    Dict=None  ,		**snake_case_	:    Optional[int] ):

							if text is None and images is None:
											raise ValueError("""You have to specify either text or images. Both cannot be none.""" )

							if text is not None:
											UpperCamelCase_:							List[Any]		=  self.tokenizer(snake_case_  ,		return_tensors=snake_case_  ,		**snake_case_ )

							if images is not None:
											UpperCamelCase_:							Any		=  self.image_processor(snake_case_  ,		return_tensors=snake_case_  ,		**snake_case_ )

							if text is not None and images is not None:
											UpperCamelCase_:							Tuple		=  image_features.pixel_values
											return encoding
							elif text is not None:
											return encoding
							else:
											return BatchEncoding(data=dict(**snake_case_ )  ,		tensor_type=snake_case_ )

			def  lowerCAmelCase__		(    self	:    List[str]  ,		*snake_case_	:    int  ,		**snake_case_	:    Optional[Any] ):
							return self.tokenizer.batch_decode(*snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    int  ,		*snake_case_	:    Optional[Any]  ,		**snake_case_	:    str ):
							return self.tokenizer.decode(*snake_case_  ,		**snake_case_ )




			@property
			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							return ["input_ids", "attention_mask", "pixel_values"]






