




"""simple docstring"""


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 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase=True    ,		__UpperCAmelCase="pt"				)						->    Tuple:
      lowerCAmelCase__      :			List[Any]   =	{"""add_prefix_space""": True} if isinstance(__UpperCAmelCase    ,		__UpperCAmelCase				) and not line.startswith(""" """				) else {}
      lowerCAmelCase__      :			Optional[Any]   =	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 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase=None    ,		)						->    Tuple:
      lowerCAmelCase__      :			Any   =	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 _lowerCamelCase    (					a_						):






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

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






   def __len__(		self       :		Optional[Any]   )			->						List[str]:

         """simple docstring"""
         return len(self.src_lens   )






   def __getitem__(		self       :		Dict   ,     UpperCamelCase       :		List[str]   )			->						Dict[str, torch.Tensor]:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	index + 1  # linecache starts at 1
         lowerCAmelCase__      :			int   =	self.prefix + linecache.getline(str(self.src_file   )   ,     UpperCamelCase   ).rstrip("""\n"""   )
         lowerCAmelCase__      :			Dict   =	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__      :			Any   =	(
             self.tokenizer.question_encoder if isinstance(self.tokenizer   ,     UpperCamelCase   ) else self.tokenizer
         )
         lowerCAmelCase__      :			Tuple   =	self.tokenizer.generator if isinstance(self.tokenizer   ,     UpperCamelCase   ) else self.tokenizer

         lowerCAmelCase__      :			str   =	encode_line(UpperCamelCase   ,     UpperCamelCase   ,     self.max_source_length   ,     """right"""   )
         lowerCAmelCase__      :			List[str]   =	encode_line(UpperCamelCase   ,     UpperCamelCase   ,     self.max_target_length   ,     """right"""   )

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






   @staticmethod
   def 	_lowerCAmelCase   (		UpperCamelCase       :		Union[str, Any]   )			->						Optional[Any]:

         """simple docstring"""
         return [len(UpperCamelCase   ) for x in Path(UpperCamelCase   ).open().readlines()]






   def 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		int   )			->						Dict[str, torch.Tensor]:

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






_A													=      getLogger(__name__)



def 	lowercase_				(			__UpperCAmelCase				)						->    int:
      return list(itertools.chain.from_iterable(__UpperCAmelCase				)				)



def 	lowercase_				(			__UpperCAmelCase				)						->    None:
      lowerCAmelCase__      :			Optional[Any]   =	get_git_info()
      save_json(__UpperCAmelCase    ,		os.path.join(__UpperCAmelCase    ,		"""git_log.json"""				)				)



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase=4    ,		**__UpperCAmelCase				)						->    str:
      with open(__UpperCAmelCase    ,		"""w"""				) as f:
            json.dump(__UpperCAmelCase    ,		__UpperCAmelCase    ,		indent=__UpperCAmelCase    ,		**__UpperCAmelCase				)



def 	lowercase_				(			__UpperCAmelCase				)						->    Optional[Any]:
      with open(__UpperCAmelCase				) as f:
            return json.load(__UpperCAmelCase				)



def 	lowercase_				(			)						->    Optional[int]:
      lowerCAmelCase__      :			Any   =	git.Repo(search_parent_directories=__UpperCAmelCase				)
      lowerCAmelCase__      :			Optional[Any]   =	{
          """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 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List:
      return list(map(__UpperCAmelCase    ,		__UpperCAmelCase				)				)



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    str:
      with open(__UpperCAmelCase    ,		"""wb"""				) as f:
            return pickle.dump(__UpperCAmelCase    ,		__UpperCAmelCase				)



def 	lowercase_				(			__UpperCAmelCase				)						->    int:

      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				):
            lowerCAmelCase__      :			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(__UpperCAmelCase				)				)				)				)



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



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[str]:
      return normalize_answer(__UpperCAmelCase				) == normalize_answer(__UpperCAmelCase				)



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Dict:
      assert len(__UpperCAmelCase				) == len(__UpperCAmelCase				)
      lowerCAmelCase__      :			int   =	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 	lowercase_				(			__UpperCAmelCase				)						->    Dict:
      return model_prefix.startswith("""rag"""				)



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











"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_A													=      logging.get_logger(__name__)


class _lowerCamelCase    (					a_						):






   def __init__(		self       :		Union[str, Any]   ,     *UpperCamelCase       :		int   ,     **UpperCamelCase       :		List[Any]   )			->						None:

         """simple docstring"""
         warnings.warn(
             """The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
             """ use CLIPImageProcessor instead."""   ,     UpperCamelCase   ,     )
         super().__init__(*UpperCamelCase   ,     **UpperCamelCase   )





