


from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""image_processor""", """tokenizer"""]
  a_  					=							"""Pix2StructImageProcessor"""
  a_  					=							("""T5Tokenizer""", """T5TokenizerFast""")






  def __init__(							self :       Union[str, Any]	,_a :       List[str]	,_a :       int ):

        '''simple docstring'''




        A_						:  Tuple          =				False
        super().__init__(_a	,_a )






  def __call__(							self :       Tuple	,_a :       Tuple=None	,_a :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,_a :       bool = True	,_a :       Union[bool, str, PaddingStrategy] = False	,_a :       Union[bool, str, TruncationStrategy] = None	,_a :       Optional[int] = None	,_a :       Optional[int] = 2048	,_a :       int = 0	,_a :       Optional[int] = None	,_a :       Optional[bool] = None	,_a :       bool = False	,_a :       bool = False	,_a :       bool = False	,_a :       bool = False	,_a :       bool = False	,_a :       bool = True	,_a :       Optional[Union[str, TensorType]] = None	,**_a :       Dict	,):

        '''simple docstring'''




        if images is None and text is None:
              raise ValueError("""You have to specify either images or text.""" )

        # Get only text
        if images is None and not self.image_processor.is_vqa:
              A_						:  str          =				self.tokenizer
              A_						:  List[Any]          =				self.tokenizer(
                  text=_a	,add_special_tokens=_a	,padding=_a	,truncation=_a	,max_length=_a	,stride=_a	,pad_to_multiple_of=_a	,return_attention_mask=_a	,return_overflowing_tokens=_a	,return_special_tokens_mask=_a	,return_offsets_mapping=_a	,return_token_type_ids=_a	,return_length=_a	,verbose=_a	,return_tensors=_a	,**_a	,)
              return text_encoding

        if not self.image_processor.is_vqa:
              # add pixel_values
              A_						:  Optional[int]          =				self.image_processor(
                  _a	,return_tensors=_a	,max_patches=_a	,**_a )
        else:
              # add pixel_values and bbox
              A_						:  List[str]          =				self.image_processor(
                  _a	,return_tensors=_a	,max_patches=_a	,header_text=_a	,**_a )

        if text is not None and not self.image_processor.is_vqa:
              A_						:  Optional[Any]          =				self.tokenizer(
                  text=_a	,add_special_tokens=_a	,padding=_a	,truncation=_a	,max_length=_a	,stride=_a	,pad_to_multiple_of=_a	,return_attention_mask=_a	,return_overflowing_tokens=_a	,return_special_tokens_mask=_a	,return_offsets_mapping=_a	,return_token_type_ids=_a	,return_length=_a	,verbose=_a	,return_tensors=_a	,**_a	,)

              if "attention_mask" in text_encoding:
                    A_						:  Union[str, Any]          =				text_encoding.pop("""attention_mask""" )
              if "input_ids" in text_encoding:
                    A_						:  Tuple          =				text_encoding.pop("""input_ids""" )
        else:
              A_						:  Tuple          =				None

        if text_encoding is not None:
              encoding_image_processor.update(_a )

        return encoding_image_processor






  def        _a							(							self :       Optional[int]	,*_a :       List[str]	,**_a :       str ):

        '''simple docstring'''




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






  def        _a							(							self :       Tuple	,*_a :       Optional[int]	,**_a :       Union[str, Any] ):

        '''simple docstring'''




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






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        A_						:  Optional[int]          =				self.tokenizer.model_input_names
        A_						:  Dict          =				self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )




'''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      lowerCamelCase					(      lowerCamelCase  :      Any							,						lowerCamelCase  :      Tuple							,						lowerCamelCase  :      int							,						lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      str=True							,						lowerCamelCase  :      Optional[Any]="pt"):
      A_						:  Optional[int]          =				{"""add_prefix_space""": True} if isinstance(lowerCamelCase							,						lowerCamelCase) and not line.startswith(""" """) else {}
      A_						:  Optional[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      lowerCamelCase					(      lowerCamelCase  :      Optional[int]							,						lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      List[Any]=None							,						):
      A_						:  Dict          =				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 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  def __init__(							self :       List[Any]	,_a :       Optional[Any]	,_a :       Tuple	,_a :       Dict	,_a :       Tuple	,_a :       Tuple="train"	,_a :       Optional[int]=None	,_a :       Any=None	,_a :       int=None	,_a :       Union[str, Any]=""	,):

        '''simple docstring'''




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






  def __len__(							self :       Tuple ):

        '''simple docstring'''




        return len(self.src_lens )






  def __getitem__(							self :       List[str]	,_a :       Tuple ):

        '''simple docstring'''




        A_						:  int          =				index + 1  # linecache starts at 1
        A_						:  Union[str, Any]          =				self.prefix + linecache.getline(str(self.src_file )	,_a ).rstrip("""\n""" )
        A_						:  Dict          =				linecache.getline(str(self.tgt_file )	,_a ).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	,_a ):
              source_line += self.tokenizer.eos_token
              tgt_line += self.tokenizer.eos_token

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

        A_						:  Optional[int]          =				encode_line(_a	,_a	,self.max_source_length	,"""right""" )
        A_						:  Optional[int]          =				encode_line(_a	,_a	,self.max_target_length	,"""right""" )

        A_						:  Optional[Any]          =				source_inputs["""input_ids"""].squeeze()
        A_						:  Dict          =				target_inputs["""input_ids"""].squeeze()
        A_						:  Union[str, Any]          =				source_inputs["""attention_mask"""].squeeze()
        return {
            "input_ids": source_ids,
            "attention_mask": src_mask,
            "decoder_input_ids": target_ids,
        }






  @staticmethod
  def        _a							(							_a :       int ):

        '''simple docstring'''




        return [len(_a ) for x in Path(_a ).open().readlines()]






  def        _a							(							self :       Optional[int]	,_a :       Dict ):

        '''simple docstring'''




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







__magic_name__			=      getLogger(__name__)




def      lowerCamelCase					(      lowerCamelCase  :      List[List]):
      return list(itertools.chain.from_iterable(lowerCamelCase))




def      lowerCamelCase					(      lowerCamelCase  :      str):
      A_						:  Union[str, Any]          =				get_git_info()
      save_json(lowerCamelCase							,						os.path.join(lowerCamelCase							,						"""git_log.json"""))




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      List[str]=4							,						**lowerCamelCase  :      List[str]):
      with open(lowerCamelCase							,						"""w""") as f:
            json.dump(lowerCamelCase							,						lowerCamelCase							,						indent=lowerCamelCase							,						**lowerCamelCase)




def      lowerCamelCase					(      lowerCamelCase  :      Any):
      with open(lowerCamelCase) as f:
            return json.load(lowerCamelCase)




def      lowerCamelCase					(      ):
      A_						:  List[str]          =				git.Repo(search_parent_directories=lowerCamelCase)
      A_						:  Union[str, Any]          =				{
          """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      lowerCamelCase					(      lowerCamelCase  :      Callable							,						lowerCamelCase  :      Iterable):
      return list(map(lowerCamelCase							,						lowerCamelCase))




def      lowerCamelCase					(      lowerCamelCase  :      int							,						lowerCamelCase  :      Union[str, Any]):
      with open(lowerCamelCase							,						"""wb""") as f:
            return pickle.dump(lowerCamelCase							,						lowerCamelCase)




def      lowerCamelCase					(      lowerCamelCase  :      List[str]):

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

      def white_space_fix(lowerCamelCase  :      List[Any]):
            return " ".join(text.split())

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

      def lower(lowerCamelCase  :      List[str]):
            return text.lower()

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




def      lowerCamelCase					(      lowerCamelCase  :      int							,						lowerCamelCase  :      int):
      A_						:  Tuple          =				normalize_answer(lowerCamelCase).split()
      A_						:  Dict          =				normalize_answer(lowerCamelCase).split()
      A_						:  int          =				Counter(lowerCamelCase) & Counter(lowerCamelCase)
      A_						:  Any          =				sum(common.values())
      if num_same == 0:
            return 0
      A_						:  Any          =				1.0 * num_same / len(lowerCamelCase)
      A_						:  Any          =				1.0 * num_same / len(lowerCamelCase)
      A_						:  Any          =				(2 * precision * recall) / (precision + recall)
      return fa




def      lowerCamelCase					(      lowerCamelCase  :      Any							,						lowerCamelCase  :      Any):
      return normalize_answer(lowerCamelCase) == normalize_answer(lowerCamelCase)




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      List[str]):
      assert len(lowerCamelCase) == len(lowerCamelCase)
      A_						:  Any          =				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      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]):
      return model_prefix.startswith("""rag""")






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