




'''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:					Dict      ,__UpperCamelCase:					Optional[int]      ,__UpperCamelCase:					List[str]      ,__UpperCamelCase:					int      ,__UpperCamelCase:					Optional[Any]=True      ,__UpperCamelCase:					Dict="pt"    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						{'add_prefix_space': True} if isinstance(__UpperCamelCase      ,__UpperCamelCase    ) and not line.startswith(' '    ) else {}
      SCREAMING_SNAKE_CASE   :      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:					Optional[int]      ,__UpperCamelCase:					str      ,__UpperCamelCase:					List[str]=None      ,):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Union[str, 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 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self,							A,							A,							A,							A,							A="train",							A=None,							A=None,							A=None,							A="",							):







             '''simple docstring'''







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







       def __len__(      self  ):







             '''simple docstring'''







             return len(self.src_lens  )







       def __getitem__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						index + 1  # linecache starts at 1
             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.prefix + linecache.getline(str(self.src_file  ),							A  ).rstrip('\n'  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						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
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						(
                 self.tokenizer.question_encoder if isinstance(self.tokenizer,							A  ) else self.tokenizer
             )
             SCREAMING_SNAKE_CASE   :      str    					=						self.tokenizer.generator if isinstance(self.tokenizer,							A  ) else self.tokenizer

             SCREAMING_SNAKE_CASE   :      int    					=						encode_line(A,							A,							self.max_source_length,							'right'  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						encode_line(A,							A,							self.max_target_length,							'right'  )

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







       @staticmethod
       def        UpperCamelCase_							(      A  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







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

UpperCamelCase_    	=				getLogger(__name__)




def   lowercase__(			__UpperCamelCase:					List[List]    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					str    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Tuple    					=						get_git_info()
      save_json(__UpperCamelCase      ,os.path.join(__UpperCamelCase      ,'git_log.json'    )    )




def   lowercase__(			__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					Optional[int]      ,__UpperCamelCase:					Dict=4      ,**__UpperCamelCase:					str    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					List[str]    ):

      """simple docstring"""






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




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      int    					=						git.Repo(search_parent_directories=__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Dict    					=						{
          '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:					Callable      ,__UpperCamelCase:					Iterable    ):

      """simple docstring"""






      return list(map(__UpperCamelCase      ,__UpperCamelCase    )    )




def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					Dict    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      def remove_articles(__UpperCamelCase:					str    ):
            return re.sub(r'\b(a|an|the)\b'      ,' '      ,__UpperCamelCase    )

      def white_space_fix(__UpperCamelCase:					Tuple    ):
            return " ".join(text.split()    )

      def remove_punc(__UpperCamelCase:					int    ):
            SCREAMING_SNAKE_CASE   :      str    					=						set(string.punctuation    )
            return "".join(ch for ch in text if ch not in exclude    )

      def lower(__UpperCamelCase:					int    ):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(__UpperCamelCase    )    )    )    )




def   lowercase__(			__UpperCamelCase:					int      ,__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						normalize_answer(__UpperCamelCase    ).split()
      SCREAMING_SNAKE_CASE   :      Any    					=						normalize_answer(__UpperCamelCase    ).split()
      SCREAMING_SNAKE_CASE   :      str    					=						Counter(__UpperCamelCase    ) & Counter(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						sum(common.values()    )
      if num_same == 0:
            return 0
      SCREAMING_SNAKE_CASE   :      List[str]    					=						1.0 * num_same / len(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Dict    					=						1.0 * num_same / len(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Any    					=						(2 * precision * recall) / (precision + recall)
      return fa




def   lowercase__(			__UpperCamelCase:					Any      ,__UpperCamelCase:					Any    ):

      """simple docstring"""






      return normalize_answer(__UpperCamelCase    ) == normalize_answer(__UpperCamelCase    )




def   lowercase__(			__UpperCamelCase:					List[str]      ,__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      assert len(__UpperCamelCase    ) == len(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						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:					Union[str, Any]    ):

      """simple docstring"""






      return model_prefix.startswith('rag'    )





def   lowercase__(			__UpperCamelCase:					Dict      ,__UpperCamelCase:					Dict      ,__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






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




"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_a								=						logging.get_logger(__name__)






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = ['pixel_values']



		def __init__(   self							,						__a = True							,						__a = None							,						__a = PILImageResampling.BICUBIC							,						__a = True							,						__a = True							,						__a = 1 / 2_55							,						__a = None							,						__a = True							,						__a = None							,						__a = None							,						**__a							,						)			->			None:






							'''simple docstring'''
							super().__init__(**__a)
							_UpperCamelCase									=    size if size is not None else {'''height''': 2_24, '''width''': 2_24}
							_UpperCamelCase									=    get_size_dict(__a)
							_UpperCamelCase									=    crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
							_UpperCamelCase									=    get_size_dict(__a							,						default_to_square=__a							,						param_name='''crop_size''')

							_UpperCamelCase									=    do_resize
							_UpperCamelCase									=    do_rescale
							_UpperCamelCase									=    do_normalize
							_UpperCamelCase									=    do_center_crop
							_UpperCamelCase									=    crop_size
							_UpperCamelCase									=    size
							_UpperCamelCase									=    resample
							_UpperCamelCase									=    rescale_factor
							_UpperCamelCase									=    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							_UpperCamelCase									=    image_std if image_std is not None else IMAGENET_DEFAULT_STD



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = PILImageResampling.BILINEAR							,						__a = None							,						**__a							,						)			->			np.ndarray:






							'''simple docstring'''
							_UpperCamelCase									=    get_size_dict(__a)

							if "shortest_edge" in size:
												_UpperCamelCase									=    get_resize_output_image_size(__a							,						size=size['''shortest_edge''']							,						default_to_square=__a)
												# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
							elif "height" in size and "width" in size:
												_UpperCamelCase									=    (size['''height'''], size['''width'''])
							else:
												raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''')
							return resize(__a							,						size=__a							,						resample=__a							,						data_format=__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = None							,						**__a							,						)			->			np.ndarray:






							'''simple docstring'''
							_UpperCamelCase									=    get_size_dict(__a)
							if "height" not in size or "width" not in size:
												raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''')
							return center_crop(__a							,						size=(size['''height'''], size['''width'''])							,						data_format=__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = None							,						**__a)			->			np.ndarray:






							'''simple docstring'''
							return rescale(__a							,						scale=__a							,						data_format=__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a = None							,						**__a							,						)			->			np.ndarray:






							'''simple docstring'''
							return normalize(__a							,						mean=__a							,						std=__a							,						data_format=__a							,						**__a)




		def 		UpperCAmelCase				(   self							,						__a							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = ChannelDimension.FIRST							,						**__a							,						)			->			BatchFeature:






							'''simple docstring'''
							_UpperCamelCase									=    do_resize if do_resize is not None else self.do_resize
							_UpperCamelCase									=    do_rescale if do_rescale is not None else self.do_rescale
							_UpperCamelCase									=    do_normalize if do_normalize is not None else self.do_normalize
							_UpperCamelCase									=    do_center_crop if do_center_crop is not None else self.do_center_crop
							_UpperCamelCase									=    crop_size if crop_size is not None else self.crop_size
							_UpperCamelCase									=    get_size_dict(__a							,						param_name='''crop_size'''							,						default_to_square=__a)
							_UpperCamelCase									=    resample if resample is not None else self.resample
							_UpperCamelCase									=    rescale_factor if rescale_factor is not None else self.rescale_factor
							_UpperCamelCase									=    image_mean if image_mean is not None else self.image_mean
							_UpperCamelCase									=    image_std if image_std is not None else self.image_std

							_UpperCamelCase									=    size if size is not None else self.size
							_UpperCamelCase									=    get_size_dict(__a)

							if not is_batched(__a):
												_UpperCamelCase									=    [images]

							if not valid_images(__a):
												raise ValueError(
												    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
												    '''torch.Tensor, tf.Tensor or jax.ndarray.''')

							if do_resize and size is None:
												raise ValueError('''Size must be specified if do_resize is True.''')

							if do_center_crop and crop_size is None:
												raise ValueError('''Crop size must be specified if do_center_crop is True.''')

							if do_rescale and rescale_factor is None:
												raise ValueError('''Rescale factor must be specified if do_rescale is True.''')

							# All transformations expect numpy arrays.
							_UpperCamelCase									=    [to_numpy_array(__a) for image in images]

							if do_resize:
												_UpperCamelCase									=    [self.resize(image=__a							,						size=__a							,						resample=__a) for image in images]

							if do_center_crop:
												_UpperCamelCase									=    [self.center_crop(image=__a							,						size=__a) for image in images]

							if do_rescale:
												_UpperCamelCase									=    [self.rescale(image=__a							,						scale=__a) for image in images]

							if do_normalize:
												_UpperCamelCase									=    [self.normalize(image=__a							,						mean=__a							,						std=__a) for image in images]

							_UpperCamelCase									=    [to_channel_dimension_format(__a							,						__a) for image in images]

							_UpperCamelCase									=    {'''pixel_values''': images}
							return BatchFeature(data=__a							,						tensor_type=__a)






