


'''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_						:  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




'''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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__magic_name__			=      logging.get_logger(__name__)


if is_vision_available():
     import PIL



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""pixel_values"""]






  def __init__(							self :       Optional[Any]	,_a :       bool = True	,_a :       Dict[str, int] = None	,_a :       PILImageResampling = PILImageResampling.BICUBIC	,_a :       bool = True	,_a :       Dict[str, int] = None	,_a :       bool = True	,_a :       Union[int, float] = 1 / 255	,_a :       bool = True	,_a :       Optional[Union[float, List[float]]] = None	,_a :       Optional[Union[float, List[float]]] = None	,_a :       bool = True	,**_a :       Dict	,):

        '''simple docstring'''




        super().__init__(**_a )
        A_						:  Tuple          =				size if size is not None else {"""shortest_edge""": 224}
        A_						:  Optional[Any]          =				get_size_dict(_a	,default_to_square=_a )
        A_						:  Tuple          =				crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        A_						:  Optional[Any]          =				get_size_dict(_a	,default_to_square=_a	,param_name="""crop_size""" )

        A_						:  Any          =				do_resize
        A_						:  List[str]          =				size
        A_						:  Union[str, Any]          =				resample
        A_						:  Dict          =				do_center_crop
        A_						:  List[str]          =				crop_size
        A_						:  Any          =				do_rescale
        A_						:  Union[str, Any]          =				rescale_factor
        A_						:  Any          =				do_normalize
        A_						:  List[str]          =				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        A_						:  List[Any]          =				image_std if image_std is not None else OPENAI_CLIP_STD
        A_						:  Tuple          =				do_convert_rgb






  def        _a							(							self :       Optional[int]	,_a :       np.ndarray	,_a :       Dict[str, int]	,_a :       PILImageResampling = PILImageResampling.BICUBIC	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       Optional[Any]	,):

        '''simple docstring'''




        A_						:  Optional[Any]          =				get_size_dict(_a	,default_to_square=_a )
        if "shortest_edge" not in size:
              raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
        A_						:  Tuple          =				get_resize_output_image_size(_a	,size=size["""shortest_edge"""]	,default_to_square=_a )
        return resize(_a	,size=_a	,resample=_a	,data_format=_a	,**_a )






  def        _a							(							self :       List[Any]	,_a :       np.ndarray	,_a :       Dict[str, int]	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       Optional[int]	,):

        '''simple docstring'''




        A_						:  Optional[int]          =				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        _a							(							self :       Any	,_a :       np.ndarray	,_a :       Union[int, float]	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       Any	,):

        '''simple docstring'''




        return rescale(_a	,scale=_a	,data_format=_a	,**_a )






  def        _a							(							self :       Any	,_a :       np.ndarray	,_a :       Union[float, List[float]]	,_a :       Union[float, List[float]]	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       List[str]	,):

        '''simple docstring'''




        return normalize(_a	,mean=_a	,std=_a	,data_format=_a	,**_a )






  def        _a							(							self :       Optional[Any]	,_a :       ImageInput	,_a :       bool = None	,_a :       Dict[str, int] = None	,_a :       PILImageResampling = None	,_a :       bool = None	,_a :       int = None	,_a :       bool = None	,_a :       float = None	,_a :       bool = None	,_a :       Optional[Union[float, List[float]]] = None	,_a :       Optional[Union[float, List[float]]] = None	,_a :       bool = None	,_a :       Optional[Union[str, TensorType]] = None	,_a :       Optional[ChannelDimension] = ChannelDimension.FIRST	,**_a :       int	,):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				do_resize if do_resize is not None else self.do_resize
        A_						:  Tuple          =				size if size is not None else self.size
        A_						:  Optional[int]          =				get_size_dict(_a	,param_name="""size"""	,default_to_square=_a )
        A_						:  List[str]          =				resample if resample is not None else self.resample
        A_						:  int          =				do_center_crop if do_center_crop is not None else self.do_center_crop
        A_						:  Any          =				crop_size if crop_size is not None else self.crop_size
        A_						:  int          =				get_size_dict(_a	,param_name="""crop_size"""	,default_to_square=_a )
        A_						:  List[Any]          =				do_rescale if do_rescale is not None else self.do_rescale
        A_						:  int          =				rescale_factor if rescale_factor is not None else self.rescale_factor
        A_						:  Any          =				do_normalize if do_normalize is not None else self.do_normalize
        A_						:  int          =				image_mean if image_mean is not None else self.image_mean
        A_						:  int          =				image_std if image_std is not None else self.image_std
        A_						:  List[str]          =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        A_						:  int          =				make_list_of_images(_a )

        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.""" )

        if do_normalize and (image_mean is None or image_std is None):
              raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
              A_						:  Optional[int]          =				[convert_to_rgb(_a ) for image in images]

        # All transformations expect numpy arrays.
        A_						:  Dict          =				[to_numpy_array(_a ) for image in images]

        if do_resize:
              A_						:  int          =				[self.resize(image=_a	,size=_a	,resample=_a ) for image in images]

        if do_center_crop:
              A_						:  Tuple          =				[self.center_crop(image=_a	,size=_a ) for image in images]

        if do_rescale:
              A_						:  List[str]          =				[self.rescale(image=_a	,scale=_a ) for image in images]

        if do_normalize:
              A_						:  Any          =				[self.normalize(image=_a	,mean=_a	,std=_a ) for image in images]

        A_						:  List[str]          =				[to_channel_dimension_format(_a	,_a ) for image in images]

        A_						:  List[str]          =				{"""pixel_values""": images}
        return BatchFeature(data=_a	,tensor_type=_a )
