

"""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    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase=True				,     __UpperCamelCase="pt"   ):
 '''simple docstring'''





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





 UpperCAmelCase__       :	str        =       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   __lowercase      (     __lowerCamelCase       ):

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






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

  def __len__(	self		:      Optional[Any]     ):






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

  def __getitem__(	self		:      Union[str, Any]       ,A		:      Optional[Any]     ):






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

   UpperCAmelCase__       :	Tuple        =       encode_line(A       ,A       ,self.max_source_length       ,"""right"""     )
   UpperCAmelCase__       :	Dict        =       encode_line(A       ,A       ,self.max_target_length       ,"""right"""     )

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

  @staticmethod
  def  __lowercase							(	A		:      int     ):






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




  def  __lowercase							(	self		:      List[Any]       ,A		:      Any     ):






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







__UpperCAmelCase			=					getLogger(__name__)
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 return list(itertools.chain.from_iterable(__UpperCamelCase   )   )
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	Dict        =       get_git_info()
 save_json(__UpperCamelCase				,     os.path.join(__UpperCamelCase				,     """git_log.json"""   )   )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase=4				,     **__UpperCamelCase   ):
 '''simple docstring'''





 with open(__UpperCamelCase				,     """w"""   ) as f:
  json.dump(__UpperCamelCase				,     __UpperCamelCase				,     indent=__UpperCamelCase				,     **__UpperCamelCase   )
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 with open(__UpperCamelCase   ) as f:
  return json.load(__UpperCamelCase   )
def 	lowerCAmelCase    (       ):
 '''simple docstring'''





 UpperCAmelCase__       :	Union[str, Any]        =       git.Repo(search_parent_directories=__UpperCamelCase   )
 UpperCAmelCase__       :	List[str]        =       {
     """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 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 return list(map(__UpperCamelCase				,     __UpperCamelCase   )   )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 with open(__UpperCamelCase				,     """wb"""   ) as f:
  return pickle.dump(__UpperCamelCase				,     __UpperCamelCase   )
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 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   ):
  UpperCAmelCase__       :	List[Any]        =       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 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	Union[str, Any]        =       normalize_answer(__UpperCamelCase   ).split()
 UpperCAmelCase__       :	Dict        =       normalize_answer(__UpperCamelCase   ).split()
 UpperCAmelCase__       :	int        =       Counter(__UpperCamelCase   ) & Counter(__UpperCamelCase   )
 UpperCAmelCase__       :	List[str]        =       sum(common.values()   )
 if num_same == 0:
  return 0
 UpperCAmelCase__       :	str        =       1.0 * num_same / len(__UpperCamelCase   )
 UpperCAmelCase__       :	Optional[int]        =       1.0 * num_same / len(__UpperCamelCase   )
 UpperCAmelCase__       :	Tuple        =       (2 * precision * recall) / (precision + recall)
 return fa
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 return normalize_answer(__UpperCamelCase   ) == normalize_answer(__UpperCamelCase   )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 assert len(__UpperCamelCase   ) == len(__UpperCamelCase   )
 UpperCAmelCase__       :	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 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 return model_prefix.startswith("""rag"""   )




def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	Optional[int]        =       {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(__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
   UpperCAmelCase__       :	Tuple        =       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, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
							import PIL

# soft dependency
if is_pytesseract_available():
							import pytesseract

__SCREAMING_SNAKE_CASE					:				List[str]									=		logging.get_logger(__name__)







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							return [
							    int(1_0_0_0 * (box[0] / width) ),
							    int(1_0_0_0 * (box[1] / height) ),
							    int(1_0_0_0 * (box[2] / width) ),
							    int(1_0_0_0 * (box[3] / height) ),
							]







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		to_pil_image(UpperCamelCase_ )
							A_    ,					A_			    =		pil_image.size
							A_			    =		pytesseract.image_to_data(UpperCamelCase_						,  lang=UpperCamelCase_						,  output_type="dict"						,  config=UpperCamelCase_ )
							A_    ,					A_    ,					A_    ,					A_    ,					A_			    =		data["text"], data["left"], data["top"], data["width"], data["height"]

							# filter empty words and corresponding coordinates
							A_			    =		[idx for idx, word in enumerate(UpperCamelCase_ ) if not word.strip()]
							A_			    =		[word for idx, word in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]
							A_			    =		[coord for idx, coord in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]
							A_			    =		[coord for idx, coord in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]
							A_			    =		[coord for idx, coord in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]
							A_			    =		[coord for idx, coord in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]

							# turn coordinates into (left, top, left+width, top+height) format
							A_			    =		[]
							for x, y, w, h in zip(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
														A_			    =		[x, y, x + w, y + h]
														actual_boxes.append(UpperCamelCase_ )

							# finally, normalize the bounding boxes
							A_			    =		[]
							for box in actual_boxes:
														normalized_boxes.append(normalize_box(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ) )

							assert len(UpperCamelCase_ ) == len(UpperCamelCase_ ), "Not as many words as there are bounding boxes"

							return words, normalized_boxes







class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Any   						=["pixel_values"]






						def __init__(  self							:      List[Any] ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Dict[str, int] = None ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BILINEAR ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      float = 1 / 2_55 ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Optional[str] = None ,  lowerCAmelCase							:      Optional[str] = "" ,  **lowerCAmelCase							:      int ,  ):
													super().__init__(**lowerCAmelCase	)
													A_			    =		size if size is not None else {"height": 2_24, "width": 2_24}
													A_			    =		get_size_dict(lowerCAmelCase	)

													A_			    =		do_resize
													A_			    =		size
													A_			    =		resample
													A_			    =		do_rescale
													A_			    =		rescale_value
													A_			    =		do_normalize
													A_			    =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													A_			    =		image_std if image_std is not None else IMAGENET_STANDARD_STD
													A_			    =		apply_ocr
													A_			    =		ocr_lang
													A_			    =		tesseract_config






						def 				_UpperCAmelCase       (  self							:      List[str] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Dict[str, int] ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BILINEAR ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      List[str] ,  ):
													A_			    =		get_size_dict(lowerCAmelCase	)
													if "height" not in size or "width" not in size:
																				raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"	)
													A_			    =		(size["height"], size["width"])
													return resize(lowerCAmelCase ,  size=lowerCAmelCase ,  resample=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[int, float] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      Tuple ,  ):
													return rescale(lowerCAmelCase ,  scale=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      int ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[float, Iterable[float]] ,  lowerCAmelCase							:      Union[float, Iterable[float]] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      int ,  ):
													return normalize(lowerCAmelCase ,  mean=lowerCAmelCase ,  std=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      ImageInput ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      Dict[str, int] = None ,  lowerCAmelCase							:      Any=None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      float = None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      Optional[str] = None ,  lowerCAmelCase							:      Optional[str] = None ,  lowerCAmelCase							:      Optional[Union[str, TensorType]] = None ,  lowerCAmelCase							:      ChannelDimension = ChannelDimension.FIRST ,  **lowerCAmelCase							:      int ,  ):
													A_			    =		do_resize if do_resize is not None else self.do_resize
													A_			    =		size if size is not None else self.size
													A_			    =		get_size_dict(lowerCAmelCase	)
													A_			    =		resample if resample is not None else self.resample
													A_			    =		do_rescale if do_rescale is not None else self.do_rescale
													A_			    =		rescale_factor if rescale_factor is not None else self.rescale_factor
													A_			    =		do_normalize if do_normalize is not None else self.do_normalize
													A_			    =		image_mean if image_mean is not None else self.image_mean
													A_			    =		image_std if image_std is not None else self.image_std
													A_			    =		apply_ocr if apply_ocr is not None else self.apply_ocr
													A_			    =		ocr_lang if ocr_lang is not None else self.ocr_lang
													A_			    =		tesseract_config if tesseract_config is not None else self.tesseract_config

													A_			    =		make_list_of_images(lowerCAmelCase	)

													if not valid_images(lowerCAmelCase	):
																				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_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("If do_normalize is True, image_mean and image_std must be specified."	)

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

													# Tesseract OCR to get words + normalized bounding boxes
													if apply_ocr:
																				requires_backends(self ,  "pytesseract"	)
																				A_			    =		[]
																				A_			    =		[]
																				for image in images:
																											A_    ,					A_			    =		apply_tesseract(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase	)
																											words_batch.append(lowerCAmelCase	)
																											boxes_batch.append(lowerCAmelCase	)

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

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

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

													A_			    =		[to_channel_dimension_format(lowerCAmelCase ,  lowerCAmelCase	) for image in images]

													A_			    =		BatchFeature(data={"pixel_values": images} ,  tensor_type=lowerCAmelCase	)

													if apply_ocr:
																				A_			    =		words_batch
																				A_			    =		boxes_batch
													return data





