

'''simple docstring'''

import os

import jsonlines
import numpy as np
from tqdm import tqdm


A_       						=							20_48
A_       						=							40_96
A_       						=							42
A_       						=							os.environ.pop("PROCESS_TRAIN", "false")
A_       						=							{"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}





def    A_  ( snake_case ):
  def choose_first(snake_case      ,       snake_case=False ):
    assert isinstance(snake_case      ,       snake_case )
    if len(snake_case ) == 1:
      SCREAMING_SNAKE_CASE:int						      =					answer[0]
      return {k: [answer[k]] for k in answer} if is_long_answer else answer
    for a in answer:
      if is_long_answer:
        SCREAMING_SNAKE_CASE:Tuple						      =					{k: [a[k]] for k in a}
      if len(a["start_token"] ) > 0:
        break
    return a

  SCREAMING_SNAKE_CASE:List[str]						      =					{"id": example["id"]}
  SCREAMING_SNAKE_CASE:Optional[int]						      =					example["annotations"]
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					annotation["yes_no_answer"]
  if 0 in yes_no_answer or 1 in yes_no_answer:
    SCREAMING_SNAKE_CASE:List[str]						      =					["yes"] if 1 in yes_no_answer else ["no"]
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[]
    SCREAMING_SNAKE_CASE:Optional[int]						      =					[]
    SCREAMING_SNAKE_CASE:Any						      =					["<cls>"]
  else:
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					["short"]
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					choose_first(annotation["short_answers"] )
    if len(out["start_token"] ) == 0:
      # answer will be long if short is not available
      SCREAMING_SNAKE_CASE:Optional[int]						      =					["long"]
      SCREAMING_SNAKE_CASE:int						      =					choose_first(annotation["long_answer"]      ,       is_long_answer=snake_case )
      SCREAMING_SNAKE_CASE:Dict						      =					[]
    answer.update(snake_case )

  # disregard some samples
  if len(answer["start_token"] ) > 1 or answer["start_token"] == answer["end_token"]:
    SCREAMING_SNAKE_CASE:int						      =					True
  else:
    SCREAMING_SNAKE_CASE:str						      =					False

  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					["start_token", "end_token", "start_byte", "end_byte", "text"]
  if not all(isinstance(answer[k]      ,       snake_case ) for k in cols ):
    raise ValueError("Issue in ID"      ,       example["id"] )

  return answer





def    A_  ( snake_case      ,       snake_case=False ):
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					_get_single_answer(snake_case )
  # bytes are of no use
  del answer["start_byte"]
  del answer["end_byte"]

  # handle yes_no answers explicitly
  if answer["category"][0] in ["yes", "no"]:  # category is list with one element
    SCREAMING_SNAKE_CASE:Dict						      =					example["document"]["tokens"]
    SCREAMING_SNAKE_CASE:Any						      =					[]
    for i in range(len(doc["token"] ) ):
      if not doc["is_html"][i]:
        context.append(doc["token"][i] )
    return {
        "context": " ".join(snake_case ),
        "answer": {
            "start_token": -100,  # ignore index in cross-entropy
            "end_token": -100,  # ignore index in cross-entropy
            "category": answer["category"],
            "span": answer["category"],  # extra
        },
    }

  # later, help in removing all no answers
  if answer["start_token"] == [-1]:
    return {
        "context": "None",
        "answer": {
            "start_token": -1,
            "end_token": -1,
            "category": "null",
            "span": "None",  # extra
        },
    }

  # handling normal samples

  SCREAMING_SNAKE_CASE:Tuple						      =					["start_token", "end_token"]
  answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} )  # e.g. [10] == 10

  SCREAMING_SNAKE_CASE:Dict						      =					example["document"]["tokens"]
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					answer["start_token"]
  SCREAMING_SNAKE_CASE:int						      =					answer["end_token"]

  SCREAMING_SNAKE_CASE:str						      =					[]
  for i in range(len(doc["token"] ) ):
    if not doc["is_html"][i]:
      context.append(doc["token"][i] )
    else:
      if answer["start_token"] > i:
        start_token -= 1
      if answer["end_token"] > i:
        end_token -= 1
  SCREAMING_SNAKE_CASE:Optional[int]						      =					" ".join(context[start_token:end_token] )

  # checking above code
  if assertion:
    SCREAMING_SNAKE_CASE:List[Any]						      =					doc["is_html"][answer["start_token"] : answer["end_token"]]
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					doc["token"][answer["start_token"] : answer["end_token"]]
    SCREAMING_SNAKE_CASE:List[Any]						      =					" ".join([old[i] for i in range(len(snake_case ) ) if not is_html[i]] )
    if new != old:
      print("ID:"      ,       example["id"] )
      print("New:"      ,       snake_case      ,       end="\n" )
      print("Old:"      ,       snake_case      ,       end="\n\n" )

  return {
      "context": " ".join(snake_case ),
      "answer": {
          "start_token": start_token,
          "end_token": end_token - 1,  # this makes it inclusive
          "category": answer["category"],  # either long or short
          "span": new,  # extra
      },
  }





def    A_  ( snake_case      ,       snake_case      ,       snake_case=2048      ,       snake_case=4096      ,       snake_case=True ):
  # overlap will be of doc_stride - q_len

  SCREAMING_SNAKE_CASE:Optional[Any]						      =					get_context_and_ans(snake_case      ,       assertion=snake_case )
  SCREAMING_SNAKE_CASE:List[str]						      =					out["answer"]

  # later, removing these samples
  if answer["start_token"] == -1:
    return {
        "example_id": example["id"],
        "input_ids": [[-1]],
        "labels": {
            "start_token": [-1],
            "end_token": [-1],
            "category": ["null"],
        },
    }

  SCREAMING_SNAKE_CASE:int						      =					tokenizer(example["question"]["text"]      ,       out["context"] ).input_ids
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					input_ids.index(tokenizer.sep_token_id ) + 1

  # return yes/no
  if answer["category"][0] in ["yes", "no"]:  # category is list with one element
    SCREAMING_SNAKE_CASE:int						      =					[]
    SCREAMING_SNAKE_CASE:List[Any]						      =					[]
    SCREAMING_SNAKE_CASE:Tuple						      =					input_ids[:q_len]
    SCREAMING_SNAKE_CASE:Dict						      =					range(snake_case      ,       len(snake_case )      ,       max_length - doc_stride )
    for i in doc_start_indices:
      SCREAMING_SNAKE_CASE:List[Any]						      =					i + max_length - q_len
      SCREAMING_SNAKE_CASE:Any						      =					input_ids[i:end_index]
      inputs.append(q_indices + slice )
      category.append(answer["category"][0] )
      if slice[-1] == tokenizer.sep_token_id:
        break

    return {
        "example_id": example["id"],
        "input_ids": inputs,
        "labels": {
            "start_token": [-100] * len(snake_case ),
            "end_token": [-100] * len(snake_case ),
            "category": category,
        },
    }

  SCREAMING_SNAKE_CASE:int						      =					out["context"].split()
  SCREAMING_SNAKE_CASE:Any						      =					splitted_context[answer["end_token"]]
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					len(
      tokenizer(
          " ".join(splitted_context[: answer["start_token"]] )      ,       add_special_tokens=snake_case      ,       ).input_ids )
  SCREAMING_SNAKE_CASE:Tuple						      =					len(
      tokenizer(" ".join(splitted_context[: answer["end_token"]] )      ,       add_special_tokens=snake_case ).input_ids )

  answer["start_token"] += q_len
  answer["end_token"] += q_len

  # fixing end token
  SCREAMING_SNAKE_CASE:str						      =					len(tokenizer(snake_case      ,       add_special_tokens=snake_case ).input_ids )
  if num_sub_tokens > 1:
    answer["end_token"] += num_sub_tokens - 1

  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					input_ids[answer["start_token"] : answer["end_token"] + 1]  # right & left are inclusive
  SCREAMING_SNAKE_CASE:Dict						      =					answer["start_token"]
  SCREAMING_SNAKE_CASE:str						      =					answer["end_token"]

  if assertion:
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tokenizer.decode(snake_case )
    if answer["span"] != new:
      print("ISSUE IN TOKENIZATION" )
      print("OLD:"      ,       answer["span"] )
      print("NEW:"      ,       snake_case      ,       end="\n\n" )

  if len(snake_case ) <= max_length:
    return {
        "example_id": example["id"],
        "input_ids": [input_ids],
        "labels": {
            "start_token": [answer["start_token"]],
            "end_token": [answer["end_token"]],
            "category": answer["category"],
        },
    }

  SCREAMING_SNAKE_CASE:str						      =					input_ids[:q_len]
  SCREAMING_SNAKE_CASE:Optional[int]						      =					range(snake_case      ,       len(snake_case )      ,       max_length - doc_stride )

  SCREAMING_SNAKE_CASE:Any						      =					[]
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[]
  SCREAMING_SNAKE_CASE:Optional[int]						      =					[]
  SCREAMING_SNAKE_CASE:Dict						      =					[]  # null, yes, no, long, short
  for i in doc_start_indices:
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					i + max_length - q_len
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					input_ids[i:end_index]
    inputs.append(q_indices + slice )
    assert len(inputs[-1] ) <= max_length, "Issue in truncating length"

    if start_token >= i and end_token <= end_index - 1:
      SCREAMING_SNAKE_CASE:Union[str, Any]						      =					start_token - i + q_len
      SCREAMING_SNAKE_CASE:Tuple						      =					end_token - i + q_len
      answers_category.append(answer["category"][0] )  # ["short"] -> "short"
    else:
      SCREAMING_SNAKE_CASE:Tuple						      =					-100
      SCREAMING_SNAKE_CASE:Any						      =					-100
      answers_category.append("null" )
    SCREAMING_SNAKE_CASE:Any						      =					inputs[-1][start_token : end_token + 1]

    answers_start_token.append(snake_case )
    answers_end_token.append(snake_case )
    if assertion:
      if new != old and new != [tokenizer.cls_token_id]:
        print("ISSUE in strided for ID:"      ,       example["id"] )
        print("New:"      ,       tokenizer.decode(snake_case ) )
        print("Old:"      ,       tokenizer.decode(snake_case )      ,       end="\n\n" )
    if slice[-1] == tokenizer.sep_token_id:
      break

  return {
      "example_id": example["id"],
      "input_ids": inputs,
      "labels": {
          "start_token": answers_start_token,
          "end_token": answers_end_token,
          "category": answers_category,
      },
  }





def    A_  ( snake_case      ,       snake_case      ,       snake_case=2048      ,       snake_case=4096      ,       snake_case=False ):
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					get_strided_contexts_and_ans(
      snake_case      ,       snake_case      ,       doc_stride=snake_case      ,       max_length=snake_case      ,       assertion=snake_case      ,       )

  return example





def    A_  ( snake_case      ,       snake_case ):
  with jsonlines.open(snake_case      ,       "a" ) as writer:
    for example in tqdm(snake_case      ,       total=len(snake_case )      ,       desc="Saving samples ... " ):
      SCREAMING_SNAKE_CASE:str						      =					example["labels"]
      for ids, start, end, cat in zip(
          example["input_ids"]      ,       labels["start_token"]      ,       labels["end_token"]      ,       labels["category"]      ,       ):
        if start == -1 and end == -1:
          continue  # leave waste samples with no answer
        if cat == "null" and np.random.rand() < 0.6:
          continue  # removing 50 % samples
        writer.write(
            {
                "input_ids": ids,
                "start_token": start,
                "end_token": end,
                "category": CATEGORY_MAPPING[cat],
            } )


if __name__ == "__main__":
    from datasets import load_dataset

    from transformers import BigBirdTokenizer

    A_       						=							load_dataset("natural_questions")
    A_       						=							BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")

    A_       						=							data["train" if PROCESS_TRAIN == "true" else "validation"]

    A_       						=							{
        "tokenizer": tokenizer,
        "doc_stride": DOC_STRIDE,
        "max_length": MAX_LENGTH,
        "assertion": False,
    }
    A_       						=							data.map(prepare_inputs, fn_kwargs=fn_kwargs)
    A_       						=							data.remove_columns(["annotations", "document", "id", "question"])
    print(data)

    np.random.seed(SEED)
    A_       						=							"nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl"
    save_to_disk(data, file_name=cache_file_name)









'''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


A_       						=							logging.get_logger(__name__)


if is_vision_available():
    import PIL





class 							_snake_case (       _a						):
   _A       :     int       =					['''pixel_values''']
   def __init__( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = PILImageResampling.BICUBIC     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Union[int, float] = 1 / 255     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,):
     super().__init__(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Any						      =					size if size is not None else {"shortest_edge": 224}
     SCREAMING_SNAKE_CASE:int						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:str						      =					crop_size if crop_size is not None else {"height": 224, "width": 224}
     SCREAMING_SNAKE_CASE:str						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__     ,param_name="crop_size"							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					do_resize
     SCREAMING_SNAKE_CASE:Optional[int]						      =					size
     SCREAMING_SNAKE_CASE:List[Any]						      =					resample
     SCREAMING_SNAKE_CASE:Any						      =					do_center_crop
     SCREAMING_SNAKE_CASE:List[Any]						      =					crop_size
     SCREAMING_SNAKE_CASE:Tuple						      =					do_rescale
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					rescale_factor
     SCREAMING_SNAKE_CASE:Dict						      =					do_normalize
     SCREAMING_SNAKE_CASE:int						      =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					image_std if image_std is not None else OPENAI_CLIP_STD
     SCREAMING_SNAKE_CASE:Optional[int]						      =					do_convert_rgb
   def     __UpperCamelCase						( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int]     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = PILImageResampling.BICUBIC     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Any     ,):
     SCREAMING_SNAKE_CASE:List[Any]						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     if "shortest_edge" not in size:
       raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''							)
     SCREAMING_SNAKE_CASE:Dict						      =					get_resize_output_image_size(SCREAMING_SNAKE_CASE__     ,size=size["shortest_edge"]     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     return resize(SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,resample=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Optional[int]     ,):
     SCREAMING_SNAKE_CASE:List[Any]						      =					get_size_dict(SCREAMING_SNAKE_CASE__							)
     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(SCREAMING_SNAKE_CASE__     ,size=(size["height"], size["width"])     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Union[int, float]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      List[Any]     ,):
     return rescale(SCREAMING_SNAKE_CASE__     ,scale=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Union[float, List[float]]     ,SCREAMING_SNAKE_CASE__			:      Union[float, List[float]]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Tuple     ,):
     return normalize(SCREAMING_SNAKE_CASE__     ,mean=SCREAMING_SNAKE_CASE__     ,std=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)







   def     __UpperCamelCase						( self			:      int     ,SCREAMING_SNAKE_CASE__			:      ImageInput     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = None     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      int = None     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, TensorType]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[ChannelDimension] = ChannelDimension.FIRST     ,**SCREAMING_SNAKE_CASE__			:      List[str]     ,):
     SCREAMING_SNAKE_CASE:Any						      =					do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE:Optional[int]						      =					size if size is not None else self.size
     SCREAMING_SNAKE_CASE:Dict						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,param_name="size"     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE:Dict						      =					do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE:Optional[int]						      =					crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE:str						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,param_name="crop_size"     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int						      =					do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE:Tuple						      =					rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE:List[Any]						      =					do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE:List[str]						      =					image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE:Any						      =					image_std if image_std is not None else self.image_std
     SCREAMING_SNAKE_CASE:str						      =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     SCREAMING_SNAKE_CASE:Any						      =					make_list_of_images(SCREAMING_SNAKE_CASE__							)

     if not valid_images(SCREAMING_SNAKE_CASE__							):
       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:
       SCREAMING_SNAKE_CASE:List[Any]						      =					[convert_to_rgb(SCREAMING_SNAKE_CASE__							) for image in images]

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE:Optional[int]						      =					[to_numpy_array(SCREAMING_SNAKE_CASE__							) for image in images]

     if do_resize:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[self.resize(image=SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,resample=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_center_crop:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[self.center_crop(image=SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE:Any						      =					[self.rescale(image=SCREAMING_SNAKE_CASE__     ,scale=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_normalize:
       SCREAMING_SNAKE_CASE:List[str]						      =					[self.normalize(image=SCREAMING_SNAKE_CASE__     ,mean=SCREAMING_SNAKE_CASE__     ,std=SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:List[Any]						      =					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:Optional[int]						      =					{"pixel_values": images}
     return BatchFeature(data=SCREAMING_SNAKE_CASE__     ,tensor_type=SCREAMING_SNAKE_CASE__							)






