






import json
import os
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


snake_case			 =						logging.get_logger(__name__)

snake_case			 =						{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

snake_case			 =						{
    """vocab_file""": {
        """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json""",
    },
    """merges_file""": {"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt"""},
}

snake_case			 =						{
    """microsoft/biogpt""": 1_024,
}


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			set()
  SCREAMING_SNAKE_CASE				:					Tuple		  =			word[0]
  for char in word[1:]:
    pairs.add((prev_char, char) )
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			char
  return pairs


class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						Dict								=		VOCAB_FILES_NAMES
     UpperCamelCase_							:						Any								=		PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_							:						str								=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_							:						Tuple								=		['''input_ids''', '''attention_mask''']


     def __init__( self				:		Tuple    ,       UpperCAmelCase_				:		Optional[Any]    ,       UpperCAmelCase_				:		Optional[int]    ,       UpperCAmelCase_				:		str="<unk>"    ,       UpperCAmelCase_				:		Union[str, Any]="<s>"    ,       UpperCAmelCase_				:		Tuple="</s>"    ,       UpperCAmelCase_				:		str="</s>"    ,       UpperCAmelCase_				:		int="<pad>"    ,       **UpperCAmelCase_				:		Tuple    ,       ):
       super().__init__(
           bos_token=UpperCAmelCase_    ,       eos_token=UpperCAmelCase_    ,       sep_token=UpperCAmelCase_    ,       unk_token=UpperCAmelCase_    ,       pad_token=UpperCAmelCase_    ,       **UpperCAmelCase_    ,       )

       try:
         import sacremoses
       except ImportError:
         raise ImportError(
             "You need to install sacremoses to use BioGptTokenizer. "
             "See https://pypi.org/project/sacremoses/ for installation."					)

       SCREAMING_SNAKE_CASE				:					List[str]		  =			"en"
       SCREAMING_SNAKE_CASE				:					int		  =			sacremoses
       # cache of sm.MosesTokenizer instance
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			{}
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			{}
       with open(UpperCAmelCase_    ,       encoding="utf-8"					) as vocab_handle:
         SCREAMING_SNAKE_CASE				:					List[str]		  =			json.load(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Dict		  =			{v: k for k, v in self.encoder.items()}
       with open(UpperCAmelCase_    ,       encoding="utf-8"					) as merges_handle:
         SCREAMING_SNAKE_CASE				:					int		  =			merges_handle.read().split("\n"					)[:-1]
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			[tuple(merge.split()[:2]					) for merge in merges]
       SCREAMING_SNAKE_CASE				:					List[str]		  =			dict(zip(UpperCAmelCase_    ,       range(len(UpperCAmelCase_					)					)					)					)
       SCREAMING_SNAKE_CASE				:					List[str]		  =			{}


     @property
     def  _A				( self				:		List[str]					):
       return len(self.encoder					)


     def  _A				( self				:		Optional[Any]					):
       return dict(self.encoder    ,       **self.added_tokens_encoder					)


     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		Optional[int]    ,       UpperCAmelCase_				:		Union[str, Any]					):
       if lang not in self.cache_moses_tokenizer:
         SCREAMING_SNAKE_CASE				:					str		  =			self.sm.MosesTokenizer(lang=UpperCAmelCase_					)
         SCREAMING_SNAKE_CASE				:					List[Any]		  =			moses_tokenizer
       return self.cache_moses_tokenizer[lang].tokenize(
           UpperCAmelCase_    ,       aggressive_dash_splits=UpperCAmelCase_    ,       return_str=UpperCAmelCase_    ,       escape=UpperCAmelCase_					)


     def  _A				( self				:		Optional[int]    ,       UpperCAmelCase_				:		Any    ,       UpperCAmelCase_				:		Optional[Any]					):
       if lang not in self.cache_moses_detokenizer:
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.sm.MosesDetokenizer(lang=UpperCAmelCase_					)
         SCREAMING_SNAKE_CASE				:					Tuple		  =			moses_detokenizer
       return self.cache_moses_detokenizer[lang].detokenize(UpperCAmelCase_					)


     def  _A				( self				:		Tuple    ,       UpperCAmelCase_				:		Optional[Any]					):
       SCREAMING_SNAKE_CASE				:					Tuple		  =			tuple(token[:-1]					) + (token[-1] + "</w>",)
       if token in self.cache:
         return self.cache[token]
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			get_pairs(UpperCAmelCase_					)

       if not pairs:
         return token + "</w>"

       while True:
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			min(UpperCAmelCase_    ,       key=lambda UpperCAmelCase_					: self.bpe_ranks.get(UpperCAmelCase_    ,       float("inf"					)					)					)
         if bigram not in self.bpe_ranks:
           break
         SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Any		  =			bigram
         SCREAMING_SNAKE_CASE				:					List[Any]		  =			[]
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			0
         while i < len(UpperCAmelCase_					):
           try:
             SCREAMING_SNAKE_CASE				:					List[str]		  =			word.index(UpperCAmelCase_    ,       UpperCAmelCase_					)
           except ValueError:
             new_word.extend(word[i:]					)
             break
           else:
             new_word.extend(word[i:j]					)
             SCREAMING_SNAKE_CASE				:					List[str]		  =			j

           if word[i] == first and i < len(UpperCAmelCase_					) - 1 and word[i + 1] == second:
             new_word.append(first + second					)
             i += 2
           else:
             new_word.append(word[i]					)
             i += 1
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			tuple(UpperCAmelCase_					)
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			new_word
         if len(UpperCAmelCase_					) == 1:
           break
         else:
           SCREAMING_SNAKE_CASE				:					List[str]		  =			get_pairs(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			" ".join(UpperCAmelCase_					)
       if word == "\n  </w>":
         SCREAMING_SNAKE_CASE				:					Tuple		  =			"\n</w>"
       SCREAMING_SNAKE_CASE				:					Tuple		  =			word
       return word


     def  _A				( self				:		int    ,       UpperCAmelCase_				:		str    ,       UpperCAmelCase_				:		List[Any]=False					):
       if bypass_tokenizer:
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			text.split()
       else:
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			self.moses_tokenize(UpperCAmelCase_    ,       self.lang					)

       SCREAMING_SNAKE_CASE				:					Tuple		  =			[]
       for token in text:
         if token:
           split_tokens.extend(list(self.bpe(UpperCAmelCase_					).split(" "					)					)					)

       return split_tokens


     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		Union[str, Any]					):
       return self.encoder.get(UpperCAmelCase_    ,       self.encoder.get(self.unk_token					)					)


     def  _A				( self				:		int    ,       UpperCAmelCase_				:		Union[str, Any]					):
       return self.decoder.get(UpperCAmelCase_    ,       self.unk_token					)


     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		List[Any]					):
       SCREAMING_SNAKE_CASE				:					int		  =			[t.replace(" "    ,       ""					).replace("</w>"    ,       " "					) for t in tokens]
       SCREAMING_SNAKE_CASE				:					str		  =			"".join(UpperCAmelCase_					).split()
       # detokenize
       SCREAMING_SNAKE_CASE				:					Any		  =			self.moses_detokenize(UpperCAmelCase_    ,       self.lang					)
       return text


     def  _A				( self				:		Optional[int]    ,       UpperCAmelCase_				:		List[int]    ,       UpperCAmelCase_				:		Optional[List[int]] = None					):
       if token_ids_a is None:
         return [self.sep_token_id] + token_ids_a
       SCREAMING_SNAKE_CASE				:					int		  =			[self.sep_token_id]
       return sep + token_ids_a + sep + token_ids_a


     def  _A				( self				:		Dict    ,       UpperCAmelCase_				:		List[int]    ,       UpperCAmelCase_				:		Optional[List[int]] = None    ,       UpperCAmelCase_				:		bool = False					):
       if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=UpperCAmelCase_    ,       token_ids_a=UpperCAmelCase_    ,       already_has_special_tokens=UpperCAmelCase_					)
       # no bos used in fairseq
       if token_ids_a is not None:
         return [1] + ([0] * len(UpperCAmelCase_					)) + [1] + ([0] * len(UpperCAmelCase_					))
       return [1] + ([0] * len(UpperCAmelCase_					))


     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		List[int]    ,       UpperCAmelCase_				:		Optional[List[int]] = None					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			[self.sep_token_id]

       # no bos used in fairseq
       if token_ids_a is None:
         return len(token_ids_a + sep					) * [0]
       return len(token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]


     def  _A				( self				:		Optional[int]    ,       UpperCAmelCase_				:		str    ,       UpperCAmelCase_				:		Optional[str] = None					):
       if not os.path.isdir(UpperCAmelCase_					):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
         return
       SCREAMING_SNAKE_CASE				:					int		  =			os.path.join(
           UpperCAmelCase_    ,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			os.path.join(
           UpperCAmelCase_    ,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]					)

       with open(UpperCAmelCase_    ,       "w"    ,       encoding="utf-8"					) as f:
         f.write(json.dumps(self.encoder    ,       indent=2    ,       sort_keys=UpperCAmelCase_    ,       ensure_ascii=UpperCAmelCase_					) + "\n"					)

       SCREAMING_SNAKE_CASE				:					List[Any]		  =			0
       with open(UpperCAmelCase_    ,       "w"    ,       encoding="utf-8"					) as writer:
         for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,       key=lambda UpperCAmelCase_					: kv[1]					):
           if index != token_index:
             logger.warning(
                 f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                 " Please check that the tokenizer is not corrupted!"					)
             SCREAMING_SNAKE_CASE				:					List[str]		  =			token_index
           writer.write(" ".join(UpperCAmelCase_					) + "\n"					)
           index += 1

       return vocab_file, merge_file


     def __getstate__( self				:		Optional[Any]					):
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			self.__dict__.copy()
       SCREAMING_SNAKE_CASE				:					Tuple		  =			None
       return state






     def __setstate__( self				:		Any    ,       UpperCAmelCase_				:		Optional[int]					):
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			d

       try:
         import sacremoses
       except ImportError:
         raise ImportError(
             "You need to install sacremoses to use XLMTokenizer. "
             "See https://pypi.org/project/sacremoses/ for installation."					)

       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			sacremoses














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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


snake_case			 =						logging.get_logger(__name__)


if is_vision_available():
   import PIL


class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						List[Any]								=		['''pixel_values''']


     def __init__( self				:		Tuple    ,       UpperCAmelCase_				:		bool = True    ,       UpperCAmelCase_				:		Dict[str, int] = None    ,       UpperCAmelCase_				:		PILImageResampling = PILImageResampling.BILINEAR    ,       UpperCAmelCase_				:		bool = True    ,       UpperCAmelCase_				:		bool = False    ,       UpperCAmelCase_				:		bool = True    ,       UpperCAmelCase_				:		bool = True    ,       UpperCAmelCase_				:		Union[int, float] = 1 / 255    ,       UpperCAmelCase_				:		bool = True    ,       UpperCAmelCase_				:		Optional[Union[float, List[float]]] = None    ,       UpperCAmelCase_				:		Optional[Union[float, List[float]]] = None    ,       **UpperCAmelCase_				:		List[str]    ,       ):
       super().__init__(**UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			size if size is not None else {"height": 2560, "width": 1920}
       if isinstance(UpperCAmelCase_    ,       (tuple, list)					):
         # The previous feature extractor size parameter was in (width, height) format
         SCREAMING_SNAKE_CASE				:					List[str]		  =			size[::-1]
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			get_size_dict(UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					int		  =			do_resize
       SCREAMING_SNAKE_CASE				:					int		  =			size
       SCREAMING_SNAKE_CASE				:					str		  =			resample
       SCREAMING_SNAKE_CASE				:					List[str]		  =			do_thumbnail
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			do_align_long_axis
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			do_pad
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			do_rescale
       SCREAMING_SNAKE_CASE				:					Tuple		  =			rescale_factor
       SCREAMING_SNAKE_CASE				:					Dict		  =			do_normalize
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       SCREAMING_SNAKE_CASE				:					Any		  =			image_std if image_std is not None else IMAGENET_STANDARD_STD


     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Dict[str, int]    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None					):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					str		  =			get_image_size(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					List[Any]		  =			size["height"], size["width"]

       if (output_width < output_height and input_width > input_height) or (
           output_width > output_height and input_width < input_height
       ):
         SCREAMING_SNAKE_CASE				:					List[Any]		  =			np.rotaa(UpperCAmelCase_    ,       3					)

       if data_format is not None:
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			to_channel_dimension_format(UpperCAmelCase_    ,       UpperCAmelCase_					)

       return image


     def  _A				( self				:		int    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Dict[str, int]    ,       UpperCAmelCase_				:		bool = False    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       ):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					List[str]		  =			size["height"], size["width"]
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Optional[int]		  =			get_image_size(UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			output_width - input_width
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			output_height - input_height

       if random_padding:
         SCREAMING_SNAKE_CASE				:					int		  =			np.random.randint(low=0    ,       high=delta_height + 1					)
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			np.random.randint(low=0    ,       high=delta_width + 1					)
       else:
         SCREAMING_SNAKE_CASE				:					List[str]		  =			delta_height // 2
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			delta_width // 2

       SCREAMING_SNAKE_CASE				:					Any		  =			delta_height - pad_top
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			delta_width - pad_left

       SCREAMING_SNAKE_CASE				:					Dict		  =			((pad_top, pad_bottom), (pad_left, pad_right))
       return pad(UpperCAmelCase_    ,       UpperCAmelCase_    ,       data_format=UpperCAmelCase_					)


     def  _A				( self				:		Optional[Any]    ,       *UpperCAmelCase_				:		int    ,       **UpperCAmelCase_				:		Union[str, Any]					):
       logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."					)
       return self.pad_image(*UpperCAmelCase_    ,       **UpperCAmelCase_					)


     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Dict[str, int]    ,       UpperCAmelCase_				:		PILImageResampling = PILImageResampling.BICUBIC    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       **UpperCAmelCase_				:		int    ,       ):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			get_image_size(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					int		  =			size["height"], size["width"]

       # We always resize to the smallest of either the input or output size.
       SCREAMING_SNAKE_CASE				:					Any		  =			min(UpperCAmelCase_    ,       UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					List[str]		  =			min(UpperCAmelCase_    ,       UpperCAmelCase_					)

       if height == input_height and width == input_width:
         return image

       if input_height > input_width:
         SCREAMING_SNAKE_CASE				:					Tuple		  =			int(input_width * height / input_height					)
       elif input_width > input_height:
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			int(input_height * width / input_width					)

       return resize(
           UpperCAmelCase_    ,       size=(height, width)    ,       resample=UpperCAmelCase_    ,       reducing_gap=2.0    ,       data_format=UpperCAmelCase_    ,       **UpperCAmelCase_					)


     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Dict[str, int]    ,       UpperCAmelCase_				:		PILImageResampling = PILImageResampling.BICUBIC    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       **UpperCAmelCase_				:		Optional[int]    ,       ):
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			get_size_dict(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			min(size["height"]    ,       size["width"]					)
       SCREAMING_SNAKE_CASE				:					Tuple		  =			get_resize_output_image_size(UpperCAmelCase_    ,       size=UpperCAmelCase_    ,       default_to_square=UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			resize(UpperCAmelCase_    ,       size=UpperCAmelCase_    ,       resample=UpperCAmelCase_    ,       data_format=UpperCAmelCase_    ,       **UpperCAmelCase_					)
       return resized_image


     def  _A				( self				:		Dict    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Union[int, float]    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       **UpperCAmelCase_				:		str    ,       ):
       return rescale(UpperCAmelCase_    ,       scale=UpperCAmelCase_    ,       data_format=UpperCAmelCase_    ,       **UpperCAmelCase_					)


     def  _A				( self				:		Tuple    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Union[float, List[float]]    ,       UpperCAmelCase_				:		Union[float, List[float]]    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       **UpperCAmelCase_				:		List[Any]    ,       ):
       return normalize(UpperCAmelCase_    ,       mean=UpperCAmelCase_    ,       std=UpperCAmelCase_    ,       data_format=UpperCAmelCase_    ,       **UpperCAmelCase_					)






     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		ImageInput    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		Dict[str, int] = None    ,       UpperCAmelCase_				:		PILImageResampling = None    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		bool = False    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		float = None    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		Optional[Union[float, List[float]]] = None    ,       UpperCAmelCase_				:		Optional[Union[float, List[float]]] = None    ,       UpperCAmelCase_				:		Optional[Union[str, TensorType]] = None    ,       UpperCAmelCase_				:		Optional[ChannelDimension] = ChannelDimension.FIRST    ,       **UpperCAmelCase_				:		Union[str, Any]    ,       ):
       SCREAMING_SNAKE_CASE				:					int		  =			do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE				:					str		  =			size if size is not None else self.size
       if isinstance(UpperCAmelCase_    ,       (tuple, list)					):
         # Previous feature extractor had size in (width, height) format
         SCREAMING_SNAKE_CASE				:					str		  =			size[::-1]
       SCREAMING_SNAKE_CASE				:					Tuple		  =			get_size_dict(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					int		  =			resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
       SCREAMING_SNAKE_CASE				:					int		  =			do_pad if do_pad is not None else self.do_pad
       SCREAMING_SNAKE_CASE				:					str		  =			do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE				:					int		  =			rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE				:					Tuple		  =			do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE				:					List[str]		  =			image_std if image_std is not None else self.image_std

       SCREAMING_SNAKE_CASE				:					Dict		  =			make_list_of_images(UpperCAmelCase_					)

       if not valid_images(UpperCAmelCase_					):
         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_pad and size is None:
         raise ValueError("Size must be specified if do_pad 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."					)

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

       if do_align_long_axis:
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			[self.align_long_axis(UpperCAmelCase_    ,       size=UpperCAmelCase_					) for image in images]

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

       if do_thumbnail:
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			[self.thumbnail(image=UpperCAmelCase_    ,       size=UpperCAmelCase_					) for image in images]

       if do_pad:
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			[self.pad_image(image=UpperCAmelCase_    ,       size=UpperCAmelCase_    ,       random_padding=UpperCAmelCase_					) for image in images]

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

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

       SCREAMING_SNAKE_CASE				:					str		  =			[to_channel_dimension_format(UpperCAmelCase_    ,       UpperCAmelCase_					) for image in images]

       SCREAMING_SNAKE_CASE				:					Tuple		  =			{"pixel_values": images}
       return BatchFeature(data=UpperCAmelCase_    ,       tensor_type=UpperCAmelCase_					)






