




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

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


_lowerCamelCase					:     List[Any]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     List[Any]					  =  {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

_lowerCamelCase					:     int					  =  {
    "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"},
}

_lowerCamelCase					:     Dict					  =  {
    "microsoft/biogpt": 1_0_2_4,
}




def 							_UpperCAmelCase     (UpperCamelCase_       :       List[Any]				):
				'''simple docstring'''
				_lowerCAmelCase			:			Optional[int]        =					set()
				_lowerCAmelCase			:			Tuple        =					word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)				)
								_lowerCAmelCase			:			Optional[Any]        =					char
				return pairs





class 				__snake_case     (_a						):
				lowerCAmelCase__			=   VOCAB_FILES_NAMES
				lowerCAmelCase__			=   PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase__			=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase__			=   ['''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						,		)     ->			Union[str, Any]:





								'''simple docstring'''




								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."""					)

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



				@property
				def 					SCREAMING_SNAKE_CASE				(					self					:						List[str]					)     ->			Dict:





								'''simple docstring'''




								return len(self.encoder					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]					)     ->			List[Any]:





								'''simple docstring'''




								return dict(self.encoder						,		**self.added_tokens_encoder					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]						,		_UpperCAmelCase					:						Optional[int]						,		_UpperCAmelCase					:						Union[str, Any]					)     ->			str:





								'''simple docstring'''




								if lang not in self.cache_moses_tokenizer:
												_lowerCAmelCase			:			str        =					self.sm.MosesTokenizer(lang=UpperCAmelCase_					)
												_lowerCAmelCase			:			List[Any]        =					moses_tokenizer
								return self.cache_moses_tokenizer[lang].tokenize(
								    UpperCAmelCase_						,		aggressive_dash_splits=UpperCAmelCase_						,		return_str=UpperCAmelCase_						,		escape=UpperCAmelCase_					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]						,		_UpperCAmelCase					:						Any						,		_UpperCAmelCase					:						Optional[Any]					)     ->			Dict:





								'''simple docstring'''




								if lang not in self.cache_moses_detokenizer:
												_lowerCAmelCase			:			Optional[int]        =					self.sm.MosesDetokenizer(lang=UpperCAmelCase_					)
												_lowerCAmelCase			:			Tuple        =					moses_detokenizer
								return self.cache_moses_detokenizer[lang].detokenize(UpperCAmelCase_					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Tuple						,		_UpperCAmelCase					:						Optional[Any]					)     ->			Dict:





								'''simple docstring'''




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

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

								while True:
												_lowerCAmelCase			:			Optional[Any]        =					min(UpperCAmelCase_						,		key=lambda _UpperCAmelCase					: self.bpe_ranks.get(UpperCAmelCase_						,		float("""inf"""					)					)					)
												if bigram not in self.bpe_ranks:
																break
												_lowerCAmelCase			:			Any        =					bigram
												_lowerCAmelCase			:			List[Any]        =					[]
												_lowerCAmelCase			:			Optional[Any]        =					0
												while i < len(UpperCAmelCase_					):
																try:
																				_lowerCAmelCase			:			List[str]        =					word.index(UpperCAmelCase_						,		UpperCAmelCase_					)
																except ValueError:
																				new_word.extend(word[i:]					)
																				break
																else:
																				new_word.extend(word[i:j]					)
																				_lowerCAmelCase			:			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
												_lowerCAmelCase			:			Optional[Any]        =					tuple(UpperCAmelCase_					)
												_lowerCAmelCase			:			Optional[Any]        =					new_word
												if len(UpperCAmelCase_					) == 1:
																break
												else:
																_lowerCAmelCase			:			List[str]        =					get_pairs(UpperCAmelCase_					)
								_lowerCAmelCase			:			Optional[Any]        =					" ".join(UpperCAmelCase_					)
								if word == "\n  </w>":
												_lowerCAmelCase			:			Tuple        =					"\n</w>"
								_lowerCAmelCase			:			Tuple        =					word
								return word



				def 					SCREAMING_SNAKE_CASE				(					self					:						int						,		_UpperCAmelCase					:						str						,		_UpperCAmelCase					:						List[Any]=False					)     ->			Optional[Any]:





								'''simple docstring'''




								if bypass_tokenizer:
												_lowerCAmelCase			:			Optional[Any]        =					text.split()
								else:
												_lowerCAmelCase			:			Union[str, Any]        =					self.moses_tokenize(UpperCAmelCase_						,		self.lang					)

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

								return split_tokens



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]						,		_UpperCAmelCase					:						Union[str, Any]					)     ->			Dict:





								'''simple docstring'''




								return self.encoder.get(UpperCAmelCase_						,		self.encoder.get(self.unk_token					)					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						int						,		_UpperCAmelCase					:						Union[str, Any]					)     ->			Any:





								'''simple docstring'''




								return self.decoder.get(UpperCAmelCase_						,		self.unk_token					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]						,		_UpperCAmelCase					:						List[Any]					)     ->			Union[str, Any]:





								'''simple docstring'''




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



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]						,		_UpperCAmelCase					:						List[int]						,		_UpperCAmelCase					:						Optional[List[int]] = None					)     ->			Union[str, Any]:





								'''simple docstring'''




								if token_ids_a is None:
												return [self.sep_token_id] + token_ids_a
								_lowerCAmelCase			:			int        =					[self.sep_token_id]
								return sep + token_ids_a + sep + token_ids_a



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						List[int]						,		_UpperCAmelCase					:						Optional[List[int]] = None						,		_UpperCAmelCase					:						bool = False					)     ->			List[Any]:





								'''simple docstring'''




								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 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]						,		_UpperCAmelCase					:						List[int]						,		_UpperCAmelCase					:						Optional[List[int]] = None					)     ->			str:





								'''simple docstring'''




								_lowerCAmelCase			:			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 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]						,		_UpperCAmelCase					:						str						,		_UpperCAmelCase					:						Optional[str] = None					)     ->			Tuple:





								'''simple docstring'''




								if not os.path.isdir(UpperCAmelCase_					):
												logger.error(f"Vocabulary path ({save_directory}) should be a directory"					)
												return
								_lowerCAmelCase			:			int        =					os.path.join(
								    UpperCAmelCase_						,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
								_lowerCAmelCase			:			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"""					)

								_lowerCAmelCase			:			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!"""					)
																				_lowerCAmelCase			:			List[str]        =					token_index
																writer.write(""" """.join(UpperCAmelCase_					) + """\n"""					)
																index += 1

								return vocab_file, merge_file



				def __getstate__(					self					:						Optional[Any]					)     ->			Union[str, Any]:





								'''simple docstring'''




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



				def __setstate__(					self					:						Any						,		_UpperCAmelCase					:						Optional[int]					)     ->			List[str]:





								'''simple docstring'''




								_lowerCAmelCase			:			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."""					)

								_lowerCAmelCase			:			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_					)






