






"""simple docstring"""

import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 						__lowerCamelCase							(					snake_case__					,				unittest.TestCase     ):





		'''simple docstring'''






		a_      :							Optional[int]  =				LongformerTokenizer
		a_      :							Any  =				True
		a_      :							int  =				LongformerTokenizerFast
		a_      :							Union[str, Any]  =				True





		def   lowerCamelCase			(  self				:      int      ):
				super().setUp()

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				lowerCAmelCase_  :				str   							=     [
				    '''l''',
				    '''o''',
				    '''w''',
				    '''e''',
				    '''r''',
				    '''s''',
				    '''t''',
				    '''i''',
				    '''d''',
				    '''n''',
				    '''\u0120''',
				    '''\u0120l''',
				    '''\u0120n''',
				    '''\u0120lo''',
				    '''\u0120low''',
				    '''er''',
				    '''\u0120lowest''',
				    '''\u0120newer''',
				    '''\u0120wider''',
				    '''<unk>''',
				]
				lowerCAmelCase_  :				Optional[Any]   							=     dict(zip(__lowercase		,     range(len(__lowercase      )      )      )      )
				lowerCAmelCase_  :				str   							=     ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
				lowerCAmelCase_  :				int   							=     {'''unk_token''': '''<unk>'''}

				lowerCAmelCase_  :				List[Any]   							=     os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["vocab_file"]      )
				lowerCAmelCase_  :				int   							=     os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["merges_file"]      )
				with open(self.vocab_file		,     "w"		,     encoding="utf-8"      ) as fp:
						fp.write(json.dumps(__lowercase      ) + "\n"      )
				with open(self.merges_file		,     "w"		,     encoding="utf-8"      ) as fp:
						fp.write("\n".join(__lowercase      )      )





		def   lowerCamelCase			(  self				:      Optional[int]		,     **a_				:      int      ):
				kwargs.update(self.special_tokens_map      )
				return self.tokenizer_class.from_pretrained(self.tmpdirname		,     **__lowercase      )





		def   lowerCamelCase			(  self				:      int		,     **a_				:      str      ):
				kwargs.update(self.special_tokens_map      )
				return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,     **__lowercase      )





		def   lowerCamelCase			(  self				:      Union[str, Any]		,     a_				:      List[str]      ):
				lowerCAmelCase_  :				List[Any]   							=     '''lower newer'''
				lowerCAmelCase_  :				Tuple   							=     '''lower newer'''
				return input_text, output_text





		def   lowerCamelCase			(  self				:      Any      ):
				lowerCAmelCase_  :				Optional[int]   							=     self.tokenizer_class(self.vocab_file		,     self.merges_file		,     **self.special_tokens_map      )
				lowerCAmelCase_  :				List[str]   							=     '''lower newer'''
				lowerCAmelCase_  :				Optional[int]   							=     ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
				lowerCAmelCase_  :				str   							=     tokenizer.tokenize(__lowercase      )  # , add_prefix_space=True)
				self.assertListEqual(__lowercase		,     __lowercase      )

				lowerCAmelCase_  :				Dict   							=     tokens + [tokenizer.unk_token]
				lowerCAmelCase_  :				str   							=     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase      )		,     __lowercase      )





		def   lowerCamelCase			(  self				:      Any      ):
				lowerCAmelCase_  :				Union[str, Any]   							=     self.get_tokenizer()

				self.assertListEqual(tokenizer.encode("Hello world!"		,     add_special_tokens=__lowercase      )		,     [0, 3_14_14, 2_32, 3_28, 2]      )
				self.assertListEqual(
				    tokenizer.encode("Hello world! cécé herlolip 418"		,     add_special_tokens=__lowercase      )		,     [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]		,     )





		@slow
		def   lowerCamelCase			(  self				:      Tuple      ):
				lowerCAmelCase_  :				List[Any]   							=     self.tokenizer_class.from_pretrained("allenai/longformer-base-4096"      )

				lowerCAmelCase_  :				Optional[int]   							=     tokenizer.encode("sequence builders"		,     add_special_tokens=__lowercase      )
				lowerCAmelCase_  :				Tuple   							=     tokenizer.encode("multi-sequence build"		,     add_special_tokens=__lowercase      )

				lowerCAmelCase_  :				Dict   							=     tokenizer.encode(
				    "sequence builders"		,     add_special_tokens=__lowercase		,     add_prefix_space=__lowercase      )
				lowerCAmelCase_  :				Optional[int]   							=     tokenizer.encode(
				    "sequence builders"		,     "multi-sequence build"		,     add_special_tokens=__lowercase		,     add_prefix_space=__lowercase      )

				lowerCAmelCase_  :				Any   							=     tokenizer.build_inputs_with_special_tokens(__lowercase      )
				lowerCAmelCase_  :				int   							=     tokenizer.build_inputs_with_special_tokens(__lowercase		,     __lowercase      )

				assert encoded_sentence == encoded_text_from_decode
				assert encoded_pair == encoded_pair_from_decode





		def   lowerCamelCase			(  self				:      Dict      ):
				lowerCAmelCase_  :				str   							=     self.get_tokenizer()

				lowerCAmelCase_  :				Tuple   							=     '''Encode this sequence.'''
				lowerCAmelCase_  :				List[str]   							=     tokenizer.byte_encoder[''' '''.encode("utf-8"      )[0]]

				# Testing encoder arguments
				lowerCAmelCase_  :				Tuple   							=     tokenizer.encode(__lowercase		,     add_special_tokens=__lowercase		,     add_prefix_space=__lowercase      )
				lowerCAmelCase_  :				List[str]   							=     tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
				self.assertNotEqual(__lowercase		,     __lowercase      )

				lowerCAmelCase_  :				int   							=     tokenizer.encode(__lowercase		,     add_special_tokens=__lowercase		,     add_prefix_space=__lowercase      )
				lowerCAmelCase_  :				List[Any]   							=     tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
				self.assertEqual(__lowercase		,     __lowercase      )

				tokenizer.add_special_tokens({"bos_token": "<s>"}      )
				lowerCAmelCase_  :				Dict   							=     tokenizer.encode(__lowercase		,     add_special_tokens=__lowercase      )
				lowerCAmelCase_  :				Tuple   							=     tokenizer.convert_ids_to_tokens(encoded[1]      )[0]
				self.assertNotEqual(__lowercase		,     __lowercase      )

				# Testing spaces after special tokens
				lowerCAmelCase_  :				Tuple   							=     '''<mask>'''
				tokenizer.add_special_tokens(
				    {"mask_token": AddedToken(__lowercase		,     lstrip=__lowercase		,     rstrip=__lowercase      )}      )  # mask token has a left space
				lowerCAmelCase_  :				str   							=     tokenizer.convert_tokens_to_ids(__lowercase      )

				lowerCAmelCase_  :				Dict   							=     '''Encode <mask> sequence'''
				lowerCAmelCase_  :				Dict   							=     '''Encode <mask>sequence'''

				lowerCAmelCase_  :				List[Any]   							=     tokenizer.encode(__lowercase      )
				lowerCAmelCase_  :				Union[str, Any]   							=     encoded.index(__lowercase      )
				lowerCAmelCase_  :				int   							=     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
				self.assertEqual(__lowercase		,     __lowercase      )

				lowerCAmelCase_  :				List[str]   							=     tokenizer.encode(__lowercase      )
				lowerCAmelCase_  :				Union[str, Any]   							=     encoded.index(__lowercase      )
				lowerCAmelCase_  :				Optional[int]   							=     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
				self.assertNotEqual(__lowercase		,     __lowercase      )





		def   lowerCamelCase			(  self				:      Tuple      ):
				pass





		def   lowerCamelCase			(  self				:      Tuple      ):
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
								lowerCAmelCase_  :				Any   							=     self.rust_tokenizer_class.from_pretrained(__lowercase		,     **__lowercase      )
								lowerCAmelCase_  :				int   							=     self.tokenizer_class.from_pretrained(__lowercase		,     **__lowercase      )
								lowerCAmelCase_  :				List[str]   							=     '''A, <mask> AllenNLP sentence.'''
								lowerCAmelCase_  :				int   							=     tokenizer_r.encode_plus(__lowercase		,     add_special_tokens=__lowercase		,     return_token_type_ids=__lowercase      )
								lowerCAmelCase_  :				Tuple   							=     tokenizer_p.encode_plus(__lowercase		,     add_special_tokens=__lowercase		,     return_token_type_ids=__lowercase      )

								# token_type_ids should put 0 everywhere
								self.assertEqual(sum(tokens_r["token_type_ids"]      )		,     sum(tokens_p["token_type_ids"]      )      )

								# attention_mask should put 1 everywhere, so sum over length should be 1
								self.assertEqual(
								    sum(tokens_r["attention_mask"]      ) / len(tokens_r["attention_mask"]      )		,     sum(tokens_p["attention_mask"]      ) / len(tokens_p["attention_mask"]      )		,     )

								lowerCAmelCase_  :				Tuple   							=     tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]      )
								lowerCAmelCase_  :				List[Any]   							=     tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]      )

								# Rust correctly handles the space before the mask while python doesnt
								self.assertSequenceEqual(tokens_p["input_ids"]		,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]      )
								self.assertSequenceEqual(tokens_r["input_ids"]		,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]      )

								self.assertSequenceEqual(
								    __lowercase		,     ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]      )
								self.assertSequenceEqual(
								    __lowercase		,     ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]      )





		def   lowerCamelCase			(  self				:      int      ):
				for trim_offsets, add_prefix_space in itertools.product([True, False]		,     repeat=2      ):
						lowerCAmelCase_  :				Tuple   							=     self.rust_tokenizer_class.from_pretrained(
						    self.tmpdirname		,     use_fast=__lowercase		,     add_prefix_space=__lowercase		,     trim_offsets=__lowercase      )

						lowerCAmelCase_  :				str   							=     json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()      )
						lowerCAmelCase_  :				Dict   							=     json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()      )

						self.assertEqual(pre_tokenizer_state["add_prefix_space"]		,     __lowercase      )

						self.assertEqual(post_processor_state["add_prefix_space"]		,     __lowercase      )
						self.assertEqual(post_processor_state["trim_offsets"]		,     __lowercase      )





		def   lowerCamelCase			(  self				:      Optional[Any]      ):
				# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
				# `trim_offsets`
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
								lowerCAmelCase_  :				Dict   							=     '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
								lowerCAmelCase_  :				Tuple   							=     f'''{text_of_1_token} {text_of_1_token}'''

								lowerCAmelCase_  :				Optional[int]   							=     self.rust_tokenizer_class.from_pretrained(
								    __lowercase		,     use_fast=__lowercase		,     add_prefix_space=__lowercase		,     trim_offsets=__lowercase      )
								lowerCAmelCase_  :				str   							=     tokenizer_r(__lowercase		,     return_offsets_mapping=__lowercase		,     add_special_tokens=__lowercase      )
								self.assertEqual(encoding.offset_mapping[0]		,     (0, len(__lowercase      ))      )
								self.assertEqual(
								    encoding.offset_mapping[1]		,     (len(__lowercase      ) + 1, len(__lowercase      ) + 1 + len(__lowercase      ))		,     )

								lowerCAmelCase_  :				int   							=     self.rust_tokenizer_class.from_pretrained(
								    __lowercase		,     use_fast=__lowercase		,     add_prefix_space=__lowercase		,     trim_offsets=__lowercase      )
								lowerCAmelCase_  :				Any   							=     tokenizer_r(__lowercase		,     return_offsets_mapping=__lowercase		,     add_special_tokens=__lowercase      )
								self.assertEqual(encoding.offset_mapping[0]		,     (0, len(__lowercase      ))      )
								self.assertEqual(
								    encoding.offset_mapping[1]		,     (len(__lowercase      ) + 1, len(__lowercase      ) + 1 + len(__lowercase      ))		,     )

								lowerCAmelCase_  :				Union[str, Any]   							=     self.rust_tokenizer_class.from_pretrained(
								    __lowercase		,     use_fast=__lowercase		,     add_prefix_space=__lowercase		,     trim_offsets=__lowercase      )
								lowerCAmelCase_  :				Any   							=     tokenizer_r(__lowercase		,     return_offsets_mapping=__lowercase		,     add_special_tokens=__lowercase      )
								self.assertEqual(encoding.offset_mapping[0]		,     (0, len(__lowercase      ))      )
								self.assertEqual(
								    encoding.offset_mapping[1]		,     (len(__lowercase      ), len(__lowercase      ) + 1 + len(__lowercase      ))		,     )

								lowerCAmelCase_  :				Optional[Any]   							=     self.rust_tokenizer_class.from_pretrained(
								    __lowercase		,     use_fast=__lowercase		,     add_prefix_space=__lowercase		,     trim_offsets=__lowercase      )
								lowerCAmelCase_  :				Union[str, Any]   							=     tokenizer_r(__lowercase		,     return_offsets_mapping=__lowercase		,     add_special_tokens=__lowercase      )
								self.assertEqual(encoding.offset_mapping[0]		,     (0, len(__lowercase      ))      )
								self.assertEqual(
								    encoding.offset_mapping[1]		,     (len(__lowercase      ), len(__lowercase      ) + 1 + len(__lowercase      ))		,     )

								lowerCAmelCase_  :				Optional[int]   							=     f''' {text}'''

								# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
								#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
								# )
								# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
								# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
								# self.assertEqual(
								#     encoding.offset_mapping[1],
								#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
								# )

								lowerCAmelCase_  :				Any   							=     self.rust_tokenizer_class.from_pretrained(
								    __lowercase		,     use_fast=__lowercase		,     add_prefix_space=__lowercase		,     trim_offsets=__lowercase      )
								lowerCAmelCase_  :				List[str]   							=     tokenizer_r(__lowercase		,     return_offsets_mapping=__lowercase		,     add_special_tokens=__lowercase      )
								self.assertEqual(encoding.offset_mapping[0]		,     (1, 1 + len(__lowercase      ))      )
								self.assertEqual(
								    encoding.offset_mapping[1]		,     (1 + len(__lowercase      ) + 1, 1 + len(__lowercase      ) + 1 + len(__lowercase      ))		,     )

								lowerCAmelCase_  :				List[str]   							=     self.rust_tokenizer_class.from_pretrained(
								    __lowercase		,     use_fast=__lowercase		,     add_prefix_space=__lowercase		,     trim_offsets=__lowercase      )
								lowerCAmelCase_  :				Tuple   							=     tokenizer_r(__lowercase		,     return_offsets_mapping=__lowercase		,     add_special_tokens=__lowercase      )
								self.assertEqual(encoding.offset_mapping[0]		,     (0, 1 + len(__lowercase      ))      )
								self.assertEqual(
								    encoding.offset_mapping[1]		,     (1 + len(__lowercase      ), 1 + len(__lowercase      ) + 1 + len(__lowercase      ))		,     )

								lowerCAmelCase_  :				str   							=     self.rust_tokenizer_class.from_pretrained(
								    __lowercase		,     use_fast=__lowercase		,     add_prefix_space=__lowercase		,     trim_offsets=__lowercase      )
								lowerCAmelCase_  :				Optional[int]   							=     tokenizer_r(__lowercase		,     return_offsets_mapping=__lowercase		,     add_special_tokens=__lowercase      )
								self.assertEqual(encoding.offset_mapping[0]		,     (0, 1 + len(__lowercase      ))      )
								self.assertEqual(
								    encoding.offset_mapping[1]		,     (1 + len(__lowercase      ), 1 + len(__lowercase      ) + 1 + len(__lowercase      ))		,     )







"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
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_vision_available, logging


if is_vision_available():
							import PIL


_UpperCamelCase					      =   logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							str									=		["""pixel_values"""]




				def __init__(  self	:str    ,     __lowercase	:bool = True    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:float = None    ,     __lowercase	:PILImageResampling = PILImageResampling.BILINEAR    ,     __lowercase	:bool = True    ,     __lowercase	:Union[int, float] = 1 / 255    ,     __lowercase	:bool = True    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     **__lowercase	:Tuple    ,     ):
											super().__init__(**__lowercase      )
											__lowerCamelCase   :   int       =size if size is not None else {'''shortest_edge''': 384}
											__lowerCamelCase   :   List[str]       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )

											__lowerCamelCase   :   Any       =do_resize
											__lowerCamelCase   :   List[str]       =size
											# Default value set here for backwards compatibility where the value in config is None
											__lowerCamelCase   :   Tuple       =crop_pct if crop_pct is not None else 224 / 256
											__lowerCamelCase   :   Any       =resample
											__lowerCamelCase   :   List[str]       =do_rescale
											__lowerCamelCase   :   Dict       =rescale_factor
											__lowerCamelCase   :   Any       =do_normalize
											__lowerCamelCase   :   int       =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowerCamelCase   :   List[Any]       =image_std if image_std is not None else IMAGENET_STANDARD_STD




				def        __lowercase	(  self	:Any    ,     __lowercase	:np.ndarray    ,     __lowercase	:Dict[str, int]    ,     __lowercase	:float    ,     __lowercase	:PILImageResampling = PILImageResampling.BICUBIC    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:int    ,     ):
											__lowerCamelCase   :   int       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )
											if "shortest_edge" not in size:
																		raise ValueError(f'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'      )
											__lowerCamelCase   :   Optional[int]       =size['''shortest_edge''']

											if shortest_edge < 384:
																		# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																		__lowerCamelCase   :   int       =int(shortest_edge / crop_pct      )
																		__lowerCamelCase   :   str       =get_resize_output_image_size(__lowercase    ,     size=__lowercase    ,     default_to_square=__lowercase      )
																		__lowerCamelCase   :   str       =resize(image=__lowercase    ,     size=__lowercase    ,     resample=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )
																		# then crop to (shortest_edge, shortest_edge)
																		return center_crop(image=__lowercase    ,     size=(shortest_edge, shortest_edge)    ,     data_format=__lowercase    ,     **__lowercase      )
											else:
																		# warping (no cropping) when evaluated at 384 or larger
																		return resize(
																		    __lowercase    ,     size=(shortest_edge, shortest_edge)    ,     resample=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




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




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




				def        __lowercase	(  self	:int    ,     __lowercase	:ImageInput    ,     __lowercase	:bool = None    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:float = None    ,     __lowercase	:PILImageResampling = None    ,     __lowercase	:bool = None    ,     __lowercase	:float = None    ,     __lowercase	:bool = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[str, TensorType]] = None    ,     __lowercase	:ChannelDimension = ChannelDimension.FIRST    ,     **__lowercase	:Optional[Any]    ,     ):
											__lowerCamelCase   :   Optional[Any]       =do_resize if do_resize is not None else self.do_resize
											__lowerCamelCase   :   int       =crop_pct if crop_pct is not None else self.crop_pct
											__lowerCamelCase   :   List[Any]       =resample if resample is not None else self.resample
											__lowerCamelCase   :   List[Any]       =do_rescale if do_rescale is not None else self.do_rescale
											__lowerCamelCase   :   List[Any]       =rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowerCamelCase   :   List[Any]       =do_normalize if do_normalize is not None else self.do_normalize
											__lowerCamelCase   :   Optional[Any]       =image_mean if image_mean is not None else self.image_mean
											__lowerCamelCase   :   str       =image_std if image_std is not None else self.image_std

											__lowerCamelCase   :   str       =size if size is not None else self.size
											__lowerCamelCase   :   Optional[int]       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )

											__lowerCamelCase   :   Optional[int]       =make_list_of_images(__lowercase      )

											if not valid_images(__lowercase      ):
																		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 or resample is None:
																		raise ValueError('''Size and resample must be specified if do_resize is True.'''      )

											if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
																		raise ValueError('''crop_pct must be specified if size < 384.'''      )

											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.'''      )

											# All transformations expect numpy arrays.
											__lowerCamelCase   :   List[Any]       =[to_numpy_array(__lowercase      ) for image in images]

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

											if do_rescale:
																		__lowerCamelCase   :   int       =[self.rescale(image=__lowercase    ,     scale=__lowercase      ) for image in images]

											if do_normalize:
																		__lowerCamelCase   :   Union[str, Any]       =[self.normalize(image=__lowercase    ,     mean=__lowercase    ,     std=__lowercase      ) for image in images]

											__lowerCamelCase   :   Union[str, Any]       =[to_channel_dimension_format(__lowercase    ,     __lowercase      ) for image in images]

											__lowerCamelCase   :   Tuple       ={'''pixel_values''': images}
											return BatchFeature(data=__lowercase    ,     tensor_type=__lowercase      )




