'''simple docstring'''




import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
						from tokenizers import Tokenizer
						from tokenizers.models import WordPiece





class        __magic_name__		(       unittest.TestCase):







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]							,     lowercase_							:    Optional[int]       ):
										lowercase_   :     Any									=			list(tokenizer_class.max_model_input_sizes.keys()       )
										for model_name in sa_models[:1]:
																	lowercase_   :     Optional[int]									=			tokenizer_class.from_pretrained(lowercase_       )
																	self.assertIsNotNone(lowercase_       )
																	self.assertIsInstance(lowercase_							,     lowercase_       )
																	self.assertIsInstance(lowercase_							,     lowercase_       )

																	for special_tok in tokenizer.all_special_tokens:
																								self.assertIsInstance(lowercase_							,     lowercase_       )
																								lowercase_   :     Optional[Any]									=			tokenizer.convert_tokens_to_ids(lowercase_       )
																								self.assertIsInstance(lowercase_							,     lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]							,     lowercase_							:    BatchEncoding							,     lowercase_							:    Optional[Callable] = None       ):
										lowercase_   :     Optional[Any]									=			pickle.dumps(lowercase_       )
										self.assertIsNotNone(lowercase_       )

										lowercase_   :     Tuple									=			pickle.loads(lowercase_       )

										# Ensure is_fast is correctly restored
										self.assertEqual(be_restored.is_fast							,     be_original.is_fast       )

										# Ensure encodings are potentially correctly restored
										if be_original.is_fast:
																	self.assertIsNotNone(be_restored.encodings       )
										else:
																	self.assertIsNone(be_restored.encodings       )

										# Ensure the keys are the same
										for original_v, restored_v in zip(be_original.values()							,     be_restored.values()       ):
																	if equal_op:
																								self.assertTrue(equal_op(lowercase_							,     lowercase_       )       )
																	else:
																								self.assertEqual(lowercase_							,     lowercase_       )







			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]       ):
										self.check_tokenizer_from_pretrained(lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										self.assertEqual(TensorType("""tf"""       )							,     TensorType.TENSORFLOW       )
										self.assertEqual(TensorType("""pt"""       )							,     TensorType.PYTORCH       )
										self.assertEqual(TensorType("""np"""       )							,     TensorType.NUMPY       )







			@require_tokenizers
			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]       ):
										import numpy as np

										lowercase_   :     Optional[int]									=			BertTokenizer.from_pretrained("""bert-base-cased"""       )
										lowercase_   :     List[Any]									=			BertTokenizerFast.from_pretrained("""bert-base-cased"""       )

										# Python no tensor
										with self.subTest("""BatchEncoding (Python, return_tensors=None)"""       ):
																	self.assert_dump_and_restore(tokenizer_p("""Small example to encode"""       )       )

										with self.subTest("""BatchEncoding (Python, return_tensors=NUMPY)"""       ):
																	self.assert_dump_and_restore(
																	    tokenizer_p("""Small example to encode"""							,     return_tensors=TensorType.NUMPY       )							,     np.array_equal       )

										with self.subTest("""BatchEncoding (Rust, return_tensors=None)"""       ):
																	self.assert_dump_and_restore(tokenizer_r("""Small example to encode"""       )       )

										with self.subTest("""BatchEncoding (Rust, return_tensors=NUMPY)"""       ):
																	self.assert_dump_and_restore(
																	    tokenizer_r("""Small example to encode"""							,     return_tensors=TensorType.NUMPY       )							,     np.array_equal       )







			@require_tf
			@require_tokenizers
			def       SCREAMING_SNAKE_CASE_	(      self							:    str       ):
										import tensorflow as tf

										def tf_array_equals(lowercase_							:    Union[str, Any]							,     lowercase_							:    int       ):
																	return tf.reduce_all(tf.equal(lowercase_							,     lowercase_       )       )

										lowercase_   :     Any									=			BertTokenizer.from_pretrained("""bert-base-cased"""       )
										lowercase_   :     Dict									=			BertTokenizerFast.from_pretrained("""bert-base-cased"""       )

										with self.subTest("""BatchEncoding (Python, return_tensors=TENSORFLOW)"""       ):
																	self.assert_dump_and_restore(
																	    tokenizer_p("""Small example to encode"""							,     return_tensors=TensorType.TENSORFLOW       )							,     lowercase_       )

										with self.subTest("""BatchEncoding (Rust, return_tensors=TENSORFLOW)"""       ):
																	self.assert_dump_and_restore(
																	    tokenizer_r("""Small example to encode"""							,     return_tensors=TensorType.TENSORFLOW       )							,     lowercase_       )







			@require_torch
			@require_tokenizers
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										import torch

										lowercase_   :     str									=			BertTokenizer.from_pretrained("""bert-base-cased"""       )
										lowercase_   :     Optional[int]									=			BertTokenizerFast.from_pretrained("""bert-base-cased"""       )

										with self.subTest("""BatchEncoding (Python, return_tensors=PYTORCH)"""       ):
																	self.assert_dump_and_restore(
																	    tokenizer_p("""Small example to encode"""							,     return_tensors=TensorType.PYTORCH       )							,     torch.equal       )

										with self.subTest("""BatchEncoding (Rust, return_tensors=PYTORCH)"""       ):
																	self.assert_dump_and_restore(
																	    tokenizer_r("""Small example to encode"""							,     return_tensors=TensorType.PYTORCH       )							,     torch.equal       )







			@require_tokenizers
			def       SCREAMING_SNAKE_CASE_	(      self							:    int       ):
										lowercase_   :     Optional[int]									=			BertTokenizer.from_pretrained("""bert-base-cased"""       )
										lowercase_   :     str									=			BertTokenizerFast.from_pretrained("""bert-base-cased"""       )

										with self.subTest("""Python Tokenizer"""       ):
																	self.assertFalse(tokenizer_p("""Small example to_encode"""       ).is_fast       )

										with self.subTest("""Rust Tokenizer"""       ):
																	self.assertTrue(tokenizer_r("""Small example to_encode"""       ).is_fast       )







			@require_tokenizers
			def       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										lowercase_   :     str									=			BertTokenizerFast.from_pretrained("""bert-base-cased"""       )
										lowercase_   :     str									=			tokenizer_r(["""Test""", """\xad""", """test"""]							,     is_split_into_words=lowercase_       )

										self.assertEqual(encoded.word_to_tokens(0       )							,     TokenSpan(start=1							,     end=2       )       )
										self.assertEqual(encoded.word_to_tokens(1       )							,     lowercase_       )
										self.assertEqual(encoded.word_to_tokens(2       )							,     TokenSpan(start=2							,     end=3       )       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										lowercase_   :     Union[str, Any]									=			BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}       )
										lowercase_   :     List[Any]									=			batch.convert_to_tensors(tensor_type="""np"""       )
										self.assertEqual(tensor_batch["""inputs"""].shape							,     (2, 3)       )
										self.assertEqual(tensor_batch["""labels"""].shape							,     (2,)       )
										# test converting the converted
										with CaptureStderr() as cs:
																	lowercase_   :     str									=			batch.convert_to_tensors(tensor_type="""np"""       )
										self.assertFalse(len(cs.err       )							,     msg=f'''should have no warning, but got {cs.err}'''       )

										lowercase_   :     Union[str, Any]									=			BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}       )
										lowercase_   :     Tuple									=			batch.convert_to_tensors(tensor_type="""np"""							,     prepend_batch_axis=lowercase_       )
										self.assertEqual(tensor_batch["""inputs"""].shape							,     (1, 3)       )
										self.assertEqual(tensor_batch["""labels"""].shape							,     (1,)       )







			@require_torch
			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										lowercase_   :     List[str]									=			BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}       )
										lowercase_   :     Optional[Any]									=			batch.convert_to_tensors(tensor_type="""pt"""       )
										self.assertEqual(tensor_batch["""inputs"""].shape							,     (2, 3)       )
										self.assertEqual(tensor_batch["""labels"""].shape							,     (2,)       )
										# test converting the converted
										with CaptureStderr() as cs:
																	lowercase_   :     str									=			batch.convert_to_tensors(tensor_type="""pt"""       )
										self.assertFalse(len(cs.err       )							,     msg=f'''should have no warning, but got {cs.err}'''       )

										lowercase_   :     List[Any]									=			BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}       )
										lowercase_   :     Union[str, Any]									=			batch.convert_to_tensors(tensor_type="""pt"""							,     prepend_batch_axis=lowercase_       )
										self.assertEqual(tensor_batch["""inputs"""].shape							,     (1, 3)       )
										self.assertEqual(tensor_batch["""labels"""].shape							,     (1,)       )







			@require_tf
			def       SCREAMING_SNAKE_CASE_	(      self							:    str       ):
										lowercase_   :     Optional[Any]									=			BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}       )
										lowercase_   :     str									=			batch.convert_to_tensors(tensor_type="""tf"""       )
										self.assertEqual(tensor_batch["""inputs"""].shape							,     (2, 3)       )
										self.assertEqual(tensor_batch["""labels"""].shape							,     (2,)       )
										# test converting the converted
										with CaptureStderr() as cs:
																	lowercase_   :     List[Any]									=			batch.convert_to_tensors(tensor_type="""tf"""       )
										self.assertFalse(len(cs.err       )							,     msg=f'''should have no warning, but got {cs.err}'''       )

										lowercase_   :     List[str]									=			BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}       )
										lowercase_   :     List[Any]									=			batch.convert_to_tensors(tensor_type="""tf"""							,     prepend_batch_axis=lowercase_       )
										self.assertEqual(tensor_batch["""inputs"""].shape							,     (1, 3)       )
										self.assertEqual(tensor_batch["""labels"""].shape							,     (1,)       )







			@require_flax
			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]       ):
										lowercase_   :     Any									=			BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}       )
										lowercase_   :     Optional[Any]									=			batch.convert_to_tensors(tensor_type="""jax"""       )
										self.assertEqual(tensor_batch["""inputs"""].shape							,     (2, 3)       )
										self.assertEqual(tensor_batch["""labels"""].shape							,     (2,)       )
										# test converting the converted
										with CaptureStderr() as cs:
																	lowercase_   :     List[str]									=			batch.convert_to_tensors(tensor_type="""jax"""       )
										self.assertFalse(len(cs.err       )							,     msg=f'''should have no warning, but got {cs.err}'''       )

										lowercase_   :     Dict									=			BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}       )
										lowercase_   :     str									=			batch.convert_to_tensors(tensor_type="""jax"""							,     prepend_batch_axis=lowercase_       )
										self.assertEqual(tensor_batch["""inputs"""].shape							,     (1, 3)       )
										self.assertEqual(tensor_batch["""labels"""].shape							,     (1,)       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]       ):
										lowercase_   :     Optional[Any]									=			[{"""input_ids""": np.array([0, 1, 2]       )}, {"""input_ids""": np.array([0, 1, 2, 3]       )}]
										lowercase_   :     Tuple									=			BertTokenizer.from_pretrained("""bert-base-cased"""       )

										lowercase_   :     Tuple									=			tokenizer.pad(lowercase_							,     padding=lowercase_       )
										self.assertTrue(isinstance(batch["""input_ids"""]							,     np.ndarray       )       )
										self.assertEqual(batch["""input_ids"""].tolist()							,     [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]       )
										lowercase_   :     Optional[Any]									=			tokenizer.pad(lowercase_							,     padding=lowercase_							,     return_tensors="""np"""       )
										self.assertTrue(isinstance(batch["""input_ids"""]							,     np.ndarray       )       )
										self.assertEqual(batch["""input_ids"""].tolist()							,     [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]       )







			@require_torch
			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]       ):
										import torch

										lowercase_   :     Tuple									=			[{"""input_ids""": torch.tensor([0, 1, 2]       )}, {"""input_ids""": torch.tensor([0, 1, 2, 3]       )}]
										lowercase_   :     List[Any]									=			BertTokenizer.from_pretrained("""bert-base-cased"""       )

										lowercase_   :     Tuple									=			tokenizer.pad(lowercase_							,     padding=lowercase_       )
										self.assertTrue(isinstance(batch["""input_ids"""]							,     torch.Tensor       )       )
										self.assertEqual(batch["""input_ids"""].tolist()							,     [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]       )
										lowercase_   :     Optional[Any]									=			tokenizer.pad(lowercase_							,     padding=lowercase_							,     return_tensors="""pt"""       )
										self.assertTrue(isinstance(batch["""input_ids"""]							,     torch.Tensor       )       )
										self.assertEqual(batch["""input_ids"""].tolist()							,     [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]       )







			@require_tf
			def       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										import tensorflow as tf

										lowercase_   :     str									=			[{"""input_ids""": tf.constant([0, 1, 2]       )}, {"""input_ids""": tf.constant([0, 1, 2, 3]       )}]
										lowercase_   :     List[Any]									=			BertTokenizer.from_pretrained("""bert-base-cased"""       )

										lowercase_   :     int									=			tokenizer.pad(lowercase_							,     padding=lowercase_       )
										self.assertTrue(isinstance(batch["""input_ids"""]							,     tf.Tensor       )       )
										self.assertEqual(batch["""input_ids"""].numpy().tolist()							,     [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]       )
										lowercase_   :     Dict									=			tokenizer.pad(lowercase_							,     padding=lowercase_							,     return_tensors="""tf"""       )
										self.assertTrue(isinstance(batch["""input_ids"""]							,     tf.Tensor       )       )
										self.assertEqual(batch["""input_ids"""].numpy().tolist()							,     [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]       )







			@require_tokenizers
			def       SCREAMING_SNAKE_CASE_	(      self							:    int       ):
										lowercase_   :     Union[str, Any]									=			Tokenizer(WordPiece(unk_token="""[UNK]"""       )       )
										PreTrainedTokenizerFast(tokenizer_object=lowercase_       )







			@require_tokenizers
			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]       ):
										lowercase_   :     str									=			Tokenizer(WordPiece(unk_token="""[UNK]"""       )       )
										with tempfile.TemporaryDirectory() as tmpdirname:
																	bert_tokenizer.save(os.path.join(lowercase_							,     """tokenizer.json"""       )       )
																	PreTrainedTokenizerFast(tokenizer_file=os.path.join(lowercase_							,     """tokenizer.json"""       )       )






'''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


_lowercase		:					Optional[int]													=		logging.get_logger(__name__)


if is_vision_available():
						import PIL





class        __magic_name__		(       _UpperCAmelCase):
			UpperCamelCase__							=			['''pixel_values''']







			def __init__(      self							:    Any							,     lowercase_							:    bool = True							,     lowercase_							:    Dict[str, int] = None							,     lowercase_							:    PILImageResampling = PILImageResampling.BICUBIC							,     lowercase_							:    bool = True							,     lowercase_							:    Dict[str, int] = None							,     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_							:    bool = True							,     **lowercase_							:    Optional[Any]							,     ):
										super().__init__(**lowercase_       )
										lowercase_   :     List[Any]									=			size if size is not None else {"""shortest_edge""": 224}
										lowercase_   :     int									=			get_size_dict(lowercase_							,     default_to_square=lowercase_       )
										lowercase_   :     List[Any]									=			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
										lowercase_   :     int									=			get_size_dict(lowercase_       )

										lowercase_   :     Tuple									=			do_resize
										lowercase_   :     int									=			size
										lowercase_   :     List[Any]									=			resample
										lowercase_   :     str									=			do_center_crop
										lowercase_   :     Optional[int]									=			crop_size
										lowercase_   :     str									=			do_rescale
										lowercase_   :     List[Any]									=			rescale_factor
										lowercase_   :     Dict									=			do_normalize
										lowercase_   :     List[str]									=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										lowercase_   :     Any									=			image_std if image_std is not None else OPENAI_CLIP_STD
										lowercase_   :     List[str]									=			do_convert_rgb







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







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]							,     lowercase_							:    np.ndarray							,     lowercase_							:    Dict[str, int]							,     lowercase_							:    Optional[Union[str, ChannelDimension]] = None							,     **lowercase_							:    Any							,     ):
										lowercase_   :     int									=			get_size_dict(lowercase_       )
										return center_crop(lowercase_							,     size=(size["""height"""], size["""width"""])							,     data_format=lowercase_							,     **lowercase_       )







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







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

										lowercase_   :     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:
																	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:
																	lowercase_   :     Dict									=			[convert_to_rgb(lowercase_       ) for image in images]

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

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

										if do_center_crop:
																	lowercase_   :     List[str]									=			[self.center_crop(image=lowercase_							,     size=lowercase_       ) for image in images]

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

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

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

										lowercase_   :     Any									=			{"""pixel_values""": images}
										return BatchFeature(data=lowercase_							,     tensor_type=lowercase_       )





