






import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


a_		:						List[Any]             =    get_tests_dir('fixtures/test_sentencepiece.model')

if is_torch_available():
				from transformers.models.mbart.modeling_mbart import shift_tokens_right

a_		:						str             =    25_00_04
a_		:						Optional[Any]             =    25_00_20

@require_sentencepiece
@require_tokenizers
class 		_snake_case  (  A__   ,    unittest.TestCase					):
			_lowercase		:    List[str]			     =							MBartaaTokenizer
			_lowercase		:    Optional[Any]			     =							MBartaaTokenizerFast
			_lowercase		:    Optional[int]			     =							True
			_lowercase		:    Dict			     =							True






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						super().setUp()

						# We have a SentencePiece fixture for testing
						SCREAMING_SNAKE_CASE						= MBartaaTokenizer(a		,					src_lang='en_XX'		,					tgt_lang='ro_RO'		,					keep_accents=a)
						tokenizer.save_pretrained(self.tmpdirname)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= '<s>'
						SCREAMING_SNAKE_CASE						= 0

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(a)		,					a)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(a)		,					a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= list(self.get_tokenizer().get_vocab().keys())

						self.assertEqual(vocab_keys[0]		,					'<s>')
						self.assertEqual(vocab_keys[1]		,					'<pad>')
						self.assertEqual(vocab_keys[-1]		,					'<mask>')
						self.assertEqual(len(a)		,					1054)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						self.assertEqual(self.get_tokenizer().vocab_size		,					1054)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= MBartaaTokenizer(a		,					src_lang='en_XX'		,					tgt_lang='ro_RO'		,					keep_accents=a)

						SCREAMING_SNAKE_CASE						= tokenizer.tokenize('This is a test')
						self.assertListEqual(a		,					['▁This', '▁is', '▁a', '▁t', 'est'])

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(a)		,					[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]		,					)

						SCREAMING_SNAKE_CASE						= tokenizer.tokenize('I was born in 92000, and this is falsé.')
						self.assertListEqual(
						    a		,					[SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '9', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', 'é', '.']		,					)
						SCREAMING_SNAKE_CASE						= tokenizer.convert_tokens_to_ids(a)
						self.assertListEqual(
						    a		,					[
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
						    ]		,					)

						SCREAMING_SNAKE_CASE						= tokenizer.convert_ids_to_tokens(a)
						self.assertListEqual(
						    a		,					[SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '<unk>', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', '<unk>', '.']		,					)






			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						# fmt: off
						SCREAMING_SNAKE_CASE						= {'input_ids': [[25_0004, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [25_0004, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_0004, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=a		,					model_name='facebook/mbart-large-50'		,					revision='d3913889c59cd5c9e456b269c376325eabad57e2'		,					)







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						if not self.test_slow_tokenizer:
									# as we don't have a slow version, we can't compare the outputs between slow and fast versions
									return

						SCREAMING_SNAKE_CASE						= (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart50', {})
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
												SCREAMING_SNAKE_CASE						= self.rust_tokenizer_class.from_pretrained(a		,					**a)
												SCREAMING_SNAKE_CASE						= self.tokenizer_class.from_pretrained(a		,					**a)

												SCREAMING_SNAKE_CASE						= tempfile.mkdtemp()

												SCREAMING_SNAKE_CASE						= tokenizer_r.save_pretrained(a)
												SCREAMING_SNAKE_CASE						= tokenizer_p.save_pretrained(a)

												# Checks it save with the same files + the tokenizer.json file for the fast one
												self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files))
												SCREAMING_SNAKE_CASE						= tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f)
												self.assertSequenceEqual(a		,					a)

												# Checks everything loads correctly in the same way
												SCREAMING_SNAKE_CASE						= tokenizer_r.from_pretrained(a)
												SCREAMING_SNAKE_CASE						= tokenizer_p.from_pretrained(a)

												# Check special tokens are set accordingly on Rust and Python
												for key in tokenizer_pp.special_tokens_map:
															self.assertTrue(hasattr(a		,					a))
															# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
															# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

												shutil.rmtree(a)

												# Save tokenizer rust, legacy_format=True
												SCREAMING_SNAKE_CASE						= tempfile.mkdtemp()

												SCREAMING_SNAKE_CASE						= tokenizer_r.save_pretrained(a		,					legacy_format=a)
												SCREAMING_SNAKE_CASE						= tokenizer_p.save_pretrained(a)

												# Checks it save with the same files
												self.assertSequenceEqual(a		,					a)

												# Checks everything loads correctly in the same way
												SCREAMING_SNAKE_CASE						= tokenizer_r.from_pretrained(a)
												SCREAMING_SNAKE_CASE						= tokenizer_p.from_pretrained(a)

												# Check special tokens are set accordingly on Rust and Python
												for key in tokenizer_pp.special_tokens_map:
															self.assertTrue(hasattr(a		,					a))

												shutil.rmtree(a)

												# Save tokenizer rust, legacy_format=False
												SCREAMING_SNAKE_CASE						= tempfile.mkdtemp()

												SCREAMING_SNAKE_CASE						= tokenizer_r.save_pretrained(a		,					legacy_format=a)
												SCREAMING_SNAKE_CASE						= tokenizer_p.save_pretrained(a)

												# Checks it saved the tokenizer.json file
												self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files))

												# Checks everything loads correctly in the same way
												SCREAMING_SNAKE_CASE						= tokenizer_r.from_pretrained(a)
												SCREAMING_SNAKE_CASE						= tokenizer_p.from_pretrained(a)

												# Check special tokens are set accordingly on Rust and Python
												for key in tokenizer_pp.special_tokens_map:
															self.assertTrue(hasattr(a		,					a))

												shutil.rmtree(a)



@require_torch
@require_sentencepiece
@require_tokenizers
class 		_snake_case  (  unittest.TestCase					):
			_lowercase		:    List[Any]			     =							'''facebook/mbart-large-50-one-to-many-mmt'''
			_lowercase		:    Any			     =							[
			    ''' UN Chief Says There Is No Military Solution in Syria''',
			    ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
			]
			_lowercase		:    Optional[int]			     =							[
			    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
			    '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
			    ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
			    ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
			]
			_lowercase		:    Any			     =							[EN_CODE, 82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2]






			@classmethod
			def    SCREAMING_SNAKE_CASE__    (       cls)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= MBartaaTokenizer.from_pretrained(
						    cls.checkpoint_name		,					src_lang='en_XX'		,					tgt_lang='ro_RO')
						SCREAMING_SNAKE_CASE						= 1
						return cls






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']		,					25_0001)
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']		,					25_0004)
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']		,					25_0020)
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']		,					25_0038)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
						self.assertListEqual(self.expected_src_tokens		,					a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						self.assertIn(a		,					self.tokenizer.all_special_ids)
						SCREAMING_SNAKE_CASE						= [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
						SCREAMING_SNAKE_CASE						= self.tokenizer.decode(a		,					skip_special_tokens=a)
						SCREAMING_SNAKE_CASE						= self.tokenizer.decode(generated_ids[1:]		,					skip_special_tokens=a)
						self.assertEqual(a		,					a)
						self.assertNotIn(self.tokenizer.eos_token		,					a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= ['this is gunna be a long sentence ' * 20]
						assert isinstance(src_text[0]		,					a)
						SCREAMING_SNAKE_CASE						= 10
						SCREAMING_SNAKE_CASE						= self.tokenizer(a		,					max_length=a		,					truncation=a).input_ids[0]
						self.assertEqual(ids[0]		,					a)
						self.assertEqual(ids[-1]		,					2)
						self.assertEqual(len(a)		,					a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'])		,					[25_0053, 25_0001])






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= tempfile.mkdtemp()
						SCREAMING_SNAKE_CASE						= self.tokenizer.fairseq_tokens_to_ids
						self.tokenizer.save_pretrained(a)
						SCREAMING_SNAKE_CASE						= MBartaaTokenizer.from_pretrained(a)
						self.assertDictEqual(new_tok.fairseq_tokens_to_ids		,					a)






			@require_torch
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= self.tokenizer(self.src_text		,					text_target=self.tgt_text		,					padding=a		,					return_tensors='pt')
						SCREAMING_SNAKE_CASE						= shift_tokens_right(batch['labels']		,					self.tokenizer.pad_token_id)

						# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
						assert batch.input_ids[1][0] == EN_CODE
						assert batch.input_ids[1][-1] == 2
						assert batch.labels[1][0] == RO_CODE
						assert batch.labels[1][-1] == 2
						assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]






			@require_torch
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						SCREAMING_SNAKE_CASE						= self.tokenizer(
						    self.src_text		,					text_target=self.tgt_text		,					padding=a		,					truncation=a		,					max_length=len(self.expected_src_tokens)		,					return_tensors='pt'		,					)
						SCREAMING_SNAKE_CASE						= shift_tokens_right(batch['labels']		,					self.tokenizer.pad_token_id)

						self.assertIsInstance(a		,					a)

						self.assertEqual((2, 14)		,					batch.input_ids.shape)
						self.assertEqual((2, 14)		,					batch.attention_mask.shape)
						SCREAMING_SNAKE_CASE						= batch.input_ids.tolist()[0]
						self.assertListEqual(self.expected_src_tokens		,					a)
						self.assertEqual(2		,					batch.decoder_input_ids[0, 0])  # decoder_start_token_id
						# Test that special tokens are reset
						self.assertEqual(self.tokenizer.prefix_tokens		,					[EN_CODE])
						self.assertEqual(self.tokenizer.suffix_tokens		,					[self.tokenizer.eos_token_id])






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self.tokenizer(self.src_text		,					padding=a		,					truncation=a		,					max_length=3		,					return_tensors='pt')
						SCREAMING_SNAKE_CASE						= self.tokenizer(
						    text_target=self.tgt_text		,					padding=a		,					truncation=a		,					max_length=10		,					return_tensors='pt')
						SCREAMING_SNAKE_CASE						= targets['input_ids']
						SCREAMING_SNAKE_CASE						= shift_tokens_right(a		,					self.tokenizer.pad_token_id)

						self.assertEqual(batch.input_ids.shape[1]		,					3)
						self.assertEqual(batch.decoder_input_ids.shape[1]		,					10)







			@require_torch
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= self.tokenizer._build_translation_inputs(
						    'A test'		,					return_tensors='pt'		,					src_lang='en_XX'		,					tgt_lang='ar_AR')

						self.assertEqual(
						    nested_simplify(a)		,					{
						        # en_XX, A, test, EOS
						        'input_ids': [[25_0004, 62, 3034, 2]],
						        'attention_mask': [[1, 1, 1, 1]],
						        # ar_AR
						        'forced_bos_token_id': 25_0001,
						    }		,					)












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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


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






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= b.T
			SCREAMING_SNAKE_CASE						= np.sum(np.square(_UpperCAmelCase)							,       axis=1)
			SCREAMING_SNAKE_CASE						= np.sum(np.square(_UpperCAmelCase)							,       axis=0)
			SCREAMING_SNAKE_CASE						= np.matmul(_UpperCAmelCase							,       _UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= aa[:, None] - 2 * ab + ba[None, :]
			return d






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= x.reshape(-1							,       3)
			SCREAMING_SNAKE_CASE						= squared_euclidean_distance(_UpperCAmelCase							,       _UpperCAmelCase)
			return np.argmin(_UpperCAmelCase							,       axis=1)







class 		_snake_case  (  A__					):
			_lowercase		:    str			     =							['''pixel_values''']






			def __init__(       self		,					a = None		,					a = True		,					a = None		,					a = PILImageResampling.BILINEAR		,					a = True		,					a = True		,					**a		,					)      ->       None:
						super().__init__(**a)
						SCREAMING_SNAKE_CASE						= size if size is not None else {'height': 256, 'width': 256}
						SCREAMING_SNAKE_CASE						= get_size_dict(a)
						SCREAMING_SNAKE_CASE						= np.array(a) if clusters is not None else None
						SCREAMING_SNAKE_CASE						= do_resize
						SCREAMING_SNAKE_CASE						= size
						SCREAMING_SNAKE_CASE						= resample
						SCREAMING_SNAKE_CASE						= do_normalize
						SCREAMING_SNAKE_CASE						= do_color_quantize






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = PILImageResampling.BILINEAR		,					a = None		,					**a		,					)      ->       np.ndarray:
						SCREAMING_SNAKE_CASE						= get_size_dict(a)
						if "height" not in size or "width" not in size:
									raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}''')
						return resize(
						    a		,					size=(size['height'], size['width'])		,					resample=a		,					data_format=a		,					**a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None		,					)      ->       np.ndarray:
						SCREAMING_SNAKE_CASE						= rescale(image=a		,					scale=1 / 1_27.5		,					data_format=a)
						SCREAMING_SNAKE_CASE						= image - 1
						return image







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None		,					a = None		,					a = None		,					a = None		,					a = None		,					a = None		,					a = None		,					a = ChannelDimension.FIRST		,					**a		,					)      ->       PIL.Image.Image:
						SCREAMING_SNAKE_CASE						= do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE						= size if size is not None else self.size
						SCREAMING_SNAKE_CASE						= get_size_dict(a)
						SCREAMING_SNAKE_CASE						= resample if resample is not None else self.resample
						SCREAMING_SNAKE_CASE						= do_normalize if do_normalize is not None else self.do_normalize
						SCREAMING_SNAKE_CASE						= do_color_quantize if do_color_quantize is not None else self.do_color_quantize
						SCREAMING_SNAKE_CASE						= clusters if clusters is not None else self.clusters
						SCREAMING_SNAKE_CASE						= np.array(a)

						SCREAMING_SNAKE_CASE						= make_list_of_images(a)

						if not valid_images(a):
									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_color_quantize and clusters is None:
									raise ValueError('Clusters must be specified if do_color_quantize is True.')

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

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

						if do_normalize:
									SCREAMING_SNAKE_CASE						= [self.normalize(image=a) for image in images]

						if do_color_quantize:
									SCREAMING_SNAKE_CASE						= [to_channel_dimension_format(a		,					ChannelDimension.LAST) for image in images]
									# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
									SCREAMING_SNAKE_CASE						= np.array(a)
									SCREAMING_SNAKE_CASE						= color_quantize(a		,					a).reshape(images.shape[:-1])

									# flatten to (batch_size, height*width)
									SCREAMING_SNAKE_CASE						= images.shape[0]
									SCREAMING_SNAKE_CASE						= images.reshape(a		,					-1)

									# We need to convert back to a list of images to keep consistent behaviour across processors.
									SCREAMING_SNAKE_CASE						= list(a)
						else:
									SCREAMING_SNAKE_CASE						= [to_channel_dimension_format(a		,					a) for image in images]

						SCREAMING_SNAKE_CASE						= {'input_ids': images}
						return BatchFeature(data=a		,					tensor_type=a)




