'''simple docstring'''




import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


__lowercase        =    get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
__lowercase        =    get_tests_dir('''fixtures/vocab.json''')
__lowercase        =    get_tests_dir('''fixtures''')






class 							a__(   unittest.TestCase      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Union[str, Any]						=			['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    0



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h""")
					self.assertIsInstance(__lowerCAmelCase			,       __lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					with tempfile.TemporaryDirectory() as tmpdirname:
							lowerCAmelCase     =    WavaVecaConfig()
							lowerCAmelCase     =    AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h""")

							# save in new folder
							model_config.save_pretrained(__lowerCAmelCase)
							processor.save_pretrained(__lowerCAmelCase)

							lowerCAmelCase     =    AutoProcessor.from_pretrained(__lowerCAmelCase)

					self.assertIsInstance(__lowerCAmelCase			,       __lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					with tempfile.TemporaryDirectory() as tmpdirname:
							# copy relevant files
							copyfile(__lowerCAmelCase			,       os.path.join(__lowerCAmelCase			,       __lowerCAmelCase))
							copyfile(__lowerCAmelCase			,       os.path.join(__lowerCAmelCase			,       """vocab.json"""))

							lowerCAmelCase     =    AutoProcessor.from_pretrained(__lowerCAmelCase)

					self.assertIsInstance(__lowerCAmelCase			,       __lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					with tempfile.TemporaryDirectory() as tmpdirname:
							lowerCAmelCase     =    WavaVecaFeatureExtractor()
							lowerCAmelCase     =    AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h""")

							lowerCAmelCase     =    WavaVecaProcessor(__lowerCAmelCase			,       __lowerCAmelCase)

							# save in new folder
							processor.save_pretrained(__lowerCAmelCase)

							# drop `processor_class` in tokenizer
							with open(os.path.join(__lowerCAmelCase			,       __lowerCAmelCase)			,       """r""") as f:
									lowerCAmelCase     =    json.load(__lowerCAmelCase)
									config_dict.pop("""processor_class""")

							with open(os.path.join(__lowerCAmelCase			,       __lowerCAmelCase)			,       """w""") as f:
									f.write(json.dumps(__lowerCAmelCase))

							lowerCAmelCase     =    AutoProcessor.from_pretrained(__lowerCAmelCase)

					self.assertIsInstance(__lowerCAmelCase			,       __lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					with tempfile.TemporaryDirectory() as tmpdirname:
							lowerCAmelCase     =    WavaVecaFeatureExtractor()
							lowerCAmelCase     =    AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h""")

							lowerCAmelCase     =    WavaVecaProcessor(__lowerCAmelCase			,       __lowerCAmelCase)

							# save in new folder
							processor.save_pretrained(__lowerCAmelCase)

							# drop `processor_class` in feature extractor
							with open(os.path.join(__lowerCAmelCase			,       __lowerCAmelCase)			,       """r""") as f:
									lowerCAmelCase     =    json.load(__lowerCAmelCase)
									config_dict.pop("""processor_class""")

							with open(os.path.join(__lowerCAmelCase			,       __lowerCAmelCase)			,       """w""") as f:
									f.write(json.dumps(__lowerCAmelCase))

							lowerCAmelCase     =    AutoProcessor.from_pretrained(__lowerCAmelCase)

					self.assertIsInstance(__lowerCAmelCase			,       __lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					with tempfile.TemporaryDirectory() as tmpdirname:
							lowerCAmelCase     =    WavaVecaConfig(processor_class="""Wav2Vec2Processor""")
							model_config.save_pretrained(__lowerCAmelCase)
							# copy relevant files
							copyfile(__lowerCAmelCase			,       os.path.join(__lowerCAmelCase			,       """vocab.json"""))
							# create emtpy sample processor
							with open(os.path.join(__lowerCAmelCase			,       __lowerCAmelCase)			,       """w""") as f:
									f.write("""{}""")

							lowerCAmelCase     =    AutoProcessor.from_pretrained(__lowerCAmelCase)

					self.assertIsInstance(__lowerCAmelCase			,       __lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					with self.assertRaises(__lowerCAmelCase):
							lowerCAmelCase     =    AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""")
					# If remote code is disabled, we can't load this config.
					with self.assertRaises(__lowerCAmelCase):
							lowerCAmelCase     =    AutoProcessor.from_pretrained(
							    """hf-internal-testing/test_dynamic_processor"""			,       trust_remote_code=__lowerCAmelCase)

					lowerCAmelCase     =    AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""			,       trust_remote_code=__lowerCAmelCase)
					self.assertTrue(processor.special_attribute_present)
					self.assertEqual(processor.__class__.__name__			,       """NewProcessor""")

					lowerCAmelCase     =    processor.feature_extractor
					self.assertTrue(feature_extractor.special_attribute_present)
					self.assertEqual(feature_extractor.__class__.__name__			,       """NewFeatureExtractor""")

					lowerCAmelCase     =    processor.tokenizer
					self.assertTrue(tokenizer.special_attribute_present)
					if is_tokenizers_available():
							self.assertEqual(tokenizer.__class__.__name__			,       """NewTokenizerFast""")

							# Test we can also load the slow version
							lowerCAmelCase     =    AutoProcessor.from_pretrained(
							    """hf-internal-testing/test_dynamic_processor"""			,       trust_remote_code=__lowerCAmelCase			,       use_fast=__lowerCAmelCase)
							lowerCAmelCase     =    new_processor.tokenizer
							self.assertTrue(new_tokenizer.special_attribute_present)
							self.assertEqual(new_tokenizer.__class__.__name__			,       """NewTokenizer""")
					else:
							self.assertEqual(tokenizer.__class__.__name__			,       """NewTokenizer""")



			def 				a_			(  self):




					"""simple docstring"""


					try:
							AutoConfig.register("""custom"""			,       __lowerCAmelCase)
							AutoFeatureExtractor.register(__lowerCAmelCase			,       __lowerCAmelCase)
							AutoTokenizer.register(__lowerCAmelCase			,       slow_tokenizer_class=__lowerCAmelCase)
							AutoProcessor.register(__lowerCAmelCase			,       __lowerCAmelCase)
							# Trying to register something existing in the Transformers library will raise an error
							with self.assertRaises(__lowerCAmelCase):
									AutoProcessor.register(__lowerCAmelCase			,       __lowerCAmelCase)

							# Now that the config is registered, it can be used as any other config with the auto-API
							lowerCAmelCase     =    CustomFeatureExtractor.from_pretrained(__lowerCAmelCase)

							with tempfile.TemporaryDirectory() as tmp_dir:
									lowerCAmelCase     =    os.path.join(__lowerCAmelCase			,       """vocab.txt""")
									with open(__lowerCAmelCase			,       """w"""			,       encoding="""utf-8""") as vocab_writer:
											vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]))
									lowerCAmelCase     =    CustomTokenizer(__lowerCAmelCase)

							lowerCAmelCase     =    CustomProcessor(__lowerCAmelCase			,       __lowerCAmelCase)

							with tempfile.TemporaryDirectory() as tmp_dir:
									processor.save_pretrained(__lowerCAmelCase)
									lowerCAmelCase     =    AutoProcessor.from_pretrained(__lowerCAmelCase)
									self.assertIsInstance(__lowerCAmelCase			,       __lowerCAmelCase)

					finally:
							if "custom" in CONFIG_MAPPING._extra_content:
									del CONFIG_MAPPING._extra_content["custom"]
							if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
									del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
							if CustomConfig in TOKENIZER_MAPPING._extra_content:
									del TOKENIZER_MAPPING._extra_content[CustomConfig]
							if CustomConfig in PROCESSOR_MAPPING._extra_content:
									del PROCESSOR_MAPPING._extra_content[CustomConfig]



			def 				a_			(  self):




					"""simple docstring"""


					class 							a__(   lowerCAmelCase__      ):


								'''simple docstring'''






								UpperCAmelCase_     :	str						=			False







					class 							a__(   lowerCAmelCase__      ):


								'''simple docstring'''






								UpperCAmelCase_     :	str						=			False







					class 							a__(   lowerCAmelCase__      ):


								'''simple docstring'''






								UpperCAmelCase_     :	Tuple						=			'''AutoFeatureExtractor'''
								UpperCAmelCase_     :	Union[str, Any]						=			'''AutoTokenizer'''
								UpperCAmelCase_     :	Union[str, Any]						=			False

					try:
							AutoConfig.register("""custom"""			,       __lowerCAmelCase)
							AutoFeatureExtractor.register(__lowerCAmelCase			,       __lowerCAmelCase)
							AutoTokenizer.register(__lowerCAmelCase			,       slow_tokenizer_class=__lowerCAmelCase)
							AutoProcessor.register(__lowerCAmelCase			,       __lowerCAmelCase)
							# If remote code is not set, the default is to use local classes.
							lowerCAmelCase     =    AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""")
							self.assertEqual(processor.__class__.__name__			,       """NewProcessor""")
							self.assertFalse(processor.special_attribute_present)
							self.assertFalse(processor.feature_extractor.special_attribute_present)
							self.assertFalse(processor.tokenizer.special_attribute_present)

							# If remote code is disabled, we load the local ones.
							lowerCAmelCase     =    AutoProcessor.from_pretrained(
							    """hf-internal-testing/test_dynamic_processor"""			,       trust_remote_code=__lowerCAmelCase)
							self.assertEqual(processor.__class__.__name__			,       """NewProcessor""")
							self.assertFalse(processor.special_attribute_present)
							self.assertFalse(processor.feature_extractor.special_attribute_present)
							self.assertFalse(processor.tokenizer.special_attribute_present)

							# If remote is enabled, we load from the Hub.
							lowerCAmelCase     =    AutoProcessor.from_pretrained(
							    """hf-internal-testing/test_dynamic_processor"""			,       trust_remote_code=__lowerCAmelCase)
							self.assertEqual(processor.__class__.__name__			,       """NewProcessor""")
							self.assertTrue(processor.special_attribute_present)
							self.assertTrue(processor.feature_extractor.special_attribute_present)
							self.assertTrue(processor.tokenizer.special_attribute_present)

					finally:
							if "custom" in CONFIG_MAPPING._extra_content:
									del CONFIG_MAPPING._extra_content["custom"]
							if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
									del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
							if CustomConfig in TOKENIZER_MAPPING._extra_content:
									del TOKENIZER_MAPPING._extra_content[CustomConfig]
							if CustomConfig in PROCESSOR_MAPPING._extra_content:
									del PROCESSOR_MAPPING._extra_content[CustomConfig]



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert""")
					self.assertEqual(processor.__class__.__name__			,       """BertTokenizerFast""")







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext""")
					self.assertEqual(processor.__class__.__name__			,       """ConvNextImageProcessor""")







@is_staging_test
class 							a__(   unittest.TestCase      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Dict						=			['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']



			@classmethod
			def 				a_			(  cls):




					"""simple docstring"""


					lowerCAmelCase     =    TOKEN
					HfFolder.save_token(__lowerCAmelCase)



			@classmethod
			def 				a_			(  cls):




					"""simple docstring"""


					try:
							delete_repo(token=cls._token			,       repo_id="""test-processor""")
					except HTTPError:
							pass

					try:
							delete_repo(token=cls._token			,       repo_id="""valid_org/test-processor-org""")
					except HTTPError:
							pass

					try:
							delete_repo(token=cls._token			,       repo_id="""test-dynamic-processor""")
					except HTTPError:
							pass



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    WavaVecaProcessor.from_pretrained(__lowerCAmelCase)
					with tempfile.TemporaryDirectory() as tmp_dir:
							processor.save_pretrained(
							    os.path.join(__lowerCAmelCase			,       """test-processor""")			,       push_to_hub=__lowerCAmelCase			,       use_auth_token=self._token)

							lowerCAmelCase     =    WavaVecaProcessor.from_pretrained(f"{USER}/test-processor")
							for k, v in processor.feature_extractor.__dict__.items():
									self.assertEqual(__lowerCAmelCase			,       getattr(new_processor.feature_extractor			,       __lowerCAmelCase))
							self.assertDictEqual(new_processor.tokenizer.get_vocab()			,       processor.tokenizer.get_vocab())



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    WavaVecaProcessor.from_pretrained(__lowerCAmelCase)

					with tempfile.TemporaryDirectory() as tmp_dir:
							processor.save_pretrained(
							    os.path.join(__lowerCAmelCase			,       """test-processor-org""")			,       push_to_hub=__lowerCAmelCase			,       use_auth_token=self._token			,       organization="""valid_org"""			,       )

							lowerCAmelCase     =    WavaVecaProcessor.from_pretrained("""valid_org/test-processor-org""")
							for k, v in processor.feature_extractor.__dict__.items():
									self.assertEqual(__lowerCAmelCase			,       getattr(new_processor.feature_extractor			,       __lowerCAmelCase))
							self.assertDictEqual(new_processor.tokenizer.get_vocab()			,       processor.tokenizer.get_vocab())







			def 				a_			(  self):




					"""simple docstring"""


					CustomFeatureExtractor.register_for_auto_class()
					CustomTokenizer.register_for_auto_class()
					CustomProcessor.register_for_auto_class()

					lowerCAmelCase     =    CustomFeatureExtractor.from_pretrained(__lowerCAmelCase)

					with tempfile.TemporaryDirectory() as tmp_dir:
							lowerCAmelCase     =    os.path.join(__lowerCAmelCase			,       """vocab.txt""")
							with open(__lowerCAmelCase			,       """w"""			,       encoding="""utf-8""") as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]))
							lowerCAmelCase     =    CustomTokenizer(__lowerCAmelCase)

					lowerCAmelCase     =    CustomProcessor(__lowerCAmelCase			,       __lowerCAmelCase)

					with tempfile.TemporaryDirectory() as tmp_dir:
							create_repo(f"{USER}/test-dynamic-processor"			,       token=self._token)
							lowerCAmelCase     =    Repository(__lowerCAmelCase			,       clone_from=f"{USER}/test-dynamic-processor"			,       token=self._token)
							processor.save_pretrained(__lowerCAmelCase)

							# This has added the proper auto_map field to the feature extractor config
							self.assertDictEqual(
							    processor.feature_extractor.auto_map			,       {
							        """AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor""",
							        """AutoProcessor""": """custom_processing.CustomProcessor""",
							    }			,       )

							# This has added the proper auto_map field to the tokenizer config
							with open(os.path.join(__lowerCAmelCase			,       """tokenizer_config.json""")) as f:
									lowerCAmelCase     =    json.load(__lowerCAmelCase)
							self.assertDictEqual(
							    tokenizer_config["""auto_map"""]			,       {
							        """AutoTokenizer""": ["""custom_tokenization.CustomTokenizer""", None],
							        """AutoProcessor""": """custom_processing.CustomProcessor""",
							    }			,       )

							# The code has been copied from fixtures
							self.assertTrue(os.path.isfile(os.path.join(__lowerCAmelCase			,       """custom_feature_extraction.py""")))
							self.assertTrue(os.path.isfile(os.path.join(__lowerCAmelCase			,       """custom_tokenization.py""")))
							self.assertTrue(os.path.isfile(os.path.join(__lowerCAmelCase			,       """custom_processing.py""")))

							repo.push_to_hub()

					lowerCAmelCase     =    AutoProcessor.from_pretrained(f"{USER}/test-dynamic-processor"			,       trust_remote_code=__lowerCAmelCase)
					# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
					self.assertEqual(new_processor.__class__.__name__			,       """CustomProcessor""")





'''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 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


__lowercase        =    logging.get_logger(__name__)






def 							snake_case__				(     _A:   Dict ,  _A:   Any					)       ->      Union[str, Any]:

		'''simple docstring'''


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






def 							snake_case__				(     _A:   Dict ,  _A:   Dict					)       ->      Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase     =    x.reshape(-1 ,  3					)
		lowerCAmelCase     =    squared_euclidean_distance(_A ,  _A					)
		return np.argmin(_A ,  axis=1					)






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






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



			def __init__(  self			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = PILImageResampling.BILINEAR			,       __lowerCAmelCase = True			,       __lowerCAmelCase = True			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)
					lowerCAmelCase     =    size if size is not None else {"""height""": 256, """width""": 256}
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					lowerCAmelCase     =    np.array(__lowerCAmelCase) if clusters is not None else None
					lowerCAmelCase     =    do_resize
					lowerCAmelCase     =    size
					lowerCAmelCase     =    resample
					lowerCAmelCase     =    do_normalize
					lowerCAmelCase     =    do_color_quantize



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = PILImageResampling.BILINEAR			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					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(
					    __lowerCAmelCase			,       size=(size["""height"""], size["""width"""])			,       resample=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    rescale(image=__lowerCAmelCase			,       scale=1 / 127.5			,       data_format=__lowerCAmelCase)
					lowerCAmelCase     =    image - 1
					return image







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = ChannelDimension.FIRST			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    do_resize if do_resize is not None else self.do_resize
					lowerCAmelCase     =    size if size is not None else self.size
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					lowerCAmelCase     =    resample if resample is not None else self.resample
					lowerCAmelCase     =    do_normalize if do_normalize is not None else self.do_normalize
					lowerCAmelCase     =    do_color_quantize if do_color_quantize is not None else self.do_color_quantize
					lowerCAmelCase     =    clusters if clusters is not None else self.clusters
					lowerCAmelCase     =    np.array(__lowerCAmelCase)

					lowerCAmelCase     =    make_list_of_images(__lowerCAmelCase)

					if not valid_images(__lowerCAmelCase):
							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.
					lowerCAmelCase     =    [to_numpy_array(__lowerCAmelCase) for image in images]

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

					if do_normalize:
							lowerCAmelCase     =    [self.normalize(image=__lowerCAmelCase) for image in images]

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

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

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

					lowerCAmelCase     =    {"""input_ids""": images}
					return BatchFeature(data=__lowerCAmelCase			,       tensor_type=__lowerCAmelCase)




