
import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
						    MODEL_MAPPING,
						    EfficientFormerForImageClassification,
						    EfficientFormerForImageClassificationWithTeacher,
						    EfficientFormerModel,
						)
						from transformers.models.efficientformer.modeling_efficientformer import (
						    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						)


if is_vision_available():
						from PIL import Image

						from transformers import EfficientFormerImageProcessor






class 					__lowercase		:


					def __init__(	self       ,       a__       ,       a__ = 1_3       ,       a__ = 6_4       ,       a__ = 2       ,       a__ = 3       ,       a__ = 3       ,       a__ = True       ,       a__ = True       ,       a__ = 1_2_8       ,       a__=[1_6, 3_2, 6_4, 1_2_8]       ,       a__ = 7       ,       a__ = 4       ,       a__ = 3_7       ,       a__ = "gelu"       ,       a__ = 0.1       ,       a__ = 0.1       ,       a__ = 1_0       ,       a__ = 0.02       ,       a__ = 2       ,       a__ = 1       ,       a__ = 1_2_8       ,       a__ = [2, 2, 2, 2]       ,       a__ = 2       ,       a__ = 2       ,       )		->							str:





									'''simple docstring'''
									A_       	=  parent
									A_       	=  batch_size
									A_       	=  image_size
									A_       	=  patch_size
									A_       	=  num_channels
									A_       	=  is_training
									A_       	=  use_labels
									A_       	=  hidden_size
									A_       	=  num_hidden_layers
									A_       	=  num_attention_heads
									A_       	=  intermediate_size
									A_       	=  hidden_act
									A_       	=  hidden_dropout_prob
									A_       	=  attention_probs_dropout_prob
									A_       	=  type_sequence_label_size
									A_       	=  initializer_range
									A_       	=  encoder_stride
									A_       	=  num_attention_outputs
									A_       	=  embed_dim
									A_       	=  embed_dim + 1
									A_       	=  resolution
									A_       	=  depths
									A_       	=  hidden_sizes
									A_       	=  dim
									A_       	=  mlp_expansion_ratio


					def        lowerCAmelCase_    (	self						)		->							List[Any]:





									'''simple docstring'''
									A_       	=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

									A_       	=  None
									if self.use_labels:
													A_       	=  ids_tensor([self.batch_size]       ,       self.type_sequence_label_size						)

									A_       	=  self.get_config()
									return config, pixel_values, labels


					def        lowerCAmelCase_    (	self						)		->							List[str]:





									'''simple docstring'''
									return EfficientFormerConfig(
									    image_size=self.image_size       ,       patch_size=self.patch_size       ,       num_channels=self.num_channels       ,       hidden_size=self.hidden_size       ,       num_hidden_layers=self.num_hidden_layers       ,       num_attention_heads=self.num_attention_heads       ,       intermediate_size=self.intermediate_size       ,       hidden_act=self.hidden_act       ,       hidden_dropout_prob=self.hidden_dropout_prob       ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,       is_decoder=a__       ,       initializer_range=self.initializer_range       ,       encoder_stride=self.encoder_stride       ,       resolution=self.resolution       ,       depths=self.depths       ,       hidden_sizes=self.hidden_sizes       ,       dim=self.dim       ,       mlp_expansion_ratio=self.mlp_expansion_ratio       ,       )


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__						)		->							str:





									'''simple docstring'''
									A_       	=  EfficientFormerModel(config=a__						)
									model.to(a__						)
									model.eval()
									A_       	=  model(a__						)
									self.parent.assertEqual(result.last_hidden_state.shape       ,       (self.batch_size, self.seq_length, self.hidden_size)						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__						)		->							Any:





									'''simple docstring'''
									A_       	=  self.type_sequence_label_size
									A_       	=  EfficientFormerForImageClassification(a__						)
									model.to(a__						)
									model.eval()
									A_       	=  model(a__       ,       labels=a__						)
									self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.type_sequence_label_size)						)

									# test greyscale images
									A_       	=  1
									A_       	=  EfficientFormerForImageClassification(a__						)
									model.to(a__						)
									model.eval()

									A_       	=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
									A_       	=  model(a__						)
									self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.type_sequence_label_size)						)


					def        lowerCAmelCase_    (	self						)		->							Dict:





									'''simple docstring'''
									A_       	=  self.prepare_config_and_inputs()
									(
									    (
									    A_
									)	,					(
									    A_
									)	,					(
									    A_
									)	,					
									)       	=  config_and_inputs
									A_       	=  {'''pixel_values''': pixel_values}
									return config, inputs_dict






@require_torch
class 					__lowercase		(					A							,       A							,       unittest.TestCase       ):
					__magic_name__						:							List[str]     	=	(
					    (
					        EfficientFormerModel,
					        EfficientFormerForImageClassificationWithTeacher,
					        EfficientFormerForImageClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					__magic_name__						:							List[Any]     	=	(
					    {
					        '''feature-extraction''': EfficientFormerModel,
					        '''image-classification''': (
					            EfficientFormerForImageClassification,
					            EfficientFormerForImageClassificationWithTeacher,
					        ),
					    }
					    if is_torch_available()
					    else {}
					)
					__magic_name__						:							Dict     	=	False

					__magic_name__						:							Union[str, Any]     	=	False
					__magic_name__						:							List[str]     	=	False
					__magic_name__						:							Any     	=	False


					def        lowerCAmelCase_    (	self						)		->							Any:





									'''simple docstring'''
									A_       	=  EfficientFormerModelTester(self						)
									A_       	=  ConfigTester(
									    self       ,       config_class=a__       ,       has_text_modality=a__       ,       hidden_size=3_7						)


					def        lowerCAmelCase_    (	self						)		->							int:





									'''simple docstring'''
									self.config_tester.run_common_tests()


					@unittest.skip(reason='''EfficientFormer does not use inputs_embeds'''						)
					def        lowerCAmelCase_    (	self						)		->							Optional[int]:





									'''simple docstring'''
									pass


					@unittest.skip(reason='''EfficientFormer does not support input and output embeddings'''						)
					def        lowerCAmelCase_    (	self						)		->							Any:





									'''simple docstring'''
									pass


					def        lowerCAmelCase_    (	self						)		->							Optional[Any]:





									'''simple docstring'''
									A_	,					A_       	=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													A_       	=  model_class(a__						)
													A_       	=  inspect.signature(model.forward						)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													A_       	=  [*signature.parameters.keys()]

													A_       	=  ['''pixel_values''']
													self.assertListEqual(arg_names[:1]       ,       a__						)


					def        lowerCAmelCase_    (	self						)		->							List[Any]:





									'''simple docstring'''
									def check_hidden_states_output(a__       ,       a__       ,       a__						):
													A_       	=  model_class(a__						)
													model.to(a__						)
													model.eval()

													with torch.no_grad():
																	A_       	=  model(**self._prepare_for_class(a__       ,       a__						)						)

													A_       	=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													A_       	=  getattr(
													    self.model_tester       ,       '''expected_num_hidden_layers'''       ,       self.model_tester.num_hidden_layers + 1						)
													self.assertEqual(len(a__						)       ,       a__						)

													if hasattr(self.model_tester       ,       '''encoder_seq_length'''						):
																	A_       	=  self.model_tester.encoder_seq_length
																	if hasattr(self.model_tester       ,       '''chunk_length'''						) and self.model_tester.chunk_length > 1:
																					A_       	=  seq_length * self.model_tester.chunk_length
													else:
																	A_       	=  self.model_tester.seq_length

													self.assertListEqual(
													    list(hidden_states[-1].shape[-2:]						)       ,       [seq_length, self.model_tester.hidden_size]       ,       )

													if config.is_encoder_decoder:
																	A_       	=  outputs.decoder_hidden_states

																	self.assertIsInstance(a__       ,       (list, tuple)						)
																	self.assertEqual(len(a__						)       ,       a__						)
																	A_       	=  getattr(self.model_tester       ,       '''seq_length'''       ,       a__						)
																	A_       	=  getattr(self.model_tester       ,       '''decoder_seq_length'''       ,       a__						)

																	self.assertListEqual(
																	    list(hidden_states[-1].shape[-2:]						)       ,       [decoder_seq_length, self.model_tester.hidden_size]       ,       )

									A_	,					A_       	=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													A_       	=  True
													check_hidden_states_output(a__       ,       a__       ,       a__						)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													A_       	=  True

													check_hidden_states_output(a__       ,       a__       ,       a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__=False						)		->							str:





									'''simple docstring'''
									A_       	=  super()._prepare_for_class(a__       ,       a__       ,       return_labels=a__						)

									if return_labels:
													if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
																	del inputs_dict["labels"]

									return inputs_dict


					def        lowerCAmelCase_    (	self						)		->							int:





									'''simple docstring'''
									A_       	=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*a__						)


					@unittest.skip(reason='''EfficientFormer does not implement masked image modeling yet'''						)
					def        lowerCAmelCase_    (	self						)		->							Optional[int]:





									'''simple docstring'''
									A_       	=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*a__						)


					def        lowerCAmelCase_    (	self						)		->							str:





									'''simple docstring'''
									A_       	=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*a__						)


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





									'''simple docstring'''
									if not self.model_tester.is_training:
													return

									A_	,					A_       	=  self.model_tester.prepare_config_and_inputs_for_common()
									A_       	=  True

									for model_class in self.all_model_classes:
													# EfficientFormerForImageClassificationWithTeacher supports inference-only
													if (
													    model_class in get_values(a__						)
													    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
													):
																	continue
													A_       	=  model_class(a__						)
													model.to(a__						)
													model.train()
													A_       	=  self._prepare_for_class(a__       ,       a__       ,       return_labels=a__						)
													A_       	=  model(**a__						).loss
													loss.backward()


					def        lowerCAmelCase_    (	self						)		->							List[str]:





									'''simple docstring'''
									A_	,					A_       	=  self.model_tester.prepare_config_and_inputs_for_common()

									A_       	=  [
									    {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
									    {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
									    {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
									]

									for model_class in self.all_model_classes:
													if (
													    model_class
													    not in [
													        *get_values(a__						),
													    ]
													    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
													):
																	continue

													for problem_type in problem_types:
																	with self.subTest(msg=F"Testing {model_class} with {problem_type['title']}"						):
																					A_       	=  problem_type['''title''']
																					A_       	=  problem_type['''num_labels''']

																					A_       	=  model_class(a__						)
																					model.to(a__						)
																					model.train()

																					A_       	=  self._prepare_for_class(a__       ,       a__       ,       return_labels=a__						)

																					if problem_type["num_labels"] > 1:
																									A_       	=  inputs['''labels'''].unsqueeze(1						).repeat(1       ,       problem_type['''num_labels''']						)

																					A_       	=  inputs['''labels'''].to(problem_type['''dtype''']						)

																					# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																					# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																					# they have the same size." which is a symptom something in wrong for the regression problem.
																					# See https://github.com/huggingface/transformers/issues/11780
																					with warnings.catch_warnings(record=a__						) as warning_list:
																									A_       	=  model(**a__						).loss
																					for w in warning_list:
																									if "Using a target size that is different to the input size" in str(w.message						):
																													raise ValueError(
																													    F"Something is going wrong in the regression problem: intercepted {w.message}"						)

																					loss.backward()


					@slow
					def        lowerCAmelCase_    (	self						)		->							int:





									'''simple docstring'''
									for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													A_       	=  EfficientFormerModel.from_pretrained(a__						)
													self.assertIsNotNone(a__						)


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





									'''simple docstring'''
									A_	,					A_       	=  self.model_tester.prepare_config_and_inputs_for_common()
									A_       	=  True

									A_       	=  getattr(self.model_tester       ,       '''seq_length'''       ,       a__						)
									A_       	=  getattr(self.model_tester       ,       '''encoder_seq_length'''       ,       a__						)
									A_       	=  getattr(self.model_tester       ,       '''key_length'''       ,       a__						)
									A_       	=  getattr(self.model_tester       ,       '''chunk_length'''       ,       a__						)

									if chunk_length is not None and hasattr(self.model_tester       ,       '''num_hashes'''						):
													A_       	=  encoder_seq_length * self.model_tester.num_hashes

									for model_class in self.all_model_classes:
													A_       	=  True
													A_       	=  False
													A_       	=  True
													A_       	=  model_class(a__						)
													model.to(a__						)
													model.eval()
													with torch.no_grad():
																	A_       	=  model(**self._prepare_for_class(a__       ,       a__						)						)
													A_       	=  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
													self.assertEqual(len(a__						)       ,       self.model_tester.num_attention_outputs						)

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													A_       	=  True
													A_       	=  model_class(a__						)
													model.to(a__						)
													model.eval()
													with torch.no_grad():
																	A_       	=  model(**self._prepare_for_class(a__       ,       a__						)						)
													A_       	=  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
													self.assertEqual(len(a__						)       ,       self.model_tester.num_attention_outputs						)

													if chunk_length is not None:
																	self.assertListEqual(
																	    list(attentions[0].shape[-4:]						)       ,       [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]       ,       )
													else:
																	self.assertListEqual(
																	    list(attentions[0].shape[-3:]						)       ,       [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]       ,       )



def        lowerCamelCase_  (   ):
				A_       	=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
				return image






@require_torch
@require_vision
class 					__lowercase		(					unittest.TestCase       ):


					@cached_property
					def        lowerCAmelCase_    (	self						)		->							int:





									'''simple docstring'''
									return (
									    EfficientFormerImageProcessor.from_pretrained('''snap-research/efficientformer-l1-300'''						)
									    if is_vision_available()
									    else None
									)


					@slow
					def        lowerCAmelCase_    (	self						)		->							Any:





									'''simple docstring'''
									A_       	=  EfficientFormerForImageClassification.from_pretrained('''snap-research/efficientformer-l1-300'''						).to(
									    a__						)

									A_       	=  self.default_image_processor
									A_       	=  prepare_img()
									A_       	=  image_processor(images=a__       ,       return_tensors='''pt'''						).to(a__						)

									# forward pass
									with torch.no_grad():
													A_       	=  model(**a__						)

									# verify the logits
									A_       	=  (1, 1_0_0_0)
									self.assertEqual(outputs.logits.shape       ,       a__						)

									A_       	=  torch.tensor([-0.05_55, 0.48_25, -0.08_52]						).to(a__						)
									self.assertTrue(torch.allclose(outputs.logits[0][:3]       ,       a__       ,       atol=1E-4						)						)


					@slow
					def        lowerCAmelCase_    (	self						)		->							Dict:





									'''simple docstring'''
									A_       	=  EfficientFormerForImageClassificationWithTeacher.from_pretrained(
									    '''snap-research/efficientformer-l1-300'''						).to(a__						)

									A_       	=  self.default_image_processor
									A_       	=  prepare_img()
									A_       	=  image_processor(images=a__       ,       return_tensors='''pt'''						).to(a__						)

									# forward pass
									with torch.no_grad():
													A_       	=  model(**a__						)

									# verify the logits
									A_       	=  (1, 1_0_0_0)
									self.assertEqual(outputs.logits.shape       ,       a__						)

									A_       	=  torch.tensor([-0.13_12, 0.43_53, -1.04_99]						).to(a__						)
									self.assertTrue(torch.allclose(outputs.logits[0][:3]       ,       a__       ,       atol=1E-4						)						)

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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


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






class 					__lowercase		(					A       ):
					__magic_name__						:							int     	=	['''pixel_values''']


					def __init__(	self       ,       a__ = True       ,       a__ = None       ,       a__ = True       ,       a__ = None       ,       a__ = PILImageResampling.BICUBIC       ,       a__ = True       ,       a__ = 1 / 2_5_5       ,       a__ = True       ,       a__ = None       ,       a__ = None       ,       **a__       ,       )		->							None:





									'''simple docstring'''
									super().__init__(**a__						)
									A_       	=  crop_size if crop_size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
									A_       	=  get_size_dict(a__       ,       param_name='''crop_size'''						)
									A_       	=  size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
									A_       	=  get_size_dict(a__						)

									A_       	=  do_center_crop
									A_       	=  crop_size
									A_       	=  do_resize
									A_       	=  size
									A_       	=  resample
									A_       	=  do_rescale
									A_       	=  rescale_factor
									A_       	=  do_normalize
									A_       	=  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									A_       	=  image_std if image_std is not None else IMAGENET_DEFAULT_STD


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = None       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									A_       	=  self.size if size is None else size
									A_       	=  get_size_dict(a__						)
									A_       	=  get_size_dict(a__       ,       param_name='''crop_size'''						)

									A_	,					A_       	=  get_image_size(a__						)
									A_       	=  min(a__       ,       a__						)
									A_       	=  (size['''height'''] / crop_size['''height''']) * min_dim
									A_       	=  (size['''width'''] / crop_size['''width''']) * min_dim
									return center_crop(a__       ,       size=(cropped_height, cropped_width)       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = PIL.Image.BICUBIC       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									A_       	=  get_size_dict(a__						)
									if "height" not in size or "width" not in size:
													raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"						)
									return resize(
									    a__       ,       size=(size['''height'''], size['''width'''])       ,       resample=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = None       ,       **a__       ,       )		->							Union[str, Any]:





									'''simple docstring'''
									return rescale(a__       ,       scale=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									return normalize(a__       ,       mean=a__       ,       std=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = ChannelDimension.FIRST       ,       **a__       ,       )		->							PIL.Image.Image:





									'''simple docstring'''
									A_       	=  do_center_crop if do_center_crop is not None else self.do_center_crop
									A_       	=  crop_size if crop_size is not None else self.crop_size
									A_       	=  get_size_dict(a__       ,       param_name='''crop_size'''						)
									A_       	=  do_resize if do_resize is not None else self.do_resize
									A_       	=  size if size is not None else self.size
									A_       	=  get_size_dict(a__						)
									A_       	=  resample if resample is not None else self.resample
									A_       	=  do_rescale if do_rescale is not None else self.do_rescale
									A_       	=  rescale_factor if rescale_factor is not None else self.rescale_factor
									A_       	=  do_normalize if do_normalize is not None else self.do_normalize
									A_       	=  image_mean if image_mean is not None else self.image_mean
									A_       	=  image_std if image_std is not None else self.image_std

									A_       	=  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_center_crop and crop_size is None:
													raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.'''						)

									if do_resize and size is None:
													raise ValueError('''Size must be specified if do_resize is True.'''						)

									if do_rescale and rescale_factor is None:
													raise ValueError('''Rescale factor must be specified if do_rescale is True.'''						)

									if do_normalize and (image_mean is None or image_std is None):
													raise ValueError('''Image mean and image standard deviation must be specified if do_normalize is True.'''						)

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

									if do_center_crop:
													A_       	=  [self.center_crop(a__       ,       a__       ,       size=a__						) for image in images]

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

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

									if do_normalize:
													A_       	=  [self.normalize(image=a__       ,       mean=a__       ,       std=a__						) for image in images]

									A_       	=  [to_channel_dimension_format(a__       ,       a__						) for image in images]

									A_       	=  {'''pixel_values''': images}
									return BatchFeature(data=a__       ,       tensor_type=a__						)