


import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
							import torch

							from transformers import (
							    NystromformerForMaskedLM,
							    NystromformerForMultipleChoice,
							    NystromformerForQuestionAnswering,
							    NystromformerForSequenceClassification,
							    NystromformerForTokenClassification,
							    NystromformerModel,
							)
							from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST

class 						A__							:




						def __init__(       self					:      Optional[Any]				,    _a					:      Optional[Any]				,    _a					:      Optional[int]=13				,    _a					:      Tuple=7				,    _a					:      Union[str, Any]=True				,    _a					:      Optional[int]=True				,    _a					:      Optional[int]=True				,    _a					:      str=True				,    _a					:      Optional[Any]=99				,    _a					:      List[Any]=32				,    _a					:      List[str]=5				,    _a					:      int=4				,    _a					:      Tuple=37				,    _a					:      int="gelu"				,    _a					:      Optional[Any]=0.1				,    _a					:      Any=0.1				,    _a					:      Optional[Any]=512				,    _a					:      Optional[Any]=16				,    _a					:      Union[str, Any]=2				,    _a					:      int=0.02				,    _a					:      List[Any]=3				,    _a					:      List[str]=4				,    _a					:      Optional[int]=None				,    )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =parent
										_SCREAMING_SNAKE_CASE			  =batch_size
										_SCREAMING_SNAKE_CASE			  =seq_length
										_SCREAMING_SNAKE_CASE			  =is_training
										_SCREAMING_SNAKE_CASE			  =use_input_mask
										_SCREAMING_SNAKE_CASE			  =use_token_type_ids
										_SCREAMING_SNAKE_CASE			  =use_labels
										_SCREAMING_SNAKE_CASE			  =vocab_size
										_SCREAMING_SNAKE_CASE			  =hidden_size
										_SCREAMING_SNAKE_CASE			  =num_hidden_layers
										_SCREAMING_SNAKE_CASE			  =num_attention_heads
										_SCREAMING_SNAKE_CASE			  =intermediate_size
										_SCREAMING_SNAKE_CASE			  =hidden_act
										_SCREAMING_SNAKE_CASE			  =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE			  =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE			  =max_position_embeddings
										_SCREAMING_SNAKE_CASE			  =type_vocab_size
										_SCREAMING_SNAKE_CASE			  =type_sequence_label_size
										_SCREAMING_SNAKE_CASE			  =initializer_range
										_SCREAMING_SNAKE_CASE			  =num_labels
										_SCREAMING_SNAKE_CASE			  =num_choices
										_SCREAMING_SNAKE_CASE			  =scope




						def 							__UpperCamelCase				(       self					:      Tuple  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size  )

										_SCREAMING_SNAKE_CASE			  =None
										if self.use_input_mask:
														_SCREAMING_SNAKE_CASE			  =random_attention_mask([self.batch_size, self.seq_length]  )

										_SCREAMING_SNAKE_CASE			  =None
										if self.use_token_type_ids:
														_SCREAMING_SNAKE_CASE			  =ids_tensor([self.batch_size, self.seq_length]				,    self.type_vocab_size  )

										_SCREAMING_SNAKE_CASE			  =None
										_SCREAMING_SNAKE_CASE			  =None
										_SCREAMING_SNAKE_CASE			  =None
										if self.use_labels:
														_SCREAMING_SNAKE_CASE			  =ids_tensor([self.batch_size]				,    self.type_sequence_label_size  )
														_SCREAMING_SNAKE_CASE			  =ids_tensor([self.batch_size, self.seq_length]				,    self.num_labels  )
														_SCREAMING_SNAKE_CASE			  =ids_tensor([self.batch_size]				,    self.num_choices  )

										_SCREAMING_SNAKE_CASE			  =self.get_config()

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     Dict:


										"""simple docstring"""





										return NystromformerConfig(
										    vocab_size=self.vocab_size				,    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				,    max_position_embeddings=self.max_position_embeddings				,    type_vocab_size=self.type_vocab_size				,    is_decoder=_a				,    initializer_range=self.initializer_range				,    )




						def 							__UpperCamelCase				(       self					:      List[Any]				,    _a					:      str				,    _a					:      Dict				,    _a					:      int				,    _a					:      Optional[int]				,    _a					:      List[str]				,    _a					:      Dict				,    _a					:      int  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =NystromformerModel(config=_a  )
										model.to(_a  )
										model.eval()
										_SCREAMING_SNAKE_CASE			  =model(_a				,    attention_mask=_a				,    token_type_ids=_a  )
										_SCREAMING_SNAKE_CASE			  =model(_a				,    token_type_ids=_a  )
										_SCREAMING_SNAKE_CASE			  =model(_a  )
										self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size)  )




						def 							__UpperCamelCase				(       self					:      Union[str, Any]				,    _a					:      List[str]				,    _a					:      Optional[Any]				,    _a					:      List[Any]				,    _a					:      Union[str, Any]				,    _a					:      Optional[Any]				,    _a					:      Optional[Any]				,    _a					:      List[Any]  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =NystromformerForMaskedLM(config=_a  )
										model.to(_a  )
										model.eval()
										_SCREAMING_SNAKE_CASE			  =model(_a				,    attention_mask=_a				,    token_type_ids=_a				,    labels=_a  )
										self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size)  )




						def 							__UpperCamelCase				(       self					:      Optional[int]				,    _a					:      Tuple				,    _a					:      Optional[Any]				,    _a					:      int				,    _a					:      List[Any]				,    _a					:      Dict				,    _a					:      Any				,    _a					:      Any  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =NystromformerForQuestionAnswering(config=_a  )
										model.to(_a  )
										model.eval()
										_SCREAMING_SNAKE_CASE			  =model(
										    _a				,    attention_mask=_a				,    token_type_ids=_a				,    start_positions=_a				,    end_positions=_a				,    )
										self.parent.assertEqual(result.start_logits.shape				,    (self.batch_size, self.seq_length)  )
										self.parent.assertEqual(result.end_logits.shape				,    (self.batch_size, self.seq_length)  )




						def 							__UpperCamelCase				(       self					:      str				,    _a					:      List[Any]				,    _a					:      Union[str, Any]				,    _a					:      Any				,    _a					:      Dict				,    _a					:      Optional[Any]				,    _a					:      str				,    _a					:      Optional[Any]  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.num_labels
										_SCREAMING_SNAKE_CASE			  =NystromformerForSequenceClassification(_a  )
										model.to(_a  )
										model.eval()
										_SCREAMING_SNAKE_CASE			  =model(_a				,    attention_mask=_a				,    token_type_ids=_a				,    labels=_a  )
										self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels)  )




						def 							__UpperCamelCase				(       self					:      List[Any]				,    _a					:      Optional[int]				,    _a					:      str				,    _a					:      int				,    _a					:      int				,    _a					:      Any				,    _a					:      List[Any]				,    _a					:      int  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.num_labels
										_SCREAMING_SNAKE_CASE			  =NystromformerForTokenClassification(config=_a  )
										model.to(_a  )
										model.eval()
										_SCREAMING_SNAKE_CASE			  =model(_a				,    attention_mask=_a				,    token_type_ids=_a				,    labels=_a  )
										self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.num_labels)  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      Dict				,    _a					:      int				,    _a					:      int				,    _a					:      Tuple				,    _a					:      List[str]				,    _a					:      Optional[int]				,    _a					:      Optional[int]  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.num_choices
										_SCREAMING_SNAKE_CASE			  =NystromformerForMultipleChoice(config=_a  )
										model.to(_a  )
										model.eval()
										_SCREAMING_SNAKE_CASE			  =input_ids.unsqueeze(1  ).expand(-1				,    self.num_choices				,    -1  ).contiguous()
										_SCREAMING_SNAKE_CASE			  =token_type_ids.unsqueeze(1  ).expand(-1				,    self.num_choices				,    -1  ).contiguous()
										_SCREAMING_SNAKE_CASE			  =input_mask.unsqueeze(1  ).expand(-1				,    self.num_choices				,    -1  ).contiguous()
										_SCREAMING_SNAKE_CASE			  =model(
										    _a				,    attention_mask=_a				,    token_type_ids=_a				,    labels=_a				,    )
										self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_choices)  )




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.prepare_config_and_inputs()
										(
										    (
										    _SCREAMING_SNAKE_CASE
										)  ,				(
										    _SCREAMING_SNAKE_CASE
										)  ,				(
										    _SCREAMING_SNAKE_CASE
										)  ,				(
										    _SCREAMING_SNAKE_CASE
										)  ,				(
										    _SCREAMING_SNAKE_CASE
										)  ,				(
										    _SCREAMING_SNAKE_CASE
										)  ,				(
										    _SCREAMING_SNAKE_CASE
										)  ,				
										)			  =config_and_inputs
										_SCREAMING_SNAKE_CASE			  ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
										return config, inputs_dict

@require_torch
class 						A__							(		UpperCamelCase__   ,     UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						(
						    (
						        NystromformerModel,
						        NystromformerForMaskedLM,
						        NystromformerForMultipleChoice,
						        NystromformerForQuestionAnswering,
						        NystromformerForSequenceClassification,
						        NystromformerForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCAmelCase             =						(
						    {
						        "feature-extraction": NystromformerModel,
						        "fill-mask": NystromformerForMaskedLM,
						        "question-answering": NystromformerForQuestionAnswering,
						        "text-classification": NystromformerForSequenceClassification,
						        "token-classification": NystromformerForTokenClassification,
						        "zero-shot": NystromformerForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						UpperCAmelCase             =						False
						UpperCAmelCase             =						False




						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =NystromformerModelTester(self  )
										_SCREAMING_SNAKE_CASE			  =ConfigTester(self				,    config_class=_a				,    hidden_size=37  )




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


										"""simple docstring"""





										self.config_tester.run_common_tests()




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_a  )




						def 							__UpperCamelCase				(       self					:      str  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
														_SCREAMING_SNAKE_CASE			  =type
														self.model_tester.create_and_check_model(*_a  )




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*_a  )




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*_a  )




						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*_a  )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*_a  )




						def 							__UpperCamelCase				(       self					:      Any  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_a  )




						@slow
						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     Tuple:


										"""simple docstring"""





										for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_SCREAMING_SNAKE_CASE			  =NystromformerModel.from_pretrained(_a  )
														self.assertIsNotNone(_a  )

@require_torch
class 						A__							(		unittest.TestCase  ):




						@slow
						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =NystromformerModel.from_pretrained('''uw-madison/nystromformer-512'''  )
										_SCREAMING_SNAKE_CASE			  =torch.tensor([[0, 1, 2, 3, 4, 5]]  )

										with torch.no_grad():
														_SCREAMING_SNAKE_CASE			  =model(_a  )[0]

										_SCREAMING_SNAKE_CASE			  =torch.Size((1, 6, 768)  )
										self.assertEqual(output.shape				,    _a  )

										_SCREAMING_SNAKE_CASE			  =torch.tensor(
										    [[[-0.45_32, -0.09_36, 0.51_37], [-0.26_76, 0.06_28, 0.61_86], [-0.36_29, -0.17_26, 0.47_16]]]  )

										self.assertTrue(torch.allclose(output[:, :3, :3]				,    _a				,    atol=1E-4  )  )




						@slow
						def 							__UpperCamelCase				(       self					:      Any  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''the [MASK] of Belgium is Brussels'''

										_SCREAMING_SNAKE_CASE			  =AutoTokenizer.from_pretrained('''uw-madison/nystromformer-512'''  )
										_SCREAMING_SNAKE_CASE			  =NystromformerForMaskedLM.from_pretrained('''uw-madison/nystromformer-512'''  )

										_SCREAMING_SNAKE_CASE			  =tokenizer(_a				,    return_tensors='''pt'''  )

										with torch.no_grad():
														_SCREAMING_SNAKE_CASE			  =model(encoding.input_ids  ).logits

										_SCREAMING_SNAKE_CASE			  =token_logits[:, 2, :].argmax(-1  )[0]

										self.assertEqual(tokenizer.decode(_a  )				,    '''capital'''  )



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
							import PIL

snake_case_						:			Union[str, Any]													=							logging.get_logger(__name__)







def 						lowerCamelCase(   a__):
				if isinstance(a__	,(list, tuple)) and isinstance(videos[0]	,(list, tuple)) and is_valid_image(videos[0][0]):
								return videos

				elif isinstance(a__	,(list, tuple)) and is_valid_image(videos[0]):
								return [videos]

				elif is_valid_image(a__):
								return [[videos]]

				raise ValueError(f"Could not make batched video from {videos}")


class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						["pixel_values"]




						def __init__(       self					:      Tuple				,    _a					:      bool = True				,    _a					:      Dict[str, int] = None				,    _a					:      PILImageResampling = PILImageResampling.BILINEAR				,    _a					:      bool = True				,    _a					:      Dict[str, int] = None				,    _a					:      bool = True				,    _a					:      Union[int, float] = 1 / 255				,    _a					:      bool = True				,    _a					:      bool = True				,    _a					:      Optional[Union[float, List[float]]] = None				,    _a					:      Optional[Union[float, List[float]]] = None				,    **_a					:      Any				,    )							->     None:


										"""simple docstring"""





										super().__init__(**_a  )
										_SCREAMING_SNAKE_CASE			  =size if size is not None else {'''shortest_edge''': 256}
										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    default_to_square=_a  )
										_SCREAMING_SNAKE_CASE			  =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    param_name='''crop_size'''  )

										_SCREAMING_SNAKE_CASE			  =do_resize
										_SCREAMING_SNAKE_CASE			  =size
										_SCREAMING_SNAKE_CASE			  =do_center_crop
										_SCREAMING_SNAKE_CASE			  =crop_size
										_SCREAMING_SNAKE_CASE			  =resample
										_SCREAMING_SNAKE_CASE			  =do_rescale
										_SCREAMING_SNAKE_CASE			  =rescale_factor
										_SCREAMING_SNAKE_CASE			  =offset
										_SCREAMING_SNAKE_CASE			  =do_normalize
										_SCREAMING_SNAKE_CASE			  =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										_SCREAMING_SNAKE_CASE			  =image_std if image_std is not None else IMAGENET_STANDARD_STD




						def 							__UpperCamelCase				(       self					:      List[Any]				,    _a					:      np.ndarray				,    _a					:      Dict[str, int]				,    _a					:      PILImageResampling = PILImageResampling.BILINEAR				,    _a					:      Optional[Union[str, ChannelDimension]] = None				,    **_a					:      List[Any]				,    )							->     np.ndarray:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    default_to_square=_a  )
										if "shortest_edge" in size:
														_SCREAMING_SNAKE_CASE			  =get_resize_output_image_size(_a				,    size['''shortest_edge''']				,    default_to_square=_a  )
										elif "height" in size and "width" in size:
														_SCREAMING_SNAKE_CASE			  =(size['''height'''], size['''width'''])
										else:
														raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"  )
										return resize(_a				,    size=_a				,    resample=_a				,    data_format=_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      int				,    _a					:      np.ndarray				,    _a					:      Dict[str, int]				,    _a					:      Optional[Union[str, ChannelDimension]] = None				,    **_a					:      Dict				,    )							->     np.ndarray:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a  )
										if "height" not in size or "width" not in size:
														raise ValueError(f"Size must have 'height' and 'width' as keys. Got {size.keys()}"  )
										return center_crop(_a				,    size=(size['''height'''], size['''width'''])				,    data_format=_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      np.ndarray				,    _a					:      Union[int, float]				,    _a					:      bool = True				,    _a					:      Optional[Union[str, ChannelDimension]] = None				,    **_a					:      List[str]				,    )							->     Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =image.astype(np.floataa  )
										if offset:
														_SCREAMING_SNAKE_CASE			  =image - (scale / 2)
										return rescale(_a				,    scale=_a				,    data_format=_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      np.ndarray				,    _a					:      Union[float, List[float]]				,    _a					:      Union[float, List[float]]				,    _a					:      Optional[Union[str, ChannelDimension]] = None				,    **_a					:      Any				,    )							->     np.ndarray:


										"""simple docstring"""





										return normalize(_a				,    mean=_a				,    std=_a				,    data_format=_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Tuple				,    _a					:      ImageInput				,    _a					:      bool = None				,    _a					:      Dict[str, int] = None				,    _a					:      PILImageResampling = None				,    _a					:      bool = None				,    _a					:      Dict[str, int] = None				,    _a					:      bool = None				,    _a					:      float = None				,    _a					:      bool = None				,    _a					:      bool = None				,    _a					:      Optional[Union[float, List[float]]] = None				,    _a					:      Optional[Union[float, List[float]]] = None				,    _a					:      Optional[ChannelDimension] = ChannelDimension.FIRST				,    )							->     np.ndarray:


										"""simple docstring"""





										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_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.'''  )

										if offset and not do_rescale:
														raise ValueError('''For offset, do_rescale must also be set to True.'''  )

										# All transformations expect numpy arrays.
										_SCREAMING_SNAKE_CASE			  =to_numpy_array(_a  )

										if do_resize:
														_SCREAMING_SNAKE_CASE			  =self.resize(image=_a				,    size=_a				,    resample=_a  )

										if do_center_crop:
														_SCREAMING_SNAKE_CASE			  =self.center_crop(_a				,    size=_a  )

										if do_rescale:
														_SCREAMING_SNAKE_CASE			  =self.rescale(image=_a				,    scale=_a				,    offset=_a  )

										if do_normalize:
														_SCREAMING_SNAKE_CASE			  =self.normalize(image=_a				,    mean=_a				,    std=_a  )

										_SCREAMING_SNAKE_CASE			  =to_channel_dimension_format(_a				,    _a  )
										return image




						def 							__UpperCamelCase				(       self					:      Tuple				,    _a					:      ImageInput				,    _a					:      bool = None				,    _a					:      Dict[str, int] = None				,    _a					:      PILImageResampling = None				,    _a					:      bool = None				,    _a					:      Dict[str, int] = None				,    _a					:      bool = None				,    _a					:      float = None				,    _a					:      bool = None				,    _a					:      bool = None				,    _a					:      Optional[Union[float, List[float]]] = None				,    _a					:      Optional[Union[float, List[float]]] = None				,    _a					:      Optional[Union[str, TensorType]] = None				,    _a					:      ChannelDimension = ChannelDimension.FIRST				,    **_a					:      str				,    )							->     PIL.Image.Image:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =do_resize if do_resize is not None else self.do_resize
										_SCREAMING_SNAKE_CASE			  =resample if resample is not None else self.resample
										_SCREAMING_SNAKE_CASE			  =do_center_crop if do_center_crop is not None else self.do_center_crop
										_SCREAMING_SNAKE_CASE			  =do_rescale if do_rescale is not None else self.do_rescale
										_SCREAMING_SNAKE_CASE			  =rescale_factor if rescale_factor is not None else self.rescale_factor
										_SCREAMING_SNAKE_CASE			  =offset if offset is not None else self.offset
										_SCREAMING_SNAKE_CASE			  =do_normalize if do_normalize is not None else self.do_normalize
										_SCREAMING_SNAKE_CASE			  =image_mean if image_mean is not None else self.image_mean
										_SCREAMING_SNAKE_CASE			  =image_std if image_std is not None else self.image_std

										_SCREAMING_SNAKE_CASE			  =size if size is not None else self.size
										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    default_to_square=_a  )
										_SCREAMING_SNAKE_CASE			  =crop_size if crop_size is not None else self.crop_size
										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    param_name='''crop_size'''  )

										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.'''  )

										_SCREAMING_SNAKE_CASE			  =make_batched(_a  )

										_SCREAMING_SNAKE_CASE			  =[
										    [
										        self._preprocess_image(
										            image=_a				,    do_resize=_a				,    size=_a				,    resample=_a				,    do_center_crop=_a				,    crop_size=_a				,    do_rescale=_a				,    rescale_factor=_a				,    offset=_a				,    do_normalize=_a				,    image_mean=_a				,    image_std=_a				,    data_format=_a				,    )
										        for img in video
										    ]
										    for video in videos
										]

										_SCREAMING_SNAKE_CASE			  ={'''pixel_values''': videos}
										return BatchFeature(data=_a				,    tensor_type=_a  )