


import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 torch import nn

   from transformers import (
       MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
       MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       MODEL_MAPPING,
       DeiTForImageClassification,
       DeiTForImageClassificationWithTeacher,
       DeiTForMaskedImageModeling,
       DeiTModel,
   )
   from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import DeiTImageProcessor
class  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       str=1_3     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=3_0     ,							SCREAMING_SNAKE_CASE__				:       int=2     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=3     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=True     ,							SCREAMING_SNAKE_CASE__				:       Tuple=True     ,							SCREAMING_SNAKE_CASE__				:       str=3_2     ,							SCREAMING_SNAKE_CASE__				:       str=5     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=4     ,							SCREAMING_SNAKE_CASE__				:       List[str]=3_7     ,							SCREAMING_SNAKE_CASE__				:       Tuple="gelu"     ,							SCREAMING_SNAKE_CASE__				:       int=0.1     ,							SCREAMING_SNAKE_CASE__				:       List[str]=0.1     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=1_0     ,							SCREAMING_SNAKE_CASE__				:       List[str]=0.02     ,							SCREAMING_SNAKE_CASE__				:       int=3     ,							SCREAMING_SNAKE_CASE__				:       str=None     ,							SCREAMING_SNAKE_CASE__				:       Any=2     ,							)		->			str:
             a_					:		Union[str, Any]							=  parent
             a_					:		str							=  batch_size
             a_					:		List[Any]							=  image_size
             a_					:		int							=  patch_size
             a_					:		Optional[int]							=  num_channels
             a_					:		int							=  is_training
             a_					:		List[Any]							=  use_labels
             a_					:		Any							=  hidden_size
             a_					:		Optional[Any]							=  num_hidden_layers
             a_					:		List[Any]							=  num_attention_heads
             a_					:		Optional[Any]							=  intermediate_size
             a_					:		Dict							=  hidden_act
             a_					:		Tuple							=  hidden_dropout_prob
             a_					:		Optional[int]							=  attention_probs_dropout_prob
             a_					:		Union[str, Any]							=  type_sequence_label_size
             a_					:		List[Any]							=  initializer_range
             a_					:		Union[str, Any]							=  scope
             a_					:		Union[str, Any]							=  encoder_stride

             # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
             a_					:		int							=  (image_size // patch_size) ** 2
             a_					:		List[str]							=  num_patches + 2
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Any:
             a_					:		Any							=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

             a_					:		Tuple							=  None
             if self.use_labels:
                    a_					:		int							=  ids_tensor([self.batch_size]     ,							self.type_sequence_label_size     )

             a_					:		Union[str, Any]							=  self.get_config()

             return config, pixel_values, labels
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			List[str]:
             return DeiTConfig(
                 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=SCREAMING_SNAKE_CASE__     ,							initializer_range=self.initializer_range     ,							encoder_stride=self.encoder_stride     ,							)
      def        SCREAMING_SNAKE_CASE							(       self				:       int     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     )		->			str:
             a_					:		Dict							=  DeiTModel(config=SCREAMING_SNAKE_CASE__     )
             model.to(SCREAMING_SNAKE_CASE__     )
             model.eval()
             a_					:		List[str]							=  model(SCREAMING_SNAKE_CASE__     )
             self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Any     )		->			Optional[int]:
             a_					:		str							=  DeiTForMaskedImageModeling(config=SCREAMING_SNAKE_CASE__     )
             model.to(SCREAMING_SNAKE_CASE__     )
             model.eval()
             a_					:		Any							=  model(SCREAMING_SNAKE_CASE__     )
             self.parent.assertEqual(
                 result.reconstruction.shape     ,							(self.batch_size, self.num_channels, self.image_size, self.image_size)     )

             # test greyscale images
             a_					:		List[str]							=  1
             a_					:		List[Any]							=  DeiTForMaskedImageModeling(SCREAMING_SNAKE_CASE__     )
             model.to(SCREAMING_SNAKE_CASE__     )
             model.eval()

             a_					:		Optional[int]							=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
             a_					:		List[Any]							=  model(SCREAMING_SNAKE_CASE__     )
             self.parent.assertEqual(result.reconstruction.shape     ,							(self.batch_size, 1, self.image_size, self.image_size)     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Optional[Any]:
             a_					:		Tuple							=  self.type_sequence_label_size
             a_					:		Dict							=  DeiTForImageClassification(SCREAMING_SNAKE_CASE__     )
             model.to(SCREAMING_SNAKE_CASE__     )
             model.eval()
             a_					:		Optional[Any]							=  model(SCREAMING_SNAKE_CASE__     ,							labels=SCREAMING_SNAKE_CASE__     )
             self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.type_sequence_label_size)     )

             # test greyscale images
             a_					:		int							=  1
             a_					:		Optional[int]							=  DeiTForImageClassification(SCREAMING_SNAKE_CASE__     )
             model.to(SCREAMING_SNAKE_CASE__     )
             model.eval()

             a_					:		Dict							=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
             a_					:		str							=  model(SCREAMING_SNAKE_CASE__     ,							labels=SCREAMING_SNAKE_CASE__     )
             self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.type_sequence_label_size)     )

      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			List[str]:
             a_					:		Optional[Any]							=  self.prepare_config_and_inputs()
             (
                 (
                 a_
             )							,		(
                 a_
             )							,		(
                 a_
             )							,		
             )					:		Optional[int]							=  config_and_inputs
             a_					:		Tuple							=  {'pixel_values': pixel_values}
             return config, inputs_dict
@require_torch
class  SCREAMING_SNAKE_CASE__  (				lowercase__						,    lowercase__						,    unittest.TestCase			):
      snake_case__				:     Dict        =						(
          (
              DeiTModel,
              DeiTForImageClassification,
              DeiTForImageClassificationWithTeacher,
              DeiTForMaskedImageModeling,
          )
          if is_torch_available()
          else ()
      )
      snake_case__				:     Dict        =						(
          {
              '''feature-extraction''': DeiTModel,
              '''image-classification''': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
          }
          if is_torch_available()
          else {}
      )

      snake_case__				:     Union[str, Any]        =						False
      snake_case__				:     Union[str, Any]        =						False
      snake_case__				:     Dict        =						False
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			List[str]:
             a_					:		List[str]							=  DeiTModelTester(self     )
             a_					:		List[str]							=  ConfigTester(self     ,							config_class=SCREAMING_SNAKE_CASE__     ,							has_text_modality=SCREAMING_SNAKE_CASE__     ,							hidden_size=3_7     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Optional[Any]:
             self.config_tester.run_common_tests()
      @unittest.skip(reason='DeiT does not use inputs_embeds'     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			List[str]:
             pass
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Union[str, Any]:
             a_							,		a_					:		Tuple							=  self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    a_					:		Optional[Any]							=  model_class(SCREAMING_SNAKE_CASE__     )
                    self.assertIsInstance(model.get_input_embeddings()     ,							(nn.Module)     )
                    a_					:		List[Any]							=  model.get_output_embeddings()
                    self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__     ,							nn.Linear     )     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			List[Any]:
             a_							,		a_					:		Dict							=  self.model_tester.prepare_config_and_inputs_for_common()

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

                    a_					:		Tuple							=  ['pixel_values']
                    self.assertListEqual(arg_names[:1]     ,							SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Optional[Any]:
             a_					:		int							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Dict:
             a_					:		Union[str, Any]							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Union[str, Any]:
             a_					:		Tuple							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       List[str]=False     )		->			Union[str, Any]:
             a_					:		List[Any]							=  super()._prepare_for_class(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							return_labels=SCREAMING_SNAKE_CASE__     )

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

             return inputs_dict
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			List[str]:
             if not self.model_tester.is_training:
                    return

             a_							,		a_					:		Dict							=  self.model_tester.prepare_config_and_inputs_for_common()
             a_					:		Optional[int]							=  True

             for model_class in self.all_model_classes:
                    # DeiTForImageClassificationWithTeacher supports inference-only
                    if (
                        model_class in get_values(SCREAMING_SNAKE_CASE__     )
                        or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
                    ):
                           continue
                    a_					:		Tuple							=  model_class(SCREAMING_SNAKE_CASE__     )
                    model.to(SCREAMING_SNAKE_CASE__     )
                    model.train()
                    a_					:		Optional[int]							=  self._prepare_for_class(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							return_labels=SCREAMING_SNAKE_CASE__     )
                    a_					:		List[Any]							=  model(**SCREAMING_SNAKE_CASE__     ).loss
                    loss.backward()
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Any:
             a_							,		a_					:		Optional[Any]							=  self.model_tester.prepare_config_and_inputs_for_common()
             if not self.model_tester.is_training:
                    return

             a_					:		Optional[Any]							=  False
             a_					:		Any							=  True

             for model_class in self.all_model_classes:
                    if model_class in get_values(SCREAMING_SNAKE_CASE__     ) or not model_class.supports_gradient_checkpointing:
                           continue
                    # DeiTForImageClassificationWithTeacher supports inference-only
                    if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                           continue
                    a_					:		Union[str, Any]							=  model_class(SCREAMING_SNAKE_CASE__     )
                    model.gradient_checkpointing_enable()
                    model.to(SCREAMING_SNAKE_CASE__     )
                    model.train()
                    a_					:		int							=  self._prepare_for_class(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							return_labels=SCREAMING_SNAKE_CASE__     )
                    a_					:		Dict							=  model(**SCREAMING_SNAKE_CASE__     ).loss
                    loss.backward()
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Optional[Any]:
             a_							,		a_					:		Union[str, Any]							=  self.model_tester.prepare_config_and_inputs_for_common()

             a_					:		List[Any]							=  [
                 {'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(SCREAMING_SNAKE_CASE__     ),
                            *get_values(SCREAMING_SNAKE_CASE__     ),
                        ]
                        or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
                    ):
                           continue

                    for problem_type in problem_types:
                           with self.subTest(msg=F"""Testing {model_class} with {problem_type["title"]}"""     ):
                                  a_					:		Optional[int]							=  problem_type['title']
                                  a_					:		Union[str, Any]							=  problem_type['num_labels']

                                  a_					:		Optional[int]							=  model_class(SCREAMING_SNAKE_CASE__     )
                                  model.to(SCREAMING_SNAKE_CASE__     )
                                  model.train()

                                  a_					:		Dict							=  self._prepare_for_class(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							return_labels=SCREAMING_SNAKE_CASE__     )

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

                                  a_					:		Tuple							=  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=SCREAMING_SNAKE_CASE__     ) as warning_list:
                                         a_					:		Optional[Any]							=  model(**SCREAMING_SNAKE_CASE__     ).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        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Any:
             for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    a_					:		Dict							=  DeiTModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
                    self.assertIsNotNone(SCREAMING_SNAKE_CASE__     )







def        SCREAMING_SNAKE_CASE_						(			)	->		Optional[Any]:

       """simple docstring"""

       a_					:		Optional[Any]							=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       return image
@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      @cached_property
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Any:
             return (
                 DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'     )
                 if is_vision_available()
                 else None
             )
      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			int:
             a_					:		Union[str, Any]							=  DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'     ).to(
                 SCREAMING_SNAKE_CASE__     )

             a_					:		Union[str, Any]							=  self.default_image_processor
             a_					:		List[Any]							=  prepare_img()
             a_					:		Any							=  image_processor(images=SCREAMING_SNAKE_CASE__     ,							return_tensors='pt'     ).to(SCREAMING_SNAKE_CASE__     )

             # forward pass
             with torch.no_grad():
                    a_					:		Any							=  model(**SCREAMING_SNAKE_CASE__     )

             # verify the logits
             a_					:		Tuple							=  torch.Size((1, 1_0_0_0)     )
             self.assertEqual(outputs.logits.shape     ,							SCREAMING_SNAKE_CASE__     )

             a_					:		str							=  torch.tensor([-1.0266, 0.1912, -1.2861]     ).to(SCREAMING_SNAKE_CASE__     )

             self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,							SCREAMING_SNAKE_CASE__     ,							atol=1E-4     )     )

      @slow
      @require_accelerate
      @require_torch_gpu
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			List[str]:
             a_					:		str							=  DeiTModel.from_pretrained(
                 'facebook/deit-base-distilled-patch16-224'     ,							torch_dtype=torch.floataa     ,							device_map='auto'     )
             a_					:		List[Any]							=  self.default_image_processor

             a_					:		int							=  prepare_img()
             a_					:		Dict							=  image_processor(images=SCREAMING_SNAKE_CASE__     ,							return_tensors='pt'     )
             a_					:		List[str]							=  inputs.pixel_values.to(SCREAMING_SNAKE_CASE__     )

             # forward pass to make sure inference works in fp16
             with torch.no_grad():
                    a_					:		Tuple							=  model(SCREAMING_SNAKE_CASE__     )






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,
    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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_      : Optional[int]  					=       logging.get_logger(__name__)
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Union[str, Any]        =						['''pixel_values''']
      def __init__(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       Optional[Dict[str, int]] = None     ,							SCREAMING_SNAKE_CASE__				:       PILImageResampling = PILImageResampling.BILINEAR     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int] = None     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       Union[int, float] = 1 / 2_5_5     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[float, List[float]]] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[float, List[float]]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			None:
             super().__init__(**SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  size if size is not None else {'shortest_edge': 2_5_6}
             a_					:		Any							=  get_size_dict(SCREAMING_SNAKE_CASE__     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
             a_					:		Optional[int]							=  get_size_dict(SCREAMING_SNAKE_CASE__     )
             a_					:		List[str]							=  do_resize
             a_					:		Dict							=  size
             a_					:		Optional[Any]							=  resample
             a_					:		Optional[int]							=  do_center_crop
             a_					:		Dict							=  crop_size
             a_					:		int							=  do_rescale
             a_					:		int							=  rescale_factor
             a_					:		Tuple							=  do_normalize
             a_					:		int							=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             a_					:		Tuple							=  image_std if image_std is not None else IMAGENET_STANDARD_STD
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int]     ,							SCREAMING_SNAKE_CASE__				:       PILImageResampling = PILImageResampling.BICUBIC     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			np.ndarray:
             a_					:		List[Any]							=  get_size_dict(SCREAMING_SNAKE_CASE__     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             if "shortest_edge" not in size:
                    raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""     )
             a_					:		Tuple							=  get_resize_output_image_size(SCREAMING_SNAKE_CASE__     ,							size=size['shortest_edge']     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             return resize(SCREAMING_SNAKE_CASE__     ,							size=SCREAMING_SNAKE_CASE__     ,							resample=SCREAMING_SNAKE_CASE__     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			np.ndarray:
             a_					:		str							=  get_size_dict(SCREAMING_SNAKE_CASE__     )
             return center_crop(SCREAMING_SNAKE_CASE__     ,							size=(size['height'], size['width'])     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       float     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			np.ndarray:
             return rescale(SCREAMING_SNAKE_CASE__     ,							scale=SCREAMING_SNAKE_CASE__     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       Union[float, List[float]]     ,							SCREAMING_SNAKE_CASE__				:       Union[float, List[float]]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       List[str]     ,							)		->			np.ndarray:
             return normalize(SCREAMING_SNAKE_CASE__     ,							mean=SCREAMING_SNAKE_CASE__     ,							std=SCREAMING_SNAKE_CASE__     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

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

             a_					:		Optional[int]							=  make_list_of_images(SCREAMING_SNAKE_CASE__     )

             if not valid_images(SCREAMING_SNAKE_CASE__     ):
                    raise ValueError(
                        'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                        'torch.Tensor, tf.Tensor or jax.ndarray.'     )

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

             if do_center_crop and crop_size is None:
                    raise ValueError('Crop size must be specified if do_center_crop is True.'     )

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

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

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

             if do_resize:
                    a_					:		str							=  [self.resize(image=SCREAMING_SNAKE_CASE__     ,							size=SCREAMING_SNAKE_CASE__     ,							resample=SCREAMING_SNAKE_CASE__     ) for image in images]

             if do_center_crop:
                    a_					:		int							=  [self.center_crop(image=SCREAMING_SNAKE_CASE__     ,							size=SCREAMING_SNAKE_CASE__     ) for image in images]

             if do_rescale:
                    a_					:		Optional[Any]							=  [self.rescale(image=SCREAMING_SNAKE_CASE__     ,							scale=SCREAMING_SNAKE_CASE__     ) for image in images]

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

             a_					:		Dict							=  [to_channel_dimension_format(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ) for image in images]

             a_					:		Tuple							=  {'pixel_values': images}
             return BatchFeature(data=SCREAMING_SNAKE_CASE__     ,							tensor_type=SCREAMING_SNAKE_CASE__     )


