


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DeiTImageProcessor



class  UpperCAmelCase_						(				unittest.TestCase):
       '''simple docstring'''






       def __init__(  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=7			,       __SCREAMING_SNAKE_CASE=3			,       __SCREAMING_SNAKE_CASE=18			,       __SCREAMING_SNAKE_CASE=30			,       __SCREAMING_SNAKE_CASE=400			,       __SCREAMING_SNAKE_CASE=True			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=True			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=True			,       __SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]			,       __SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]			,       ):




              """simple docstring"""




              UpperCamelCase     :						Optional[int] 						=		size if size is not None else {"""height""": 20, """width""": 20}
              UpperCamelCase     :						List[str] 						=		crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

              UpperCamelCase     :						Union[str, Any] 						=		parent
              UpperCamelCase     :						List[Any] 						=		batch_size
              UpperCamelCase     :						Any 						=		num_channels
              UpperCamelCase     :						Tuple 						=		image_size
              UpperCamelCase     :						Dict 						=		min_resolution
              UpperCamelCase     :						List[Any] 						=		max_resolution
              UpperCamelCase     :						Dict 						=		do_resize
              UpperCamelCase     :						Optional[Any] 						=		size
              UpperCamelCase     :						Dict 						=		do_center_crop
              UpperCamelCase     :						Optional[int] 						=		crop_size
              UpperCamelCase     :						Union[str, Any] 						=		do_normalize
              UpperCamelCase     :						Dict 						=		image_mean
              UpperCamelCase     :						int 						=		image_std






       def 			_lowercase (  self    ):




              """simple docstring"""




              return {
                  "do_resize": self.do_resize,
                  "size": self.size,
                  "do_center_crop": self.do_center_crop,
                  "crop_size": self.crop_size,
                  "do_normalize": self.do_normalize,
                  "image_mean": self.image_mean,
                  "image_std": self.image_std,
              }




@require_torch
@require_vision
class  UpperCAmelCase_						(				__lowerCAmelCase,  unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase   :  List[str]				=       DeiTImageProcessor if is_vision_available() else None
       __UpperCamelCase   :  Any				=       True






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[int] 						=		DeiTImageProcessingTester(self    )






       @property
       def 			_lowercase (  self    ):




              """simple docstring"""




              return self.image_processor_tester.prepare_image_processor_dict()






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Any 						=		self.image_processing_class(**self.image_processor_dict    )
              self.assertTrue(hasattr(_UpperCamelCase			,       '''do_resize'''    )    )
              self.assertTrue(hasattr(_UpperCamelCase			,       '''size'''    )    )
              self.assertTrue(hasattr(_UpperCamelCase			,       '''do_center_crop'''    )    )
              self.assertTrue(hasattr(_UpperCamelCase			,       '''center_crop'''    )    )
              self.assertTrue(hasattr(_UpperCamelCase			,       '''do_normalize'''    )    )
              self.assertTrue(hasattr(_UpperCamelCase			,       '''image_mean'''    )    )
              self.assertTrue(hasattr(_UpperCamelCase			,       '''image_std'''    )    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						str 						=		self.image_processing_class.from_dict(self.image_processor_dict    )
              self.assertEqual(image_processor.size			,       {'''height''': 20, '''width''': 20}    )
              self.assertEqual(image_processor.crop_size			,       {'''height''': 18, '''width''': 18}    )

              UpperCamelCase     :						int 						=		self.image_processing_class.from_dict(self.image_processor_dict			,       size=42			,       crop_size=84    )
              self.assertEqual(image_processor.size			,       {'''height''': 42, '''width''': 42}    )
              self.assertEqual(image_processor.crop_size			,       {'''height''': 84, '''width''': 84}    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              pass






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						str 						=		self.image_processing_class(**self.image_processor_dict    )
              # create random PIL images
              UpperCamelCase     :						str 						=		prepare_image_inputs(self.image_processor_tester			,       equal_resolution=_UpperCamelCase    )
              for image in image_inputs:
                     self.assertIsInstance(_UpperCamelCase			,       Image.Image    )

              # Test not batched input
              UpperCamelCase     :						Dict 						=		image_processing(image_inputs[0]			,       return_tensors='''pt'''    ).pixel_values
              self.assertEqual(
                  encoded_images.shape			,       (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size['''height'''],
                      self.image_processor_tester.crop_size['''width'''],
                  )			,       )

              # Test batched
              UpperCamelCase     :						Union[str, Any] 						=		image_processing(_UpperCamelCase			,       return_tensors='''pt'''    ).pixel_values
              self.assertEqual(
                  encoded_images.shape			,       (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size['''height'''],
                      self.image_processor_tester.crop_size['''width'''],
                  )			,       )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[int] 						=		self.image_processing_class(**self.image_processor_dict    )
              # create random numpy tensors
              UpperCamelCase     :						Dict 						=		prepare_image_inputs(self.image_processor_tester			,       equal_resolution=_UpperCamelCase			,       numpify=_UpperCamelCase    )
              for image in image_inputs:
                     self.assertIsInstance(_UpperCamelCase			,       np.ndarray    )

              # Test not batched input
              UpperCamelCase     :						int 						=		image_processing(image_inputs[0]			,       return_tensors='''pt'''    ).pixel_values
              self.assertEqual(
                  encoded_images.shape			,       (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size['''height'''],
                      self.image_processor_tester.crop_size['''width'''],
                  )			,       )

              # Test batched
              UpperCamelCase     :						List[str] 						=		image_processing(_UpperCamelCase			,       return_tensors='''pt'''    ).pixel_values
              self.assertEqual(
                  encoded_images.shape			,       (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size['''height'''],
                      self.image_processor_tester.crop_size['''width'''],
                  )			,       )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[Any] 						=		self.image_processing_class(**self.image_processor_dict    )
              # create random PyTorch tensors
              UpperCamelCase     :						Union[str, Any] 						=		prepare_image_inputs(self.image_processor_tester			,       equal_resolution=_UpperCamelCase			,       torchify=_UpperCamelCase    )
              for image in image_inputs:
                     self.assertIsInstance(_UpperCamelCase			,       torch.Tensor    )

              # Test not batched input
              UpperCamelCase     :						Any 						=		image_processing(image_inputs[0]			,       return_tensors='''pt'''    ).pixel_values
              self.assertEqual(
                  encoded_images.shape			,       (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size['''height'''],
                      self.image_processor_tester.crop_size['''width'''],
                  )			,       )

              # Test batched
              UpperCamelCase     :						Any 						=		image_processing(_UpperCamelCase			,       return_tensors='''pt'''    ).pixel_values
              self.assertEqual(
                  encoded_images.shape			,       (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size['''height'''],
                      self.image_processor_tester.crop_size['''width'''],
                  )			,       )


import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


lowerCAmelCase      :				List[Any]     			=logging.get_logger(__name__)

lowerCAmelCase      :				Optional[int]     			={
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}




class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      _snake_case     		=							'vit-hybrid'



      def __init__(       self  :					str     ,	_UpperCamelCase  :					int=None     ,	_UpperCamelCase  :					Union[str, Any]=768     ,	_UpperCamelCase  :					List[Any]=12     ,	_UpperCamelCase  :					List[Any]=12     ,	_UpperCamelCase  :					Optional[int]=3072     ,	_UpperCamelCase  :					Optional[int]="gelu"     ,	_UpperCamelCase  :					int=0.0     ,	_UpperCamelCase  :					Tuple=0.0     ,	_UpperCamelCase  :					Dict=0.0_2     ,	_UpperCamelCase  :					Dict=1E-1_2     ,	_UpperCamelCase  :					Union[str, Any]=224     ,	_UpperCamelCase  :					str=1     ,	_UpperCamelCase  :					Optional[int]=3     ,	_UpperCamelCase  :					Dict=[1, 1024, 24, 24]     ,	_UpperCamelCase  :					Optional[Any]=True     ,	**_UpperCamelCase  :					List[Any]     ,	)					->str:


          """simple docstring"""


          super().__init__(**_UpperCamelCase)

          if backbone_config is None:
              logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone.""")
              _lowerCamelCase  :		Tuple			    =    {
                  """global_padding""": """same""",
                  """layer_type""": """bottleneck""",
                  """depths""": [3, 4, 9],
                  """out_features""": ["""stage3"""],
                  """embedding_dynamic_padding""": True,
              }

          if isinstance(_UpperCamelCase     ,	_UpperCamelCase):
              if "model_type" in backbone_config:
                  _lowerCamelCase  :		int			    =    CONFIG_MAPPING[backbone_config["""model_type"""]]
              else:
                  logger.info(
                      """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.""")
                  _lowerCamelCase  :		Optional[int]			    =    BitConfig
              _lowerCamelCase  :		List[Any]			    =    backbone_config_class(**_UpperCamelCase)

          _lowerCamelCase  :		Any			    =    backbone_featmap_shape
          _lowerCamelCase  :		Dict			    =    backbone_config
          _lowerCamelCase  :		Tuple			    =    hidden_size
          _lowerCamelCase  :		Union[str, Any]			    =    num_hidden_layers
          _lowerCamelCase  :		Optional[Any]			    =    num_attention_heads
          _lowerCamelCase  :		Optional[Any]			    =    intermediate_size
          _lowerCamelCase  :		List[Any]			    =    hidden_act
          _lowerCamelCase  :		Dict			    =    hidden_dropout_prob
          _lowerCamelCase  :		Optional[Any]			    =    attention_probs_dropout_prob
          _lowerCamelCase  :		Union[str, Any]			    =    initializer_range
          _lowerCamelCase  :		Any			    =    layer_norm_eps
          _lowerCamelCase  :		Optional[int]			    =    image_size
          _lowerCamelCase  :		Any			    =    patch_size
          _lowerCamelCase  :		Optional[Any]			    =    num_channels
          _lowerCamelCase  :		List[str]			    =    qkv_bias




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->Dict[str, any]:


          """simple docstring"""


          _lowerCamelCase  :		Dict			    =    copy.deepcopy(self.__dict__)
          _lowerCamelCase  :		List[Any]			    =    self.backbone_config.to_dict()
          _lowerCamelCase  :		List[str]			    =    self.__class__.model_type
          return output




