
'''simple docstring'''



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


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import CLIPImageProcessor





class      A							(    unittest.TestCase			):



			def __init__(   self    ,       lowerCamelCase__    ,       lowerCamelCase__=7    ,       lowerCamelCase__=3    ,       lowerCamelCase__=18    ,       lowerCamelCase__=30    ,       lowerCamelCase__=400    ,       lowerCamelCase__=True    ,       lowerCamelCase__=None    ,       lowerCamelCase__=True    ,       lowerCamelCase__=None    ,       lowerCamelCase__=True    ,       lowerCamelCase__=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]    ,       lowerCamelCase__=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]    ,       lowerCamelCase__=True    ,       )     ->						Union[str, Any]:




									'''simple docstring'''



									lowercase__								=    size if size is not None else {"""shortest_edge""": 20}
									lowercase__								=    crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
									lowercase__								=    parent
									lowercase__								=    batch_size
									lowercase__								=    num_channels
									lowercase__								=    image_size
									lowercase__								=    min_resolution
									lowercase__								=    max_resolution
									lowercase__								=    do_resize
									lowercase__								=    size
									lowercase__								=    do_center_crop
									lowercase__								=    crop_size
									lowercase__								=    do_normalize
									lowercase__								=    image_mean
									lowercase__								=    image_std
									lowercase__								=    do_convert_rgb



			def A__	(   self    )     ->						Tuple:




									'''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,
									    "do_convert_rgb": self.do_convert_rgb,
									}



			def A__	(   self    ,       lowerCamelCase__=False    ,       lowerCamelCase__=False    ,       lowerCamelCase__=False    )     ->						Any:




									'''simple docstring'''



									assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

									if equal_resolution:
															lowercase__								=    []
															for i in range(self.batch_size    ):
																					image_inputs.append(
																					    np.random.randint(
																					        255    ,       size=(self.num_channels, self.max_resolution, self.max_resolution)    ,       dtype=np.uinta    )    )
									else:
															lowercase__								=    []
															for i in range(self.batch_size    ):
																					lowercase__	,  lowercase__								=    np.random.choice(np.arange(self.min_resolution    ,       self.max_resolution    )    ,       2    )
																					image_inputs.append(np.random.randint(255    ,       size=(self.num_channels, width, height)    ,       dtype=np.uinta    )    )

									if not numpify and not torchify:
															# PIL expects the channel dimension as last dimension
															lowercase__								=    [Image.fromarray(np.moveaxis(__UpperCamelCase    ,       0    ,       -1    )    ) for x in image_inputs]

									if torchify:
															lowercase__								=    [torch.from_numpy(__UpperCamelCase    ) for x in image_inputs]

									return image_inputs





@require_torch
@require_vision
class      A							(    UpperCAmelCase_						,  unittest.TestCase			):
			lowerCamelCase							:							Union[str, Any]    						=					CLIPImageProcessor if is_vision_available() else None



			def A__	(   self    )     ->						str:




									'''simple docstring'''



									lowercase__								=    CLIPImageProcessingTester(self    )



			@property
			def A__	(   self    )     ->						Union[str, Any]:




									'''simple docstring'''



									return self.image_processor_tester.prepare_image_processor_dict()



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




									'''simple docstring'''



									lowercase__								=    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"""    )    )
									self.assertTrue(hasattr(__UpperCamelCase    ,       """do_convert_rgb"""    )    )



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




									'''simple docstring'''



									lowercase__								=    self.image_processing_class.from_dict(self.image_processor_dict    )
									self.assertEqual(image_processor.size    ,       {"""shortest_edge""": 20}    )
									self.assertEqual(image_processor.crop_size    ,       {"""height""": 18, """width""": 18}    )

									lowercase__								=    self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42    ,       crop_size=84    )
									self.assertEqual(image_processor.size    ,       {"""shortest_edge""": 42}    )
									self.assertEqual(image_processor.crop_size    ,       {"""height""": 84, """width""": 84}    )



			def A__	(   self    )     ->						Any:




									'''simple docstring'''



									pass



			def A__	(   self    )     ->						int:




									'''simple docstring'''



									lowercase__								=    self.image_processing_class(**self.image_processor_dict    )
									# create random PIL images
									lowercase__								=    self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase    )
									for image in image_inputs:
															self.assertIsInstance(__UpperCamelCase    ,       Image.Image    )

									# Test not batched input
									lowercase__								=    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
									lowercase__								=    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 A__	(   self    )     ->						Tuple:




									'''simple docstring'''



									lowercase__								=    self.image_processing_class(**self.image_processor_dict    )
									# create random numpy tensors
									lowercase__								=    self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase    ,       numpify=__UpperCamelCase    )
									for image in image_inputs:
															self.assertIsInstance(__UpperCamelCase    ,       np.ndarray    )

									# Test not batched input
									lowercase__								=    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
									lowercase__								=    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 A__	(   self    )     ->						Tuple:




									'''simple docstring'''



									lowercase__								=    self.image_processing_class(**self.image_processor_dict    )
									# create random PyTorch tensors
									lowercase__								=    self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase    ,       torchify=__UpperCamelCase    )
									for image in image_inputs:
															self.assertIsInstance(__UpperCamelCase    ,       torch.Tensor    )

									# Test not batched input
									lowercase__								=    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
									lowercase__								=    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"""],
									    )    ,       )





@require_torch
@require_vision
class      A							(    UpperCAmelCase_						,  unittest.TestCase			):
			lowerCamelCase							:							Any    						=					CLIPImageProcessor if is_vision_available() else None



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




									'''simple docstring'''



									lowercase__								=    CLIPImageProcessingTester(self    ,       num_channels=4    )
									lowercase__								=    3



			@property
			def A__	(   self    )     ->						int:




									'''simple docstring'''



									return self.image_processor_tester.prepare_image_processor_dict()



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




									'''simple docstring'''



									lowercase__								=    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"""    )    )
									self.assertTrue(hasattr(__UpperCamelCase    ,       """do_convert_rgb"""    )    )



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




									'''simple docstring'''



									pass



			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



									lowercase__								=    self.image_processing_class(**self.image_processor_dict    )
									# create random PIL images
									lowercase__								=    self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase    )
									for image in image_inputs:
															self.assertIsInstance(__UpperCamelCase    ,       Image.Image    )

									# Test not batched input
									lowercase__								=    image_processing(image_inputs[0]    ,       return_tensors="""pt"""    ).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,       (
									        1,
									        self.expected_encoded_image_num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )    ,       )

									# Test batched
									lowercase__								=    image_processing(__UpperCamelCase    ,       return_tensors="""pt"""    ).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,       (
									        self.image_processor_tester.batch_size,
									        self.expected_encoded_image_num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )    ,       )








'''simple docstring'''



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


__A       =      logging.get_logger(__name__)

__A       =      {
    "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      A							(    __UpperCAmelCase			):
			lowerCamelCase							:							List[str]    						=					"""vit-hybrid"""



			def __init__(   self    ,       lowerCamelCase__=None    ,       lowerCamelCase__=768    ,       lowerCamelCase__=12    ,       lowerCamelCase__=12    ,       lowerCamelCase__=3_072    ,       lowerCamelCase__="gelu"    ,       lowerCamelCase__=0.0    ,       lowerCamelCase__=0.0    ,       lowerCamelCase__=0.02    ,       lowerCamelCase__=1e-12    ,       lowerCamelCase__=224    ,       lowerCamelCase__=1    ,       lowerCamelCase__=3    ,       lowerCamelCase__=[1, 1_024, 24, 24]    ,       lowerCamelCase__=True    ,       **lowerCamelCase__    ,       )     ->						Optional[Any]:




									'''simple docstring'''



									super().__init__(**lowerCamelCase__    )

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

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

									lowercase__								=    backbone_featmap_shape
									lowercase__								=    backbone_config
									lowercase__								=    hidden_size
									lowercase__								=    num_hidden_layers
									lowercase__								=    num_attention_heads
									lowercase__								=    intermediate_size
									lowercase__								=    hidden_act
									lowercase__								=    hidden_dropout_prob
									lowercase__								=    attention_probs_dropout_prob
									lowercase__								=    initializer_range
									lowercase__								=    layer_norm_eps
									lowercase__								=    image_size
									lowercase__								=    patch_size
									lowercase__								=    num_channels
									lowercase__								=    qkv_bias



			def A__	(   self    )     ->						Dict[str, any]:




									'''simple docstring'''



									lowercase__								=    copy.deepcopy(self.__dict__    )
									lowercase__								=    self.backbone_config.to_dict()
									lowercase__								=    self.__class__.model_type
									return output






