

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_video_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import VideoMAEImageProcessor



class 					A__(unittest.TestCase       ):
						"""simple docstring"""






						def __init__(						self     ,			_lowercase     ,			_lowercase=7     ,			_lowercase=3     ,			_lowercase=10     ,			_lowercase=18     ,			_lowercase=30     ,			_lowercase=400     ,			_lowercase=True     ,			_lowercase=None     ,			_lowercase=True     ,			_lowercase=[0.5, 0.5, 0.5]     ,			_lowercase=[0.5, 0.5, 0.5]     ,			_lowercase=None     ,			)    ->   str:
									a_	:	Optional[int]          =   size if size is not None else {'shortest_edge': 18}
									a_	:	Union[str, Any]          =   crop_size if crop_size is not None else {'height': 18, 'width': 18}

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





						def  UpperCamelCase__				(						self						)    ->   Dict:
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "crop_size": self.crop_size,
									}







@require_torch
@require_vision
class 					A__(a__,     unittest.TestCase       ):
						"""simple docstring"""






						_A						:							List[str]									= VideoMAEImageProcessor if is_vision_available() else None





						def  UpperCamelCase__				(						self						)    ->   Dict:
									a_	:	Any          =   VideoMAEImageProcessingTester(self						)





						@property
						def  UpperCamelCase__				(						self						)    ->   List[str]:
									return self.image_processor_tester.prepare_image_processor_dict()





						def  UpperCamelCase__				(						self						)    ->   Union[str, Any]:
									a_	:	Optional[Any]          =   self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,			"""image_mean"""						)						)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,			"""image_std"""						)						)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,			"""do_normalize"""						)						)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,			"""do_resize"""						)						)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,			"""do_center_crop"""						)						)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,			"""size"""						)						)





						def  UpperCamelCase__				(						self						)    ->   int:
									a_	:	Tuple          =   self.image_processing_class.from_dict(self.image_processor_dict						)
									self.assertEqual(image_processor.size     ,			{"""shortest_edge""": 18}						)
									self.assertEqual(image_processor.crop_size     ,			{"""height""": 18, """width""": 18}						)

									a_	:	Any          =   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  UpperCamelCase__				(						self						)    ->   Union[str, Any]:
									pass





						def  UpperCamelCase__				(						self						)    ->   Union[str, Any]:
									# Initialize image_processing
									a_	:	List[Any]          =   self.image_processing_class(**self.image_processor_dict						)
									# create random PIL videos
									a_	:	Dict          =   prepare_video_inputs(self.image_processor_tester     ,			equal_resolution=SCREAMING_SNAKE_CASE_						)
									for video in video_inputs:
												self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,			SCREAMING_SNAKE_CASE_						)
												self.assertIsInstance(video[0]     ,			Image.Image						)

									# Test not batched input
									a_	:	str          =   image_processing(video_inputs[0]     ,			return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_videos.shape     ,			(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )     ,			)

									# Test batched
									a_	:	str          =   image_processing(SCREAMING_SNAKE_CASE_     ,			return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_videos.shape     ,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )     ,			)





						def  UpperCamelCase__				(						self						)    ->   Dict:
									# Initialize image_processing
									a_	:	Union[str, Any]          =   self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									a_	:	Optional[Any]          =   prepare_video_inputs(self.image_processor_tester     ,			equal_resolution=SCREAMING_SNAKE_CASE_     ,			numpify=SCREAMING_SNAKE_CASE_						)
									for video in video_inputs:
												self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,			SCREAMING_SNAKE_CASE_						)
												self.assertIsInstance(video[0]     ,			np.ndarray						)

									# Test not batched input
									a_	:	int          =   image_processing(video_inputs[0]     ,			return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_videos.shape     ,			(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )     ,			)

									# Test batched
									a_	:	Optional[Any]          =   image_processing(SCREAMING_SNAKE_CASE_     ,			return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_videos.shape     ,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )     ,			)





						def  UpperCamelCase__				(						self						)    ->   Optional[Any]:
									# Initialize image_processing
									a_	:	List[Any]          =   self.image_processing_class(**self.image_processor_dict						)
									# create random PyTorch tensors
									a_	:	Optional[int]          =   prepare_video_inputs(self.image_processor_tester     ,			equal_resolution=SCREAMING_SNAKE_CASE_     ,			torchify=SCREAMING_SNAKE_CASE_						)
									for video in video_inputs:
												self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,			SCREAMING_SNAKE_CASE_						)
												self.assertIsInstance(video[0]     ,			torch.Tensor						)

									# Test not batched input
									a_	:	List[Any]          =   image_processing(video_inputs[0]     ,			return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_videos.shape     ,			(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )     ,			)

									# Test batched
									a_	:	Optional[Any]          =   image_processing(SCREAMING_SNAKE_CASE_     ,			return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_videos.shape     ,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        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


__snake_case							:			int      		= logging.get_logger(__name__)

__snake_case							:			Dict      		= {
    """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__(a_       ):
						"""simple docstring"""






						_A						:							Dict									= '''vit-hybrid'''





						def __init__(						self     ,			_lowercase=None     ,			_lowercase=768     ,			_lowercase=12     ,			_lowercase=12     ,			_lowercase=3_072     ,			_lowercase="gelu"     ,			_lowercase=0.0     ,			_lowercase=0.0     ,			_lowercase=0.0_2     ,			_lowercase=1e-12     ,			_lowercase=224     ,			_lowercase=1     ,			_lowercase=3     ,			_lowercase=[1, 1_024, 24, 24]     ,			_lowercase=True     ,			**_lowercase     ,			)    ->   Tuple:
									super().__init__(**_lowercase						)

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

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

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





						def  UpperCamelCase__				(						self						)    ->   Dict[str, any]:
									a_	:	List[str]          =   copy.deepcopy(self.__dict__						)
									a_	:	Optional[int]          =   self.backbone_config.to_dict()
									a_	:	List[str]          =   self.__class__.model_type
									return output


