

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 ViTImageProcessor






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







			def __init__(	self							:       Tuple					,					a							:       int					,					a							:       Optional[int]=13					,					a							:       Optional[int]=3					,					a							:       int=224					,					a							:       Optional[int]=30					,					a							:       int=400					,					a							:       Union[str, Any]=True					,					a							:       int=None					,					a							:       Tuple=True					,					a							:       Tuple=[0.5, 0.5, 0.5]					,					a							:       Optional[int]=[0.5, 0.5, 0.5]					,					)							->				Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				size if size is not None else {"height": 18, "width": 18}
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				parent
				SCREAMING_SNAKE_CASE      :					int    =				batch_size
				SCREAMING_SNAKE_CASE      :					int    =				num_channels
				SCREAMING_SNAKE_CASE      :					Any    =				image_size
				SCREAMING_SNAKE_CASE      :					Tuple    =				min_resolution
				SCREAMING_SNAKE_CASE      :					str    =				max_resolution
				SCREAMING_SNAKE_CASE      :					int    =				do_resize
				SCREAMING_SNAKE_CASE      :					List[Any]    =				size
				SCREAMING_SNAKE_CASE      :					int    =				do_normalize
				SCREAMING_SNAKE_CASE      :					Tuple    =				image_mean
				SCREAMING_SNAKE_CASE      :					Tuple    =				image_std






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

				"""simple docstring"""

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






@require_torch
@require_vision
class _UpperCamelCase							(							__A							,		unittest.TestCase      ):
			'''simple docstring'''







			lowerCamelCase__							=ViTImageProcessor if is_vision_available() else None
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				EfficientFormerImageProcessorTester(self		)
			@property
			def 							__UpperCamelCase       (	self							:       Any		)							->				List[str]:

				"""simple docstring"""

				return self.image_proc_tester.prepare_image_processor_dict()
			def 							__UpperCamelCase       (	self							:       List[Any]		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.image_processing_class(**self.image_processor_dict		)
				self.assertTrue(hasattr(a					,					"image_mean"		)		)
				self.assertTrue(hasattr(a					,					"image_std"		)		)
				self.assertTrue(hasattr(a					,					"do_normalize"		)		)
				self.assertTrue(hasattr(a					,					"do_resize"		)		)
				self.assertTrue(hasattr(a					,					"size"		)		)
			def 							__UpperCamelCase       (	self							:       int		)							->				str:

				"""simple docstring"""

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Tuple    =				self.image_processing_class(**self.image_processor_dict		)
				# create random PIL images
				SCREAMING_SNAKE_CASE      :					Any    =				prepare_image_inputs(self.image_proc_tester					,					equal_resolution=a		)
				for image in image_inputs:
					self.assertIsInstance(a					,					Image.Image		)

				# Test not batched input
				SCREAMING_SNAKE_CASE      :					List[str]    =				image_processor(image_inputs[0]					,					return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape					,					(
				        1,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )					,					)

				# Test batched
				SCREAMING_SNAKE_CASE      :					str    =				image_processor(a					,					return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape					,					(
				        self.image_proc_tester.batch_size,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )					,					)
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				self.image_processing_class(**self.image_processor_dict		)
				# create random numpy tensors
				SCREAMING_SNAKE_CASE      :					int    =				prepare_image_inputs(self.image_proc_tester					,					equal_resolution=a					,					numpify=a		)
				for image in image_inputs:
					self.assertIsInstance(a					,					np.ndarray		)

				# Test not batched input
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				image_processor(image_inputs[0]					,					return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape					,					(
				        1,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )					,					)

				# Test batched
				SCREAMING_SNAKE_CASE      :					Any    =				image_processor(a					,					return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape					,					(
				        self.image_proc_tester.batch_size,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )					,					)






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				self.image_processing_class(**self.image_processor_dict		)
				# create random PyTorch tensors
				SCREAMING_SNAKE_CASE      :					Any    =				prepare_image_inputs(self.image_proc_tester					,					equal_resolution=a					,					torchify=a		)
				for image in image_inputs:
					self.assertIsInstance(a					,					torch.Tensor		)

				# Test not batched input
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				image_processor(image_inputs[0]					,					return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape					,					(
				        1,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )					,					)

				# Test batched
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				image_processor(a					,					return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape					,					(
				        self.image_proc_tester.batch_size,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )					,					)






'''simple docstring'''







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase_					:    Dict      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    Optional[int]      	=			{
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='deta'
				__a            ={
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				}




				def __init__(  self     :					List[str]					,				__a     :					List[str]=None					,				__a     :					Dict=9_00					,				__a     :					str=20_48					,				__a     :					Tuple=6					,				__a     :					List[str]=20_48					,				__a     :					str=8					,				__a     :					Union[str, Any]=6					,				__a     :					int=10_24					,				__a     :					List[Any]=8					,				__a     :					Dict=0.0					,				__a     :					Tuple=True					,				__a     :					Optional[Any]="relu"					,				__a     :					Tuple=2_56					,				__a     :					Optional[Any]=0.1					,				__a     :					int=0.0					,				__a     :					List[Any]=0.0					,				__a     :					Optional[int]=0.02					,				__a     :					str=1.0					,				__a     :					Dict=True					,				__a     :					Dict=False					,				__a     :					Optional[int]="sine"					,				__a     :					Any=5					,				__a     :					List[str]=4					,				__a     :					Optional[int]=4					,				__a     :					List[str]=True					,				__a     :					str=3_00					,				__a     :					int=True					,				__a     :					int=True					,				__a     :					Tuple=1					,				__a     :					Optional[int]=5					,				__a     :					Tuple=2					,				__a     :					Dict=1					,				__a     :					Optional[int]=1					,				__a     :					Any=5					,				__a     :					Optional[int]=2					,				__a     :					Dict=0.1					,				__a     :					str=0.25					,				**__a     :					Tuple					,				):
								if backbone_config is None:
												logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."					)
												_a								= CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]					)
								else:
												if isinstance(__a					,				__a					):
																_a								= backbone_config.pop("model_type"					)
																_a								= CONFIG_MAPPING[backbone_model_type]
																_a								= config_class.from_dict(__a					)

								_a								= backbone_config
								_a								= num_queries
								_a								= max_position_embeddings
								_a								= d_model
								_a								= encoder_ffn_dim
								_a								= encoder_layers
								_a								= encoder_attention_heads
								_a								= decoder_ffn_dim
								_a								= decoder_layers
								_a								= decoder_attention_heads
								_a								= dropout
								_a								= attention_dropout
								_a								= activation_dropout
								_a								= activation_function
								_a								= init_std
								_a								= init_xavier_std
								_a								= encoder_layerdrop
								_a								= auxiliary_loss
								_a								= position_embedding_type
								# deformable attributes
								_a								= num_feature_levels
								_a								= encoder_n_points
								_a								= decoder_n_points
								_a								= two_stage
								_a								= two_stage_num_proposals
								_a								= with_box_refine
								_a								= assign_first_stage
								if two_stage is True and with_box_refine is False:
												raise ValueError("If two_stage is True, with_box_refine must be True."					)
								# Hungarian matcher
								_a								= class_cost
								_a								= bbox_cost
								_a								= giou_cost
								# Loss coefficients
								_a								= mask_loss_coefficient
								_a								= dice_loss_coefficient
								_a								= bbox_loss_coefficient
								_a								= giou_loss_coefficient
								_a								= eos_coefficient
								_a								= focal_alpha
								super().__init__(is_encoder_decoder=__a					,				**__a					)




				@property
				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								return self.encoder_attention_heads




				@property
				def   UpperCamelCase__   (  self     :					Dict					):
								return self.d_model




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= copy.deepcopy(self.__dict__					)
								_a								= self.backbone_config.to_dict()
								_a								= self.__class__.model_type
								return output
