
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor






class 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		def __init__(      self				:			Optional[Any]  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			bool = True  ,	UpperCamelCase__				:			Dict[str, int] = None  ,	UpperCamelCase__				:			int = 32  ,	UpperCamelCase__				:			bool = True  ,	UpperCamelCase__				:			Union[int, float] = 1 / 255  ,	UpperCamelCase__				:			bool = True  ,	UpperCamelCase__				:			bool = True  ,	UpperCamelCase__				:			Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073]  ,	UpperCamelCase__				:			Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711]  ,	UpperCamelCase__				:			bool = True  ,	UpperCamelCase__				:			Union[str, Any]=7  ,	UpperCamelCase__				:			str=30  ,	UpperCamelCase__				:			List[Any]=400  ,	UpperCamelCase__				:			Union[str, Any]=3  ,	)						->			Tuple:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						parent
				snake_case  :       str     			=						do_resize
				snake_case  :       Union[str, Any]     			=						size if size is not None else {'''shortest_edge''': 288}
				snake_case  :       Optional[Any]     			=						size_divisor
				snake_case  :       List[str]     			=						do_rescale
				snake_case  :       Union[str, Any]     			=						rescale_factor
				snake_case  :       str     			=						do_normalize
				snake_case  :       int     			=						do_center_crop
				snake_case  :       Optional[int]     			=						image_mean
				snake_case  :       Optional[Any]     			=						image_std
				snake_case  :       Optional[int]     			=						do_pad
				snake_case  :       List[str]     			=						batch_size
				snake_case  :       Optional[Any]     			=						num_channels
				snake_case  :       Union[str, Any]     			=						min_resolution
				snake_case  :       str     			=						max_resolution



		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			Tuple:






				"""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,
				    "size_divisor": self.size_divisor,
				}



		def       lowerCAmelCase				(      self				:			int  ,	UpperCamelCase__				:			Dict  ,	UpperCamelCase__				:			str=False      )						->			List[str]:






				"""simple docstring"""







				if not batched:
						snake_case  :       Union[str, Any]     			=						self.size['''shortest_edge''']
						snake_case  :       Dict     			=						image_inputs[0]
						if isinstance(UpperCamelCase__  ,	Image.Image      ):
								snake_case			,snake_case  :       int     			=						image.size
						else:
								snake_case			,snake_case  :       int     			=						image.shape[1], image.shape[2]
						snake_case  :       Tuple     			=						size / min(UpperCamelCase__  ,	UpperCamelCase__      )
						if h < w:
								snake_case			,snake_case  :       Optional[int]     			=						size, scale * w
						else:
								snake_case			,snake_case  :       Union[str, Any]     			=						scale * h, size

						snake_case  :       Optional[Any]     			=						int((1333 / 800) * size      )
						if max(UpperCamelCase__  ,	UpperCamelCase__      ) > max_size:
								snake_case  :       Tuple     			=						max_size / max(UpperCamelCase__  ,	UpperCamelCase__      )
								snake_case  :       Optional[int]     			=						newh * scale
								snake_case  :       Optional[Any]     			=						neww * scale

						snake_case			,snake_case  :       Optional[int]     			=						int(newh + 0.5      ), int(neww + 0.5      )
						snake_case			,snake_case  :       Optional[Any]     			=						(
						    newh // self.size_divisor * self.size_divisor,
						    neww // self.size_divisor * self.size_divisor,
						)

				else:
						snake_case  :       List[str]     			=						[]
						for image in image_inputs:
								snake_case			,snake_case  :       Optional[int]     			=						self.get_expected_values([image]      )
								expected_values.append((expected_height, expected_width)      )
						snake_case  :       Union[str, Any]     			=						max(UpperCamelCase__  ,	key=lambda UpperCamelCase__      : item[0]      )[0]
						snake_case  :       Any     			=						max(UpperCamelCase__  ,	key=lambda UpperCamelCase__      : item[1]      )[1]

				return expected_height, expected_width







@require_torch
@require_vision
class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						BridgeTowerImageProcessor if is_vision_available() else None



		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			Tuple:






				"""simple docstring"""







				snake_case  :       Dict     			=						BridgeTowerImageProcessingTester(self      )



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






				"""simple docstring"""







				return self.image_processor_tester.prepare_image_processor_dict()



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






				"""simple docstring"""







				snake_case  :       str     			=						self.image_processing_class(**self.image_processor_dict      )
				self.assertTrue(hasattr(UpperCamelCase__  ,	'''image_mean'''      )      )
				self.assertTrue(hasattr(UpperCamelCase__  ,	'''image_std'''      )      )
				self.assertTrue(hasattr(UpperCamelCase__  ,	'''do_normalize'''      )      )
				self.assertTrue(hasattr(UpperCamelCase__  ,	'''do_resize'''      )      )
				self.assertTrue(hasattr(UpperCamelCase__  ,	'''size'''      )      )
				self.assertTrue(hasattr(UpperCamelCase__  ,	'''size_divisor'''      )      )



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Tuple:






				"""simple docstring"""







				pass



		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			Any:






				"""simple docstring"""







				snake_case  :       int     			=						self.image_processing_class(**self.image_processor_dict      )
				# create random PIL images
				snake_case  :       Optional[int]     			=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=UpperCamelCase__      )
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__  ,	Image.Image      )

				# Test not batched input
				snake_case  :       Any     			=						image_processing(image_inputs[0]  ,	return_tensors='''pt'''      ).pixel_values

				snake_case			,snake_case  :       List[Any]     			=						self.image_processor_tester.get_expected_values(UpperCamelCase__      )
				self.assertEqual(
				    encoded_images.shape  ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,	)

				# Test batched
				snake_case  :       Union[str, Any]     			=						image_processing(UpperCamelCase__  ,	return_tensors='''pt'''      ).pixel_values

				snake_case			,snake_case  :       int     			=						self.image_processor_tester.get_expected_values(UpperCamelCase__  ,	batched=UpperCamelCase__      )
				self.assertEqual(
				    encoded_images.shape  ,	(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )  ,	)



		def       lowerCAmelCase				(      self				:			List[str]      )						->			int:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						self.image_processing_class(**self.image_processor_dict      )
				# create random numpy tensors
				snake_case  :       int     			=						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
				snake_case  :       int     			=						image_processing(image_inputs[0]  ,	return_tensors='''pt'''      ).pixel_values

				snake_case			,snake_case  :       Any     			=						self.image_processor_tester.get_expected_values(UpperCamelCase__      )
				self.assertEqual(
				    encoded_images.shape  ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,	)

				# Test batched
				snake_case  :       Optional[int]     			=						image_processing(UpperCamelCase__  ,	return_tensors='''pt'''      ).pixel_values

				snake_case			,snake_case  :       Any     			=						self.image_processor_tester.get_expected_values(UpperCamelCase__  ,	batched=UpperCamelCase__      )
				self.assertEqual(
				    encoded_images.shape  ,	(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )  ,	)



		def       lowerCAmelCase				(      self				:			Any      )						->			str:






				"""simple docstring"""







				snake_case  :       Any     			=						self.image_processing_class(**self.image_processor_dict      )
				# create random PyTorch tensors
				snake_case  :       Optional[int]     			=						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
				snake_case  :       Union[str, Any]     			=						image_processing(image_inputs[0]  ,	return_tensors='''pt'''      ).pixel_values

				snake_case			,snake_case  :       Dict     			=						self.image_processor_tester.get_expected_values(UpperCamelCase__      )
				self.assertEqual(
				    encoded_images.shape  ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,	)

				# Test batched
				snake_case  :       Optional[Any]     			=						image_processing(UpperCamelCase__  ,	return_tensors='''pt'''      ).pixel_values

				snake_case			,snake_case  :       Optional[int]     			=						self.image_processor_tester.get_expected_values(UpperCamelCase__  ,	batched=UpperCamelCase__      )
				self.assertEqual(
				    encoded_images.shape  ,	(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )  ,	)




'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {
    "hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						"""yolos"""



		def __init__(      self				:			Any  ,	UpperCamelCase__				:			Optional[Any]=768  ,	UpperCamelCase__				:			Any=12  ,	UpperCamelCase__				:			Optional[int]=12  ,	UpperCamelCase__				:			Union[str, Any]=3072  ,	UpperCamelCase__				:			str="gelu"  ,	UpperCamelCase__				:			Union[str, Any]=0.0  ,	UpperCamelCase__				:			Union[str, Any]=0.0  ,	UpperCamelCase__				:			Union[str, Any]=0.02  ,	UpperCamelCase__				:			List[Any]=1e-1_2  ,	UpperCamelCase__				:			List[Any]=[512, 864]  ,	UpperCamelCase__				:			int=16  ,	UpperCamelCase__				:			Union[str, Any]=3  ,	UpperCamelCase__				:			Union[str, Any]=True  ,	UpperCamelCase__				:			int=100  ,	UpperCamelCase__				:			Optional[Any]=True  ,	UpperCamelCase__				:			int=False  ,	UpperCamelCase__				:			Dict=1  ,	UpperCamelCase__				:			Optional[int]=5  ,	UpperCamelCase__				:			List[str]=2  ,	UpperCamelCase__				:			Union[str, Any]=5  ,	UpperCamelCase__				:			Union[str, Any]=2  ,	UpperCamelCase__				:			int=0.1  ,	**UpperCamelCase__				:			Optional[Any]  ,	)						->			Any:






				"""simple docstring"""







				super().__init__(**UpperCamelCase__      )

				snake_case  :       List[str]     			=						hidden_size
				snake_case  :       Union[str, Any]     			=						num_hidden_layers
				snake_case  :       List[str]     			=						num_attention_heads
				snake_case  :       Any     			=						intermediate_size
				snake_case  :       int     			=						hidden_act
				snake_case  :       Optional[Any]     			=						hidden_dropout_prob
				snake_case  :       int     			=						attention_probs_dropout_prob
				snake_case  :       Union[str, Any]     			=						initializer_range
				snake_case  :       List[Any]     			=						layer_norm_eps
				snake_case  :       Tuple     			=						image_size
				snake_case  :       Any     			=						patch_size
				snake_case  :       str     			=						num_channels
				snake_case  :       Optional[int]     			=						qkv_bias
				snake_case  :       Optional[Any]     			=						num_detection_tokens
				snake_case  :       Dict     			=						use_mid_position_embeddings
				snake_case  :       List[Any]     			=						auxiliary_loss
				# Hungarian matcher
				snake_case  :       List[str]     			=						class_cost
				snake_case  :       Optional[Any]     			=						bbox_cost
				snake_case  :       Tuple     			=						giou_cost
				# Loss coefficients
				snake_case  :       Union[str, Any]     			=						bbox_loss_coefficient
				snake_case  :       List[Any]     			=						giou_loss_coefficient
				snake_case  :       Tuple     			=						eos_coefficient







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						version.parse("""1.11"""					)



		@property
		def       lowerCAmelCase				(      self				:			int      )						->			Mapping[str, Mapping[int, str]]:






				"""simple docstring"""







				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]      )



		@property
		def       lowerCAmelCase				(      self				:			int      )						->			float:






				"""simple docstring"""







				return 1e-4



		@property
		def       lowerCAmelCase				(      self				:			Tuple      )						->			int:






				"""simple docstring"""







				return 12


