




"""simple docstring"""







import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	     =       {
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   A_				      =       '''detr'''
   A_				      =       ['''past_key_values''']
   A_				      =       {
       '''hidden_size''': '''d_model''',
       '''num_attention_heads''': '''encoder_attention_heads''',
   }







   def __init__(		self       ,					lowerCamelCase_=True       ,					lowerCamelCase_=None       ,					lowerCamelCase_=3       ,					lowerCamelCase_=1_0_0       ,					lowerCamelCase_=6       ,					lowerCamelCase_=2_0_4_8       ,					lowerCamelCase_=8       ,					lowerCamelCase_=6       ,					lowerCamelCase_=2_0_4_8       ,					lowerCamelCase_=8       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=True       ,					lowerCamelCase_="relu"       ,					lowerCamelCase_=2_5_6       ,					lowerCamelCase_=0.1       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=0.02       ,					lowerCamelCase_=1.0       ,					lowerCamelCase_=False       ,					lowerCamelCase_="sine"       ,					lowerCamelCase_="resnet50"       ,					lowerCamelCase_=True       ,					lowerCamelCase_=False       ,					lowerCamelCase_=1       ,					lowerCamelCase_=5       ,					lowerCamelCase_=2       ,					lowerCamelCase_=1       ,					lowerCamelCase_=1       ,					lowerCamelCase_=5       ,					lowerCamelCase_=2       ,					lowerCamelCase_=0.1       ,					**lowerCamelCase_       ,					)				->   List[Any]:
       if backbone_config is not None and use_timm_backbone:
           raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''')

       if not use_timm_backbone:
           if backbone_config is None:
               logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''')
               UpperCamelCase              =	CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
           elif isinstance(lowerCamelCase_       ,					lowerCamelCase_):
               UpperCamelCase              =	backbone_config.get('''model_type''')
               UpperCamelCase              =	CONFIG_MAPPING[backbone_model_type]
               UpperCamelCase              =	config_class.from_dict(lowerCamelCase_)
           # set timm attributes to None
           UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase              =	None, None, None

       UpperCamelCase              =	use_timm_backbone
       UpperCamelCase              =	backbone_config
       UpperCamelCase              =	num_channels
       UpperCamelCase              =	num_queries
       UpperCamelCase              =	d_model
       UpperCamelCase              =	encoder_ffn_dim
       UpperCamelCase              =	encoder_layers
       UpperCamelCase              =	encoder_attention_heads
       UpperCamelCase              =	decoder_ffn_dim
       UpperCamelCase              =	decoder_layers
       UpperCamelCase              =	decoder_attention_heads
       UpperCamelCase              =	dropout
       UpperCamelCase              =	attention_dropout
       UpperCamelCase              =	activation_dropout
       UpperCamelCase              =	activation_function
       UpperCamelCase              =	init_std
       UpperCamelCase              =	init_xavier_std
       UpperCamelCase              =	encoder_layerdrop
       UpperCamelCase              =	decoder_layerdrop
       UpperCamelCase              =	encoder_layers
       UpperCamelCase              =	auxiliary_loss
       UpperCamelCase              =	position_embedding_type
       UpperCamelCase              =	backbone
       UpperCamelCase              =	use_pretrained_backbone
       UpperCamelCase              =	dilation
       # Hungarian matcher
       UpperCamelCase              =	class_cost
       UpperCamelCase              =	bbox_cost
       UpperCamelCase              =	giou_cost
       # Loss coefficients
       UpperCamelCase              =	mask_loss_coefficient
       UpperCamelCase              =	dice_loss_coefficient
       UpperCamelCase              =	bbox_loss_coefficient
       UpperCamelCase              =	giou_loss_coefficient
       UpperCamelCase              =	eos_coefficient
       super().__init__(is_encoder_decoder=lowerCamelCase_       ,					**lowerCamelCase_)







   @property
   def   UpperCAmelCase__			(		self)				->   int:
       return self.encoder_attention_heads







   @property
   def   UpperCAmelCase__			(		self)				->   int:
       return self.d_model







   @classmethod
   def   UpperCAmelCase__			(		cls       ,					lowerCamelCase_       ,					**lowerCamelCase_)				->   List[str]:
       return cls(backbone_config=lowerCamelCase_       ,					**lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Dict[str, any]:
       UpperCamelCase              =	copy.deepcopy(self.__dict__)
       if output["backbone_config"] is not None:
           UpperCamelCase              =	self.backbone_config.to_dict()
       UpperCamelCase              =	self.__class__.model_type
       return output

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   A_				      =       version.parse('''1.11'''			)







   @property
   def   UpperCAmelCase__			(		self)				->   Mapping[str, Mapping[int, str]]:
       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
               ('''pixel_mask''', {0: '''batch'''}),
           ])







   @property
   def   UpperCAmelCase__			(		self)				->   float:
       return 1e-5







   @property
   def   UpperCAmelCase__			(		self)				->   int:
       return 1_2




'''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, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor






class 				lowercase__      (				unittest.TestCase  ):





	def __init__(					self   :							int					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							Optional[Any]=13					,lowerCamelCase__   :							List[Any]=3					,lowerCamelCase__   :							Optional[Any]=224					,lowerCamelCase__   :							int=30					,lowerCamelCase__   :							str=400					,lowerCamelCase__   :							str=True					,lowerCamelCase__   :							Any=None					,lowerCamelCase__   :							Tuple=True					,lowerCamelCase__   :							Tuple=[0.5, 0.5, 0.5]					,lowerCamelCase__   :							Optional[int]=[0.5, 0.5, 0.5]					,):







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





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''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 				lowercase__      (				lowercase		,   unittest.TestCase  ):
	lowercase__    							=     ViTImageProcessor if is_vision_available() else None





	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  EfficientFormerImageProcessorTester(self					)





	@property
	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							return self.image_proc_tester.prepare_image_processor_dict()





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(lowerCamelCase__					,'image_mean'					)					)
							self.assertTrue(hasattr(lowerCamelCase__					,'image_std'					)					)
							self.assertTrue(hasattr(lowerCamelCase__					,'do_normalize'					)					)
							self.assertTrue(hasattr(lowerCamelCase__					,'do_resize'					)					)
							self.assertTrue(hasattr(lowerCamelCase__					,'size'					)					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							pass





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							# Initialize image_processor
							_UpperCamelCase   :						str												=  self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							_UpperCamelCase   :						Optional[Any]												=  prepare_image_inputs(self.image_proc_tester					,equal_resolution=lowerCamelCase__					)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__					,Image.Image					)

							# Test not batched input
							_UpperCamelCase   :						int												=  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
							_UpperCamelCase   :						int												=  image_processor(lowerCamelCase__					,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]					):







							'''simple docstring'''
							# Initialize image_processor
							_UpperCamelCase   :						Any												=  self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							_UpperCamelCase   :						Optional[int]												=  prepare_image_inputs(self.image_proc_tester					,equal_resolution=lowerCamelCase__					,numpify=lowerCamelCase__					)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__					,np.ndarray					)

							# Test not batched input
							_UpperCamelCase   :						Union[str, 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
							_UpperCamelCase   :						Optional[Any]												=  image_processor(lowerCamelCase__					,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   :							Optional[int]					):







							'''simple docstring'''
							# Initialize image_processor
							_UpperCamelCase   :						Any												=  self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							_UpperCamelCase   :						Any												=  prepare_image_inputs(self.image_proc_tester					,equal_resolution=lowerCamelCase__					,torchify=lowerCamelCase__					)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__					,torch.Tensor					)

							# Test not batched input
							_UpperCamelCase   :						Dict												=  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
							_UpperCamelCase   :						Any												=  image_processor(lowerCamelCase__					,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'],
							    )					,)


