"""simple docstring"""





import copy

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


_UpperCamelCase     :		Optional[int]											=					logging.get_logger(__name__)

_UpperCamelCase     :		Dict											=					{
    "SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}


class   UpperCAmelCase_			(					_a):
      lowerCamelCase__	:					str										=						"deformable_detr"
      lowerCamelCase__	:					List[Any]										=						{
          "hidden_size": "d_model",
          "num_attention_heads": "encoder_attention_heads",
      }


      def __init__(     self	,    a=True	,    a=None	,    a=3	,    a=3_0_0	,    a=1_0_2_4	,    a=6	,    a=1_0_2_4	,    a=8	,    a=6	,    a=1_0_2_4	,    a=8	,    a=0.0	,    a=True	,    a="relu"	,    a=2_5_6	,    a=0.1	,    a=0.0	,    a=0.0	,    a=0.02	,    a=1.0	,    a=True	,    a=False	,    a="sine"	,    a="resnet50"	,    a=True	,    a=False	,    a=4	,    a=4	,    a=4	,    a=False	,    a=3_0_0	,    a=False	,    a=1	,    a=5	,    a=2	,    a=1	,    a=1	,    a=5	,    a=2	,    a=0.1	,    a=0.25	,    a=False	,    **a	,    ) ->      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.'							)
                           lowercase__    :	List[Any]     					=			CONFIG_MAPPING['resnet'](out_features=['stage4']							)
                    elif isinstance(a	,    a							):
                           lowercase__    :	Union[str, Any]     					=			backbone_config.get('model_type'							)
                           lowercase__    :	List[Any]     					=			CONFIG_MAPPING[backbone_model_type]
                           lowercase__    :	Union[str, Any]     					=			config_class.from_dict(a							)
             lowercase__    :	List[Any]     					=			use_timm_backbone
             lowercase__    :	int     					=			backbone_config
             lowercase__    :	Optional[int]     					=			num_channels
             lowercase__    :	Optional[Any]     					=			num_queries
             lowercase__    :	List[Any]     					=			max_position_embeddings
             lowercase__    :	List[str]     					=			d_model
             lowercase__    :	List[str]     					=			encoder_ffn_dim
             lowercase__    :	List[str]     					=			encoder_layers
             lowercase__    :	List[Any]     					=			encoder_attention_heads
             lowercase__    :	str     					=			decoder_ffn_dim
             lowercase__    :	Optional[Any]     					=			decoder_layers
             lowercase__    :	List[Any]     					=			decoder_attention_heads
             lowercase__    :	Optional[Any]     					=			dropout
             lowercase__    :	int     					=			attention_dropout
             lowercase__    :	Union[str, Any]     					=			activation_dropout
             lowercase__    :	Tuple     					=			activation_function
             lowercase__    :	Optional[int]     					=			init_std
             lowercase__    :	List[str]     					=			init_xavier_std
             lowercase__    :	Tuple     					=			encoder_layerdrop
             lowercase__    :	List[str]     					=			auxiliary_loss
             lowercase__    :	Tuple     					=			position_embedding_type
             lowercase__    :	Tuple     					=			backbone
             lowercase__    :	List[Any]     					=			use_pretrained_backbone
             lowercase__    :	List[Any]     					=			dilation
             # deformable attributes
             lowercase__    :	Dict     					=			num_feature_levels
             lowercase__    :	Optional[Any]     					=			encoder_n_points
             lowercase__    :	Union[str, Any]     					=			decoder_n_points
             lowercase__    :	List[Any]     					=			two_stage
             lowercase__    :	str     					=			two_stage_num_proposals
             lowercase__    :	Optional[int]     					=			with_box_refine
             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
             lowercase__    :	int     					=			class_cost
             lowercase__    :	List[Any]     					=			bbox_cost
             lowercase__    :	Optional[Any]     					=			giou_cost
             # Loss coefficients
             lowercase__    :	List[Any]     					=			mask_loss_coefficient
             lowercase__    :	List[Any]     					=			dice_loss_coefficient
             lowercase__    :	Tuple     					=			bbox_loss_coefficient
             lowercase__    :	int     					=			giou_loss_coefficient
             lowercase__    :	Any     					=			eos_coefficient
             lowercase__    :	str     					=			focal_alpha
             lowercase__    :	Any     					=			disable_custom_kernels
             super().__init__(is_encoder_decoder=a	,    **a							)


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


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


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	Dict     					=			copy.deepcopy(self.__dict__							)
             if self.backbone_config is not None:
                    lowercase__    :	Optional[Any]     					=			self.backbone_config.to_dict()
             lowercase__    :	Optional[Any]     					=			self.__class__.model_type
             return output


"""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 MobileViTImageProcessor


class   UpperCAmelCase_			(					unittest.TestCase):


      def __init__(     self	,    a	,    a=7	,    a=3	,    a=1_8	,    a=3_0	,    a=4_0_0	,    a=True	,    a=None	,    a=True	,    a=None	,    a=True	,    ) ->      List[str]:
             lowercase__    :	Tuple     					=			size if size is not None else {'shortest_edge': 2_0}
             lowercase__    :	Union[str, Any]     					=			crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
             lowercase__    :	Optional[int]     					=			parent
             lowercase__    :	Optional[int]     					=			batch_size
             lowercase__    :	str     					=			num_channels
             lowercase__    :	Any     					=			image_size
             lowercase__    :	Optional[Any]     					=			min_resolution
             lowercase__    :	int     					=			max_resolution
             lowercase__    :	List[Any]     					=			do_resize
             lowercase__    :	List[str]     					=			size
             lowercase__    :	str     					=			do_center_crop
             lowercase__    :	List[Any]     					=			crop_size
             lowercase__    :	Union[str, Any]     					=			do_flip_channel_order


      def 	_UpperCAmelCase (     self							) ->      int:
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "do_flip_channel_order": self.do_flip_channel_order,
             }





@require_torch
@require_vision
class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Optional[Any]										=						MobileViTImageProcessor if is_vision_available() else None


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Tuple     					=			MobileViTImageProcessingTester(self							)


      @property
      def 	_UpperCAmelCase (     self							) ->      int:
             return self.image_processor_tester.prepare_image_processor_dict()


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__    :	List[str]     					=			self.image_processing_class(**self.image_processor_dict							)
             self.assertTrue(hasattr(a	,    'do_resize'							)							)
             self.assertTrue(hasattr(a	,    'size'							)							)
             self.assertTrue(hasattr(a	,    'do_center_crop'							)							)
             self.assertTrue(hasattr(a	,    'center_crop'							)							)
             self.assertTrue(hasattr(a	,    'do_flip_channel_order'							)							)


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Optional[int]     					=			self.image_processing_class.from_dict(self.image_processor_dict							)
             self.assertEqual(image_processor.size	,    {'shortest_edge': 2_0}							)
             self.assertEqual(image_processor.crop_size	,    {'height': 1_8, 'width': 1_8}							)

             lowercase__    :	str     					=			self.image_processing_class.from_dict(self.image_processor_dict	,    size=4_2	,    crop_size=8_4							)
             self.assertEqual(image_processor.size	,    {'shortest_edge': 4_2}							)
             self.assertEqual(image_processor.crop_size	,    {'height': 8_4, 'width': 8_4}							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             pass


      def 	_UpperCAmelCase (     self							) ->      str:
             # Initialize image_processing
             lowercase__    :	Any     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PIL images
             lowercase__    :	List[str]     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    Image.Image							)

             # Test not batched input
             lowercase__    :	Union[str, Any]     					=			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__    :	List[Any]     					=			image_processing(a	,    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 	_UpperCAmelCase (     self							) ->      Tuple:
             # Initialize image_processing
             lowercase__    :	Dict     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random numpy tensors
             lowercase__    :	Tuple     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a	,    numpify=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    np.ndarray							)

             # Test not batched input
             lowercase__    :	List[Any]     					=			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__    :	Any     					=			image_processing(a	,    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 	_UpperCAmelCase (     self							) ->      Dict:
             # Initialize image_processing
             lowercase__    :	str     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PyTorch tensors
             lowercase__    :	Optional[Any]     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a	,    torchify=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    torch.Tensor							)

             # Test not batched input
             lowercase__    :	Union[str, Any]     					=			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__    :	Tuple     					=			image_processing(a	,    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'],
                 )	,    )

