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


lowerCAmelCase			:							Tuple     =logging.get_logger(__name__)

lowerCAmelCase			:							Dict     ={
    "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'''






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



						def __init__(					self     :				Any    ,  _UpperCamelCase     :				Dict=True    ,  _UpperCamelCase     :				List[Any]=None    ,  _UpperCamelCase     :				Any=3    ,  _UpperCamelCase     :				Optional[Any]=100    ,  _UpperCamelCase     :				int=6    ,  _UpperCamelCase     :				Optional[Any]=2048    ,  _UpperCamelCase     :				Union[str, Any]=8    ,  _UpperCamelCase     :				str=6    ,  _UpperCamelCase     :				List[str]=2048    ,  _UpperCamelCase     :				Optional[int]=8    ,  _UpperCamelCase     :				str=0.0    ,  _UpperCamelCase     :				List[Any]=0.0    ,  _UpperCamelCase     :				Any=True    ,  _UpperCamelCase     :				Optional[Any]="relu"    ,  _UpperCamelCase     :				Tuple=256    ,  _UpperCamelCase     :				Any=0.1    ,  _UpperCamelCase     :				Optional[Any]=0.0    ,  _UpperCamelCase     :				Dict=0.0    ,  _UpperCamelCase     :				Dict=0.0_2    ,  _UpperCamelCase     :				Dict=1.0    ,  _UpperCamelCase     :				Optional[int]=False    ,  _UpperCamelCase     :				Dict="sine"    ,  _UpperCamelCase     :				Dict="resnet50"    ,  _UpperCamelCase     :				Optional[int]=True    ,  _UpperCamelCase     :				Tuple=False    ,  _UpperCamelCase     :				str=1    ,  _UpperCamelCase     :				int=5    ,  _UpperCamelCase     :				Optional[int]=2    ,  _UpperCamelCase     :				List[Any]=1    ,  _UpperCamelCase     :				Union[str, Any]=1    ,  _UpperCamelCase     :				str=5    ,  _UpperCamelCase     :				List[Any]=2    ,  _UpperCamelCase     :				List[str]=0.1    ,  **_UpperCamelCase     :				Any    ,  )				->str:


										"""simple docstring"""


										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.""")
																		_lowerCamelCase     :		Optional[Any] 						=						CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""])
														elif isinstance(_UpperCamelCase    ,  _UpperCamelCase):
																		_lowerCamelCase     :		Dict 						=						backbone_config.get("""model_type""")
																		_lowerCamelCase     :		List[str] 						=						CONFIG_MAPPING[backbone_model_type]
																		_lowerCamelCase     :		Tuple 						=						config_class.from_dict(_UpperCamelCase)
														# set timm attributes to None
														_lowerCamelCase     :		Union[str, Any] 						=						None, None, None

										_lowerCamelCase     :		Optional[int] 						=						use_timm_backbone
										_lowerCamelCase     :		Union[str, Any] 						=						backbone_config
										_lowerCamelCase     :		Dict 						=						num_channels
										_lowerCamelCase     :		Optional[int] 						=						num_queries
										_lowerCamelCase     :		Tuple 						=						d_model
										_lowerCamelCase     :		Union[str, Any] 						=						encoder_ffn_dim
										_lowerCamelCase     :		Optional[Any] 						=						encoder_layers
										_lowerCamelCase     :		str 						=						encoder_attention_heads
										_lowerCamelCase     :		List[Any] 						=						decoder_ffn_dim
										_lowerCamelCase     :		List[str] 						=						decoder_layers
										_lowerCamelCase     :		Tuple 						=						decoder_attention_heads
										_lowerCamelCase     :		List[str] 						=						dropout
										_lowerCamelCase     :		Any 						=						attention_dropout
										_lowerCamelCase     :		List[str] 						=						activation_dropout
										_lowerCamelCase     :		Optional[int] 						=						activation_function
										_lowerCamelCase     :		List[Any] 						=						init_std
										_lowerCamelCase     :		Optional[Any] 						=						init_xavier_std
										_lowerCamelCase     :		Optional[Any] 						=						encoder_layerdrop
										_lowerCamelCase     :		Optional[int] 						=						decoder_layerdrop
										_lowerCamelCase     :		Union[str, Any] 						=						encoder_layers
										_lowerCamelCase     :		List[Any] 						=						auxiliary_loss
										_lowerCamelCase     :		int 						=						position_embedding_type
										_lowerCamelCase     :		int 						=						backbone
										_lowerCamelCase     :		Optional[int] 						=						use_pretrained_backbone
										_lowerCamelCase     :		List[str] 						=						dilation
										# Hungarian matcher
										_lowerCamelCase     :		int 						=						class_cost
										_lowerCamelCase     :		Optional[Any] 						=						bbox_cost
										_lowerCamelCase     :		int 						=						giou_cost
										# Loss coefficients
										_lowerCamelCase     :		str 						=						mask_loss_coefficient
										_lowerCamelCase     :		List[str] 						=						dice_loss_coefficient
										_lowerCamelCase     :		Dict 						=						bbox_loss_coefficient
										_lowerCamelCase     :		Optional[int] 						=						giou_loss_coefficient
										_lowerCamelCase     :		int 						=						eos_coefficient
										super().__init__(is_encoder_decoder=_UpperCamelCase    ,  **_UpperCamelCase)



						@property
						def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->int:


										"""simple docstring"""


										return self.encoder_attention_heads



						@property
						def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->int:


										"""simple docstring"""


										return self.d_model



						@classmethod
						def 	_SCREAMING_SNAKE_CASE    (					cls     :				List[str]    ,  _UpperCamelCase     :				PretrainedConfig    ,  **_UpperCamelCase     :				List[str])				->List[Any]:


										"""simple docstring"""


										return cls(backbone_config=_UpperCamelCase    ,  **_UpperCamelCase)




						def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Dict[str, any]:


										"""simple docstring"""


										_lowerCamelCase     :		Tuple 						=						copy.deepcopy(self.__dict__)
										if output["backbone_config"] is not None:
														_lowerCamelCase     :		List[str] 						=						self.backbone_config.to_dict()
										_lowerCamelCase     :		List[Any] 						=						self.__class__.model_type
										return output





class 	__snake_case	( __lowerCAmelCase		):





						'''simple docstring'''






						_snake_case    		=       version.parse('1.11'		)



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


										"""simple docstring"""


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



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


										"""simple docstring"""


										return 1E-5




						@property
						def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->int:


										"""simple docstring"""


										return 12





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


lowerCAmelCase			:							Tuple     =logging.get_logger(__name__)




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       ['pixel_values']



      def __init__(					self     :				Optional[Any]    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				Dict[str, int] = None    ,  _UpperCamelCase     :				PILImageResampling = PIL.Image.BICUBIC    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				Dict[str, int] = None    ,  _UpperCamelCase     :				Union[int, float] = 1 / 255    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				Optional[Union[float, List[float]]] = None    ,  _UpperCamelCase     :				Optional[Union[float, List[float]]] = None    ,  **_UpperCamelCase     :				str    ,  )				->None:


          """simple docstring"""


          super().__init__(**_UpperCamelCase)
          _lowerCamelCase     :		Tuple 						=						size if size is not None else {"""height""": 256, """width""": 256}
          _lowerCamelCase     :		Optional[Any] 						=						get_size_dict(_UpperCamelCase)
          _lowerCamelCase     :		Any 						=						crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
          _lowerCamelCase     :		Any 						=						get_size_dict(_UpperCamelCase    ,  param_name="""crop_size""")

          _lowerCamelCase     :		int 						=						do_resize
          _lowerCamelCase     :		int 						=						size
          _lowerCamelCase     :		Optional[int] 						=						resample
          _lowerCamelCase     :		int 						=						do_center_crop
          _lowerCamelCase     :		Optional[Any] 						=						crop_size
          _lowerCamelCase     :		Union[str, Any] 						=						do_rescale
          _lowerCamelCase     :		List[str] 						=						rescale_factor
          _lowerCamelCase     :		List[Any] 						=						do_normalize
          _lowerCamelCase     :		Dict 						=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          _lowerCamelCase     :		int 						=						image_std if image_std is not None else IMAGENET_STANDARD_STD



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				np.ndarray    ,  _UpperCamelCase     :				Dict[str, int]    ,  _UpperCamelCase     :				PILImageResampling = PIL.Image.BICUBIC    ,  _UpperCamelCase     :				Optional[Union[str, ChannelDimension]] = None    ,  **_UpperCamelCase     :				Union[str, Any]    ,  )				->np.ndarray:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						get_size_dict(_UpperCamelCase)
          if "height" not in size or "width" not in size:
              raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""")
          return resize(
              _UpperCamelCase    ,  size=(size["""height"""], size["""width"""])    ,  resample=_UpperCamelCase    ,  data_format=_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				np.ndarray    ,  _UpperCamelCase     :				Dict[str, int]    ,  _UpperCamelCase     :				Optional[Union[str, ChannelDimension]] = None    ,  **_UpperCamelCase     :				List[str]    ,  )				->np.ndarray:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						get_size_dict(_UpperCamelCase)
          if "height" not in size or "width" not in size:
              raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""")
          return center_crop(_UpperCamelCase    ,  size=(size["""height"""], size["""width"""])    ,  data_format=_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				np.ndarray    ,  _UpperCamelCase     :				Union[int, float]    ,  _UpperCamelCase     :				Optional[Union[str, ChannelDimension]] = None    ,  **_UpperCamelCase     :				Union[str, Any]    ,  )				->str:


          """simple docstring"""


          return rescale(_UpperCamelCase    ,  scale=_UpperCamelCase    ,  data_format=_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				np.ndarray    ,  _UpperCamelCase     :				Union[float, List[float]]    ,  _UpperCamelCase     :				Union[float, List[float]]    ,  _UpperCamelCase     :				Optional[Union[str, ChannelDimension]] = None    ,  **_UpperCamelCase     :				Union[str, Any]    ,  )				->np.ndarray:


          """simple docstring"""


          return normalize(_UpperCamelCase    ,  mean=_UpperCamelCase    ,  std=_UpperCamelCase    ,  data_format=_UpperCamelCase    ,  **_UpperCamelCase)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				ImageInput    ,  _UpperCamelCase     :				bool = None    ,  _UpperCamelCase     :				Dict[str, int] = None    ,  _UpperCamelCase     :				Tuple=None    ,  _UpperCamelCase     :				bool = None    ,  _UpperCamelCase     :				Dict[str, int] = None    ,  _UpperCamelCase     :				bool = None    ,  _UpperCamelCase     :				float = None    ,  _UpperCamelCase     :				bool = None    ,  _UpperCamelCase     :				Optional[Union[float, List[float]]] = None    ,  _UpperCamelCase     :				Optional[Union[float, List[float]]] = None    ,  _UpperCamelCase     :				Optional[Union[str, TensorType]] = None    ,  _UpperCamelCase     :				ChannelDimension = ChannelDimension.FIRST    ,  **_UpperCamelCase     :				List[Any]    ,  )				->PIL.Image.Image:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						do_resize if do_resize is not None else self.do_resize
          _lowerCamelCase     :		List[str] 						=						resample if resample is not None else self.resample
          _lowerCamelCase     :		Dict 						=						do_center_crop if do_center_crop is not None else self.do_center_crop
          _lowerCamelCase     :		Optional[Any] 						=						do_rescale if do_rescale is not None else self.do_rescale
          _lowerCamelCase     :		Dict 						=						rescale_factor if rescale_factor is not None else self.rescale_factor
          _lowerCamelCase     :		Dict 						=						do_normalize if do_normalize is not None else self.do_normalize
          _lowerCamelCase     :		int 						=						image_mean if image_mean is not None else self.image_mean
          _lowerCamelCase     :		Dict 						=						image_std if image_std is not None else self.image_std

          _lowerCamelCase     :		Optional[Any] 						=						size if size is not None else self.size
          _lowerCamelCase     :		Optional[int] 						=						get_size_dict(_UpperCamelCase)
          _lowerCamelCase     :		List[str] 						=						crop_size if crop_size is not None else self.crop_size
          _lowerCamelCase     :		Dict 						=						get_size_dict(_UpperCamelCase    ,  param_name="""crop_size""")

          _lowerCamelCase     :		int 						=						make_list_of_images(_UpperCamelCase)

          if not valid_images(_UpperCamelCase):
              raise ValueError(
                  """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                  """torch.Tensor, tf.Tensor or jax.ndarray.""")

          if do_resize and size is None or resample is None:
              raise ValueError("""Size and resample must be specified if do_resize is True.""")

          if do_center_crop and crop_size is None:
              raise ValueError("""Crop size must be specified if do_center_crop is True.""")

          if do_rescale and rescale_factor is None:
              raise ValueError("""Rescale factor must be specified if do_rescale is True.""")

          if do_normalize and (image_mean is None or image_std is None):
              raise ValueError("""Image mean and std must be specified if do_normalize is True.""")

          # All transformations expect numpy arrays.
          _lowerCamelCase     :		Union[str, Any] 						=						[to_numpy_array(_UpperCamelCase) for image in images]

          if do_resize:
              _lowerCamelCase     :		Any 						=						[self.resize(image=_UpperCamelCase    ,  size=_UpperCamelCase    ,  resample=_UpperCamelCase) for image in images]

          if do_center_crop:
              _lowerCamelCase     :		str 						=						[self.center_crop(image=_UpperCamelCase    ,  size=_UpperCamelCase) for image in images]

          if do_rescale:
              _lowerCamelCase     :		Optional[int] 						=						[self.rescale(image=_UpperCamelCase    ,  scale=_UpperCamelCase) for image in images]

          if do_normalize:
              _lowerCamelCase     :		List[str] 						=						[self.normalize(image=_UpperCamelCase    ,  mean=_UpperCamelCase    ,  std=_UpperCamelCase) for image in images]

          _lowerCamelCase     :		List[str] 						=						[to_channel_dimension_format(_UpperCamelCase    ,  _UpperCamelCase) for image in images]

          _lowerCamelCase     :		int 						=						{"""pixel_values""": images}
          return BatchFeature(data=_UpperCamelCase    ,  tensor_type=_UpperCamelCase)




