
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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


if is_vision_available():
  import PIL

lowercase  :					int						    =							logging.get_logger(__name__)







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[List[ImageInput]]:
       if isinstance(SCREAMING_SNAKE_CASE__					,	(list, tuple)				) and isinstance(videos[0]					,	(list, tuple)				) and is_valid_image(videos[0][0]				):
              return videos

       elif isinstance(SCREAMING_SNAKE_CASE__					,	(list, tuple)				) and is_valid_image(videos[0]				):
              return [videos]

       elif is_valid_image(SCREAMING_SNAKE_CASE__				):
              return [[videos]]

       raise ValueError(f"Could not make batched video from {videos}"				)




class __snake_case		(      lowerCAmelCase     ):
    _a		:						Dict=				["pixel_values"]
    def __init__(  self		,snake_case = True		,snake_case = None		,snake_case = PILImageResampling.BILINEAR		,snake_case = True		,snake_case = None		,snake_case = True		,snake_case = 1 / 255		,snake_case = True		,snake_case = True		,snake_case = None		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**snake_case      )
           lowercase							:     int       	=      size if size is not None else {"""shortest_edge""": 256}
           lowercase							:     Union[str, Any]       	=      get_size_dict(snake_case		,default_to_square=snake_case      )
           lowercase							:     str       	=      crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
           lowercase							:     Tuple       	=      get_size_dict(snake_case		,param_name="""crop_size"""      )

           lowercase							:     Union[str, Any]       	=      do_resize
           lowercase							:     str       	=      size
           lowercase							:     Optional[int]       	=      do_center_crop
           lowercase							:     List[Any]       	=      crop_size
           lowercase							:     List[str]       	=      resample
           lowercase							:     Any       	=      do_rescale
           lowercase							:     Union[str, Any]       	=      rescale_factor
           lowercase							:     Any       	=      offset
           lowercase							:     Optional[int]       	=      do_normalize
           lowercase							:     Optional[int]       	=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           lowercase							:     Tuple       	=      image_std if image_std is not None else IMAGENET_STANDARD_STD
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case = PILImageResampling.BILINEAR		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      get_size_dict(snake_case		,default_to_square=snake_case      )
           if "shortest_edge" in size:
                  lowercase							:     str       	=      get_resize_output_image_size(snake_case		,size["""shortest_edge"""]		,default_to_square=snake_case      )
           elif "height" in size and "width" in size:
                  lowercase							:     Optional[int]       	=      (size["""height"""], size["""width"""])
           else:
                  raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"      )
           return resize(snake_case		,size=snake_case		,resample=snake_case		,data_format=snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      get_size_dict(snake_case      )
           if "height" not in size or "width" not in size:
                  raise ValueError(f"Size must have 'height' and 'width' as keys. Got {size.keys()}"      )
           return center_crop(snake_case		,size=(size["""height"""], size["""width"""])		,data_format=snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case = True		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      image.astype(np.floataa      )
           if offset:
                  lowercase							:     Optional[Any]       	=      image - (scale / 2)
           return rescale(snake_case		,scale=snake_case		,data_format=snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           return normalize(snake_case		,mean=snake_case		,std=snake_case		,data_format=snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = ChannelDimension.FIRST		,):



           '''simple docstring'''







           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."""      )

           if offset and not do_rescale:
                  raise ValueError("""For offset, do_rescale must also be set to True."""      )

           # All transformations expect numpy arrays.
           lowercase							:     Optional[int]       	=      to_numpy_array(snake_case      )

           if do_resize:
                  lowercase							:     Union[str, Any]       	=      self.resize(image=snake_case		,size=snake_case		,resample=snake_case      )

           if do_center_crop:
                  lowercase							:     Optional[Any]       	=      self.center_crop(snake_case		,size=snake_case      )

           if do_rescale:
                  lowercase							:     Optional[int]       	=      self.rescale(image=snake_case		,scale=snake_case		,offset=snake_case      )

           if do_normalize:
                  lowercase							:     Any       	=      self.normalize(image=snake_case		,mean=snake_case		,std=snake_case      )

           lowercase							:     Any       	=      to_channel_dimension_format(snake_case		,snake_case      )
           return image
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = ChannelDimension.FIRST		,**snake_case		,):



           '''simple docstring'''







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

           lowercase							:     int       	=      size if size is not None else self.size
           lowercase							:     List[Any]       	=      get_size_dict(snake_case		,default_to_square=snake_case      )
           lowercase							:     str       	=      crop_size if crop_size is not None else self.crop_size
           lowercase							:     List[Any]       	=      get_size_dict(snake_case		,param_name="""crop_size"""      )

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

           lowercase							:     List[Any]       	=      make_batched(snake_case      )

           lowercase							:     Any       	=      [
               [
                   self._preprocess_image(
                       image=snake_case		,do_resize=snake_case		,size=snake_case		,resample=snake_case		,do_center_crop=snake_case		,crop_size=snake_case		,do_rescale=snake_case		,rescale_factor=snake_case		,offset=snake_case		,do_normalize=snake_case		,image_mean=snake_case		,image_std=snake_case		,data_format=snake_case		,)
                   for img in video
               ]
               for video in videos
           ]

           lowercase							:     Optional[int]       	=      {"""pixel_values""": videos}
           return BatchFeature(data=snake_case		,tensor_type=snake_case      )








"""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


lowerCamelCase_					=      logging.get_logger(__name__)

lowerCamelCase_					=      {
    '''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 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				'''detr'''
	__magic_name__	      =				['''past_key_values''']
	__magic_name__	      =				{
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	}





	def __init__(      self					: Dict	,	lowerCAmelCase_					: str=True	,	lowerCAmelCase_					: Union[str, Any]=None	,	lowerCAmelCase_					: Dict=3	,	lowerCAmelCase_					: List[Any]=100	,	lowerCAmelCase_					: int=6	,	lowerCAmelCase_					: Union[str, Any]=2_048	,	lowerCAmelCase_					: int=8	,	lowerCAmelCase_					: int=6	,	lowerCAmelCase_					: Union[str, Any]=2_048	,	lowerCAmelCase_					: Any=8	,	lowerCAmelCase_					: Dict=0.0	,	lowerCAmelCase_					: Union[str, Any]=0.0	,	lowerCAmelCase_					: Dict=True	,	lowerCAmelCase_					: List[Any]="relu"	,	lowerCAmelCase_					: List[Any]=256	,	lowerCAmelCase_					: Tuple=0.1	,	lowerCAmelCase_					: Optional[Any]=0.0	,	lowerCAmelCase_					: List[Any]=0.0	,	lowerCAmelCase_					: List[Any]=0.0_2	,	lowerCAmelCase_					: Optional[int]=1.0	,	lowerCAmelCase_					: str=False	,	lowerCAmelCase_					: Tuple="sine"	,	lowerCAmelCase_					: str="resnet50"	,	lowerCAmelCase_					: List[Any]=True	,	lowerCAmelCase_					: int=False	,	lowerCAmelCase_					: Optional[Any]=1	,	lowerCAmelCase_					: int=5	,	lowerCAmelCase_					: Optional[Any]=2	,	lowerCAmelCase_					: List[Any]=1	,	lowerCAmelCase_					: List[Any]=1	,	lowerCAmelCase_					: Dict=5	,	lowerCAmelCase_					: Optional[Any]=2	,	lowerCAmelCase_					: Union[str, Any]=0.1	,	**lowerCAmelCase_					: Dict	,	)						->			int:
				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_     : Tuple			     =					CONFIG_MAPPING["resnet"](out_features=["stage4"]						)
							elif isinstance(lowerCAmelCase_	,	lowerCAmelCase_						):
										UpperCAmelCase_     : Dict			     =					backbone_config.get("model_type"						)
										UpperCAmelCase_     : Dict			     =					CONFIG_MAPPING[backbone_model_type]
										UpperCAmelCase_     : Tuple			     =					config_class.from_dict(lowerCAmelCase_						)
							# set timm attributes to None
							UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					None, None, None

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





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			int:
				return self.encoder_attention_heads





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





	@classmethod
	def      _SCREAMING_SNAKE_CASE      (      cls					: Union[str, Any]	,	lowerCAmelCase_					: PretrainedConfig	,	**lowerCAmelCase_					: Tuple						)						->			List[Any]:
				return cls(backbone_config=lowerCAmelCase_	,	**lowerCAmelCase_						)





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







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				version.parse('''1.11'''     )





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





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			float:
				return 1e-5





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			int:
				return 12



