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						,							_lowerCamelCase						,							_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 List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_tf_available():
    import tensorflow as tf

    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
    from ..tf_utils import stable_softmax

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowerCAmelCase			:							Optional[Any]     =logging.get_logger(__name__)




@add_end_docstrings(__lowerCAmelCase		)
class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def __init__(					self     :				str    ,  *_UpperCamelCase     :				int    ,  **_UpperCamelCase     :				List[str])				->Tuple:


          """simple docstring"""


          super().__init__(*_UpperCamelCase    ,  **_UpperCamelCase)
          requires_backends(self    ,  """vision""")
          self.check_model_type(
              TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
              if self.framework == """tf"""
              else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  _UpperCamelCase     :				List[str]=None)				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						{}
          if top_k is not None:
              _lowerCamelCase     :		str 						=						top_k
          return {}, {}, postprocess_params



      def __call__(					self     :				Optional[int]    ,  _UpperCamelCase     :				Union[str, List[str], "Image.Image", List["Image.Image"]]    ,  **_UpperCamelCase     :				Optional[int])				->Dict:


          """simple docstring"""


          return super().__call__(_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Optional[int])				->str:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						load_image(_UpperCamelCase)
          _lowerCamelCase     :		Any 						=						self.image_processor(images=_UpperCamelCase    ,  return_tensors=self.framework)
          return model_inputs



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str    ,  _UpperCamelCase     :				Union[str, Any])				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						self.model(**_UpperCamelCase)
          return model_outputs




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				List[str]=5)				->str:


          """simple docstring"""


          if top_k > self.model.config.num_labels:
              _lowerCamelCase     :		Union[str, Any] 						=						self.model.config.num_labels

          if self.framework == "pt":
              _lowerCamelCase     :		Optional[Any] 						=						model_outputs.logits.softmax(-1)[0]
              _lowerCamelCase						,							_lowerCamelCase     :		Dict 						=						probs.topk(_UpperCamelCase)
          elif self.framework == "tf":
              _lowerCamelCase     :		List[Any] 						=						stable_softmax(model_outputs.logits    ,  axis=-1)[0]
              _lowerCamelCase     :		List[Any] 						=						tf.math.top_k(_UpperCamelCase    ,  k=_UpperCamelCase)
              _lowerCamelCase						,							_lowerCamelCase     :		str 						=						topk.values.numpy(), topk.indices.numpy()
          else:
              raise ValueError(F"""Unsupported framework: {self.framework}""")

          _lowerCamelCase     :		str 						=						scores.tolist()
          _lowerCamelCase     :		str 						=						ids.tolist()
          return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCamelCase    ,  _UpperCamelCase)]




