





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


_A					:				str				=			logging.get_logger(__name__)

_A					:				List[Any]				=			{
    """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	(  lowercase_    ):


    '''simple docstring'''







    lowerCamelCase__    :		str				=	"detr"
    lowerCamelCase__    :		List[str]				=	["past_key_values"]
    lowerCamelCase__    :		Optional[int]				=	{
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }



    def __init__(       self   ,				A_=True   ,				A_=None   ,				A_=3   ,				A_=1_00   ,				A_=6   ,				A_=20_48   ,				A_=8   ,				A_=6   ,				A_=20_48   ,				A_=8   ,				A_=0.0   ,				A_=0.0   ,				A_=True   ,				A_="relu"   ,				A_=2_56   ,				A_=0.1   ,				A_=0.0   ,				A_=0.0   ,				A_=0.02   ,				A_=1.0   ,				A_=False   ,				A_="sine"   ,				A_="resnet50"   ,				A_=True   ,				A_=False   ,				A_=1   ,				A_=5   ,				A_=2   ,				A_=1   ,				A_=1   ,				A_=5   ,				A_=2   ,				A_=0.1   ,				**A_   ,				):





        '''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.'''     )
                SCREAMING_SNAKE_CASE__											=			CONFIG_MAPPING["""resnet"""](out_features=['''stage4''']     )
            elif isinstance(a__   ,				a__     ):
                SCREAMING_SNAKE_CASE__											=			backbone_config.get('''model_type'''     )
                SCREAMING_SNAKE_CASE__											=			CONFIG_MAPPING[backbone_model_type]
                SCREAMING_SNAKE_CASE__											=			config_class.from_dict(a__     )
            # set timm attributes to None
            SCREAMING_SNAKE_CASE__											=			None, None, None

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



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.encoder_attention_heads



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.d_model



    @classmethod
    def  lowercase_					(       cls   ,				A_   ,				**A_     ):





        '''simple docstring'''

        return cls(backbone_config=a__   ,				**a__     )




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			copy.deepcopy(self.__dict__     )
        if output["backbone_config"] is not None:
            SCREAMING_SNAKE_CASE__											=			self.backbone_config.to_dict()
        SCREAMING_SNAKE_CASE__											=			self.__class__.model_type
        return output





class 					__snake_case	(  lowercase_    ):


    '''simple docstring'''







    lowerCamelCase__    :		Any				=	version.parse("""1.11"""    )



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

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



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return 1E-5




    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return 12








# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
     import torch




class       snake_case__ (				TensorFormatter[Mapping, "torch.Tensor", Mapping]):

       '''simple docstring'''



       def __init__(				self      ,    a__=None      ,    **a__     )			->   Tuple:






            '''simple docstring'''





            super().__init__(features=a__     )
            __snake_case      :int 							=    torch_tensor_kwargs
            import torch  # noqa import torch at initialization



       def        __lowercase    (				self      ,    a__     )			->   List[str]:






            '''simple docstring'''





            import torch

            if isinstance(a__      ,    a__     ) and column:
                 if all(
                     isinstance(a__      ,    torch.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype
                     for x in column     ):
                      return torch.stack(a__     )
            return column



       def        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            import torch

            if isinstance(a__      ,    (str, bytes, type(a__     ))     ):
                 return value
            elif isinstance(a__      ,    (np.character, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.character     ):
                 return value.tolist()

            __snake_case      :Optional[int] 							=    {}

            if isinstance(a__      ,    (np.number, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.integer     ):
                 __snake_case      :List[Any] 							=    {"""dtype""": torch.intaa}
            elif isinstance(a__      ,    (np.number, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.floating     ):
                 __snake_case      :Tuple 							=    {"""dtype""": torch.floataa}
            elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                 import PIL.Image

                 if isinstance(a__      ,    PIL.Image.Image     ):
                      __snake_case      :Union[str, Any] 							=    np.asarray(a__     )
            return torch.tensor(a__      ,    **{**default_dtype, **self.torch_tensor_kwargs}     )



       def        __lowercase    (				self      ,    a__     )			->   str:






            '''simple docstring'''





            import torch

            # support for torch, tf, jax etc.
            if hasattr(a__      ,    """__array__"""     ) and not isinstance(a__      ,    torch.Tensor     ):
                 __snake_case      :Optional[Any] 							=    data_struct.__array__()
            # support for nested types like struct of list of struct
            if isinstance(a__      ,    np.ndarray     ):
                 if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                      return self._consolidate([self.recursive_tensorize(a__     ) for substruct in data_struct]     )
            elif isinstance(a__      ,    (list, tuple)     ):
                 return self._consolidate([self.recursive_tensorize(a__     ) for substruct in data_struct]     )
            return self._tensorize(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            return map_nested(self._recursive_tensorize      ,    a__      ,    map_list=a__     )



       def        __lowercase    (				self      ,    a__     )			->   Mapping:






            '''simple docstring'''





            __snake_case      :Tuple 							=    self.numpy_arrow_extractor().extract_row(a__     )
            __snake_case      :Any 							=    self.python_features_decoder.decode_row(a__     )
            return self.recursive_tensorize(a__     )



       def        __lowercase    (				self      ,    a__     )			->   "torch.Tensor":






            '''simple docstring'''





            __snake_case      :List[str] 							=    self.numpy_arrow_extractor().extract_column(a__     )
            __snake_case      :List[Any] 							=    self.python_features_decoder.decode_column(a__      ,    pa_table.column_names[0]     )
            __snake_case      :Optional[Any] 							=    self.recursive_tensorize(a__     )
            __snake_case      :Any 							=    self._consolidate(a__     )
            return column



       def        __lowercase    (				self      ,    a__     )			->   Mapping:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    self.numpy_arrow_extractor().extract_batch(a__     )
            __snake_case      :Tuple 							=    self.python_features_decoder.decode_batch(a__     )
            __snake_case      :Tuple 							=    self.recursive_tensorize(a__     )
            for column_name in batch:
                 __snake_case      :str 							=    self._consolidate(batch[column_name]     )
            return batch


