






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   a__	(						_UpperCamelCase    ):
       lowerCamelCase__:     List[Any]							    =     """detr"""
       lowerCamelCase__:     Tuple							    =     ["""past_key_values"""]
       lowerCamelCase__:     Optional[int]							    =     {
           """hidden_size""": """d_model""",
           """num_attention_heads""": """encoder_attention_heads""",
       }

       def __init__(   self      :     Optional[Any]      ,							lowerCamelCase_      :     Optional[int]=True      ,							lowerCamelCase_      :     List[Any]=None      ,							lowerCamelCase_      :     Union[str, Any]=3      ,							lowerCamelCase_      :     str=1_0_0      ,							lowerCamelCase_      :     Tuple=6      ,							lowerCamelCase_      :     Optional[Any]=2_0_4_8      ,							lowerCamelCase_      :     List[Any]=8      ,							lowerCamelCase_      :     Optional[int]=6      ,							lowerCamelCase_      :     Optional[Any]=2_0_4_8      ,							lowerCamelCase_      :     Union[str, Any]=8      ,							lowerCamelCase_      :     Any=0.0      ,							lowerCamelCase_      :     Union[str, Any]=0.0      ,							lowerCamelCase_      :     Tuple=True      ,							lowerCamelCase_      :     int="relu"      ,							lowerCamelCase_      :     Optional[Any]=2_5_6      ,							lowerCamelCase_      :     Any=0.1      ,							lowerCamelCase_      :     Dict=0.0      ,							lowerCamelCase_      :     Union[str, Any]=0.0      ,							lowerCamelCase_      :     Any=0.0_2      ,							lowerCamelCase_      :     Optional[int]=1.0      ,							lowerCamelCase_      :     Optional[Any]=False      ,							lowerCamelCase_      :     int="sine"      ,							lowerCamelCase_      :     Tuple="resnet50"      ,							lowerCamelCase_      :     Union[str, Any]=True      ,							lowerCamelCase_      :     Union[str, Any]=False      ,							lowerCamelCase_      :     Optional[Any]=1      ,							lowerCamelCase_      :     Dict=5      ,							lowerCamelCase_      :     int=2      ,							lowerCamelCase_      :     Optional[Any]=1      ,							lowerCamelCase_      :     Optional[int]=1      ,							lowerCamelCase_      :     List[Any]=5      ,							lowerCamelCase_      :     Union[str, Any]=2      ,							lowerCamelCase_      :     Tuple=0.1      ,							**lowerCamelCase_      :     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."""    )
                      a_    :     Any										=    CONFIG_MAPPING['''resnet'''](out_features=["""stage4"""]    )
                 elif isinstance(_UpperCAmelCase      ,							_UpperCAmelCase    ):
                      a_    :     Optional[int]										=    backbone_config.get("""model_type"""    )
                      a_    :     Tuple										=    CONFIG_MAPPING[backbone_model_type]
                      a_    :     Union[str, Any]										=    config_class.from_dict(_UpperCAmelCase    )
                 # set timm attributes to None
                 a_    :     Union[str, Any]										=    None, None, None

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

       @property
       def 						UpperCAmelCase(   self      :     Tuple    ):
            return self.encoder_attention_heads

       @property
       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            return self.d_model

       @classmethod
       def 						UpperCAmelCase(   cls      :     str      ,							lowerCamelCase_      :     int      ,							**lowerCamelCase_      :     Optional[Any]    ):
            return cls(backbone_config=_UpperCAmelCase      ,							**_UpperCAmelCase    )

       def 						UpperCAmelCase(   self      :     str    ):
            a_    :     Dict										=    copy.deepcopy(self.__dict__    )
            if output["backbone_config"] is not None:
                 a_    :     Tuple										=    self.backbone_config.to_dict()
            a_    :     Dict										=    self.__class__.model_type
            return output


class   a__	(						_UpperCamelCase    ):
       lowerCamelCase__:     List[str]							    =     version.parse("""1.11"""    )

       @property
       def 						UpperCAmelCase(   self      :     Dict    ):
            return OrderedDict(
                [
                    ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                    ("""pixel_mask""", {0: """batch"""}),
                ]    )

       @property
       def 						UpperCAmelCase(   self      :     int    ):
            return 1E-5

       @property
       def 						UpperCAmelCase(   self      :     int    ):
            return 1_2







import numpy as np

__lowerCamelCase        =     [
    ['''a''', '''b''', '''c''', '''d''', '''e'''],
    ['''f''', '''g''', '''h''', '''i''', '''k'''],
    ['''l''', '''m''', '''n''', '''o''', '''p'''],
    ['''q''', '''r''', '''s''', '''t''', '''u'''],
    ['''v''', '''w''', '''x''', '''y''', '''z'''],
]


class   a__	:

       def __init__(   self      :     Union[str, Any]    ):
            a_    :     List[Any]										=    np.array(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     str    ):
            a_			,			a_    :     Optional[int]										=    np.where(letter == self.SQUARE    )
            a_    :     Union[str, Any]										=    np.concatenate([indexa + 1, indexa + 1]    )
            return indexes

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     int    ):
            a_    :     str										=    self.SQUARE[indexa - 1, indexa - 1]
            return letter

       def 						UpperCAmelCase(   self      :     Optional[Any]      ,							lowerCamelCase_      :     str    ):
            a_    :     List[str]										=    message.lower()
            a_    :     str										=    message.replace(""" """      ,							""""""    )
            a_    :     Union[str, Any]										=    message.replace("""j"""      ,							"""i"""    )

            a_    :     Optional[Any]										=    np.empty((2, len(lowerCamelCase_    ))    )
            for letter_index in range(len(lowerCamelCase_    )    ):
                 a_    :     int										=    self.letter_to_numbers(message[letter_index]    )

                 a_    :     str										=    numbers[0]
                 a_    :     int										=    numbers[1]

            a_    :     int										=    first_step.reshape(2 * len(lowerCamelCase_    )    )
            a_    :     Optional[Any]										=    """"""
            for numbers_index in range(len(lowerCamelCase_    )    ):
                 a_    :     Optional[Any]										=    int(second_step[numbers_index * 2]    )
                 a_    :     Optional[Any]										=    int(second_step[(numbers_index * 2) + 1]    )
                 a_    :     Optional[Any]										=    self.numbers_to_letter(lowerCamelCase_      ,							lowerCamelCase_    )
                 a_    :     List[str]										=    encoded_message + letter

            return encoded_message

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     str    ):
            a_    :     Optional[Any]										=    message.lower()
            message.replace(""" """      ,							""""""    )
            a_    :     int										=    np.empty(2 * len(lowerCamelCase_    )    )
            for letter_index in range(len(lowerCamelCase_    )    ):
                 a_    :     str										=    self.letter_to_numbers(message[letter_index]    )
                 a_    :     Optional[int]										=    numbers[0]
                 a_    :     Optional[Any]										=    numbers[1]

            a_    :     Tuple										=    first_step.reshape((2, len(lowerCamelCase_    ))    )
            a_    :     Optional[int]										=    """"""
            for numbers_index in range(len(lowerCamelCase_    )    ):
                 a_    :     Dict										=    int(second_step[0, numbers_index]    )
                 a_    :     Tuple										=    int(second_step[1, numbers_index]    )
                 a_    :     Tuple										=    self.numbers_to_letter(lowerCamelCase_      ,							lowerCamelCase_    )
                 a_    :     Union[str, Any]										=    decoded_message + letter

            return decoded_message



