





import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
A__:    Union[str, Any]  	=	logging.get_logger('''transformers.models.speecht5''')

A__:    int  	=	{
    '''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
    '''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
    '''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
    '''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
A__:    Any  	=	{
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
A__:    Tuple  	=	{
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
    '''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
    '''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
    '''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
A__:    Optional[int]  	=	{
    '''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
    '''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
    '''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
    '''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
    '''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
    '''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
    '''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
    '''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
A__:    List[str]  	=	{
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
A__:    Tuple  	=	{
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
A__:    int  	=	{
    '''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
    '''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
    '''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
    '''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
    '''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
    '''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
    '''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
    '''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
    '''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
A__:    Tuple  	=	{
    '''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
    '''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
    '''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
    '''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
    '''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
    '''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
    '''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
    '''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
    '''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
    '''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
    '''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
    '''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
    '''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
A__:    int  	=	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
A__:    Optional[int]  	=	{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
A__:    Any  	=	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
A__:    List[Any]  	=	[]
A__:    List[str]  	=	[
    '''encoder.version''',
    '''encoder.layers.*.norm_k.weight''',
    '''encoder.layers.*.norm_k.bias''',
    '''decoder.version''',
    '''decoder.layers.*.norm_k.weight''',
    '''decoder.layers.*.norm_k.bias''',
    '''decoder.pos_emb.pe_k''',
    '''speech_encoder_prenet.embed_positions._float_tensor''',
    '''text_decoder_prenet.embed_positions._float_tensor''',
]
A__:    Optional[int]  	=	IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
A__:    str  	=	IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
A__:    Tuple  	=	IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]



def 				lowerCAmelCase_ (							A_    ,A_    ,A_    ,A_    ,A_):
    for attribute in key.split("."):
        UpperCamelCase__:	Optional[int]					=     getattr(A_    ,A_)

    if weight_type is not None:
        UpperCamelCase__:	Tuple					=     getattr(A_    ,A_).shape
    else:
        UpperCamelCase__:	List[Any]					=     hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
            F" {value.shape} for {full_name}")

    if weight_type == "weight":
        UpperCamelCase__:	Tuple					=     value
    elif weight_type == "weight_g":
        UpperCamelCase__:	List[Any]					=     value
    elif weight_type == "weight_v":
        UpperCamelCase__:	List[str]					=     value
    elif weight_type == "bias":
        UpperCamelCase__:	List[Any]					=     value
    elif weight_type == "running_mean":
        UpperCamelCase__:	List[Any]					=     value
    elif weight_type == "running_var":
        UpperCamelCase__:	Tuple					=     value
    elif weight_type == "num_batches_tracked":
        UpperCamelCase__:	Any					=     value
    else:
        UpperCamelCase__:	int					=     value

    logger.info(F"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.")



def 				lowerCAmelCase_ (							A_    ,A_):
    for key in ignore_keys:
        if key.endswith(".*"):
            if name.startswith(key[:-1]):
                return True
        elif ".*." in key:
            UpperCamelCase__				, UpperCamelCase__:	Optional[int]					=     key.split(".*.")
            if prefix in name and suffix in name:
                return True
        elif key in name:
            return True
    return False



def 				lowerCAmelCase_ (							A_    ,A_    ,A_):
    UpperCamelCase__:	str					=     []

    if task == "s2t":
        UpperCamelCase__:	Optional[Any]					=     hf_model.speechta.encoder.prenet.feature_encoder
        UpperCamelCase__:	int					=     MAPPING_S2T
        UpperCamelCase__:	Any					=     IGNORE_KEYS_S2T
    elif task == "t2s":
        UpperCamelCase__:	Optional[Any]					=     None
        UpperCamelCase__:	int					=     MAPPING_T2S
        UpperCamelCase__:	Dict					=     IGNORE_KEYS_T2S
    elif task == "s2s":
        UpperCamelCase__:	Optional[Any]					=     hf_model.speechta.encoder.prenet.feature_encoder
        UpperCamelCase__:	Tuple					=     MAPPING_S2S
        UpperCamelCase__:	List[str]					=     IGNORE_KEYS_S2S
    else:
        raise ValueError(F"Unsupported task: {task}")

    for name, value in fairseq_dict.items():
        if should_ignore(A_    ,A_):
            logger.info(F"{name} was ignored")
            continue

        UpperCamelCase__:	Union[str, Any]					=     False
        if "conv_layers" in name:
            load_conv_layer(
                A_    ,A_    ,A_    ,A_    ,hf_model.config.feat_extract_norm == "group"    ,)
            UpperCamelCase__:	Dict					=     True
        else:
            for key, mapped_key in MAPPING.items():
                # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

                if "*" in key:
                    UpperCamelCase__				, UpperCamelCase__:	List[str]					=     key.split(".*.")
                    if prefix in name and suffix in name:
                        UpperCamelCase__:	List[Any]					=     suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                if key in name:
                    UpperCamelCase__:	int					=     True
                    if "*" in mapped_key:
                        UpperCamelCase__:	Dict					=     name.split(A_)[0].split(".")[-2]
                        UpperCamelCase__:	Any					=     mapped_key.replace("*"    ,A_)
                    if "weight_g" in name:
                        UpperCamelCase__:	List[Any]					=     "weight_g"
                    elif "weight_v" in name:
                        UpperCamelCase__:	Optional[int]					=     "weight_v"
                    elif "bias" in name:
                        UpperCamelCase__:	Dict					=     "bias"
                    elif "weight" in name:
                        UpperCamelCase__:	Union[str, Any]					=     "weight"
                    elif "running_mean" in name:
                        UpperCamelCase__:	Optional[Any]					=     "running_mean"
                    elif "running_var" in name:
                        UpperCamelCase__:	Optional[int]					=     "running_var"
                    elif "num_batches_tracked" in name:
                        UpperCamelCase__:	Tuple					=     "num_batches_tracked"
                    else:
                        UpperCamelCase__:	Optional[int]					=     None
                    set_recursively(A_    ,A_    ,A_    ,A_    ,A_)
                continue
        if not is_used:
            unused_weights.append(A_)

    logger.warning(F"Unused weights: {unused_weights}")



def 				lowerCAmelCase_ (							A_    ,A_    ,A_    ,A_    ,A_):
    UpperCamelCase__:	int					=     full_name.split("conv_layers.")[-1]
    UpperCamelCase__:	Dict					=     name.split(".")
    UpperCamelCase__:	int					=     int(items[0])
    UpperCamelCase__:	Any					=     int(items[1])

    if type_id == 0:
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                raise ValueError(
                    F"{full_name} has size {value.shape}, but"
                    F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.")
            UpperCamelCase__:	List[str]					=     value
            logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}.")
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                raise ValueError(
                    F"{full_name} has size {value.shape}, but"
                    F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.")
            UpperCamelCase__:	Tuple					=     value
            logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}.")
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                raise ValueError(
                    F"{full_name} has size {value.shape}, but"
                    F" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.")
            UpperCamelCase__:	List[Any]					=     value
            logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                raise ValueError(
                    F"{full_name} has size {value.shape}, but"
                    F" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.")
            UpperCamelCase__:	Optional[int]					=     value
            logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
    else:
        unused_weights.append(A_)



@torch.no_grad()
def 				lowerCAmelCase_ (							A_    ,A_    ,A_    ,A_=None    ,A_=None    ,A_=None    ,):
    if config_path is not None:
        UpperCamelCase__:	Tuple					=     SpeechTaConfig.from_pretrained(A_)
    else:
        UpperCamelCase__:	Any					=     SpeechTaConfig()

    if task == "s2t":
        UpperCamelCase__:	Optional[Any]					=     config.max_text_positions
        UpperCamelCase__:	List[Any]					=     SpeechTaForSpeechToText(A_)
    elif task == "t2s":
        UpperCamelCase__:	List[str]					=     18_76
        UpperCamelCase__:	Optional[Any]					=     6_00
        UpperCamelCase__:	Union[str, Any]					=     config.max_speech_positions
        UpperCamelCase__:	Any					=     SpeechTaForTextToSpeech(A_)
    elif task == "s2s":
        UpperCamelCase__:	List[Any]					=     18_76
        UpperCamelCase__:	List[str]					=     config.max_speech_positions
        UpperCamelCase__:	Optional[Any]					=     SpeechTaForSpeechToSpeech(A_)
    else:
        raise ValueError(F"Unknown task name: {task}")

    if vocab_path:
        UpperCamelCase__:	Tuple					=     SpeechTaTokenizer(A_    ,model_max_length=config.max_text_positions)

        # Mask token behaves like a normal word, i.e. include the space before it
        UpperCamelCase__:	Optional[int]					=     AddedToken("<mask>"    ,lstrip=A_    ,rstrip=A_)
        UpperCamelCase__:	str					=     mask_token
        tokenizer.add_special_tokens({"mask_token": mask_token})
        tokenizer.add_tokens(["<ctc_blank>"])

    UpperCamelCase__:	int					=     SpeechTaFeatureExtractor()
    UpperCamelCase__:	int					=     SpeechTaProcessor(tokenizer=A_    ,feature_extractor=A_)
    processor.save_pretrained(A_)

    UpperCamelCase__:	Tuple					=     torch.load(A_)
    recursively_load_weights(fairseq_checkpoint["model"]    ,A_    ,A_)

    model.save_pretrained(A_)

    if repo_id:
        print("Pushing to the hub...")
        processor.push_to_hub(A_)
        model.push_to_hub(A_)


if __name__ == "__main__":
   A__:    Any  	=	argparse.ArgumentParser()
   parser.add_argument(
       '''--task''',
       default='''s2t''',
       type=str,
       help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
   )
   parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
   parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
   parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
   parser.add_argument(
       '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
   )
   parser.add_argument(
       '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
   )

   A__:    List[str]  	=	parser.parse_args()
   convert_speechta_checkpoint(
       args.task,
       args.checkpoint_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.vocab_path,
       args.push_to_hub,
   )








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A__:    str  	=	logging.get_logger(__name__)

A__:    Union[str, Any]  	=	{
    '''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json''',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}




class        _a    (    UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__  		=       """deformable_detr"""
      UpperCamelCase__  		=       {
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
      }






      def __init__(		self:			Dict	,    __lowerCamelCase:			Optional[Any]=True	,    __lowerCamelCase:			List[str]=None	,    __lowerCamelCase:			Tuple=3	,    __lowerCamelCase:			Union[str, Any]=300	,    __lowerCamelCase:			Optional[Any]=1024	,    __lowerCamelCase:			Optional[int]=6	,    __lowerCamelCase:			Optional[int]=1024	,    __lowerCamelCase:			List[str]=8	,    __lowerCamelCase:			Any=6	,    __lowerCamelCase:			Tuple=1024	,    __lowerCamelCase:			List[str]=8	,    __lowerCamelCase:			Optional[int]=0.0	,    __lowerCamelCase:			Union[str, Any]=True	,    __lowerCamelCase:			List[str]="relu"	,    __lowerCamelCase:			Tuple=256	,    __lowerCamelCase:			Dict=0.1	,    __lowerCamelCase:			Optional[Any]=0.0	,    __lowerCamelCase:			Optional[int]=0.0	,    __lowerCamelCase:			Any=0.02	,    __lowerCamelCase:			int=1.0	,    __lowerCamelCase:			Optional[Any]=True	,    __lowerCamelCase:			str=False	,    __lowerCamelCase:			Any="sine"	,    __lowerCamelCase:			Optional[Any]="resnet50"	,    __lowerCamelCase:			Optional[int]=True	,    __lowerCamelCase:			int=False	,    __lowerCamelCase:			int=4	,    __lowerCamelCase:			int=4	,    __lowerCamelCase:			Any=4	,    __lowerCamelCase:			Any=False	,    __lowerCamelCase:			List[str]=300	,    __lowerCamelCase:			Dict=False	,    __lowerCamelCase:			str=1	,    __lowerCamelCase:			int=5	,    __lowerCamelCase:			str=2	,    __lowerCamelCase:			Dict=1	,    __lowerCamelCase:			Tuple=1	,    __lowerCamelCase:			Union[str, Any]=5	,    __lowerCamelCase:			Optional[Any]=2	,    __lowerCamelCase:			List[str]=0.1	,    __lowerCamelCase:			Dict=0.25	,    __lowerCamelCase:			Dict=False	,    **__lowerCamelCase:			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."					)
                  UpperCamelCase__:	Union[str, Any]					=     CONFIG_MAPPING["resnet"](out_features=["stage4"]					)
              elif isinstance(__lowerCamelCase	,    __lowerCamelCase					):
                  UpperCamelCase__:	Tuple					=     backbone_config.get("model_type"					)
                  UpperCamelCase__:	Optional[int]					=     CONFIG_MAPPING[backbone_model_type]
                  UpperCamelCase__:	Tuple					=     config_class.from_dict(__lowerCamelCase					)
          UpperCamelCase__:	Dict					=     use_timm_backbone
          UpperCamelCase__:	Any					=     backbone_config
          UpperCamelCase__:	Optional[int]					=     num_channels
          UpperCamelCase__:	int					=     num_queries
          UpperCamelCase__:	List[str]					=     max_position_embeddings
          UpperCamelCase__:	Dict					=     d_model
          UpperCamelCase__:	List[Any]					=     encoder_ffn_dim
          UpperCamelCase__:	Union[str, Any]					=     encoder_layers
          UpperCamelCase__:	Tuple					=     encoder_attention_heads
          UpperCamelCase__:	Tuple					=     decoder_ffn_dim
          UpperCamelCase__:	Optional[int]					=     decoder_layers
          UpperCamelCase__:	int					=     decoder_attention_heads
          UpperCamelCase__:	Optional[Any]					=     dropout
          UpperCamelCase__:	List[str]					=     attention_dropout
          UpperCamelCase__:	List[Any]					=     activation_dropout
          UpperCamelCase__:	List[Any]					=     activation_function
          UpperCamelCase__:	Union[str, Any]					=     init_std
          UpperCamelCase__:	List[str]					=     init_xavier_std
          UpperCamelCase__:	Optional[Any]					=     encoder_layerdrop
          UpperCamelCase__:	List[str]					=     auxiliary_loss
          UpperCamelCase__:	Optional[int]					=     position_embedding_type
          UpperCamelCase__:	Optional[Any]					=     backbone
          UpperCamelCase__:	Any					=     use_pretrained_backbone
          UpperCamelCase__:	Union[str, Any]					=     dilation
          # deformable attributes
          UpperCamelCase__:	Union[str, Any]					=     num_feature_levels
          UpperCamelCase__:	Optional[Any]					=     encoder_n_points
          UpperCamelCase__:	Tuple					=     decoder_n_points
          UpperCamelCase__:	Any					=     two_stage
          UpperCamelCase__:	Optional[int]					=     two_stage_num_proposals
          UpperCamelCase__:	str					=     with_box_refine
          if two_stage is True and with_box_refine is False:
              raise ValueError("If two_stage is True, with_box_refine must be True."					)
          # Hungarian matcher
          UpperCamelCase__:	Any					=     class_cost
          UpperCamelCase__:	str					=     bbox_cost
          UpperCamelCase__:	List[str]					=     giou_cost
          # Loss coefficients
          UpperCamelCase__:	Optional[Any]					=     mask_loss_coefficient
          UpperCamelCase__:	List[Any]					=     dice_loss_coefficient
          UpperCamelCase__:	Optional[Any]					=     bbox_loss_coefficient
          UpperCamelCase__:	List[Any]					=     giou_loss_coefficient
          UpperCamelCase__:	Dict					=     eos_coefficient
          UpperCamelCase__:	List[Any]					=     focal_alpha
          UpperCamelCase__:	Dict					=     disable_custom_kernels
          super().__init__(is_encoder_decoder=__lowerCamelCase	,    **__lowerCamelCase					)






      @property
      def 			UpperCAmelCase_       (		self:			Union[str, Any]					):
          '''simple docstring'''


          return self.encoder_attention_heads






      @property
      def 			UpperCAmelCase_       (		self:			Any					):
          '''simple docstring'''


          return self.d_model






      def 			UpperCAmelCase_       (		self:			int					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[int]					=     copy.deepcopy(self.__dict__					)
          if self.backbone_config is not None:
              UpperCamelCase__:	Tuple					=     self.backbone_config.to_dict()
          UpperCamelCase__:	Optional[int]					=     self.__class__.model_type
          return output

