
"""simple docstring"""




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__      :	List[str]          =    logging.get_logger("""transformers.models.speecht5""")

A__      :	List[str]          =    {
    '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__      :	int          =    {
    '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__      :	str          =    {
    '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__      :	Any          =    {
    '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__      :	Any          =    {
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
A__      :	int          =    {
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
A__      :	Any          =    {
    '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__      :	Optional[Any]          =    {
    '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__      :	str          =    {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
A__      :	str          =    {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
A__      :	Union[str, Any]          =    {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
A__      :	int          =    []
A__      :	Optional[Any]          =    [
    '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[Any]          =    IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
A__      :	Union[str, Any]          =    IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
A__      :	Optional[int]          =    IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]



def 							a__ (				lowerCAmelCase	:       List[str]       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     for attribute in key.split("."		):
          UpperCAmelCase__							: Dict 							=						getattr(lowercase_       ,    lowercase_		)

     if weight_type is not None:
          UpperCAmelCase__							: Tuple 							=						getattr(lowercase_       ,    lowercase_		).shape
     else:
          UpperCAmelCase__							: int 							=						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__							: Any 							=						value
     elif weight_type == "weight_g":
          UpperCAmelCase__							: Union[str, Any] 							=						value
     elif weight_type == "weight_v":
          UpperCAmelCase__							: Optional[Any] 							=						value
     elif weight_type == "bias":
          UpperCAmelCase__							: str 							=						value
     elif weight_type == "running_mean":
          UpperCAmelCase__							: List[Any] 							=						value
     elif weight_type == "running_var":
          UpperCAmelCase__							: str 							=						value
     elif weight_type == "num_batches_tracked":
          UpperCAmelCase__							: List[Any] 							=						value
     else:
          UpperCAmelCase__							: Optional[Any] 							=						value

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



def 							a__ (				lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     for key in ignore_keys:
          if key.endswith(".*"		):
               if name.startswith(key[:-1]		):
                    return True
          elif ".*." in key:
               UpperCAmelCase__							: List[Any] 							=						key.split(".*."		)
               if prefix in name and suffix in name:
                    return True
          elif key in name:
               return True
     return False



def 							a__ (				lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       List[str]       ,    lowerCAmelCase	:       Optional[Any]		):






     '''simple docstring'''
     UpperCAmelCase__							: Any 							=						[]

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

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

          UpperCAmelCase__							: Optional[Any] 							=						False
          if "conv_layers" in name:
               load_conv_layer(
                   lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    hf_model.config.feat_extract_norm == "group"       ,    )
               UpperCAmelCase__							: Any 							=						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__							: Tuple 							=						key.split(".*."		)
                         if prefix in name and suffix in name:
                              UpperCAmelCase__							: Optional[Any] 							=						suffix

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

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



def 							a__ (				lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Any		):






     '''simple docstring'''
     UpperCAmelCase__							: int 							=						full_name.split("conv_layers."		)[-1]
     UpperCAmelCase__							: Optional[int] 							=						name.split("."		)
     UpperCAmelCase__							: Any 							=						int(items[0]		)
     UpperCAmelCase__							: Dict 							=						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__							: Optional[int] 							=						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__							: List[str] 							=						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__							: int 							=						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__							: str 							=						value
               logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
     else:
          unused_weights.append(lowercase_		)



@torch.no_grad()
def 							a__ (				lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       List[Any]=None       ,    lowerCAmelCase	:       Tuple=None       ,    lowerCAmelCase	:       Dict=None       ,    ):






     '''simple docstring'''
     if config_path is not None:
          UpperCAmelCase__							: Tuple 							=						SpeechTaConfig.from_pretrained(lowercase_		)
     else:
          UpperCAmelCase__							: Tuple 							=						SpeechTaConfig()

     if task == "s2t":
          UpperCAmelCase__							: Optional[int] 							=						config.max_text_positions
          UpperCAmelCase__							: Any 							=						SpeechTaForSpeechToText(lowercase_		)
     elif task == "t2s":
          UpperCAmelCase__							: List[Any] 							=						1876
          UpperCAmelCase__							: str 							=						600
          UpperCAmelCase__							: List[Any] 							=						config.max_speech_positions
          UpperCAmelCase__							: Tuple 							=						SpeechTaForTextToSpeech(lowercase_		)
     elif task == "s2s":
          UpperCAmelCase__							: Dict 							=						1876
          UpperCAmelCase__							: int 							=						config.max_speech_positions
          UpperCAmelCase__							: Any 							=						SpeechTaForSpeechToSpeech(lowercase_		)
     else:
          raise ValueError(F"Unknown task name: {task}"		)

     if vocab_path:
          UpperCAmelCase__							: Any 							=						SpeechTaTokenizer(lowercase_       ,    model_max_length=config.max_text_positions		)

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

     UpperCAmelCase__							: Dict 							=						SpeechTaFeatureExtractor()
     UpperCAmelCase__							: List[Any] 							=						SpeechTaProcessor(tokenizer=lowercase_       ,    feature_extractor=lowercase_		)
     processor.save_pretrained(lowercase_		)

     UpperCAmelCase__							: List[Any] 							=						torch.load(lowercase_		)
     recursively_load_weights(fairseq_checkpoint["model"]       ,    lowercase_       ,    lowercase_		)

     model.save_pretrained(lowercase_		)

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


if __name__ == "__main__":
    A__      :	Union[str, 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,
    )





"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from packaging import version

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


A__      :	Union[str, Any]          =    logging.get_logger(__name__)

A__      :	Optional[int]          =    {
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							'table-transformer'
     _A								=							['past_key_values']
     _A								=							{
         'hidden_size': 'd_model',
         'num_attention_heads': 'encoder_attention_heads',
     }



     def __init__(      self ,			__UpperCamelCase=True ,			__UpperCamelCase=None ,			__UpperCamelCase=3 ,			__UpperCamelCase=1_00 ,			__UpperCamelCase=6 ,			__UpperCamelCase=20_48 ,			__UpperCamelCase=8 ,			__UpperCamelCase=6 ,			__UpperCamelCase=20_48 ,			__UpperCamelCase=8 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=True ,			__UpperCamelCase="relu" ,			__UpperCamelCase=2_56 ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=0.02 ,			__UpperCamelCase=1.0 ,			__UpperCamelCase=False ,			__UpperCamelCase="sine" ,			__UpperCamelCase="resnet50" ,			__UpperCamelCase=True ,			__UpperCamelCase=False ,			__UpperCamelCase=1 ,			__UpperCamelCase=5 ,			__UpperCamelCase=2 ,			__UpperCamelCase=1 ,			__UpperCamelCase=1 ,			__UpperCamelCase=5 ,			__UpperCamelCase=2 ,			__UpperCamelCase=0.1 ,			**__UpperCamelCase ,			)->					List[Any]:
          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__							: Any 							=						CONFIG_MAPPING["resnet"](out_features=["stage4"]  )
               elif isinstance(__UpperCamelCase ,			__UpperCamelCase  ):
                    UpperCAmelCase__							: int 							=						backbone_config.get("model_type"  )
                    UpperCAmelCase__							: Optional[Any] 							=						CONFIG_MAPPING[backbone_model_type]
                    UpperCAmelCase__							: int 							=						config_class.from_dict(__UpperCamelCase  )
               # set timm attributes to None
               UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: List[str] 							=						None, None, None

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



     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return self.encoder_attention_heads



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







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							version.parse('1.11'				)



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



     @property
     def  lowerCAmelCase__       (      self  )->					float:
          return 1E-5



     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return 12



