





import copy
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


UpperCAmelCase__						:			List[str]									=logging.get_logger(__name__)

UpperCAmelCase__						:			Optional[int]									={
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}




class       __A     (						snake_case__    ):
				__A      =				'''conditional_detr'''
				__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_=300   , UpperCAmelCase_=6   , UpperCAmelCase_=2048   , UpperCAmelCase_=8   , UpperCAmelCase_=6   , UpperCAmelCase_=2048   , UpperCAmelCase_=8   , UpperCAmelCase_=0.0   , UpperCAmelCase_=0.0   , UpperCAmelCase_=True   , UpperCAmelCase_="relu"   , UpperCAmelCase_=256   , UpperCAmelCase_=0.1   , UpperCAmelCase_=0.0   , UpperCAmelCase_=0.0   , UpperCAmelCase_=0.0_2   , UpperCAmelCase_=1.0   , UpperCAmelCase_=False   , UpperCAmelCase_="sine"   , UpperCAmelCase_="resnet50"   , UpperCAmelCase_=True   , UpperCAmelCase_=False   , UpperCAmelCase_=2   , UpperCAmelCase_=5   , UpperCAmelCase_=2   , UpperCAmelCase_=1   , UpperCAmelCase_=1   , UpperCAmelCase_=2   , UpperCAmelCase_=5   , UpperCAmelCase_=2   , UpperCAmelCase_=0.2_5   , **UpperCAmelCase_   , ):
								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	 =CONFIG_MAPPING['resnet'](out_features=["""stage4"""]							)
												elif isinstance(_A   , _A							):
																lowerCamelCase	 =backbone_config.get("""model_type"""							)
																lowerCamelCase	 =CONFIG_MAPPING[backbone_model_type]
																lowerCamelCase	 =config_class.from_dict(_A							)

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


				@property
				def _snake_case       (		self							):
								return self.encoder_attention_heads


				@property
				def _snake_case       (		self							):
								return self.d_model


				def _snake_case       (		self							):
								lowerCamelCase	 =copy.deepcopy(self.__dict__							)
								if self.backbone_config is not None:
												lowerCamelCase	 =self.backbone_config.to_dict()
								lowerCamelCase	 =self.__class__.model_type
								return output




class       __A     (						snake_case__    ):
				__A      =				version.parse("""1.11"""    )


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


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


				@property
				def _snake_case       (		self							):
								return 12












import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase__						:			int									=logging.get_logger(__name__)





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Any:
				lowerCamelCase	 =UniSpeechSatForSequenceClassification.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""projector.weight"""]
				lowerCamelCase	 =downstream_dict["""projector.bias"""]
				lowerCamelCase	 =downstream_dict["""model.post_net.linear.weight"""]
				lowerCamelCase	 =downstream_dict["""model.post_net.linear.bias"""]
				return model





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  int:
				lowerCamelCase	 =UniSpeechSatForAudioFrameClassification.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""model.linear.weight"""]
				lowerCamelCase	 =downstream_dict["""model.linear.bias"""]
				return model





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Dict:
				lowerCamelCase	 =UniSpeechSatForXVector.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""connector.weight"""]
				lowerCamelCase	 =downstream_dict["""connector.bias"""]
				for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
								lowerCamelCase	 =downstream_dict[
								    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
								]
								lowerCamelCase	 =downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
				lowerCamelCase	 =downstream_dict["""objective.W"""]
				return model





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  str:
				lowerCamelCase	 =torch.load(_UpperCAmelCase			,      map_location="""cpu"""				)

				lowerCamelCase	 =checkpoint["""Downstream"""]

				lowerCamelCase	 =UniSpeechSatConfig.from_pretrained(_UpperCAmelCase				)
				lowerCamelCase	 =WavaVecaFeatureExtractor.from_pretrained(
				    _UpperCAmelCase			,      return_attention_mask=_UpperCAmelCase			,      do_normalize=_UpperCAmelCase				)

				lowerCamelCase	 =hf_config.architectures[0]
				if arch.endswith("""ForSequenceClassification"""				):
								lowerCamelCase	 =convert_classification(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				elif arch.endswith("""ForAudioFrameClassification"""				):
								lowerCamelCase	 =convert_diarization(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				elif arch.endswith("""ForXVector"""				):
								lowerCamelCase	 =convert_xvector(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				else:
								raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}"""				)

				if hf_config.use_weighted_layer_sum:
								lowerCamelCase	 =checkpoint["""Featurizer"""]["""weights"""]

				hf_feature_extractor.save_pretrained(_UpperCAmelCase				)
				hf_model.save_pretrained(_UpperCAmelCase				)


if __name__ == "__main__":
							UpperCAmelCase__						:			Dict									=argparse.ArgumentParser()
							parser.add_argument(
							    '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
							)
							parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
							parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
							parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
							UpperCAmelCase__						:			List[Any]									=parser.parse_args()
							convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





