



import argparse

import torch

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


logging.set_verbosity_info()
lowerCamelCase__			= logging.get_logger(__name__)

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			Tuple    ):
    """simple docstring"""







    __a       =		UniSpeechSatForSequenceClassification.from_pretrained(__UpperCAmelCase				,     config=__UpperCAmelCase    )
    __a       =		downstream_dict["""projector.weight"""]
    __a       =		downstream_dict["""projector.bias"""]
    __a       =		downstream_dict["""model.post_net.linear.weight"""]
    __a       =		downstream_dict["""model.post_net.linear.bias"""]
    return model

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Any				,     _SCREAMING_SNAKE_CASE       :			List[Any]				,     _SCREAMING_SNAKE_CASE       :			str    ):
    """simple docstring"""







    __a       =		UniSpeechSatForAudioFrameClassification.from_pretrained(__UpperCAmelCase				,     config=__UpperCAmelCase    )
    __a       =		downstream_dict["""model.linear.weight"""]
    __a       =		downstream_dict["""model.linear.bias"""]
    return model

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Union[str, Any]				,     _SCREAMING_SNAKE_CASE       :			List[Any]				,     _SCREAMING_SNAKE_CASE       :			Optional[Any]    ):
    """simple docstring"""







    __a       =		UniSpeechSatForXVector.from_pretrained(__UpperCAmelCase				,     config=__UpperCAmelCase    )
    __a       =		downstream_dict["""connector.weight"""]
    __a       =		downstream_dict["""connector.bias"""]
    for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
        __a       =		downstream_dict[
            f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
        ]
        __a       =		downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

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

@torch.no_grad()
def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Union[str, Any]				,     _SCREAMING_SNAKE_CASE       :			Dict				,     _SCREAMING_SNAKE_CASE       :			Optional[int]				,     _SCREAMING_SNAKE_CASE       :			Union[str, Any]    ):
    """simple docstring"""







    __a       =		torch.load(__UpperCAmelCase				,     map_location="""cpu"""    )

    __a       =		checkpoint["""Downstream"""]

    __a       =		UniSpeechSatConfig.from_pretrained(__UpperCAmelCase    )
    __a       =		WavaVecaFeatureExtractor.from_pretrained(
        __UpperCAmelCase				,     return_attention_mask=__UpperCAmelCase				,     do_normalize=__UpperCAmelCase    )

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

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

    hf_feature_extractor.save_pretrained(__UpperCAmelCase    )
    hf_model.save_pretrained(__UpperCAmelCase    )


if __name__ == "__main__":
      lowerCamelCase__			= 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.""")
      lowerCamelCase__			= parser.parse_args()
      convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	torch.FloatTensor






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      lowerCamelCase__					):





							@register_to_config
							def __init__(   self		:		Tuple      ,				__lowercase		:		int = 16      ,				__lowercase		:		int = 88      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		int = 1      ,				__lowercase		:		float = 0.0      ,				__lowercase		:		int = 32      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		bool = False      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		str = "geglu"      ,				__lowercase		:		bool = True      ,				__lowercase		:		bool = True      ,				):

											'''simple docstring'''
											super().__init__()
											__a       =		num_attention_heads
											__a       =		attention_head_dim
											__a       =		num_attention_heads * attention_head_dim

											__a       =		in_channels

											__a       =		torch.nn.GroupNorm(num_groups=__lowercase      ,				num_channels=__lowercase      ,				eps=1E-6      ,				affine=__lowercase	)
											__a       =		nn.Linear(__lowercase      ,				__lowercase	)

											# 3. Define transformers blocks
											__a       =		nn.ModuleList(
											    [
											        BasicTransformerBlock(
											            __lowercase      ,				__lowercase      ,				__lowercase      ,				dropout=__lowercase      ,				cross_attention_dim=__lowercase      ,				activation_fn=__lowercase      ,				attention_bias=__lowercase      ,				double_self_attention=__lowercase      ,				norm_elementwise_affine=__lowercase      ,				)
											        for d in range(__lowercase	)
											    ]	)

											__a       =		nn.Linear(__lowercase      ,				__lowercase	)







							def 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		str=None      ,				__lowercase		:		List[Any]=None      ,				__lowercase		:		List[Any]=None      ,				__lowercase		:		int=1      ,				__lowercase		:		Union[str, Any]=None      ,				__lowercase		:		bool = True      ,				):

											'''simple docstring'''
											__a     ,					__a     ,					__a     ,					__a       =		hidden_states.shape
											__a       =		batch_frames // num_frames

											__a       =		hidden_states

											__a       =		hidden_states[None, :].reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
											__a       =		hidden_states.permute(0      ,				2      ,				1      ,				3      ,				4	)

											__a       =		self.norm(__lowercase	)
											__a       =		hidden_states.permute(0      ,				3      ,				4      ,				2      ,				1	).reshape(batch_size * height * width      ,				__lowercase      ,				__lowercase	)

											__a       =		self.proj_in(__lowercase	)

											# 2. Blocks
											for block in self.transformer_blocks:
															__a       =		block(
															    __lowercase      ,				encoder_hidden_states=__lowercase      ,				timestep=__lowercase      ,				cross_attention_kwargs=__lowercase      ,				class_labels=__lowercase      ,				)

											# 3. Output
											__a       =		self.proj_out(__lowercase	)
											__a       =		(
											    hidden_states[None, None, :]
											    .reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
											    .permute(0      ,				3      ,				4      ,				1      ,				2	)
											    .contiguous()
											)
											__a       =		hidden_states.reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)

											__a       =		hidden_states + residual

											if not return_dict:
															return (output,)

											return TransformerTemporalModelOutput(sample=__lowercase	)
