






import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

SCREAMING_SNAKE_CASE								=  {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'mask_emb': 'masked_spec_embed',
}

def        _lowerCamelCase		(  __A      :							Tuple						,						__A      :							str						,						__A      :							int						,						__A      :							Union[str, Any]						,						__A      :							int	) ->   List[str]:
    for attribute in key.split('''.'''	):
        _UpperCAmelCase			: Union[str, Any]     					=			getattr(_lowerCamelCase						,						_lowerCamelCase	)

    if weight_type is not None:
        _UpperCAmelCase			: Union[str, Any]     					=			getattr(_lowerCamelCase						,						_lowerCamelCase	).shape
    else:
        _UpperCAmelCase			: Union[str, Any]     					=			hf_pointer.shape

    assert hf_shape == value.shape, (
        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			: int     					=			value
    elif weight_type == "weight_g":
        _UpperCAmelCase			: Union[str, Any]     					=			value
    elif weight_type == "weight_v":
        _UpperCAmelCase			: int     					=			value
    elif weight_type == "bias":
        _UpperCAmelCase			: int     					=			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        _lowerCamelCase		(  __A      :							Union[str, Any]						,						__A      :							Any	) ->   Optional[int]:
    _UpperCAmelCase			: Optional[Any]     					=			[]
    _UpperCAmelCase			: Dict     					=			fairseq_model.state_dict()

    _UpperCAmelCase			: List[str]     					=			hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        _UpperCAmelCase			: List[str]     					=			False
        if "conv_layers" in name:
            load_conv_layer(
                _lowerCamelCase						,						_lowerCamelCase						,						_lowerCamelCase						,						_lowerCamelCase						,						hf_model.config.feat_extract_norm == '''group'''						,						)
            _UpperCAmelCase			: List[str]     					=			True
        else:
            for key, mapped_key in MAPPING.items():
                _UpperCAmelCase			: Optional[Any]     					=			mapped_key

                if key in name:
                    _UpperCAmelCase			: Optional[int]     					=			True
                    if "*" in mapped_key:
                        _UpperCAmelCase			: Optional[Any]     					=			name.split(_lowerCamelCase	)[0].split('''.'''	)[-2]
                        _UpperCAmelCase			: Tuple     					=			mapped_key.replace('''*'''						,						_lowerCamelCase	)
                    if "weight_g" in name:
                        _UpperCAmelCase			: str     					=			"""weight_g"""
                    elif "weight_v" in name:
                        _UpperCAmelCase			: List[str]     					=			"""weight_v"""
                    elif "weight" in name:
                        _UpperCAmelCase			: str     					=			"""weight"""
                    elif "bias" in name:
                        _UpperCAmelCase			: Any     					=			"""bias"""
                    else:
                        _UpperCAmelCase			: Optional[int]     					=			None
                    set_recursively(_lowerCamelCase						,						_lowerCamelCase						,						_lowerCamelCase						,						_lowerCamelCase						,						_lowerCamelCase	)
                continue
        if not is_used:
            unused_weights.append(_lowerCamelCase	)

    logger.warning(f'''Unused weights: {unused_weights}'''	)

def        _lowerCamelCase		(  __A      :							Optional[int]						,						__A      :							Optional[Any]						,						__A      :							List[str]						,						__A      :							int						,						__A      :							Union[str, Any]	) ->   Dict:
    _UpperCAmelCase			: List[Any]     					=			full_name.split('''conv_layers.'''	)[-1]
    _UpperCAmelCase			: List[Any]     					=			name.split('''.'''	)
    _UpperCAmelCase			: str     					=			int(items[0]	)
    _UpperCAmelCase			: List[str]     					=			int(items[1]	)

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                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:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                f'''{full_name} has size {value.shape}, but'''
                f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
            )
            _UpperCAmelCase			: Optional[Any]     					=			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:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                " found."
            )
            _UpperCAmelCase			: str     					=			value
            logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                f'''{full_name} has size {value.shape}, but'''
                f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
            )
            _UpperCAmelCase			: List[str]     					=			value
            logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
    else:
        unused_weights.append(_lowerCamelCase	)

def        _lowerCamelCase		(  __A      :							int	) ->   Optional[int]:
    _UpperCAmelCase			: Dict     					=			HubertConfig()
    _UpperCAmelCase			: Dict     					=			model.config

    _UpperCAmelCase			: str     					=			fs_config.activation_dropout
    _UpperCAmelCase			: List[str]     					=			False
    _UpperCAmelCase			: Union[str, Any]     					=			fs_config.attention_dropout
    _UpperCAmelCase			: List[str]     					=			False
    _UpperCAmelCase			: str     					=			eval(fs_config.extractor_conv_feature_layers	)
    _UpperCAmelCase			: Optional[int]     					=			[x[0] for x in conv_layers]
    _UpperCAmelCase			: Tuple     					=			[x[1] for x in conv_layers]
    _UpperCAmelCase			: Optional[Any]     					=			[x[2] for x in conv_layers]
    _UpperCAmelCase			: Optional[Any]     					=			"""gelu"""
    _UpperCAmelCase			: List[str]     					=			"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
    _UpperCAmelCase			: Optional[int]     					=			False
    _UpperCAmelCase			: Any     					=			0.0
    _UpperCAmelCase			: Dict     					=			0.0
    _UpperCAmelCase			: Optional[Any]     					=			fs_config.activation_fn
    _UpperCAmelCase			: Dict     					=			fs_config.dropout
    _UpperCAmelCase			: Union[str, Any]     					=			fs_config.encoder_embed_dim
    _UpperCAmelCase			: str     					=			0.02
    _UpperCAmelCase			: Dict     					=			fs_config.encoder_ffn_embed_dim
    _UpperCAmelCase			: Union[str, Any]     					=			1e-5
    _UpperCAmelCase			: Union[str, Any]     					=			0.0
    _UpperCAmelCase			: Dict     					=			fs_config.encoder_attention_heads
    _UpperCAmelCase			: str     					=			fs_config.conv_pos_groups
    _UpperCAmelCase			: Optional[Any]     					=			fs_config.conv_pos
    _UpperCAmelCase			: Dict     					=			len(_lowerCamelCase	)
    _UpperCAmelCase			: str     					=			fs_config.encoder_layers

    return config

@torch.no_grad()
def        _lowerCamelCase		(  __A      :							List[Any]						,						__A      :							int=None	) ->   Dict:
    _UpperCAmelCase			: Any     					=			distilhubert().model.model

    if config_path is not None:
        _UpperCAmelCase			: int     					=			HubertConfig.from_pretrained(_lowerCamelCase	)
    else:
        _UpperCAmelCase			: List[Any]     					=			convert_config(_lowerCamelCase	)
    _UpperCAmelCase			: List[Any]     					=			model.eval()

    _UpperCAmelCase			: Dict     					=			WavaVecaFeatureExtractor(
        feature_size=1						,						sampling_rate=16_000						,						padding_value=0						,						do_normalize=_lowerCamelCase						,						return_attention_mask=_lowerCamelCase						,						)
    _UpperCAmelCase			: int     					=			HubertModel(_lowerCamelCase	)

    recursively_load_weights(_lowerCamelCase						,						_lowerCamelCase	)

    feature_extractor.save_pretrained(_lowerCamelCase	)
    hf_model.save_pretrained(_lowerCamelCase	)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE								=  argparse.ArgumentParser()
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
 SCREAMING_SNAKE_CASE								=  parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)












from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 def __init__(		self							,					_A							,					_A)	->						Dict:

     """simple docstring"""



     super().__init__()
     self.register_modules(unet=_A							,					scheduler=_A)


 @torch.no_grad()
 def __call__(		self							,					_A = 1							,					_A = None							,					_A = 1000							,					_A = "pil"							,					_A = True							,					)	->						Union[ImagePipelineOutput, Tuple]:

     """simple docstring"""



     if isinstance(self.unet.config.sample_size							,					_A):
         _UpperCAmelCase			: List[str]     					=			(
             batch_size,
             self.unet.config.in_channels,
             self.unet.config.sample_size,
             self.unet.config.sample_size,
         )
     else:
         _UpperCAmelCase			: Union[str, Any]     					=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

     if self.device.type == "mps":
         # randn does not work reproducibly on mps
         _UpperCAmelCase			: int     					=			randn_tensor(_A							,					generator=_A)
         _UpperCAmelCase			: Tuple     					=			image.to(self.device)
     else:
         _UpperCAmelCase			: Any     					=			randn_tensor(_A							,					generator=_A							,					device=self.device)

     # set step values
     self.scheduler.set_timesteps(_A)

     for t in self.progress_bar(self.scheduler.timesteps):
         # 1. predict noise model_output
         _UpperCAmelCase			: Optional[Any]     					=			self.unet(_A							,					_A).sample

         # 2. compute previous image: x_t -> x_t-1
         _UpperCAmelCase			: Dict     					=			self.scheduler.step(_A							,					_A							,					_A							,					generator=_A).prev_sample

     _UpperCAmelCase			: Optional[int]     					=			(image / 2 + 0.5).clamp(0							,					1)
     _UpperCAmelCase			: int     					=			image.cpu().permute(0							,					2							,					3							,					1).numpy()
     if output_type == "pil":
         _UpperCAmelCase			: str     					=			self.numpy_to_pil(_A)

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=_A)




