

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


snake_case_        =			{
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [32, 64],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

snake_case_        =			{
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [192, 192 * 2, 192 * 3, 192 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

snake_case_        =			{
    '''sample_size''': 256,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

snake_case_        =			{
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

snake_case_        =			{
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

snake_case_        =			{
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Union[str, Any]      ):



     '''simple docstring'''





     if isinstance(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ):
          return v
     if v.lower() in ("yes", "true", "t", "y", "1"):
          return True
     elif v.lower() in ("no", "false", "f", "n", "0"):
          return False
     else:
          raise argparse.ArgumentTypeError('boolean value expected'      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   Dict     ,					SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   Tuple=False      ):



     '''simple docstring'''





     lowercase__   :       Union[str, Any]				=	checkpoint[f"""{old_prefix}.in_layers.0.weight"""]
     lowercase__   :       Union[str, Any]				=	checkpoint[f"""{old_prefix}.in_layers.0.bias"""]
     lowercase__   :       List[Any]				=	checkpoint[f"""{old_prefix}.in_layers.2.weight"""]
     lowercase__   :       Union[str, Any]				=	checkpoint[f"""{old_prefix}.in_layers.2.bias"""]
     lowercase__   :       int				=	checkpoint[f"""{old_prefix}.emb_layers.1.weight"""]
     lowercase__   :       List[Any]				=	checkpoint[f"""{old_prefix}.emb_layers.1.bias"""]
     lowercase__   :       List[Any]				=	checkpoint[f"""{old_prefix}.out_layers.0.weight"""]
     lowercase__   :       str				=	checkpoint[f"""{old_prefix}.out_layers.0.bias"""]
     lowercase__   :       Any				=	checkpoint[f"""{old_prefix}.out_layers.3.weight"""]
     lowercase__   :       Optional[Any]				=	checkpoint[f"""{old_prefix}.out_layers.3.bias"""]

     if has_skip:
          lowercase__   :       List[Any]				=	checkpoint[f"""{old_prefix}.skip_connection.weight"""]
          lowercase__   :       Dict				=	checkpoint[f"""{old_prefix}.skip_connection.bias"""]

     return new_checkpoint

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   Dict     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]     ,					SCREAMING_SNAKE_CASE_		:   List[str]     ,					SCREAMING_SNAKE_CASE_		:   int=None      ):



     '''simple docstring'''





     lowercase__				,			lowercase__				,			lowercase__   :       List[Any]				=	checkpoint[f"""{old_prefix}.qkv.weight"""].chunk(3     ,					dim=0      )
     lowercase__				,			lowercase__				,			lowercase__   :       int				=	checkpoint[f"""{old_prefix}.qkv.bias"""].chunk(3     ,					dim=0      )

     lowercase__   :       Union[str, Any]				=	checkpoint[f"""{old_prefix}.norm.weight"""]
     lowercase__   :       List[str]				=	checkpoint[f"""{old_prefix}.norm.bias"""]

     lowercase__   :       Tuple				=	weight_q.squeeze(-1      ).squeeze(-1      )
     lowercase__   :       Dict				=	bias_q.squeeze(-1      ).squeeze(-1      )
     lowercase__   :       Tuple				=	weight_k.squeeze(-1      ).squeeze(-1      )
     lowercase__   :       List[Any]				=	bias_k.squeeze(-1      ).squeeze(-1      )
     lowercase__   :       List[str]				=	weight_v.squeeze(-1      ).squeeze(-1      )
     lowercase__   :       Tuple				=	bias_v.squeeze(-1      ).squeeze(-1      )

     lowercase__   :       Optional[Any]				=	(
         checkpoint[f"""{old_prefix}.proj_out.weight"""].squeeze(-1      ).squeeze(-1      )
     )
     lowercase__   :       Any				=	checkpoint[f"""{old_prefix}.proj_out.bias"""].squeeze(-1      ).squeeze(-1      )

     return new_checkpoint

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   List[Any]      ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	torch.load(SCREAMING_SNAKE_CASE_     ,					map_location='cpu'      )
     lowercase__   :       Optional[Any]				=	{}

     lowercase__   :       Tuple				=	checkpoint['time_embed.0.weight']
     lowercase__   :       Tuple				=	checkpoint['time_embed.0.bias']
     lowercase__   :       Optional[int]				=	checkpoint['time_embed.2.weight']
     lowercase__   :       str				=	checkpoint['time_embed.2.bias']

     if unet_config["num_class_embeds"] is not None:
          lowercase__   :       int				=	checkpoint['label_emb.weight']

     lowercase__   :       Tuple				=	checkpoint['input_blocks.0.0.weight']
     lowercase__   :       Optional[Any]				=	checkpoint['input_blocks.0.0.bias']

     lowercase__   :       Optional[Any]				=	unet_config['down_block_types']
     lowercase__   :       Tuple				=	unet_config['layers_per_block']
     lowercase__   :       Optional[Any]				=	unet_config['attention_head_dim']
     lowercase__   :       Optional[Any]				=	unet_config['block_out_channels']
     lowercase__   :       int				=	1
     lowercase__   :       Optional[int]				=	channels_list[0]

     for i, layer_type in enumerate(SCREAMING_SNAKE_CASE_      ):
          lowercase__   :       Optional[Any]				=	channels_list[i]
          lowercase__   :       Tuple				=	current_channels != prev_channels
          if layer_type == "ResnetDownsampleBlock2D":
               for j in range(SCREAMING_SNAKE_CASE_      ):
                    lowercase__   :       int				=	f"""down_blocks.{i}.resnets.{j}"""
                    lowercase__   :       List[Any]				=	f"""input_blocks.{current_layer}.0"""
                    lowercase__   :       Any				=	True if j == 0 and downsample_block_has_skip else False
                    lowercase__   :       Dict				=	convert_resnet(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					has_skip=SCREAMING_SNAKE_CASE_      )
                    current_layer += 1

          elif layer_type == "AttnDownBlock2D":
               for j in range(SCREAMING_SNAKE_CASE_      ):
                    lowercase__   :       Union[str, Any]				=	f"""down_blocks.{i}.resnets.{j}"""
                    lowercase__   :       int				=	f"""input_blocks.{current_layer}.0"""
                    lowercase__   :       List[str]				=	True if j == 0 and downsample_block_has_skip else False
                    lowercase__   :       Any				=	convert_resnet(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					has_skip=SCREAMING_SNAKE_CASE_      )
                    lowercase__   :       List[str]				=	f"""down_blocks.{i}.attentions.{j}"""
                    lowercase__   :       Optional[int]				=	f"""input_blocks.{current_layer}.1"""
                    lowercase__   :       Any				=	convert_attention(
                        SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
                    current_layer += 1

          if i != len(SCREAMING_SNAKE_CASE_      ) - 1:
               lowercase__   :       Tuple				=	f"""down_blocks.{i}.downsamplers.0"""
               lowercase__   :       Tuple				=	f"""input_blocks.{current_layer}.0"""
               lowercase__   :       Any				=	convert_resnet(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
               current_layer += 1

          lowercase__   :       Union[str, Any]				=	current_channels

     # hardcoded the mid-block for now
     lowercase__   :       Union[str, Any]				=	'mid_block.resnets.0'
     lowercase__   :       str				=	'middle_block.0'
     lowercase__   :       Any				=	convert_resnet(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Any				=	'mid_block.attentions.0'
     lowercase__   :       int				=	'middle_block.1'
     lowercase__   :       Dict				=	convert_attention(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Dict				=	'mid_block.resnets.1'
     lowercase__   :       str				=	'middle_block.2'
     lowercase__   :       Any				=	convert_resnet(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

     lowercase__   :       str				=	0
     lowercase__   :       int				=	unet_config['up_block_types']

     for i, layer_type in enumerate(SCREAMING_SNAKE_CASE_      ):
          if layer_type == "ResnetUpsampleBlock2D":
               for j in range(layers_per_block + 1      ):
                    lowercase__   :       Any				=	f"""up_blocks.{i}.resnets.{j}"""
                    lowercase__   :       Optional[Any]				=	f"""output_blocks.{current_layer}.0"""
                    lowercase__   :       Tuple				=	convert_resnet(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					has_skip=SCREAMING_SNAKE_CASE_      )
                    current_layer += 1

               if i != len(SCREAMING_SNAKE_CASE_      ) - 1:
                    lowercase__   :       List[str]				=	f"""up_blocks.{i}.upsamplers.0"""
                    lowercase__   :       Any				=	f"""output_blocks.{current_layer-1}.1"""
                    lowercase__   :       Any				=	convert_resnet(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
          elif layer_type == "AttnUpBlock2D":
               for j in range(layers_per_block + 1      ):
                    lowercase__   :       Optional[Any]				=	f"""up_blocks.{i}.resnets.{j}"""
                    lowercase__   :       Union[str, Any]				=	f"""output_blocks.{current_layer}.0"""
                    lowercase__   :       List[str]				=	convert_resnet(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					has_skip=SCREAMING_SNAKE_CASE_      )
                    lowercase__   :       Any				=	f"""up_blocks.{i}.attentions.{j}"""
                    lowercase__   :       Any				=	f"""output_blocks.{current_layer}.1"""
                    lowercase__   :       int				=	convert_attention(
                        SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
                    current_layer += 1

               if i != len(SCREAMING_SNAKE_CASE_      ) - 1:
                    lowercase__   :       Optional[Any]				=	f"""up_blocks.{i}.upsamplers.0"""
                    lowercase__   :       str				=	f"""output_blocks.{current_layer-1}.2"""
                    lowercase__   :       Dict				=	convert_resnet(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

     lowercase__   :       List[str]				=	checkpoint['out.0.weight']
     lowercase__   :       List[Any]				=	checkpoint['out.0.bias']
     lowercase__   :       int				=	checkpoint['out.2.weight']
     lowercase__   :       Optional[int]				=	checkpoint['out.2.bias']

     return new_checkpoint


if __name__ == "__main__":
       snake_case_        =			argparse.ArgumentParser()

       parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
       parser.add_argument(
           '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
       )
       parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

       snake_case_        =			parser.parse_args()
       snake_case_        =			strabool(args.class_cond)

       snake_case_        =			os.path.basename(args.unet_path)
       print(F'''Checkpoint: {ckpt_name}''')

       # Get U-Net config
       if "imagenet64" in ckpt_name:
              snake_case_        =			IMAGENET_64_UNET_CONFIG
       elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              snake_case_        =			LSUN_256_UNET_CONFIG
       elif "test" in ckpt_name:
              snake_case_        =			TEST_UNET_CONFIG
       else:
              raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

       if not args.class_cond:
              snake_case_        =			None

       snake_case_        =			con_pt_to_diffuser(args.unet_path, unet_config)

       snake_case_        =			UNetaDModel(**unet_config)
       image_unet.load_state_dict(converted_unet_ckpt)

       # Get scheduler config
       if "cd" in ckpt_name or "test" in ckpt_name:
              snake_case_        =			CD_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
              snake_case_        =			CT_IMAGENET_64_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              snake_case_        =			CT_LSUN_256_SCHEDULER_CONFIG
       else:
              raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

       snake_case_        =			CMStochasticIterativeScheduler(**scheduler_config)

       snake_case_        =			ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
       consistency_model.save_pretrained(args.dump_path)




import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

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''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
snake_case_        =			[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]     ,					SCREAMING_SNAKE_CASE_		:   Dict      ):



     '''simple docstring'''





     for attribute in key.split('.'      ):
          lowercase__   :       Optional[Any]				=	getattr(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

     if weight_type is not None:
          lowercase__   :       Union[str, Any]				=	getattr(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ).shape
     else:
          lowercase__   :       Optional[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":
          lowercase__   :       Optional[int]				=	value
     elif weight_type == "weight_g":
          lowercase__   :       Union[str, Any]				=	value
     elif weight_type == "weight_v":
          lowercase__   :       Tuple				=	value
     elif weight_type == "bias":
          lowercase__   :       Any				=	value
     else:
          lowercase__   :       Union[str, Any]				=	value

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

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   Any      ):



     '''simple docstring'''





     lowercase__   :       Optional[int]				=	[]
     lowercase__   :       Union[str, Any]				=	fairseq_model.state_dict()

     lowercase__   :       Optional[int]				=	hf_model.unispeech_sat.feature_extractor

     for name, value in fairseq_dict.items():
          lowercase__   :       Tuple				=	False
          if "conv_layers" in name:
               load_conv_layer(
                   SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					hf_model.config.feat_extract_norm == 'group'     ,					)
               lowercase__   :       Optional[int]				=	True
          else:
               for key, mapped_key in MAPPING.items():
                    lowercase__   :       List[str]				=	'unispeech_sat.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                    if key in name or key.split('w2v_model.'      )[-1] == name.split('.'      )[0]:
                         if "layer_norm_for_extract" in name and (".".join(name.split('.'      )[:-1]      ) != key):
                              # special case since naming is very similar
                              continue
                         lowercase__   :       Tuple				=	True
                         if "*" in mapped_key:
                              lowercase__   :       Any				=	name.split(SCREAMING_SNAKE_CASE_      )[0].split('.'      )[-2]
                              lowercase__   :       Optional[Any]				=	mapped_key.replace('*'     ,					SCREAMING_SNAKE_CASE_      )
                         if "weight_g" in name:
                              lowercase__   :       int				=	'weight_g'
                         elif "weight_v" in name:
                              lowercase__   :       Any				=	'weight_v'
                         elif "bias" in name:
                              lowercase__   :       str				=	'bias'
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              lowercase__   :       Union[str, Any]				=	'weight'
                         else:
                              lowercase__   :       Union[str, Any]				=	None
                         set_recursively(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
                    continue
          if not is_used:
               unused_weights.append(SCREAMING_SNAKE_CASE_      )

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

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]      ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	full_name.split('conv_layers.'      )[-1]
     lowercase__   :       Dict				=	name.split('.'      )
     lowercase__   :       List[str]				=	int(items[0]      )
     lowercase__   :       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."""      )
               lowercase__   :       List[Any]				=	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."""      )
               lowercase__   :       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:
               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[layer_id].layer_norm.bias.data.shape} was found."""      )
               lowercase__   :       str				=	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[layer_id].layer_norm.weight.data.shape} was found."""      )
               lowercase__   :       int				=	value
               logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
     else:
          unused_weights.append(SCREAMING_SNAKE_CASE_      )

@torch.no_grad()
def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   List[str]=None     ,					SCREAMING_SNAKE_CASE_		:   List[Any]=None     ,					SCREAMING_SNAKE_CASE_		:   int=True      ):



     '''simple docstring'''





     if config_path is not None:
          lowercase__   :       Any				=	UniSpeechSatConfig.from_pretrained(SCREAMING_SNAKE_CASE_      )
     else:
          lowercase__   :       Dict				=	UniSpeechSatConfig()

     lowercase__   :       str				=	''

     if is_finetuned:
          lowercase__   :       Any				=	UniSpeechSatForCTC(SCREAMING_SNAKE_CASE_      )
     else:
          lowercase__   :       Optional[Any]				=	UniSpeechSatForPreTraining(SCREAMING_SNAKE_CASE_      )

     lowercase__				,			lowercase__				,			lowercase__   :       List[Any]				=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]     ,					arg_overrides={'data': '/'.join(dict_path.split('/'      )[:-1]      )}      )
     lowercase__   :       List[str]				=	model[0].eval()

     recursively_load_weights(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

     hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
       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('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
       parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
       parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
       parser.add_argument(
           '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
       )
       snake_case_        =			parser.parse_args()
       convert_unispeech_sat_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )

