





"""simple docstring"""




import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


UpperCamelCase_	 ="""pcuenq/k-upscaler"""





def 			a_							(				_lowercase			,		_lowercase			,		*, _lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Tuple      =       {
						    # norm1
						    F"""{diffusers_resnet_prefix}.norm1.linear.weight""": checkpoint[F"""{resnet_prefix}.main.0.mapper.weight"""],
						    F"""{diffusers_resnet_prefix}.norm1.linear.bias""": checkpoint[F"""{resnet_prefix}.main.0.mapper.bias"""],
						    # conv1
						    F"""{diffusers_resnet_prefix}.conv1.weight""": checkpoint[F"""{resnet_prefix}.main.2.weight"""],
						    F"""{diffusers_resnet_prefix}.conv1.bias""": checkpoint[F"""{resnet_prefix}.main.2.bias"""],
						    # norm2
						    F"""{diffusers_resnet_prefix}.norm2.linear.weight""": checkpoint[F"""{resnet_prefix}.main.4.mapper.weight"""],
						    F"""{diffusers_resnet_prefix}.norm2.linear.bias""": checkpoint[F"""{resnet_prefix}.main.4.mapper.bias"""],
						    # conv2
						    F"""{diffusers_resnet_prefix}.conv2.weight""": checkpoint[F"""{resnet_prefix}.main.6.weight"""],
						    F"""{diffusers_resnet_prefix}.conv2.bias""": checkpoint[F"""{resnet_prefix}.main.6.bias"""],
						}

						if resnet.conv_shortcut is not None:
												rv.update(
												    {
												        F"""{diffusers_resnet_prefix}.conv_shortcut.weight""": checkpoint[F"""{resnet_prefix}.skip.weight"""],
												    }  )

						return rv





def 			a_							(				_lowercase			,		*, _lowercase			,		_lowercase  ):
						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   str      =       checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3			,		dim=0  )
						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   str      =       checkpoint[F"""{attention_prefix}.qkv_proj.bias"""].chunk(3			,		dim=0  )
						_UpperCamelCase     :   List[str]      =       {
						    # norm
						    F"""{diffusers_attention_prefix}.norm1.linear.weight""": checkpoint[F"""{attention_prefix}.norm_in.mapper.weight"""],
						    F"""{diffusers_attention_prefix}.norm1.linear.bias""": checkpoint[F"""{attention_prefix}.norm_in.mapper.bias"""],
						    # to_q
						    F"""{diffusers_attention_prefix}.attn1.to_q.weight""": weight_q.squeeze(-1  ).squeeze(-1  ),
						    F"""{diffusers_attention_prefix}.attn1.to_q.bias""": bias_q,
						    # to_k
						    F"""{diffusers_attention_prefix}.attn1.to_k.weight""": weight_k.squeeze(-1  ).squeeze(-1  ),
						    F"""{diffusers_attention_prefix}.attn1.to_k.bias""": bias_k,
						    # to_v
						    F"""{diffusers_attention_prefix}.attn1.to_v.weight""": weight_v.squeeze(-1  ).squeeze(-1  ),
						    F"""{diffusers_attention_prefix}.attn1.to_v.bias""": bias_v,
						    # to_out
						    F"""{diffusers_attention_prefix}.attn1.to_out.0.weight""": checkpoint[F"""{attention_prefix}.out_proj.weight"""]
						    .squeeze(-1  )
						    .squeeze(-1  ),
						    F"""{diffusers_attention_prefix}.attn1.to_out.0.bias""": checkpoint[F"""{attention_prefix}.out_proj.bias"""],
						}

						return rv





def 			a_							(				_lowercase			,		*, _lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase  ,				_UpperCamelCase     :   Optional[Any]      =       checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2			,		dim=0  )
						_UpperCamelCase  ,				_UpperCamelCase     :   Tuple      =       checkpoint[F"""{attention_prefix}.kv_proj.bias"""].chunk(2			,		dim=0  )

						_UpperCamelCase     :   Tuple      =       {
						    # norm2 (ada groupnorm)
						    F"""{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight""": checkpoint[
						        F"""{attention_prefix}.norm_dec.mapper.weight"""
						    ],
						    F"""{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias""": checkpoint[
						        F"""{attention_prefix}.norm_dec.mapper.bias"""
						    ],
						    # layernorm on encoder_hidden_state
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight""": checkpoint[
						        F"""{attention_prefix}.norm_enc.weight"""
						    ],
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias""": checkpoint[
						        F"""{attention_prefix}.norm_enc.bias"""
						    ],
						    # to_q
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight""": checkpoint[
						        F"""{attention_prefix}.q_proj.weight"""
						    ]
						    .squeeze(-1  )
						    .squeeze(-1  ),
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias""": checkpoint[
						        F"""{attention_prefix}.q_proj.bias"""
						    ],
						    # to_k
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight""": weight_k.squeeze(-1  ).squeeze(-1  ),
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias""": bias_k,
						    # to_v
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight""": weight_v.squeeze(-1  ).squeeze(-1  ),
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias""": bias_v,
						    # to_out
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight""": checkpoint[
						        F"""{attention_prefix}.out_proj.weight"""
						    ]
						    .squeeze(-1  )
						    .squeeze(-1  ),
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias""": checkpoint[
						        F"""{attention_prefix}.out_proj.bias"""
						    ],
						}

						return rv





def 			a_							(				_lowercase			,		_lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Optional[int]      =       '''inner_model.u_net.u_blocks''' if block_type == '''up''' else '''inner_model.u_net.d_blocks'''
						_UpperCamelCase     :   Any      =       F"""{block_prefix}.{block_idx}"""

						_UpperCamelCase     :   Union[str, Any]      =       {}

						if not hasattr(_lowercase			,		'''attentions'''  ):
												_UpperCamelCase     :   Optional[int]      =       1  # resnet only
						elif not block.attentions[0].add_self_attention:
												_UpperCamelCase     :   Optional[Any]      =       2  # resnet -> cross-attention
						else:
												_UpperCamelCase     :   Union[str, Any]      =       3  # resnet -> self-attention -> cross-attention)

						for resnet_idx, resnet in enumerate(block.resnets  ):
												# diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
												_UpperCamelCase     :   Union[str, Any]      =       F"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
												_UpperCamelCase     :   Tuple      =       n * resnet_idx if block_type == '''up''' else n * resnet_idx + 1
												_UpperCamelCase     :   List[Any]      =       F"""{block_prefix}.{idx}""" if block_type == '''up''' else F"""{block_prefix}.{idx}"""

												diffusers_checkpoint.update(
												    resnet_to_diffusers_checkpoint(
												        _lowercase			,		_lowercase			,		diffusers_resnet_prefix=_lowercase			,		resnet_prefix=_lowercase  )  )

						if hasattr(_lowercase			,		'''attentions'''  ):
												for attention_idx, attention in enumerate(block.attentions  ):
																		_UpperCamelCase     :   List[Any]      =       F"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
																		_UpperCamelCase     :   List[Any]      =       n * attention_idx + 1 if block_type == '''up''' else n * attention_idx + 2
																		_UpperCamelCase     :   Optional[int]      =       F"""{block_prefix}.{idx}"""
																		_UpperCamelCase     :   Optional[int]      =       F"""{block_prefix}.{idx }"""
																		_UpperCamelCase     :   List[Any]      =       1 if not attention.add_self_attention else 2
																		_UpperCamelCase     :   Optional[int]      =       (
																		    n * attention_idx + cross_attention_index
																		    if block_type == '''up'''
																		    else n * attention_idx + cross_attention_index + 1
																		)
																		_UpperCamelCase     :   Tuple      =       F"""{block_prefix}.{idx }"""

																		diffusers_checkpoint.update(
																		    cross_attn_to_diffusers_checkpoint(
																		        _lowercase			,		diffusers_attention_prefix=_lowercase			,		diffusers_attention_index=2			,		attention_prefix=_lowercase			,		)  )

																		if attention.add_self_attention is True:
																								diffusers_checkpoint.update(
																								    self_attn_to_diffusers_checkpoint(
																								        _lowercase			,		diffusers_attention_prefix=_lowercase			,		attention_prefix=_lowercase			,		)  )

						return diffusers_checkpoint





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   List[Any]      =       {}

						# pre-processing
						diffusers_checkpoint.update(
						    {
						        '''conv_in.weight''': checkpoint['''inner_model.proj_in.weight'''],
						        '''conv_in.bias''': checkpoint['''inner_model.proj_in.bias'''],
						    }  )

						# timestep and class embedding
						diffusers_checkpoint.update(
						    {
						        '''time_proj.weight''': checkpoint['''inner_model.timestep_embed.weight'''].squeeze(-1  ),
						        '''time_embedding.linear_1.weight''': checkpoint['''inner_model.mapping.0.weight'''],
						        '''time_embedding.linear_1.bias''': checkpoint['''inner_model.mapping.0.bias'''],
						        '''time_embedding.linear_2.weight''': checkpoint['''inner_model.mapping.2.weight'''],
						        '''time_embedding.linear_2.bias''': checkpoint['''inner_model.mapping.2.bias'''],
						        '''time_embedding.cond_proj.weight''': checkpoint['''inner_model.mapping_cond.weight'''],
						    }  )

						# down_blocks
						for down_block_idx, down_block in enumerate(model.down_blocks  ):
												diffusers_checkpoint.update(block_to_diffusers_checkpoint(_lowercase			,		_lowercase			,		_lowercase			,		'''down'''  )  )

						# up_blocks
						for up_block_idx, up_block in enumerate(model.up_blocks  ):
												diffusers_checkpoint.update(block_to_diffusers_checkpoint(_lowercase			,		_lowercase			,		_lowercase			,		'''up'''  )  )

						# post-processing
						diffusers_checkpoint.update(
						    {
						        '''conv_out.weight''': checkpoint['''inner_model.proj_out.weight'''],
						        '''conv_out.bias''': checkpoint['''inner_model.proj_out.bias'''],
						    }  )

						return diffusers_checkpoint





def 			a_							(				_lowercase  ):
						_UpperCamelCase     :   int      =       original_config['''input_channels'''] + original_config['''unet_cond_dim''']
						_UpperCamelCase     :   Any      =       original_config['''input_channels'''] + (1 if original_config['''has_variance'''] else 0)

						_UpperCamelCase     :   Tuple      =       original_config['''channels''']

						assert (
						    len(set(original_config['''depths''']  )  ) == 1
						), "UNet2DConditionModel currently do not support blocks with different number of layers"
						_UpperCamelCase     :   int      =       original_config['''depths'''][0]

						_UpperCamelCase     :   Optional[int]      =       original_config['''mapping_cond_dim''']
						_UpperCamelCase     :   Optional[int]      =       original_config['''cross_cond_dim''']

						_UpperCamelCase     :   Tuple      =       []
						_UpperCamelCase     :   List[str]      =       []
						for s, c in zip(original_config['''self_attn_depths''']			,		original_config['''cross_attn_depths''']  ):
												if s:
																		_UpperCamelCase     :   str      =       '''self'''
																		_UpperCamelCase     :   List[Any]      =       '''cross''' if c else None
												elif c:
																		_UpperCamelCase     :   Union[str, Any]      =       '''cross'''
																		_UpperCamelCase     :   int      =       None
												else:
																		_UpperCamelCase     :   int      =       None
																		_UpperCamelCase     :   List[Any]      =       None
												attna_types.append(_lowercase  )
												attna_types.append(_lowercase  )

						_UpperCamelCase     :   Dict      =       UNetaDConditionModel(
						    in_channels=_lowercase			,		out_channels=_lowercase			,		down_block_types=('''KDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''')			,		mid_block_type=_lowercase			,		up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')			,		block_out_channels=_lowercase			,		layers_per_block=_lowercase			,		act_fn='''gelu'''			,		norm_num_groups=_lowercase			,		cross_attention_dim=_lowercase			,		attention_head_dim=64			,		time_cond_proj_dim=_lowercase			,		resnet_time_scale_shift='''scale_shift'''			,		time_embedding_type='''fourier'''			,		timestep_post_act='''gelu'''			,		conv_in_kernel=1			,		conv_out_kernel=1			,		)

						return unet





def 			a_							(				_lowercase  ):
						_UpperCamelCase     :   Tuple      =       torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''  )

						_UpperCamelCase     :   str      =       huggingface_hub.hf_hub_download(_lowercase			,		'''config_laion_text_cond_latent_upscaler_2.json'''  )
						_UpperCamelCase     :   List[Any]      =       huggingface_hub.hf_hub_download(
						    _lowercase			,		'''laion_text_cond_latent_upscaler_2_1_00470000_slim.pth'''  )
						print(F"""loading original model configuration from {orig_config_path}"""  )
						print(F"""loading original model checkpoint from {orig_weights_path}"""  )

						print('''converting to diffusers unet'''  )
						_UpperCamelCase     :   Any      =       K.config.load_config(open(_lowercase  )  )['''model''']
						_UpperCamelCase     :   Tuple      =       unet_model_from_original_config(_lowercase  )

						_UpperCamelCase     :   List[Any]      =       torch.load(_lowercase			,		map_location=_lowercase  )['''model_ema''']
						_UpperCamelCase     :   Optional[int]      =       unet_to_diffusers_checkpoint(_lowercase			,		_lowercase  )

						model.load_state_dict(_lowercase			,		strict=_lowercase  )
						model.save_pretrained(args.dump_path  )
						print(F"""saving converted unet model in {args.dump_path}"""  )


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

						parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
						UpperCamelCase_	 =parser.parse_args()

						main(args)









import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowercase_      :							Dict    							=					logging.get_logger(__name__)

lowercase_      :							str    							=					{
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
    'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj',
    'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
    'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense',
    'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
    'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense',
    'output.dense': 'encoder.encoder.layer.*.output.dense',
    'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
    'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings',
    'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm',
    'encoder.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		):

						'''simple docstring'''


						for attribute in key.split("."		):
												_UpperCAmelCase						=	getattr(snake_case_      ,       snake_case_		)

						if weight_type is not None:
												_UpperCAmelCase						=	getattr(snake_case_      ,       snake_case_		).shape
						else:
												_UpperCAmelCase						=	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						=	value
						elif weight_type == "weight_g":
												_UpperCAmelCase						=	value
						elif weight_type == "weight_v":
												_UpperCAmelCase						=	value
						elif weight_type == "bias":
												_UpperCAmelCase						=	value
						else:
												_UpperCAmelCase						=	value

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


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	[]
						_UpperCAmelCase						=	fairseq_model.state_dict()

						_UpperCAmelCase						=	hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												_UpperCAmelCase						=	False
												if "conv_layers" in name:
																		load_conv_layer(
																		    snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       hf_model.config.feat_extract_norm == "group"      ,       )
																		_UpperCAmelCase						=	True
												else:
																		for key, mapped_key in MAPPING.items():
																								_UpperCAmelCase						=	"sew_d." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

																								if key in name or key.split("w2v_model."		)[-1] == name.split("."		)[0]:
																														_UpperCAmelCase						=	True
																														if "*" in mapped_key:
																																				_UpperCAmelCase						=	name.split(snake_case_		)[0].split("."		)[-2]
																																				if not layer_index.isnumeric():
																																										continue
																																				_UpperCAmelCase						=	mapped_key.replace("*"      ,       snake_case_		)
																														if "weight_g" in name:
																																				_UpperCAmelCase						=	"weight_g"
																														elif "weight_v" in name:
																																				_UpperCAmelCase						=	"weight_v"
																														elif "weight" in name:
																																				_UpperCAmelCase						=	"weight"
																														elif "bias" in name:
																																				_UpperCAmelCase						=	"bias"
																														else:
																																				_UpperCAmelCase						=	None
																														set_recursively(snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		)
																								continue
												if not is_used:
																		unused_weights.append(snake_case_		)

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


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	full_name.split("conv_layers."		)[-1]
						_UpperCAmelCase						=	name.split("."		)
						_UpperCAmelCase						=	int(items[0]		)
						_UpperCAmelCase						=	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						=	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						=	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						=	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						=	value
																		logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
						else:
												unused_weights.append(snake_case_		)


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	SEWDConfig()
						if is_finetuned:
												_UpperCAmelCase						=	model.wav_encoder.wav_model.cfg
						else:
												_UpperCAmelCase						=	model.cfg

						_UpperCAmelCase						=	fs_config.conv_bias
						_UpperCAmelCase						=	eval(fs_config.conv_feature_layers		)
						_UpperCAmelCase						=	[x[0] for x in conv_layers]
						_UpperCAmelCase						=	[x[1] for x in conv_layers]
						_UpperCAmelCase						=	[x[2] for x in conv_layers]
						_UpperCAmelCase						=	"gelu"
						_UpperCAmelCase						=	"layer" if fs_config.extractor_mode == "layer_norm" else "group"
						_UpperCAmelCase						=	0.0
						_UpperCAmelCase						=	fs_config.activation_fn.name
						_UpperCAmelCase						=	fs_config.encoder_embed_dim
						_UpperCAmelCase						=	0.02
						_UpperCAmelCase						=	fs_config.encoder_ffn_embed_dim
						_UpperCAmelCase						=	1e-5
						_UpperCAmelCase						=	fs_config.encoder_layerdrop
						_UpperCAmelCase						=	fs_config.encoder_attention_heads
						_UpperCAmelCase						=	fs_config.conv_pos_groups
						_UpperCAmelCase						=	fs_config.conv_pos
						_UpperCAmelCase						=	len(snake_case_		)
						_UpperCAmelCase						=	fs_config.encoder_layers
						_UpperCAmelCase						=	fs_config.squeeze_factor
						# DeBERTa-specific parameters:
						_UpperCAmelCase						=	fs_config.max_position_embeddings
						_UpperCAmelCase						=	fs_config.position_buckets
						_UpperCAmelCase						=	fs_config.share_att_key
						_UpperCAmelCase						=	fs_config.relative_attention
						_UpperCAmelCase						=	fs_config.position_biased_input
						_UpperCAmelCase						=	tuple(fs_config.pos_att_type.split("|"		)		)
						_UpperCAmelCase						=	fs_config.norm_rel_ebd

						# take care of any params that are overridden by the Wav2VecCtc model
						if is_finetuned:
												_UpperCAmelCase						=	model.cfg
												_UpperCAmelCase						=	fs_config.final_dropout
												_UpperCAmelCase						=	fs_config.layerdrop
						_UpperCAmelCase						=	fs_config.activation_dropout
						_UpperCAmelCase						=	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
						_UpperCAmelCase						=	fs_config.attention_dropout
						_UpperCAmelCase						=	fs_config.dropout_input
						_UpperCAmelCase						=	fs_config.dropout
						_UpperCAmelCase						=	fs_config.mask_channel_length
						_UpperCAmelCase						=	fs_config.mask_channel_prob
						_UpperCAmelCase						=	fs_config.mask_length
						_UpperCAmelCase						=	fs_config.mask_prob

						_UpperCAmelCase						=	"Wav2Vec2FeatureExtractor"
						_UpperCAmelCase						=	"Wav2Vec2CTCTokenizer"

						return config


@torch.no_grad()
def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_=None      ,       snake_case_=None      ,       snake_case_=True		):

						'''simple docstring'''


						if is_finetuned:
												_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase						=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path]      ,       arg_overrides={"data": "/".join(dict_path.split("/"		)[:-1]		)}		)
						else:
												_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase						=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

						if config_path is not None:
												_UpperCAmelCase						=	SEWDConfig.from_pretrained(snake_case_		)
						else:
												_UpperCAmelCase						=	convert_config(model[0]      ,       snake_case_		)
						_UpperCAmelCase						=	model[0].eval()

						_UpperCAmelCase						=	True if config.feat_extract_norm == "layer" else False
						_UpperCAmelCase						=	WavaVecaFeatureExtractor(
						    feature_size=1      ,       sampling_rate=1_6000      ,       padding_value=0      ,       do_normalize=snake_case_      ,       return_attention_mask=snake_case_      ,       )

						if is_finetuned:
												if dict_path:
																		_UpperCAmelCase						=	Dictionary.load(snake_case_		)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		_UpperCAmelCase						=	target_dict.pad_index
																		_UpperCAmelCase						=	target_dict.bos_index
																		_UpperCAmelCase						=	target_dict.pad_index
																		_UpperCAmelCase						=	target_dict.bos_index
																		_UpperCAmelCase						=	target_dict.eos_index
																		_UpperCAmelCase						=	len(target_dict.symbols		)
																		_UpperCAmelCase						=	os.path.join(snake_case_      ,       "vocab.json"		)
																		if not os.path.isdir(snake_case_		):
																								logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(snake_case_		)		)
																								return
																		os.makedirs(snake_case_      ,       exist_ok=snake_case_		)
																		with open(snake_case_      ,       "w"      ,       encoding="utf-8"		) as vocab_handle:
																								json.dump(target_dict.indices      ,       snake_case_		)
																		_UpperCAmelCase						=	WavaVecaCTCTokenizer(
																		    snake_case_      ,       unk_token=target_dict.unk_word      ,       pad_token=target_dict.pad_word      ,       bos_token=target_dict.bos_word      ,       eos_token=target_dict.eos_word      ,       word_delimiter_token="|"      ,       do_lower_case=snake_case_      ,       )
																		_UpperCAmelCase						=	WavaVecaProcessor(feature_extractor=snake_case_      ,       tokenizer=snake_case_		)
																		processor.save_pretrained(snake_case_		)

												_UpperCAmelCase						=	SEWDForCTC(snake_case_		)
						else:
												_UpperCAmelCase						=	SEWDModel(snake_case_		)
												feature_extractor.save_pretrained(snake_case_		)

						recursively_load_weights(snake_case_      ,       snake_case_      ,       snake_case_		)

						hf_model.save_pretrained(snake_case_		)


if __name__ == "__main__":
				lowercase_      :							str    							=					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(
				    '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
				)
				lowercase_      :							Tuple    							=					parser.parse_args()
				convert_sew_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
				)




