



"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class 							_UpperCAmelCase							(				a   ):





					'''simple docstring'''



					a__             =4_2



class 							_UpperCAmelCase							(				a	,a   ):





					'''simple docstring'''



					@register_to_config
					def __init__(      self ,			A = None ,			A = 3 ,			A = 3 ,			A = False ,			A = "positional" ,			A = 0 ,			A = True ,			A = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D") ,			A = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D") ,			A = (2_2_4, 4_4_8, 6_7_2, 8_9_6) ,			A = 2 ,			A = 1 ,			A = 1 ,			A = "conv" ,			A = "conv" ,			A = "silu" ,			A = 8 ,			A = 3_2 ,			A = 1E-5 ,			A = "default" ,			A = True ,			A = None ,			A = None ,			)							->      List[str]:
									super().__init__()

									_UpperCAmelCase							:						int       							=					sample_size
									_UpperCAmelCase							:						Tuple       							=					block_out_channels[0] * 4

									# Check inputs
									if len(A	) != len(A	):
													raise ValueError(
													    f'Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.'	)

									if len(A	) != len(A	):
													raise ValueError(
													    f'Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.'	)

									# input
									_UpperCAmelCase							:						int       							=					nn.Convad(A ,			block_out_channels[0] ,			kernel_size=3 ,			padding=(1, 1)	)

									# time
									if time_embedding_type == "fourier":
													_UpperCAmelCase							:						str       							=					GaussianFourierProjection(embedding_size=block_out_channels[0] ,			scale=1_6	)
													_UpperCAmelCase							:						Optional[int]       							=					2 * block_out_channels[0]
									elif time_embedding_type == "positional":
													_UpperCAmelCase							:						List[str]       							=					Timesteps(block_out_channels[0] ,			A ,			A	)
													_UpperCAmelCase							:						List[Any]       							=					block_out_channels[0]

									_UpperCAmelCase							:						List[str]       							=					TimestepEmbedding(A ,			A	)

									# class embedding
									if class_embed_type is None and num_class_embeds is not None:
													_UpperCAmelCase							:						Optional[int]       							=					nn.Embedding(A ,			A	)
									elif class_embed_type == "timestep":
													_UpperCAmelCase							:						Union[str, Any]       							=					TimestepEmbedding(A ,			A	)
									elif class_embed_type == "identity":
													_UpperCAmelCase							:						int       							=					nn.Identity(A ,			A	)
									else:
													_UpperCAmelCase							:						Dict       							=					None

									_UpperCAmelCase							:						Dict       							=					nn.ModuleList([]	)
									_UpperCAmelCase							:						str       							=					None
									_UpperCAmelCase							:						str       							=					nn.ModuleList([]	)

									# down
									_UpperCAmelCase							:						List[Any]       							=					block_out_channels[0]
									for i, down_block_type in enumerate(A	):
													_UpperCAmelCase							:						Any       							=					output_channel
													_UpperCAmelCase							:						Dict       							=					block_out_channels[i]
													_UpperCAmelCase							:						Any       							=					i == len(A	) - 1

													_UpperCAmelCase							:						Union[str, Any]       							=					get_down_block(
													    A ,			num_layers=A ,			in_channels=A ,			out_channels=A ,			temb_channels=A ,			add_downsample=not is_final_block ,			resnet_eps=A ,			resnet_act_fn=A ,			resnet_groups=A ,			attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel ,			downsample_padding=A ,			resnet_time_scale_shift=A ,			downsample_type=A ,			)
													self.down_blocks.append(A	)

									# mid
									_UpperCAmelCase							:						List[str]       							=					UNetMidBlockaD(
									    in_channels=block_out_channels[-1] ,			temb_channels=A ,			resnet_eps=A ,			resnet_act_fn=A ,			output_scale_factor=A ,			resnet_time_scale_shift=A ,			attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1] ,			resnet_groups=A ,			add_attention=A ,			)

									# up
									_UpperCAmelCase							:						Dict       							=					list(reversed(A	)	)
									_UpperCAmelCase							:						List[Any]       							=					reversed_block_out_channels[0]
									for i, up_block_type in enumerate(A	):
													_UpperCAmelCase							:						Optional[int]       							=					output_channel
													_UpperCAmelCase							:						Optional[int]       							=					reversed_block_out_channels[i]
													_UpperCAmelCase							:						int       							=					reversed_block_out_channels[min(i + 1 ,			len(A	) - 1	)]

													_UpperCAmelCase							:						str       							=					i == len(A	) - 1

													_UpperCAmelCase							:						Dict       							=					get_up_block(
													    A ,			num_layers=layers_per_block + 1 ,			in_channels=A ,			out_channels=A ,			prev_output_channel=A ,			temb_channels=A ,			add_upsample=not is_final_block ,			resnet_eps=A ,			resnet_act_fn=A ,			resnet_groups=A ,			attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel ,			resnet_time_scale_shift=A ,			upsample_type=A ,			)
													self.up_blocks.append(A	)
													_UpperCAmelCase							:						Union[str, Any]       							=					output_channel

									# out
									_UpperCAmelCase							:						Any       							=					norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,			3_2	)
									_UpperCAmelCase							:						Union[str, Any]       							=					nn.GroupNorm(num_channels=block_out_channels[0] ,			num_groups=A ,			eps=A	)
									_UpperCAmelCase							:						List[Any]       							=					nn.SiLU()
									_UpperCAmelCase							:						Any       							=					nn.Convad(block_out_channels[0] ,			A ,			kernel_size=3 ,			padding=1	)





					def 				__lowerCAmelCase			(      self ,			A ,			A ,			A = None ,			A = True ,			)							->      Union[UNetaDOutput, Tuple]:
									# 0. center input if necessary
									if self.config.center_input_sample:
													_UpperCAmelCase							:						List[str]       							=					2 * sample - 1.0

									# 1. time
									_UpperCAmelCase							:						Optional[int]       							=					timestep
									if not torch.is_tensor(A	):
													_UpperCAmelCase							:						Tuple       							=					torch.tensor([timesteps] ,			dtype=torch.long ,			device=sample.device	)
									elif torch.is_tensor(A	) and len(timesteps.shape	) == 0:
													_UpperCAmelCase							:						int       							=					timesteps[None].to(sample.device	)

									# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
									_UpperCAmelCase							:						Optional[Any]       							=					timesteps * torch.ones(sample.shape[0] ,			dtype=timesteps.dtype ,			device=timesteps.device	)

									_UpperCAmelCase							:						Union[str, Any]       							=					self.time_proj(A	)

									# timesteps does not contain any weights and will always return f32 tensors
									# but time_embedding might actually be running in fp16. so we need to cast here.
									# there might be better ways to encapsulate this.
									_UpperCAmelCase							:						List[Any]       							=					t_emb.to(dtype=self.dtype	)
									_UpperCAmelCase							:						Optional[Any]       							=					self.time_embedding(A	)

									if self.class_embedding is not None:
													if class_labels is None:
																	raise ValueError('''class_labels should be provided when doing class conditioning'''	)

													if self.config.class_embed_type == "timestep":
																	_UpperCAmelCase							:						List[str]       							=					self.time_proj(A	)

													_UpperCAmelCase							:						Optional[Any]       							=					self.class_embedding(A	).to(dtype=self.dtype	)
													_UpperCAmelCase							:						Optional[Any]       							=					emb + class_emb

									# 2. pre-process
									_UpperCAmelCase							:						Optional[int]       							=					sample
									_UpperCAmelCase							:						Tuple       							=					self.conv_in(A	)

									# 3. down
									_UpperCAmelCase							:						str       							=					(sample,)
									for downsample_block in self.down_blocks:
													if hasattr(A ,			'''skip_conv'''	):
																	_UpperCAmelCase							:						List[Any]       							=					downsample_block(
																	    hidden_states=A ,			temb=A ,			skip_sample=A	)
													else:
																	_UpperCAmelCase							:						List[Any]       							=					downsample_block(hidden_states=A ,			temb=A	)

													down_block_res_samples += res_samples

									# 4. mid
									_UpperCAmelCase							:						List[Any]       							=					self.mid_block(A ,			A	)

									# 5. up
									_UpperCAmelCase							:						Union[str, Any]       							=					None
									for upsample_block in self.up_blocks:
													_UpperCAmelCase							:						Dict       							=					down_block_res_samples[-len(upsample_block.resnets	) :]
													_UpperCAmelCase							:						Any       							=					down_block_res_samples[: -len(upsample_block.resnets	)]

													if hasattr(A ,			'''skip_conv'''	):
																	_UpperCAmelCase							:						Any       							=					upsample_block(A ,			A ,			A ,			A	)
													else:
																	_UpperCAmelCase							:						List[Any]       							=					upsample_block(A ,			A ,			A	)

        # 6. post-process
									_UpperCAmelCase							:						Dict       							=					self.conv_norm_out(A	)
									_UpperCAmelCase							:						Any       							=					self.conv_act(A	)
									_UpperCAmelCase							:						Optional[Any]       							=					self.conv_out(A	)

									if skip_sample is not None:
													sample += skip_sample

									if self.config.time_embedding_type == "fourier":
													_UpperCAmelCase							:						Union[str, Any]       							=					timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]	)))	)
													_UpperCAmelCase							:						Union[str, Any]       							=					sample / timesteps

									if not return_dict:
													return (sample,)

									return UNetaDOutput(sample=A	)







"""simple docstring"""
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()
_lowerCAmelCase					:int  =      logging.get_logger(__name__)

_lowerCAmelCase					:Dict  =      {
    '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 				lowerCamelCase_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	Union[str, Any]  , UpperCamelCase__	:	Optional[int]  , UpperCamelCase__	:	Any  , UpperCamelCase__	:	int							):
    for attribute in key.split('''.'''							):
        _UpperCAmelCase							:						Optional[Any]       							=					getattr(UpperCamelCase__  , UpperCamelCase__							)

    if weight_type is not None:
        _UpperCAmelCase							:						Union[str, Any]       							=					getattr(UpperCamelCase__  , UpperCamelCase__							).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							:						Dict       							=					value
    elif weight_type == "weight_g":
        _UpperCAmelCase							:						Optional[int]       							=					value
    elif weight_type == "weight_v":
        _UpperCAmelCase							:						List[Any]       							=					value
    elif weight_type == "bias":
        _UpperCAmelCase							:						Union[str, Any]       							=					value
    else:
        _UpperCAmelCase							:						Optional[int]       							=					value

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





def 				lowerCamelCase_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	Optional[Any]  , UpperCamelCase__	:	int							):
    _UpperCAmelCase							:						Dict       							=					[]
    _UpperCAmelCase							:						Optional[int]       							=					fairseq_model.state_dict()

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

    for name, value in fairseq_dict.items():
        _UpperCAmelCase							:						str       							=					False
        if "conv_layers" in name:
            load_conv_layer(
                UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , hf_model.config.feat_extract_norm == '''group'''  , )
            _UpperCAmelCase							:						Any       							=					True
        else:
            for key, mapped_key in MAPPING.items():
                _UpperCAmelCase							:						str       							=					'''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							:						Dict       							=					True
                    if "*" in mapped_key:
                        _UpperCAmelCase							:						List[str]       							=					name.split(UpperCamelCase__							)[0].split('''.'''							)[-2]
                        if not layer_index.isnumeric():
                            continue
                        _UpperCAmelCase							:						str       							=					mapped_key.replace('''*'''  , UpperCamelCase__							)
                    if "weight_g" in name:
                        _UpperCAmelCase							:						int       							=					'''weight_g'''
                    elif "weight_v" in name:
                        _UpperCAmelCase							:						Optional[int]       							=					'''weight_v'''
                    elif "weight" in name:
                        _UpperCAmelCase							:						int       							=					'''weight'''
                    elif "bias" in name:
                        _UpperCAmelCase							:						int       							=					'''bias'''
                    else:
                        _UpperCAmelCase							:						Dict       							=					None
                    set_recursively(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)
                continue
        if not is_used:
            unused_weights.append(UpperCamelCase__							)

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





def 				lowerCamelCase_		(UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	Optional[Any]  , UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	Any  , UpperCamelCase__	:	str							):
    _UpperCAmelCase							:						List[Any]       							=					full_name.split('''conv_layers.'''							)[-1]
    _UpperCAmelCase							:						Any       							=					name.split('''.'''							)
    _UpperCAmelCase							:						Union[str, Any]       							=					int(items[0]							)
    _UpperCAmelCase							:						int       							=					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							:						Tuple       							=					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							:						Union[str, 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							:						Union[str, Any]       							=					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							:						int       							=					value
            logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
    else:
        unused_weights.append(UpperCamelCase__							)





def 				lowerCamelCase_		(UpperCamelCase__	:	List[Any]  , UpperCamelCase__	:	Any							):
    _UpperCAmelCase							:						Any       							=					SEWDConfig()
    if is_finetuned:
        _UpperCAmelCase							:						str       							=					model.wav_encoder.wav_model.cfg
    else:
        _UpperCAmelCase							:						Optional[Any]       							=					model.cfg

    _UpperCAmelCase							:						Optional[Any]       							=					fs_config.conv_bias
    _UpperCAmelCase							:						Optional[int]       							=					eval(fs_config.conv_feature_layers							)
    _UpperCAmelCase							:						Dict       							=					[x[0] for x in conv_layers]
    _UpperCAmelCase							:						int       							=					[x[1] for x in conv_layers]
    _UpperCAmelCase							:						Optional[int]       							=					[x[2] for x in conv_layers]
    _UpperCAmelCase							:						int       							=					'''gelu'''
    _UpperCAmelCase							:						int       							=					'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    _UpperCAmelCase							:						Tuple       							=					0.0
    _UpperCAmelCase							:						List[Any]       							=					fs_config.activation_fn.name
    _UpperCAmelCase							:						Optional[Any]       							=					fs_config.encoder_embed_dim
    _UpperCAmelCase							:						Optional[int]       							=					0.02
    _UpperCAmelCase							:						List[Any]       							=					fs_config.encoder_ffn_embed_dim
    _UpperCAmelCase							:						Dict       							=					1E-5
    _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.encoder_layerdrop
    _UpperCAmelCase							:						Tuple       							=					fs_config.encoder_attention_heads
    _UpperCAmelCase							:						Optional[Any]       							=					fs_config.conv_pos_groups
    _UpperCAmelCase							:						Any       							=					fs_config.conv_pos
    _UpperCAmelCase							:						Optional[Any]       							=					len(UpperCamelCase__							)
    _UpperCAmelCase							:						Any       							=					fs_config.encoder_layers
    _UpperCAmelCase							:						Dict       							=					fs_config.squeeze_factor
    # DeBERTa-specific parameters:
    _UpperCAmelCase							:						int       							=					fs_config.max_position_embeddings
    _UpperCAmelCase							:						Any       							=					fs_config.position_buckets
    _UpperCAmelCase							:						Optional[int]       							=					fs_config.share_att_key
    _UpperCAmelCase							:						Dict       							=					fs_config.relative_attention
    _UpperCAmelCase							:						List[Any]       							=					fs_config.position_biased_input
    _UpperCAmelCase							:						str       							=					tuple(fs_config.pos_att_type.split('''|'''							)							)
    _UpperCAmelCase							:						List[Any]       							=					fs_config.norm_rel_ebd

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

    _UpperCAmelCase							:						int       							=					'''Wav2Vec2FeatureExtractor'''
    _UpperCAmelCase							:						str       							=					'''Wav2Vec2CTCTokenizer'''

    return config





@torch.no_grad()
def 				lowerCamelCase_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	int  , UpperCamelCase__	:	Tuple=None  , UpperCamelCase__	:	Any=None  , UpperCamelCase__	:	Union[str, Any]=True							):

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

    if config_path is not None:
        _UpperCAmelCase							:						List[str]       							=					SEWDConfig.from_pretrained(UpperCamelCase__							)
    else:
        _UpperCAmelCase							:						Optional[Any]       							=					convert_config(model[0]  , UpperCamelCase__							)
    _UpperCAmelCase							:						Dict       							=					model[0].eval()

    _UpperCAmelCase							:						List[str]       							=					True if config.feat_extract_norm == '''layer''' else False
    _UpperCAmelCase							:						Optional[int]       							=					WavaVecaFeatureExtractor(
        feature_size=1  , sampling_rate=1_6000  , padding_value=0  , do_normalize=UpperCamelCase__  , return_attention_mask=UpperCamelCase__  , )

    if is_finetuned:
        if dict_path:
            _UpperCAmelCase							:						List[str]       							=					Dictionary.load(UpperCamelCase__							)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            _UpperCAmelCase							:						Union[str, Any]       							=					target_dict.pad_index
            _UpperCAmelCase							:						Union[str, Any]       							=					target_dict.bos_index
            _UpperCAmelCase							:						int       							=					target_dict.pad_index
            _UpperCAmelCase							:						str       							=					target_dict.bos_index
            _UpperCAmelCase							:						Tuple       							=					target_dict.eos_index
            _UpperCAmelCase							:						List[str]       							=					len(target_dict.symbols							)
            _UpperCAmelCase							:						Optional[Any]       							=					os.path.join(UpperCamelCase__  , '''vocab.json'''							)
            if not os.path.isdir(UpperCamelCase__							):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(UpperCamelCase__							)							)
                return
            os.makedirs(UpperCamelCase__  , exist_ok=UpperCamelCase__							)
            with open(UpperCamelCase__  , '''w'''  , encoding='''utf-8'''							) as vocab_handle:
                json.dump(target_dict.indices  , UpperCamelCase__							)
            _UpperCAmelCase							:						Any       							=					WavaVecaCTCTokenizer(
                UpperCamelCase__  , 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=UpperCamelCase__  , )
            _UpperCAmelCase							:						Dict       							=					WavaVecaProcessor(feature_extractor=UpperCamelCase__  , tokenizer=UpperCamelCase__							)
            processor.save_pretrained(UpperCamelCase__							)

        _UpperCAmelCase							:						Tuple       							=					SEWDForCTC(UpperCamelCase__							)
    else:
        _UpperCAmelCase							:						Any       							=					SEWDModel(UpperCamelCase__							)
        feature_extractor.save_pretrained(UpperCamelCase__							)

    recursively_load_weights(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)

    hf_model.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  _lowerCAmelCase					:Union[str, Any]  =      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'
  )
  _lowerCAmelCase					:int  =      parser.parse_args()
  convert_sew_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
  )


