


'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def 	_A  (       A							,A=0.999							,A="cosine"							,)  ->				Tuple:
	if alpha_transform_type == "cosine":

		def alpha_bar_fn(A							):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(A							):
			return math.exp(t * -12.0							)

	else:
		raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''							)

	lowercase						:		List[Any]   					=		[]
	for i in range(A							):
		lowercase						:		int   					=		i / num_diffusion_timesteps
		lowercase						:		Union[str, Any]   					=		(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(A							) / alpha_bar_fn(A							)							,A							)							)
	return torch.tensor(A							,dtype=torch.floataa							)






class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				_snake_case						       =				[e.name for e in KarrasDiffusionSchedulers]
				_snake_case						       =				2

				@register_to_config
				def __init__(     self							,      a_ = 1_0_0_0							,      a_ = 0.0_00_85							,      a_ = 0.0_12							,      a_ = "linear"							,      a_ = None							,      a_ = "epsilon"							,      a_ = "linspace"							,      a_ = 0							,      )    ->  str:
					if trained_betas is not None:
						lowercase						:		Optional[Any]   					=		torch.tensor(a_							,      dtype=torch.floataa							)
					elif beta_schedule == "linear":
						lowercase						:		str   					=		torch.linspace(a_							,      a_							,      a_							,      dtype=torch.floataa							)
					elif beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						lowercase						:		Tuple   					=		(
						    torch.linspace(beta_start**0.5							,      beta_end**0.5							,      a_							,      dtype=torch.floataa							) ** 2
						)
					elif beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						lowercase						:		Union[str, Any]   					=		betas_for_alpha_bar(a_							)
					else:
						raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''							)

					lowercase						:		str   					=		1.0 - self.betas
					lowercase						:		Optional[Any]   					=		torch.cumprod(self.alphas							,      dim=0							)

					#  set all values
					self.set_timesteps(a_							,      a_							,      a_							)

				def 	a__		(     self							,      a_							,      a_=None							)    ->  List[Any]:
					if schedule_timesteps is None:
						lowercase						:		List[Any]   					=		self.timesteps

					lowercase						:		List[Any]   					=		(schedule_timesteps == timestep).nonzero()

					# The sigma index that is taken for the **very** first `step`
					# is always the second index (or the last index if there is only 1)
					# This way we can ensure we don't accidentally skip a sigma in
					# case we start in the middle of the denoising schedule (e.g. for image-to-image)
					if len(self._index_counter							) == 0:
						lowercase						:		Any   					=		1 if len(a_							) > 1 else 0
					else:
						lowercase						:		Optional[Any]   					=		timestep.cpu().item() if torch.is_tensor(a_							) else timestep
						lowercase						:		List[Any]   					=		self._index_counter[timestep_int]

					return indices[pos].item()

				@property
				def 	a__		(     self							)    ->  Any:
					# standard deviation of the initial noise distribution
					if self.config.timestep_spacing in ["linspace", "trailing"]:
						return self.sigmas.max()

					return (self.sigmas.max() ** 2 + 1) ** 0.5

				def 	a__		(     self							,      a_							,      a_							,      )    ->  torch.FloatTensor:
					lowercase						:		List[str]   					=		self.index_for_timestep(a_							)

					if self.state_in_first_order:
						lowercase						:		str   					=		self.sigmas[step_index]
					else:
						lowercase						:		List[str]   					=		self.sigmas_interpol[step_index]

					lowercase						:		Dict   					=		sample / ((sigma**2 + 1) ** 0.5)
					return sample

				def 	a__		(     self							,      a_							,      a_ = None							,      a_ = None							,      )    ->  List[str]:
					lowercase						:		List[Any]   					=		num_inference_steps

					lowercase						:		List[Any]   					=		num_train_timesteps or self.config.num_train_timesteps

					# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
					if self.config.timestep_spacing == "linspace":
						lowercase						:		Tuple   					=		np.linspace(0							,      num_train_timesteps - 1							,      a_							,      dtype=a_							)[::-1].copy()
					elif self.config.timestep_spacing == "leading":
						lowercase						:		List[Any]   					=		num_train_timesteps // self.num_inference_steps
						# creates integer timesteps by multiplying by ratio
						# casting to int to avoid issues when num_inference_step is power of 3
						lowercase						:		Tuple   					=		(np.arange(0							,      a_							) * step_ratio).round()[::-1].copy().astype(a_							)
						timesteps += self.config.steps_offset
					elif self.config.timestep_spacing == "trailing":
						lowercase						:		Union[str, Any]   					=		num_train_timesteps / self.num_inference_steps
						# creates integer timesteps by multiplying by ratio
						# casting to int to avoid issues when num_inference_step is power of 3
						lowercase						:		Dict   					=		(np.arange(a_							,      0							,      -step_ratio							)).round().copy().astype(a_							)
						timesteps -= 1
					else:
						raise ValueError(
						    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''							)

					lowercase						:		Optional[int]   					=		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
					lowercase						:		Dict   					=		torch.from_numpy(np.log(a_							)							).to(a_							)

					lowercase						:		Dict   					=		np.interp(a_							,      np.arange(0							,      len(a_							)							)							,      a_							)
					lowercase						:		Dict   					=		np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
					lowercase						:		Optional[int]   					=		torch.from_numpy(a_							).to(device=a_							)

					# interpolate sigmas
					lowercase						:		Union[str, Any]   					=		sigmas.log().lerp(sigmas.roll(1							).log()							,      0.5							).exp()

					lowercase						:		str   					=		torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2							), sigmas[-1:]]							)
					lowercase						:		Optional[Any]   					=		torch.cat(
					    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2							), sigmas_interpol[-1:]]							)

					if str(a_							).startswith("mps"							):
						# mps does not support float64
						lowercase						:		Optional[int]   					=		torch.from_numpy(a_							).to(a_							,      dtype=torch.floataa							)
					else:
						lowercase						:		Union[str, Any]   					=		torch.from_numpy(a_							).to(a_							)

					# interpolate timesteps
					lowercase						:		Optional[Any]   					=		self.sigma_to_t(a_							).to(a_							,      dtype=timesteps.dtype							)
					lowercase						:		Tuple   					=		torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])							,      dim=-1							).flatten()

					lowercase						:		Optional[Any]   					=		torch.cat([timesteps[:1], interleaved_timesteps]							)

					lowercase						:		List[str]   					=		None

					# for exp beta schedules, such as the one for `pipeline_shap_e.py`
					# we need an index counter
					lowercase						:		List[str]   					=		defaultdict(a_							)

				def 	a__		(     self							,      a_							)    ->  List[str]:
					# get log sigma
					lowercase						:		Optional[int]   					=		sigma.log()

					# get distribution
					lowercase						:		int   					=		log_sigma - self.log_sigmas[:, None]

					# get sigmas range
					lowercase						:		List[str]   					=		dists.ge(0							).cumsum(dim=0							).argmax(dim=0							).clamp(max=self.log_sigmas.shape[0] - 2							)
					lowercase						:		Dict   					=		low_idx + 1

					lowercase						:		int   					=		self.log_sigmas[low_idx]
					lowercase						:		Union[str, Any]   					=		self.log_sigmas[high_idx]

					# interpolate sigmas
					lowercase						:		Optional[Any]   					=		(low - log_sigma) / (low - high)
					lowercase						:		Optional[Any]   					=		w.clamp(0							,      1							)

					# transform interpolation to time range
					lowercase						:		int   					=		(1 - w) * low_idx + w * high_idx
					lowercase						:		int   					=		t.view(sigma.shape							)
					return t

				@property
				def 	a__		(     self							)    ->  Any:
					return self.sample is None

				def 	a__		(     self							,      a_							,      a_							,      a_							,      a_ = True							,      )    ->  Union[SchedulerOutput, Tuple]:
					lowercase						:		List[str]   					=		self.index_for_timestep(a_							)

					# advance index counter by 1
					lowercase						:		Union[str, Any]   					=		timestep.cpu().item() if torch.is_tensor(a_							) else timestep
					self._index_counter[timestep_int] += 1

					if self.state_in_first_order:
						lowercase						:		Any   					=		self.sigmas[step_index]
						lowercase						:		str   					=		self.sigmas_interpol[step_index + 1]
						lowercase						:		Union[str, Any]   					=		self.sigmas[step_index + 1]
					else:
						# 2nd order / KDPM2's method
						lowercase						:		Tuple   					=		self.sigmas[step_index - 1]
						lowercase						:		List[Any]   					=		self.sigmas_interpol[step_index]
						lowercase						:		int   					=		self.sigmas[step_index]

					# currently only gamma=0 is supported. This usually works best anyways.
					# We can support gamma in the future but then need to scale the timestep before
					# passing it to the model which requires a change in API
					lowercase						:		List[str]   					=		0
					lowercase						:		Union[str, Any]   					=		sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

					# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
					if self.config.prediction_type == "epsilon":
						lowercase						:		Union[str, Any]   					=		sigma_hat if self.state_in_first_order else sigma_interpol
						lowercase						:		int   					=		sample - sigma_input * model_output
					elif self.config.prediction_type == "v_prediction":
						lowercase						:		List[str]   					=		sigma_hat if self.state_in_first_order else sigma_interpol
						lowercase						:		Any   					=		model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
						    sample / (sigma_input**2 + 1)
						)
					elif self.config.prediction_type == "sample":
						raise NotImplementedError("prediction_type not implemented yet: sample"							)
					else:
						raise ValueError(
						    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''							)

					if self.state_in_first_order:
						# 2. Convert to an ODE derivative for 1st order
						lowercase						:		Union[str, Any]   					=		(sample - pred_original_sample) / sigma_hat
						# 3. delta timestep
						lowercase						:		List[Any]   					=		sigma_interpol - sigma_hat

						# store for 2nd order step
						lowercase						:		Any   					=		sample
					else:
						# DPM-Solver-2
						# 2. Convert to an ODE derivative for 2nd order
						lowercase						:		List[str]   					=		(sample - pred_original_sample) / sigma_interpol

						# 3. delta timestep
						lowercase						:		List[Any]   					=		sigma_next - sigma_hat

						lowercase						:		List[str]   					=		self.sample
						lowercase						:		Dict   					=		None

					lowercase						:		Dict   					=		sample + derivative * dt

					if not return_dict:
						return (prev_sample,)

					return SchedulerOutput(prev_sample=a_							)

				def 	a__		(     self							,      a_							,      a_							,      a_							,      )    ->  torch.FloatTensor:
					# Make sure sigmas and timesteps have the same device and dtype as original_samples
					lowercase						:		Union[str, Any]   					=		self.sigmas.to(device=original_samples.device							,      dtype=original_samples.dtype							)
					if original_samples.device.type == "mps" and torch.is_floating_point(a_							):
						# mps does not support float64
						lowercase						:		Union[str, Any]   					=		self.timesteps.to(original_samples.device							,      dtype=torch.floataa							)
						lowercase						:		Tuple   					=		timesteps.to(original_samples.device							,      dtype=torch.floataa							)
					else:
						lowercase						:		Optional[int]   					=		self.timesteps.to(original_samples.device							)
						lowercase						:		Tuple   					=		timesteps.to(original_samples.device							)

					lowercase						:		Optional[int]   					=		[self.index_for_timestep(a_							,      a_							) for t in timesteps]

					lowercase						:		List[Any]   					=		sigmas[step_indices].flatten()
					while len(sigma.shape							) < len(original_samples.shape							):
						lowercase						:		List[str]   					=		sigma.unsqueeze(-1							)

					lowercase						:		Tuple   					=		original_samples + noise * sigma
					return noisy_samples


				def __len__(     self							)    ->  str:
					return self.config.num_train_timesteps






'''simple docstring'''
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase   : int             =				logging.get_logger(__name__)

lowerCAmelCase   : Dict             =				{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """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""",
    """mask_emb""": """masked_spec_embed""",
}
lowerCAmelCase   : Optional[Any]             =				[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]




def 	_A  (       A							,A							,A							,A							,A							)  ->				Tuple:
	for attribute in key.split("."							):
		lowercase						:		Dict   					=		getattr(A							,A							)

	if weight_type is not None:
		lowercase						:		List[str]   					=		getattr(A							,A							).shape
	else:
		lowercase						:		Any   					=		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						:		List[Any]   					=		value
	elif weight_type == "weight_g":
		lowercase						:		List[Any]   					=		value
	elif weight_type == "weight_v":
		lowercase						:		int   					=		value
	elif weight_type == "bias":
		lowercase						:		Any   					=		value
	elif weight_type == "running_mean":
		lowercase						:		Tuple   					=		value
	elif weight_type == "running_var":
		lowercase						:		Dict   					=		value
	elif weight_type == "num_batches_tracked":
		lowercase						:		Optional[int]   					=		value
	elif weight_type == "inv_freq":
		lowercase						:		List[Any]   					=		value
	else:
		lowercase						:		Dict   					=		value

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




def 	_A  (       A							,A							,A							)  ->				int:
	lowercase						:		Optional[int]   					=		[]
	lowercase						:		Tuple   					=		fairseq_model.state_dict()

	lowercase						:		Optional[Any]   					=		hf_model.wavaveca_conformer.feature_extractor

	for name, value in fairseq_dict.items():
		lowercase						:		str   					=		False
		if "conv_layers" in name:
			load_conv_layer(
			    A							,A							,A							,A							,hf_model.config.feat_extract_norm == "group"							,)
			lowercase						:		Dict   					=		True
		else:
			for key, mapped_key in MAPPING.items():
				lowercase						:		List[str]   					=		"wav2vec2_conformer." + 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]:
					lowercase						:		List[str]   					=		True
					if "*" in mapped_key:
						lowercase						:		int   					=		name.split(A							)[0].split("."							)[-2]
						lowercase						:		Optional[Any]   					=		mapped_key.replace("*"							,A							)
					if "pos_bias_u" in name:
						lowercase						:		str   					=		None
					elif "pos_bias_v" in name:
						lowercase						:		Optional[int]   					=		None
					elif "weight_g" in name:
						lowercase						:		int   					=		"weight_g"
					elif "weight_v" in name:
						lowercase						:		Dict   					=		"weight_v"
					elif "bias" in name:
						lowercase						:		List[str]   					=		"bias"
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						lowercase						:		Optional[int]   					=		"weight"
					elif "running_mean" in name:
						lowercase						:		Optional[int]   					=		"running_mean"
					elif "inv_freq" in name:
						lowercase						:		Dict   					=		"inv_freq"
					elif "running_var" in name:
						lowercase						:		int   					=		"running_var"
					elif "num_batches_tracked" in name:
						lowercase						:		Optional[Any]   					=		"num_batches_tracked"
					else:
						lowercase						:		int   					=		None
					set_recursively(A							,A							,A							,A							,A							)
				continue
		if not is_used:
			unused_weights.append(A							)

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




def 	_A  (       A							,A							,A							,A							,A							)  ->				List[str]:
	lowercase						:		Tuple   					=		full_name.split("conv_layers."							)[-1]
	lowercase						:		Optional[Any]   					=		name.split("."							)
	lowercase						:		str   					=		int(items[0]							)
	lowercase						:		List[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						:		Union[str, 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						:		str   					=		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.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''							)
			lowercase						:		int   					=		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.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''							)
			lowercase						:		str   					=		value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
	else:
		unused_weights.append(A							)




@torch.no_grad()
def 	_A  (       A							,A							,A=None							,A=None							,A=True							)  ->				Optional[Any]:
	if config_path is not None:
		lowercase						:		Tuple   					=		WavaVecaConformerConfig.from_pretrained(A							,hidden_act="swish"							)
	else:
		lowercase						:		Any   					=		WavaVecaConformerConfig()

	if "rope" in checkpoint_path:
		lowercase						:		str   					=		"rotary"

	if is_finetuned:
		if dict_path:
			lowercase						:		List[str]   					=		Dictionary.load(A							)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			lowercase						:		Optional[int]   					=		target_dict.pad_index
			lowercase						:		Optional[int]   					=		target_dict.bos_index
			lowercase						:		Optional[Any]   					=		target_dict.eos_index
			lowercase						:		str   					=		len(target_dict.symbols							)
			lowercase						:		List[Any]   					=		os.path.join(A							,"vocab.json"							)
			if not os.path.isdir(A							):
				logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(A							)							)
				return
			os.makedirs(A							,exist_ok=A							)
			lowercase						:		Any   					=		target_dict.indices

			# fairseq has the <pad> and <s> switched
			lowercase						:		Any   					=		0
			lowercase						:		Tuple   					=		1
			with open(A							,"w"							,encoding="utf-8"							) as vocab_handle:
				json.dump(A							,A							)
			lowercase						:		Tuple   					=		WavaVecaCTCTokenizer(
			    A							,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=A							,)
			lowercase						:		Dict   					=		True if config.feat_extract_norm == "layer" else False
			lowercase						:		List[str]   					=		WavaVecaFeatureExtractor(
			    feature_size=1							,sampling_rate=1_6_0_0_0							,padding_value=0							,do_normalize=A							,return_attention_mask=A							,)
			lowercase						:		List[str]   					=		WavaVecaProcessor(feature_extractor=A							,tokenizer=A							)
			processor.save_pretrained(A							)

		lowercase						:		Any   					=		WavaVecaConformerForCTC(A							)
	else:
		lowercase						:		str   					=		WavaVecaConformerForPreTraining(A							)

	if is_finetuned:
		lowercase					,		lowercase					,		lowercase						:		List[str]   					=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]							,arg_overrides={"data": "/".join(dict_path.split("/"							)[:-1]							)}							)
	else:
		lowercase						:		List[str]   					=		argparse.Namespace(task="audio_pretraining"							)
		lowercase						:		Union[str, Any]   					=		fairseq.tasks.setup_task(A							)

		lowercase					,		lowercase					,		lowercase						:		Optional[Any]   					=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,task=A							)

	lowercase						:		List[str]   					=		model[0].eval()

	recursively_load_weights(A							,A							,not is_finetuned							)

	hf_wavavec.save_pretrained(A							)


if __name__ == "__main__":
					lowerCAmelCase   : 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(
					    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
					)
					lowerCAmelCase   : int             =				parser.parse_args()
					convert_wavaveca_conformer_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)


