


'''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()
__UpperCAmelCase       							=				logging.get_logger(__name__)

__UpperCAmelCase       							=				{
    '''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''',
}
__UpperCAmelCase       							=				[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def    _snake_case  (	A					,       A					,       A					,       A					,       A			)		->       Optional[int]:
							for attribute in key.split('''.'''			):
														lowerCAmelCase__			  =      getattr(A					,       A			)

							if weight_type is not None:
														lowerCAmelCase__			  =      getattr(A					,       A			).shape
							else:
														lowerCAmelCase__			  =      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":
														lowerCAmelCase__			  =      value
							elif weight_type == "weight_g":
														lowerCAmelCase__			  =      value
							elif weight_type == "weight_v":
														lowerCAmelCase__			  =      value
							elif weight_type == "bias":
														lowerCAmelCase__			  =      value
							elif weight_type == "running_mean":
														lowerCAmelCase__			  =      value
							elif weight_type == "running_var":
														lowerCAmelCase__			  =      value
							elif weight_type == "num_batches_tracked":
														lowerCAmelCase__			  =      value
							elif weight_type == "inv_freq":
														lowerCAmelCase__			  =      value
							else:
														lowerCAmelCase__			  =      value

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


def    _snake_case  (	A					,       A					,       A			)		->       int:
							lowerCAmelCase__			  =      []
							lowerCAmelCase__			  =      fairseq_model.state_dict()

							lowerCAmelCase__			  =      hf_model.wavaveca_conformer.feature_extractor

							for name, value in fairseq_dict.items():
														lowerCAmelCase__			  =      False
														if "conv_layers" in name:
																					load_conv_layer(
																					    A					,       A					,       A					,       A					,       hf_model.config.feat_extract_norm == '''group'''					,       )
																					lowerCAmelCase__			  =      True
														else:
																					for key, mapped_key in MAPPING.items():
																												lowerCAmelCase__			  =      '''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]:
																																			lowerCAmelCase__			  =      True
																																			if "*" in mapped_key:
																																										lowerCAmelCase__			  =      name.split(A			)[0].split('''.'''			)[-2]
																																										lowerCAmelCase__			  =      mapped_key.replace('''*'''					,       A			)
																																			if "pos_bias_u" in name:
																																										lowerCAmelCase__			  =      None
																																			elif "pos_bias_v" in name:
																																										lowerCAmelCase__			  =      None
																																			elif "weight_g" in name:
																																										lowerCAmelCase__			  =      '''weight_g'''
																																			elif "weight_v" in name:
																																										lowerCAmelCase__			  =      '''weight_v'''
																																			elif "bias" in name:
																																										lowerCAmelCase__			  =      '''bias'''
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										lowerCAmelCase__			  =      '''weight'''
																																			elif "running_mean" in name:
																																										lowerCAmelCase__			  =      '''running_mean'''
																																			elif "inv_freq" in name:
																																										lowerCAmelCase__			  =      '''inv_freq'''
																																			elif "running_var" in name:
																																										lowerCAmelCase__			  =      '''running_var'''
																																			elif "num_batches_tracked" in name:
																																										lowerCAmelCase__			  =      '''num_batches_tracked'''
																																			else:
																																										lowerCAmelCase__			  =      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    _snake_case  (	A					,       A					,       A					,       A					,       A			)		->       Optional[Any]:
							lowerCAmelCase__			  =      full_name.split('''conv_layers.'''			)[-1]
							lowerCAmelCase__			  =      name.split('''.'''			)
							lowerCAmelCase__			  =      int(items[0]			)
							lowerCAmelCase__			  =      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."""			)
																					lowerCAmelCase__			  =      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."""			)
																					lowerCAmelCase__			  =      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."""			)
																					lowerCAmelCase__			  =      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."""			)
																					lowerCAmelCase__			  =      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    _snake_case  (	A					,       A					,       A=None					,       A=None					,       A=True			)		->       str:
							if config_path is not None:
														lowerCAmelCase__			  =      WavaVecaConformerConfig.from_pretrained(A					,       hidden_act='''swish'''			)
							else:
														lowerCAmelCase__			  =      WavaVecaConformerConfig()

							if "rope" in checkpoint_path:
														lowerCAmelCase__			  =      '''rotary'''

							if is_finetuned:
														if dict_path:
																					lowerCAmelCase__			  =      Dictionary.load(A			)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					lowerCAmelCase__			  =      target_dict.pad_index
																					lowerCAmelCase__			  =      target_dict.bos_index
																					lowerCAmelCase__			  =      target_dict.eos_index
																					lowerCAmelCase__			  =      len(target_dict.symbols			)
																					lowerCAmelCase__			  =      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			)
																					lowerCAmelCase__			  =      target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					lowerCAmelCase__			  =      0
																					lowerCAmelCase__			  =      1
																					with open(A					,       '''w'''					,       encoding='''utf-8'''			) as vocab_handle:
																												json.dump(A					,       A			)
																					lowerCAmelCase__			  =      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					,       )
																					lowerCAmelCase__			  =      True if config.feat_extract_norm == '''layer''' else False
																					lowerCAmelCase__			  =      WavaVecaFeatureExtractor(
																					    feature_size=1					,       sampling_rate=16000					,       padding_value=0					,       do_normalize=A					,       return_attention_mask=A					,       )
																					lowerCAmelCase__			  =      WavaVecaProcessor(feature_extractor=A					,       tokenizer=A			)
																					processor.save_pretrained(A			)

														lowerCAmelCase__			  =      WavaVecaConformerForCTC(A			)
							else:
														lowerCAmelCase__			  =      WavaVecaConformerForPreTraining(A			)

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

														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,       task=A			)

							lowerCAmelCase__			  =      model[0].eval()

							recursively_load_weights(A					,       A					,       not is_finetuned			)

							hf_wavavec.save_pretrained(A			)


if __name__ == "__main__":
		__UpperCAmelCase       							=				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'''
		)
		__UpperCAmelCase       							=				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
		)






from __future__ import annotations

from math import pi





def      _UpperCamelCase  (a__ :float ,      a__ :float ,      a__ :float							):






							"""simple docstring"""





							if (inductance, frequency, reactance).count(0							) != 1:
														raise ValueError("""One and only one argument must be 0"""							)
							if inductance < 0:
														raise ValueError("""Inductance cannot be negative"""							)
							if frequency < 0:
														raise ValueError("""Frequency cannot be negative"""							)
							if reactance < 0:
														raise ValueError("""Inductive reactance cannot be negative"""							)
							if inductance == 0:
														return {"inductance": reactance / (2 * pi * frequency)}
							elif frequency == 0:
														return {"frequency": reactance / (2 * pi * inductance)}
							elif reactance == 0:
														return {"reactance": 2 * pi * frequency * inductance}
							else:
														raise ValueError("""Exactly one argument must be 0"""							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




