






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "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",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
UpperCamelCase								=		[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    List[Any]:
							_lowercase							:   int 							=   {}
							with open(SCREAMING_SNAKE_CASE     ,     'r'				) as file:
														for line_number, line in enumerate(SCREAMING_SNAKE_CASE				):
																					_lowercase							:   Any 							=   line.strip()
																					if line:
																												_lowercase							:   str 							=   line.split()
																												_lowercase							:   List[Any] 							=   line_number
																												_lowercase							:   str 							=   words[0]
																												_lowercase							:   str 							=   value
							return result






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Any:
							for attribute in key.split('.'				):
														_lowercase							:   List[str] 							=   getattr(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)

							_lowercase							:   List[Any] 							=   None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(SCREAMING_SNAKE_CASE				):
																					_lowercase							:   Optional[Any] 							=   PARAM_MAPPING[full_name.split('.'				)[-1]]
																					_lowercase							:   int 							=   'param'

							if weight_type is not None and weight_type != "param":
														_lowercase							:   Any 							=   getattr(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				).shape
							elif weight_type is not None and weight_type == "param":
														_lowercase							:   Optional[Any] 							=   hf_pointer
														for attribute in hf_param_name.split('.'				):
																					_lowercase							:   Dict 							=   getattr(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
														_lowercase							:   Tuple 							=   shape_pointer.shape

														# let's reduce dimension
														_lowercase							:   Any 							=   value[0]
							else:
														_lowercase							:   Dict 							=   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							:   Tuple 							=   value
							elif weight_type == "weight_g":
														_lowercase							:   List[Any] 							=   value
							elif weight_type == "weight_v":
														_lowercase							:   Tuple 							=   value
							elif weight_type == "bias":
														_lowercase							:   Optional[Any] 							=   value
							elif weight_type == "param":
														for attribute in hf_param_name.split('.'				):
																					_lowercase							:   Any 							=   getattr(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
														_lowercase							:   Optional[Any] 							=   value
							else:
														_lowercase							:   Optional[int] 							=   value

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






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Tuple:
							_lowercase							:   int 							=   None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(SCREAMING_SNAKE_CASE				):
																					_lowercase							:   Any 							=   PARAM_MAPPING[full_name.split('.'				)[-1]]
																					_lowercase							:   Tuple 							=   'param'

							if weight_type is not None and weight_type != "param":
														_lowercase							:   str 							=   '.'.join([key, weight_type]				)
							elif weight_type is not None and weight_type == "param":
														_lowercase							:   Union[str, Any] 							=   '.'.join([key, hf_param_name]				)
							else:
														_lowercase							:   str 							=   key

							_lowercase							:   Optional[Any] 							=   value if 'lm_head' in full_key else value[0]


UpperCamelCase								=		{
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE=None     ,     SCREAMING_SNAKE_CASE=None				)     ->    Union[str, Any]:
							_lowercase							:   List[Any] 							=   False
							for key, mapped_key in MAPPING.items():
														_lowercase							:   str 							=   'wav2vec2.' + 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							:   Tuple 							=   True
																					if "*" in mapped_key:
																												_lowercase							:   Optional[Any] 							=   name.split(SCREAMING_SNAKE_CASE				)[0].split('.'				)[-2]
																												_lowercase							:   Union[str, Any] 							=   mapped_key.replace('*'     ,     SCREAMING_SNAKE_CASE				)
																					if "weight_g" in name:
																												_lowercase							:   int 							=   'weight_g'
																					elif "weight_v" in name:
																												_lowercase							:   Any 							=   'weight_v'
																					elif "bias" in name:
																												_lowercase							:   Tuple 							=   'bias'
																					elif "weight" in name:
																												# TODO: don't match quantizer.weight_proj
																												_lowercase							:   Dict 							=   'weight'
																					else:
																												_lowercase							:   Optional[Any] 							=   None
																					if hf_dict is not None:
																												rename_dict(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																					else:
																												set_recursively(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																					return is_used
							return is_used






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    List[str]:
							_lowercase							:   List[Any] 							=   []
							_lowercase							:   List[str] 							=   fairseq_model.state_dict()

							_lowercase							:   Tuple 							=   hf_model.wavaveca.feature_extractor

							for name, value in fairseq_dict.items():
														_lowercase							:   List[Any] 							=   False
														if "conv_layers" in name:
																					load_conv_layer(
																					    SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     hf_model.config.feat_extract_norm == 'group'     ,     )
																					_lowercase							:   List[Any] 							=   True
														else:
																					_lowercase							:   Optional[int] 							=   load_wavaveca_layer(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
														if not is_used:
																					unused_weights.append(SCREAMING_SNAKE_CASE				)

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






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    List[str]:
							_lowercase							:   Optional[int] 							=   full_name.split('conv_layers.'				)[-1]
							_lowercase							:   Union[str, Any] 							=   name.split('.'				)
							_lowercase							:   str 							=   int(items[0]				)
							_lowercase							:   Optional[int] 							=   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							:   str 							=   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							:   Tuple 							=   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							:   Dict 							=   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(SCREAMING_SNAKE_CASE				)






@torch.no_grad()
def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE=None     ,     SCREAMING_SNAKE_CASE=None     ,     SCREAMING_SNAKE_CASE=True     ,     SCREAMING_SNAKE_CASE=False				)     ->    Dict:
							if config_path is not None:
														_lowercase							:   Any 							=   WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE				)
							else:
														_lowercase							:   List[Any] 							=   WavaVecaConfig()

							if is_seq_class:
														_lowercase							:   int 							=   read_txt_into_dict(SCREAMING_SNAKE_CASE				)
														_lowercase							:   Union[str, Any] 							=   idalabel
														_lowercase							:   List[Any] 							=   WavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE				)
														_lowercase							:   Tuple 							=   WavaVecaFeatureExtractor(
														    feature_size=1     ,     sampling_rate=16_000     ,     padding_value=0     ,     do_normalize=SCREAMING_SNAKE_CASE     ,     return_attention_mask=SCREAMING_SNAKE_CASE     ,     )
														feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE				)

							elif is_finetuned:
														if dict_path:
																					_lowercase							:   Tuple 							=   Dictionary.load(SCREAMING_SNAKE_CASE				)

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

																					# fairseq has the <pad> and <s> switched
																					_lowercase							:   Dict 							=   0
																					_lowercase							:   Union[str, Any] 							=   1
																					with open(SCREAMING_SNAKE_CASE     ,     'w'     ,     encoding='utf-8'				) as vocab_handle:
																												json.dump(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																					_lowercase							:   Any 							=   WavaVecaCTCTokenizer(
																					    SCREAMING_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=SCREAMING_SNAKE_CASE     ,     )
																					_lowercase							:   Union[str, Any] 							=   True if config.feat_extract_norm == 'layer' else False
																					_lowercase							:   int 							=   WavaVecaFeatureExtractor(
																					    feature_size=1     ,     sampling_rate=16_000     ,     padding_value=0     ,     do_normalize=SCREAMING_SNAKE_CASE     ,     return_attention_mask=SCREAMING_SNAKE_CASE     ,     )
																					_lowercase							:   str 							=   WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE     ,     tokenizer=SCREAMING_SNAKE_CASE				)
																					processor.save_pretrained(SCREAMING_SNAKE_CASE				)

														_lowercase							:   Union[str, Any] 							=   WavaVecaForCTC(SCREAMING_SNAKE_CASE				)
							else:
														_lowercase							:   Any 							=   WavaVecaForPreTraining(SCREAMING_SNAKE_CASE				)

							if is_finetuned or is_seq_class:
														_lowercase							,							_lowercase							,							_lowercase							:   Any 							=   fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]     ,     arg_overrides={'data': '/'.join(dict_path.split('/'				)[:-1]				)}				)
							else:
														_lowercase							:   Optional[Any] 							=   argparse.Namespace(task='audio_pretraining'				)
														_lowercase							:   Optional[int] 							=   fairseq.tasks.setup_task(SCREAMING_SNAKE_CASE				)

														_lowercase							,							_lowercase							,							_lowercase							:   Any 							=   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,     task=SCREAMING_SNAKE_CASE				)

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

							recursively_load_weights(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     not is_finetuned				)

							hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE				)


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"
							)
							parser.add_argument(
							    "--is_seq_class",
							    action="store_true",
							    help="Whether the model to convert is a fine-tuned sequence classification model or not",
							)
							UpperCamelCase								=		parser.parse_args()

							UpperCamelCase								=		not args.not_finetuned and not args.is_seq_class
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.dict_path,
							    is_finetuned,
							    args.is_seq_class,
							)









from __future__ import annotations

from math import pow, sqrt

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      if (resistance, reactance, impedance).count(0			) != 1:
            raise ValueError("One and only one argument must be 0"			)
      if resistance == 0:
            return {"resistance": sqrt(pow(UpperCAmelCase__ , 2			) - pow(UpperCAmelCase__ , 2			)			)}
      elif reactance == 0:
            return {"reactance": sqrt(pow(UpperCAmelCase__ , 2			) - pow(UpperCAmelCase__ , 2			)			)}
      elif impedance == 0:
            return {"impedance": sqrt(pow(UpperCAmelCase__ , 2			) + pow(UpperCAmelCase__ , 2			)			)}
      else:
            raise ValueError("Exactly one argument must be 0"			)


if __name__ == "__main__":
     import doctest

     doctest.testmod()