


import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_UpperCAmelCase			:  Tuple     =logging.get_logger(__name__)

_UpperCAmelCase			:  int     ={
    """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""",
    """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			:  List[str]     =[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> Optional[Any]:
				for attribute in key.split('''.''' ):
								lowerCAmelCase_ :      Union[str, Any]    				=				getattr(lowerCAmelCase_ ,   lowerCAmelCase_ )

				if weight_type is not None:
								lowerCAmelCase_ :      Any    				=				getattr(lowerCAmelCase_ ,   lowerCAmelCase_ ).shape
				else:
								lowerCAmelCase_ :      int    				=				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":
								lowerCAmelCase_ :      Optional[Any]    				=				value
				elif weight_type == "weight_g":
								lowerCAmelCase_ :      Any    				=				value
				elif weight_type == "weight_v":
								lowerCAmelCase_ :      List[str]    				=				value
				elif weight_type == "bias":
								lowerCAmelCase_ :      Tuple    				=				value
				else:
								lowerCAmelCase_ :      str    				=				value

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



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> Union[str, Any]:
				lowerCAmelCase_ :      List[str]    				=				[]
				lowerCAmelCase_ :      List[str]    				=				fairseq_model.state_dict()

				lowerCAmelCase_ :      Dict    				=				hf_model.feature_extractor

				# if encoder has different dim to decoder -> use proj_weight
				lowerCAmelCase_ :      Any    				=				None

				for name, value in fairseq_dict.items():
								lowerCAmelCase_ :      int    				=				False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   hf_model.config.feat_extract_norm == '''group''' ,   )
												lowerCAmelCase_ :      List[str]    				=				True
								elif name.split('''.''' )[0] == "proj":
												lowerCAmelCase_ :      int    				=				fairseq_model.proj
												lowerCAmelCase_ :      List[Any]    				=				True
								else:
												for key, mapped_key in MAPPING.items():
																if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
																				lowerCAmelCase_ :      Any    				=				True
																				if "*" in mapped_key:
																								lowerCAmelCase_ :      str    				=				name.split(lowerCAmelCase_ )[0].split('''.''' )[-2]
																								lowerCAmelCase_ :      Optional[Any]    				=				mapped_key.replace('''*''' ,   lowerCAmelCase_ )
																				if "weight_g" in name:
																								lowerCAmelCase_ :      List[str]    				=				'''weight_g'''
																				elif "weight_v" in name:
																								lowerCAmelCase_ :      str    				=				'''weight_v'''
																				elif "bias" in name:
																								lowerCAmelCase_ :      Any    				=				'''bias'''
																				elif "weight" in name:
																								lowerCAmelCase_ :      List[Any]    				=				'''weight'''
																				else:
																								lowerCAmelCase_ :      Any    				=				None
																				set_recursively(lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )
																continue
								if not is_used:
												unused_weights.append(lowerCAmelCase_ )

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

				return proj_weight



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> Dict:
				lowerCAmelCase_ :      List[str]    				=				full_name.split('''conv_layers.''' )[-1]
				lowerCAmelCase_ :      Optional[int]    				=				name.split('''.''' )
				lowerCAmelCase_ :      Any    				=				int(items[0] )
				lowerCAmelCase_ :      List[str]    				=				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."""
												)
												lowerCAmelCase_ :      int    				=				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."""
												)
												lowerCAmelCase_ :      int    				=				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."
												)
												lowerCAmelCase_ :      Dict    				=				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."""
												)
												lowerCAmelCase_ :      Optional[int]    				=				value
												logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
				else:
								unused_weights.append(lowerCAmelCase_ )



def 			lowerCAmelCase (		lowerCAmelCase_ )-> Any:
				lowerCAmelCase_				,  lowerCAmelCase_ :      Tuple    				=				emb.weight.shape
				lowerCAmelCase_ :      List[str]    				=				nn.Linear(lowerCAmelCase_ ,   lowerCAmelCase_ ,   bias=lowerCAmelCase_ )
				lowerCAmelCase_ :      Optional[Any]    				=				emb.weight.data
				return lin_layer



def 			lowerCAmelCase (		lowerCAmelCase_ )-> int:
				with open(lowerCAmelCase_ ,   '''r''' ,   encoding='''utf-8''' ) as f:
								lowerCAmelCase_ :      Optional[Any]    				=				f.readlines()
								lowerCAmelCase_ :      str    				=				[line.split(''' ''' )[0] for line in lines]

				lowerCAmelCase_ :      Optional[Any]    				=				len(lowerCAmelCase_ )

				lowerCAmelCase_ :      List[str]    				=				{
				    '''<s>''': 0,
				    '''<pad>''': 1,
				    '''</s>''': 2,
				    '''<unk>''': 3,
				}

				vocab_dict.update(dict(zip(lowerCAmelCase_ ,   range(4 ,   num_words + 4 ) ) ) )
				return vocab_dict



@torch.no_grad()
def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   )-> Tuple:
				lowerCAmelCase_ :      int    				=				WavaVecaConfig.from_pretrained(lowerCAmelCase_ )
				lowerCAmelCase_ :      Optional[Any]    				=				SpeechaTextaConfig.from_pretrained(
				    lowerCAmelCase_ ,   vocab_size=lowerCAmelCase_ ,   decoder_layers=lowerCAmelCase_ ,   do_stable_layer_norm=lowerCAmelCase_ )

				lowerCAmelCase_ :      Union[str, Any]    				=				WavaVecaFeatureExtractor(
				    feature_size=1 ,   sampling_rate=16_000 ,   padding_value=0 ,   do_normalize=lowerCAmelCase_ ,   return_attention_mask=lowerCAmelCase_ ,   )

				lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      Tuple    				=				fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path] ,   arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
				lowerCAmelCase_ :      Dict    				=				model[0].eval()

				# set weights for wav2vec2 encoder
				lowerCAmelCase_ :      Tuple    				=				WavaVecaModel(lowerCAmelCase_ )
				lowerCAmelCase_ :      List[Any]    				=				recursively_load_weights_wavaveca(model.encoder ,   lowerCAmelCase_ )

				lowerCAmelCase_ :      Optional[Any]    				=				SpeechaTextaForCausalLM(lowerCAmelCase_ )
				lowerCAmelCase_				,  lowerCAmelCase_ :      Union[str, Any]    				=				hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,   strict=lowerCAmelCase_ )

				# set output linear layer
				unexpected_keys.remove('''embed_out''' )
				lowerCAmelCase_ :      Any    				=				nn.Parameter(model.decoder.embed_out.detach() )

				# layer norm is init to identity matrix so leaving it is fine
				logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}""" )
				logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}""" )

				lowerCAmelCase_ :      int    				=				SpeechEncoderDecoderModel(encoder=lowerCAmelCase_ ,   decoder=lowerCAmelCase_ )
				lowerCAmelCase_ :      Optional[int]    				=				False

				# add projection layer
				lowerCAmelCase_ :      Any    				=				nn.Parameter(projection_layer.weight )
				lowerCAmelCase_ :      Optional[Any]    				=				nn.Parameter(projection_layer.bias )

				lowerCAmelCase_ :      Any    				=				create_vocab_dict(lowerCAmelCase_ )

				with open(os.path.join(lowerCAmelCase_ ,   '''vocab.json''' ) ,   '''w''' ) as fp:
								json.dump(lowerCAmelCase_ ,   lowerCAmelCase_ )

				lowerCAmelCase_ :      List[Any]    				=				SpeechaTextaTokenizer(os.path.join(lowerCAmelCase_ ,   '''vocab.json''' ) )
				tokenizer.save_pretrained(lowerCAmelCase_ )

				lowerCAmelCase_ :      int    				=				hf_wavavec.config.to_dict()
				lowerCAmelCase_ :      List[Any]    				=				tokenizer.pad_token_id
				lowerCAmelCase_ :      List[str]    				=				tokenizer.bos_token_id
				lowerCAmelCase_ :      Optional[int]    				=				tokenizer.eos_token_id
				lowerCAmelCase_ :      Optional[int]    				=				'''speech_to_text_2'''
				lowerCAmelCase_ :      Tuple    				=				'''wav2vec2'''

				lowerCAmelCase_ :      Optional[int]    				=				SpeechEncoderDecoderConfig.from_dict(lowerCAmelCase_ )

				hf_wavavec.save_pretrained(lowerCAmelCase_ )
				feature_extractor.save_pretrained(lowerCAmelCase_ )


if __name__ == "__main__":
						_UpperCAmelCase			:  List[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(
						    """--encoder_config_path""",
						    default="""facebook/wav2vec2-large-lv60""",
						    type=str,
						    help="""Path to hf encoder wav2vec2 checkpoint config""",
						)
						parser.add_argument(
						    """--decoder_config_path""",
						    default="""facebook/s2t-small-mustc-en-fr-st""",
						    type=str,
						    help="""Path to hf decoder s2t checkpoint config""",
						)
						parser.add_argument("""--vocab_size""", default=1_0224, type=int, help="""Vocab size of decoder""")
						parser.add_argument("""--num_decoder_layers""", default=7, type=int, help="""Number of decoder layers""")

						_UpperCAmelCase			:  Tuple     =parser.parse_args()
						convert_wavaveca_checkpoint(
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.dict_path,
						    encoder_config_path=args.encoder_config_path,
						    decoder_config_path=args.decoder_config_path,
						    vocab_size=args.vocab_size,
						    num_decoder_layers=args.num_decoder_layers,
						)



from __future__ import annotations

from math import pi



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> dict[str, float]:

				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()