"""simple docstring"""







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()
__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE    	={
    "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",
}
__SCREAMING_SNAKE_CASE    	=[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Any     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     Optional[int]     ,			__SCREAMING_SNAKE_CASE							:     List[str]     ,			__SCREAMING_SNAKE_CASE							:     List[Any]      ):
     for attribute in key.split('.'      ):
          lowercase_	:				List[str]													=	getattr(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

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

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



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     List[str]     ,			__SCREAMING_SNAKE_CASE							:     Optional[int]      ):
     lowercase_	:				Union[str, Any]													=	[]
     lowercase_	:				int													=	fairseq_model.state_dict()

     lowercase_	:				Dict													=	hf_model.feature_extractor

     # if encoder has different dim to decoder -> use proj_weight
     lowercase_	:				Optional[Any]													=	None

     for name, value in fairseq_dict.items():
          lowercase_	:				Optional[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_	:				Optional[int]													=	True
          elif name.split('.'      )[0] == "proj":
               lowercase_	:				int													=	fairseq_model.proj
               lowercase_	:				Union[str, Any]													=	True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split('w2v_model.'      )[-1] == name.split('.'      )[0]:
                         lowercase_	:				List[str]													=	True
                         if "*" in mapped_key:
                              lowercase_	:				Optional[Any]													=	name.split(__SCREAMING_SNAKE_CASE      )[0].split('.'      )[-2]
                              lowercase_	:				str													=	mapped_key.replace('*'     ,			__SCREAMING_SNAKE_CASE      )
                         if "weight_g" in name:
                              lowercase_	:				Union[str, Any]													=	'weight_g'
                         elif "weight_v" in name:
                              lowercase_	:				str													=	'weight_v'
                         elif "bias" in name:
                              lowercase_	:				Optional[int]													=	'bias'
                         elif "weight" in name:
                              lowercase_	:				Tuple													=	'weight'
                         else:
                              lowercase_	:				Union[str, Any]													=	None
                         set_recursively(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
                    continue
          if not is_used:
               unused_weights.append(__SCREAMING_SNAKE_CASE      )

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

     return proj_weight



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optional[Any]     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     List[str]     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     List[str]      ):
     lowercase_	:				List[str]													=	full_name.split('conv_layers.'      )[-1]
     lowercase_	:				List[str]													=	name.split('.'      )
     lowercase_	:				Tuple													=	int(items[0]      )
     lowercase_	:				Any													=	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.'''
               )
               lowercase_	:				List[Any]													=	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.'''
               )
               lowercase_	:				Optional[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."
               )
               lowercase_	:				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.'''
               )
               lowercase_	:				Tuple													=	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      )



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optional[Any]      ):
     lowercase_							,  lowercase_	:				Dict													=	emb.weight.shape
     lowercase_	:				Optional[Any]													=	nn.Linear(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			bias=__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Dict													=	emb.weight.data
     return lin_layer



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Union[str, Any]      ):
     with open(__SCREAMING_SNAKE_CASE     ,			'r'     ,			encoding='utf-8'      ) as f:
          lowercase_	:				Tuple													=	f.readlines()
          lowercase_	:				Union[str, Any]													=	[line.split(' '      )[0] for line in lines]

     lowercase_	:				Any													=	len(__SCREAMING_SNAKE_CASE      )

     lowercase_	:				Any													=	{
         '<s>': 0,
         '<pad>': 1,
         '</s>': 2,
         '<unk>': 3,
     }

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



@torch.no_grad()
def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     Optional[int]     ,			__SCREAMING_SNAKE_CASE							:     Tuple     ,			__SCREAMING_SNAKE_CASE							:     Optional[int]     ,			__SCREAMING_SNAKE_CASE							:     List[str]     ,			__SCREAMING_SNAKE_CASE							:     Any     ,			):
     lowercase_	:				Tuple													=	WavaVecaConfig.from_pretrained(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				str													=	SpeechaTextaConfig.from_pretrained(
         __SCREAMING_SNAKE_CASE     ,			vocab_size=__SCREAMING_SNAKE_CASE     ,			decoder_layers=__SCREAMING_SNAKE_CASE     ,			do_stable_layer_norm=__SCREAMING_SNAKE_CASE      )

     lowercase_	:				Tuple													=	WavaVecaFeatureExtractor(
         feature_size=1     ,			sampling_rate=1_60_00     ,			padding_value=0     ,			do_normalize=__SCREAMING_SNAKE_CASE     ,			return_attention_mask=__SCREAMING_SNAKE_CASE     ,			)

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

     # set weights for wav2vec2 encoder
     lowercase_	:				Dict													=	WavaVecaModel(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Dict													=	recursively_load_weights_wavaveca(model.encoder     ,			__SCREAMING_SNAKE_CASE      )

     lowercase_	:				List[Any]													=	SpeechaTextaForCausalLM(__SCREAMING_SNAKE_CASE      )
     lowercase_							,  lowercase_	:				str													=	hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()     ,			strict=__SCREAMING_SNAKE_CASE      )

     # set output linear layer
     unexpected_keys.remove('embed_out'      )
     lowercase_	:				List[str]													=	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}'''      )

     lowercase_	:				int													=	SpeechEncoderDecoderModel(encoder=__SCREAMING_SNAKE_CASE     ,			decoder=__SCREAMING_SNAKE_CASE      )
     lowercase_	:				str													=	False

     # add projection layer
     lowercase_	:				Any													=	nn.Parameter(projection_layer.weight      )
     lowercase_	:				Tuple													=	nn.Parameter(projection_layer.bias      )

     lowercase_	:				str													=	create_vocab_dict(__SCREAMING_SNAKE_CASE      )

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

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

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

     lowercase_	:				Optional[Any]													=	SpeechEncoderDecoderConfig.from_dict(__SCREAMING_SNAKE_CASE      )

     hf_wavavec.save_pretrained(__SCREAMING_SNAKE_CASE      )
     feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE    	=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")

       __SCREAMING_SNAKE_CASE    	=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,
       )


"""simple docstring"""







from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optional[int]      ):
     return {key.lstrip('-'      ): value for key, value in zip(unknown_args[::2]     ,			unknown_args[1::2]      )}



def 			lowercase__(       ):
     lowercase_	:				Any													=	ArgumentParser(
         'HuggingFace Datasets CLI tool'     ,			usage='datasets-cli <command> [<args>]'     ,			allow_abbrev=__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Tuple													=	parser.add_subparsers(help='datasets-cli command helpers'      )
     set_verbosity_info()

     # Register commands
     ConvertCommand.register_subcommand(__SCREAMING_SNAKE_CASE      )
     EnvironmentCommand.register_subcommand(__SCREAMING_SNAKE_CASE      )
     TestCommand.register_subcommand(__SCREAMING_SNAKE_CASE      )
     RunBeamCommand.register_subcommand(__SCREAMING_SNAKE_CASE      )
     DummyDataCommand.register_subcommand(__SCREAMING_SNAKE_CASE      )

     # Parse args
     lowercase_							,  lowercase_	:				Dict													=	parser.parse_known_args()
     if not hasattr(__SCREAMING_SNAKE_CASE     ,			'func'      ):
          parser.print_help()
          exit(1      )
     lowercase_	:				int													=	parse_unknown_args(__SCREAMING_SNAKE_CASE      )

     # Run
     lowercase_	:				List[Any]													=	args.func(__SCREAMING_SNAKE_CASE     ,			**__SCREAMING_SNAKE_CASE      )
     service.run()


if __name__ == "__main__":
       main()

