"""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							:  Optional[int]  ,    __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  Dict  ,    __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  List[Any]   ):







     for attribute in key.split('.'   ):
          lowercase_			:       Dict									=			getattr(_lowercase  ,    _lowercase   )

     if weight_type is not None:
          lowercase_			:       Optional[Any]									=			getattr(_lowercase  ,    _lowercase   ).shape
     else:
          lowercase_			:       List[Any]									=			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_			:       Dict									=			value
     elif weight_type == "weight_v":
          lowercase_			:       int									=			value
     elif weight_type == "bias":
          lowercase_			:       Dict									=			value
     else:
          lowercase_			:       Tuple									=			value

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



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

     lowercase_			:       Union[str, Any]									=			hf_model.feature_extractor

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

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

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

     return proj_weight



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]   ):
     lowercase_			:       Tuple									=			full_name.split('conv_layers.'   )[-1]
     lowercase_			:       Union[str, Any]									=			name.split('.'   )
     lowercase_			:       int									=			int(items[0]   )
     lowercase_			:       List[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_			:       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.'''
               )
               lowercase_			:       Union[str, 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_			:       Tuple									=			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_			:       str									=			value
               logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''   )
     else:
          unused_weights.append(_lowercase   )



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



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[Any]   ):







     with open(_lowercase  ,    'r'  ,    encoding='utf-8'   ) as f:
          lowercase_			:       Dict									=			f.readlines()
          lowercase_			:       Any									=			[line.split(' '   )[0] for line in lines]

     lowercase_			:       int									=			len(_lowercase   )

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

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



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

     lowercase_			:       List[Any]									=			WavaVecaFeatureExtractor(
         feature_size=1  ,    sampling_rate=1_60_00  ,    padding_value=0  ,    do_normalize=_lowercase  ,    return_attention_mask=_lowercase  ,    )

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

     # set weights for wav2vec2 encoder
     lowercase_			:       Any									=			WavaVecaModel(_lowercase   )
     lowercase_			:       Optional[int]									=			recursively_load_weights_wavaveca(model.encoder  ,    _lowercase   )

     lowercase_			:       Union[str, Any]									=			SpeechaTextaForCausalLM(_lowercase   )
     lowercase_							,						lowercase_			:       Tuple									=			hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()  ,    strict=_lowercase   )

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

     lowercase_			:       List[Any]									=			SpeechEncoderDecoderModel(encoder=_lowercase  ,    decoder=_lowercase   )
     lowercase_			:       List[Any]									=			False

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

     lowercase_			:       List[Any]									=			create_vocab_dict(_lowercase   )

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

     lowercase_			:       Dict									=			SpeechaTextaTokenizer(os.path.join(_lowercase  ,    'vocab.json'   )   )
     tokenizer.save_pretrained(_lowercase   )

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

     lowercase_			:       List[Any]									=			SpeechEncoderDecoderConfig.from_dict(_lowercase   )

     hf_wavavec.save_pretrained(_lowercase   )
     feature_extractor.save_pretrained(_lowercase   )


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 diffusers.utils.testing_utils import require_onnxruntime







@require_onnxruntime
class  UpperCamelCase	:
   pass

