
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()
_lowercase											=    logging.get_logger(__name__)

_lowercase											=    {
    """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""",
}
_lowercase											=    [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Tuple			,							UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     Tuple			,							UpperCAmelCase_	:     Any			,							UpperCAmelCase_	:     List[str]				)			->						Dict:
       for attribute in key.split('''.'''				):
              SCREAMING_SNAKE_CASE_							:				Tuple		=getattr(UpperCAmelCase_			,							UpperCAmelCase_				)

       if weight_type is not None:
              SCREAMING_SNAKE_CASE_							:				Dict		=getattr(UpperCAmelCase_			,							UpperCAmelCase_				).shape
       else:
              SCREAMING_SNAKE_CASE_							:				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":
              SCREAMING_SNAKE_CASE_							:				Tuple		=value
       elif weight_type == "weight_g":
              SCREAMING_SNAKE_CASE_							:				Tuple		=value
       elif weight_type == "weight_v":
              SCREAMING_SNAKE_CASE_							:				Tuple		=value
       elif weight_type == "bias":
              SCREAMING_SNAKE_CASE_							:				str		=value
       else:
              SCREAMING_SNAKE_CASE_							:				int		=value

       logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Tuple			,							UpperCAmelCase_	:     List[str]				)			->						Tuple:
       SCREAMING_SNAKE_CASE_							:				List[Any]		=[]
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=fairseq_model.state_dict()

       SCREAMING_SNAKE_CASE_							:				Optional[int]		=hf_model.feature_extractor

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

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

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

       return proj_weight
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     List[str]			,							UpperCAmelCase_	:     List[str]			,							UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     Any				)			->						Tuple:
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=full_name.split('''conv_layers.'''				)[-1]
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=name.split('''.'''				)
       SCREAMING_SNAKE_CASE_							:				Tuple		=int(items[0]				)
       SCREAMING_SNAKE_CASE_							:				Union[str, 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.'
                     )
                     SCREAMING_SNAKE_CASE_							:				Dict		=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.'
                     )
                     SCREAMING_SNAKE_CASE_							:				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:
                     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."
                     )
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=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.'
                     )
                     SCREAMING_SNAKE_CASE_							:				Any		=value
                     logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
       else:
              unused_weights.append(UpperCAmelCase_				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     List[str]				)			->						Dict:
       SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Tuple		=emb.weight.shape
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=nn.Linear(UpperCAmelCase_			,							UpperCAmelCase_			,							bias=UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Tuple		=emb.weight.data
       return lin_layer
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     List[str]				)			->						Any:
       with open(UpperCAmelCase_			,							'''r'''			,							encoding='''utf-8'''				) as f:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=f.readlines()
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=[line.split(''' '''				)[0] for line in lines]

       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=len(UpperCAmelCase_				)

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

       vocab_dict.update(dict(zip(UpperCAmelCase_			,							range(4			,							num_words + 4				)				)				)				)
       return vocab_dict
@torch.no_grad()
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Union[str, Any]			,							UpperCAmelCase_	:     List[str]			,							UpperCAmelCase_	:     Dict			,							UpperCAmelCase_	:     Dict			,							UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     Any			,							UpperCAmelCase_	:     Union[str, Any]			,							)			->						Union[str, Any]:
       SCREAMING_SNAKE_CASE_							:				str		=WavaVecaConfig.from_pretrained(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Any		=SpeechaTextaConfig.from_pretrained(
           UpperCAmelCase_			,							vocab_size=UpperCAmelCase_			,							decoder_layers=UpperCAmelCase_			,							do_stable_layer_norm=UpperCAmelCase_				)

       SCREAMING_SNAKE_CASE_							:				Dict		=WavaVecaFeatureExtractor(
           feature_size=1			,							sampling_rate=1_6_0_0_0			,							padding_value=0			,							do_normalize=UpperCAmelCase_			,							return_attention_mask=UpperCAmelCase_			,							)

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

       # set weights for wav2vec2 encoder
       SCREAMING_SNAKE_CASE_							:				Tuple		=WavaVecaModel(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Tuple		=recursively_load_weights_wavaveca(model.encoder			,							UpperCAmelCase_				)

       SCREAMING_SNAKE_CASE_							:				str		=SpeechaTextaForCausalLM(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Dict		=hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()			,							strict=UpperCAmelCase_				)

       # set output linear layer
       unexpected_keys.remove('''embed_out'''				)
       SCREAMING_SNAKE_CASE_							:				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}'				)

       SCREAMING_SNAKE_CASE_							:				Any		=SpeechEncoderDecoderModel(encoder=UpperCAmelCase_			,							decoder=UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				List[str]		=False

       # add projection layer
       SCREAMING_SNAKE_CASE_							:				str		=nn.Parameter(projection_layer.weight				)
       SCREAMING_SNAKE_CASE_							:				int		=nn.Parameter(projection_layer.bias				)

       SCREAMING_SNAKE_CASE_							:				Any		=create_vocab_dict(UpperCAmelCase_				)

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

       SCREAMING_SNAKE_CASE_							:				Any		=SpeechaTextaTokenizer(os.path.join(UpperCAmelCase_			,							'''vocab.json'''				)				)
       tokenizer.save_pretrained(UpperCAmelCase_				)

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

       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=SpeechEncoderDecoderConfig.from_dict(UpperCAmelCase_				)

       hf_wavavec.save_pretrained(UpperCAmelCase_				)
       feature_extractor.save_pretrained(UpperCAmelCase_				)


if __name__ == "__main__":
 _lowercase											=    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""")

 _lowercase											=    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,
 )


def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     list				)			->						float:
       SCREAMING_SNAKE_CASE_							:				Dict		=0
       while len(UpperCAmelCase_				) > 1:
              SCREAMING_SNAKE_CASE_							:				Tuple		=0
              # Consider two files with minimum cost to be merged
              for _ in range(2				):
                     SCREAMING_SNAKE_CASE_							:				int		=files.index(min(UpperCAmelCase_				)				)
                     temp += files[min_index]
                     files.pop(UpperCAmelCase_				)
              files.append(UpperCAmelCase_				)
              optimal_merge_cost += temp
       return optimal_merge_cost


if __name__ == "__main__":
 import doctest

 doctest.testmod()
