



'''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()
lowerCamelCase	 =       logging.get_logger(__name__)

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



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							for attribute in key.split('.'					):
														__lowercase       	=getattr(lowerCamelCase_  ,   lowerCamelCase_					)

							if weight_type is not None:
														__lowercase       	=getattr(lowerCamelCase_  ,   lowerCamelCase_					).shape
							else:
														__lowercase       	=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       	=value
							elif weight_type == "weight_g":
														__lowercase       	=value
							elif weight_type == "weight_v":
														__lowercase       	=value
							elif weight_type == "bias":
														__lowercase       	=value
							else:
														__lowercase       	=value

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



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=[]
							__lowercase       	=fairseq_model.state_dict()

							__lowercase       	=hf_model.feature_extractor

							# if encoder has different dim to decoder -> use proj_weight
							__lowercase       	=None

							for name, value in fairseq_dict.items():
														__lowercase       	=False
														if "conv_layers" in name:
																					load_conv_layer(
																					    lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_  ,   hf_model.config.feat_extract_norm == 'group'  ,   )
																					__lowercase       	=True
														elif name.split('.'					)[0] == "proj":
																					__lowercase       	=fairseq_model.proj
																					__lowercase       	=True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split('w2v_model.'					)[-1] == name.split('.'					)[0]:
																																			__lowercase       	=True
																																			if "*" in mapped_key:
																																										__lowercase       	=name.split(lowerCamelCase_					)[0].split('.'					)[-2]
																																										__lowercase       	=mapped_key.replace('*'  ,   lowerCamelCase_					)
																																			if "weight_g" in name:
																																										__lowercase       	="""weight_g"""
																																			elif "weight_v" in name:
																																										__lowercase       	="""weight_v"""
																																			elif "bias" in name:
																																										__lowercase       	="""bias"""
																																			elif "weight" in name:
																																										__lowercase       	="""weight"""
																																			else:
																																										__lowercase       	=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   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=full_name.split('conv_layers.'					)[-1]
							__lowercase       	=name.split('.'					)
							__lowercase       	=int(items[0]					)
							__lowercase       	=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       	=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       	=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       	=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       	=value
																					logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
							else:
														unused_weights.append(lowerCamelCase_					)



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=emb.weight.shape
							__lowercase       	=nn.Linear(lowerCamelCase_  ,   lowerCamelCase_  ,   bias=lowerCamelCase_					)
							__lowercase       	=emb.weight.data
							return lin_layer



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



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

							__lowercase       	=len(lowerCamelCase_					)

							__lowercase       	={
							    """<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   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   ):



							"""simple docstring"""



							__lowercase       	=WavaVecaConfig.from_pretrained(lowerCamelCase_					)
							__lowercase       	=SpeechaTextaConfig.from_pretrained(
							    lowerCamelCase_  ,   vocab_size=lowerCamelCase_  ,   decoder_layers=lowerCamelCase_  ,   do_stable_layer_norm=lowerCamelCase_					)

							__lowercase       	=WavaVecaFeatureExtractor(
							    feature_size=1  ,   sampling_rate=16_000  ,   padding_value=0  ,   do_normalize=lowerCamelCase_  ,   return_attention_mask=lowerCamelCase_  ,   )

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

							# set weights for wav2vec2 encoder
							__lowercase       	=WavaVecaModel(lowerCamelCase_					)
							__lowercase       	=recursively_load_weights_wavaveca(model.encoder  ,   lowerCamelCase_					)

							__lowercase       	=SpeechaTextaForCausalLM(lowerCamelCase_					)
							__lowercase       	=hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()  ,   strict=lowerCamelCase_					)

							# set output linear layer
							unexpected_keys.remove('embed_out'					)
							__lowercase       	=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       	=SpeechEncoderDecoderModel(encoder=lowerCamelCase_  ,   decoder=lowerCamelCase_					)
							__lowercase       	=False

							# add projection layer
							__lowercase       	=nn.Parameter(projection_layer.weight					)
							__lowercase       	=nn.Parameter(projection_layer.bias					)

							__lowercase       	=create_vocab_dict(lowerCamelCase_					)

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

							__lowercase       	=SpeechaTextaTokenizer(os.path.join(lowerCamelCase_  ,   'vocab.json'					)					)
							tokenizer.save_pretrained(lowerCamelCase_					)

							__lowercase       	=hf_wavavec.config.to_dict()
							__lowercase       	=tokenizer.pad_token_id
							__lowercase       	=tokenizer.bos_token_id
							__lowercase       	=tokenizer.eos_token_id
							__lowercase       	="""speech_to_text_2"""
							__lowercase       	="""wav2vec2"""

							__lowercase       	=SpeechEncoderDecoderConfig.from_dict(lowerCamelCase_					)

							hf_wavavec.save_pretrained(lowerCamelCase_					)
							feature_extractor.save_pretrained(lowerCamelCase_					)


if __name__ == "__main__":
		lowerCamelCase	 =       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""")

		lowerCamelCase	 =       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'''



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							if not isinstance(_lowerCAmelCase  ,   _lowerCAmelCase					):
														raise TypeError('only integers accepted as input'					)
							else:
														__lowercase       	=str(abs(_lowerCAmelCase					)					)
														__lowercase       	=[list(_lowerCAmelCase					) for char in range(len(_lowerCAmelCase					)					)]
														for index in range(len(_lowerCAmelCase					)					):
																					num_transpositions[index].pop(_lowerCAmelCase					)
														return max(
														    int(''.join(list(_lowerCAmelCase					)					)					) for transposition in num_transpositions					)


if __name__ == "__main__":
		__import__("""doctest""").testmod()



