



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    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": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
lowercase__			   =  [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: List[Any]  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: List[Any]  ,       _lowerCamelCase				: List[str]					):


							for attribute in key.split("""."""					):
														if is_finetuned:
																					if attribute in ["quantizer", "project_q", "project_hid"]:
																												# those layers are only relevant for pretraining and should be dropped
																												return

																					if attribute == "ctc_proj":
																												# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
																												__a		:		int									=       """lm_head"""

														__a		:		Optional[Any]									=       getattr(_lowerCamelCase  ,       _lowerCamelCase					)

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

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





def       __magic_name__    (   _lowerCamelCase				: str  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: Union[str, Any]					):
							__a		:		Dict									=       []
							__a		:		List[Any]									=       fairseq_model.state_dict()

							__a		:		Union[str, Any]									=       hf_model.unispeech.feature_extractor

							for name, value in fairseq_dict.items():
														__a		:		str									=       False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       hf_model.config.feat_extract_norm == """group"""  ,       )
																					__a		:		Optional[Any]									=       True
														else:
																					for key, mapped_key in MAPPING.items():
																												__a		:		Any									=       """unispeech.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																												if key in name or key.split("""w2v_model."""					)[-1] == name.split("""."""					)[0]:
																																			__a		:		Tuple									=       True
																																			if "*" in mapped_key:
																																										__a		:		str									=       name.split(_lowerCamelCase					)[0].split("""."""					)[-2]
																																										__a		:		Union[str, Any]									=       mapped_key.replace("""*"""  ,       _lowerCamelCase					)
																																			if "weight_g" in name:
																																										__a		:		Union[str, Any]									=       """weight_g"""
																																			elif "weight_v" in name:
																																										__a		:		Optional[int]									=       """weight_v"""
																																			elif "bias" in name:
																																										__a		:		Any									=       """bias"""
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										__a		:		Optional[Any]									=       """weight"""
																																			else:
																																										__a		:		Tuple									=       None
																																			set_recursively(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
																												continue
														if not is_used:
																					unused_weights.append(_lowerCamelCase					)

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





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





@torch.no_grad()
def       __magic_name__    (   _lowerCamelCase				: str  ,       _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: Any=None  ,       _lowerCamelCase				: Any=None  ,       _lowerCamelCase				: Dict=True					):


							if config_path is not None:
														__a		:		Tuple									=       UniSpeechConfig.from_pretrained(_lowerCamelCase					)
							else:
														__a		:		Tuple									=       UniSpeechConfig()

							if is_finetuned:
														if dict_path:
																					__a		:		Tuple									=       Dictionary.load_from_json(_lowerCamelCase					)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					__a		:		int									=       target_dict.pad_index
																					__a		:		Dict									=       target_dict.bos_index
																					__a		:		Tuple									=       target_dict.eos_index
																					__a		:		Tuple									=       len(target_dict.symbols					)
																					__a		:		str									=       os.path.join(_lowerCamelCase  ,       """vocab.json"""					)
																					if not os.path.isdir(_lowerCamelCase					):
																												logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_lowerCamelCase					)					)
																												return
																					os.makedirs(_lowerCamelCase  ,       exist_ok=_lowerCamelCase					)
																					__a		:		Optional[Any]									=       target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					__a		:		Any									=       4_2
																					__a		:		Optional[int]									=       4_3
																					with open(_lowerCamelCase  ,       """w"""  ,       encoding="""utf-8"""					) as vocab_handle:
																												json.dump(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Optional[int]									=       WavaVecaPhonemeCTCTokenizer(
																					    _lowerCamelCase  ,       unk_token=target_dict.unk_word  ,       pad_token=target_dict.pad_word  ,       bos_token=target_dict.bos_word  ,       eos_token=target_dict.eos_word  ,       word_delimiter_token="""|"""  ,       do_lower_case=_lowerCamelCase  ,       )
																					__a		:		str									=       True if config.feat_extract_norm == """layer""" else False
																					__a		:		str									=       WavaVecaFeatureExtractor(
																					    feature_size=1  ,       sampling_rate=1_6_0_0_0  ,       padding_value=0  ,       do_normalize=_lowerCamelCase  ,       return_attention_mask=_lowerCamelCase  ,       )
																					__a		:		Optional[int]									=       WavaVecaProcessor(feature_extractor=_lowerCamelCase  ,       tokenizer=_lowerCamelCase					)
																					processor.save_pretrained(_lowerCamelCase					)

														__a		:		List[str]									=       UniSpeechForCTC(_lowerCamelCase					)
							else:
														__a		:		int									=       UniSpeechForPreTraining(_lowerCamelCase					)

							if is_finetuned:
														__a							,     __a							,     __a		:		int									=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]  ,       arg_overrides={"""data""": """/""".join(dict_path.split("""/"""					)[:-1]					), """w2v_path""": checkpoint_path}					)
							else:
														__a							,     __a							,     __a		:		Optional[int]									=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					)

							__a		:		List[Any]									=       model[0].eval()

							recursively_load_weights(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)

							hf_unispeech.save_pretrained(_lowerCamelCase					)


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("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
	parser.add_argument(
	    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
	)
	lowercase__			   =  parser.parse_args()
	convert_unispeech_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)






"""simple docstring"""

def       __magic_name__    (   _lowerCamelCase				: list[int]					):
							if not nums:  # Makes sure that the list is not empty
														raise ValueError("""List is empty"""					)

							__a		:		Any									=       sum(_lowerCamelCase					) / len(_lowerCamelCase					)  # Calculate the average
							return sum(abs(x - average					) for x in nums					) / len(_lowerCamelCase					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

