



__snake_case       :	dict[str, float]            ={
    "km/h": 1.0,
    "m/s": 3.6,
    "mph": 1.609344,
    "knot": 1.852,
}

__snake_case       :	dict[str, float]            ={
    "km/h": 1.0,
    "m/s": 0.277777778,
    "mph": 0.621371192,
    "knot": 0.539956803,
}






def 			lowerCAmelCase__	(					lowerCamelCase_	:	float ,lowerCamelCase_	:	str ,lowerCamelCase_	:	str):





				'''simple docstring'''

				if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
								lowerCAmelCase__  :       Optional[Any]				=  (
								    f"""Incorrect 'from_type' or 'to_type' value: {unit_from!r}, {unit_to!r}\n"""
								    f"""Valid values are: {', '.join(lowerCamelCase_)}"""
								)
								raise ValueError(lowerCamelCase_)
				return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to] ,3)


if __name__ == "__main__":
							import doctest

							doctest.testmod()






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()
__snake_case       :	Dict            =logging.get_logger(__name__)

__snake_case       :	Union[str, Any]            ={
    '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',
}
__snake_case       :	Tuple            =[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Dict ,lowerCamelCase_	:	Union[str, Any] ,lowerCamelCase_	:	int ,lowerCamelCase_	:	int ,lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				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
																lowerCAmelCase__  :       str				=  '''lm_head'''

								lowerCAmelCase__  :       List[str]				=  getattr(lowerCamelCase_ ,lowerCamelCase_)

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

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Dict ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  []
				lowerCAmelCase__  :       Optional[Any]				=  fairseq_model.state_dict()

				lowerCAmelCase__  :       str				=  hf_model.unispeech.feature_extractor

				for name, value in fairseq_dict.items():
								lowerCAmelCase__  :       Union[str, Any]				=  False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,hf_model.config.feat_extract_norm == '''group''' ,)
												lowerCAmelCase__  :       Tuple				=  True
								else:
												for key, mapped_key in MAPPING.items():
																lowerCAmelCase__  :       List[str]				=  '''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]:
																				lowerCAmelCase__  :       Dict				=  True
																				if "*" in mapped_key:
																								lowerCAmelCase__  :       Tuple				=  name.split(lowerCamelCase_)[0].split('''.''')[-2]
																								lowerCAmelCase__  :       str				=  mapped_key.replace('''*''' ,lowerCamelCase_)
																				if "weight_g" in name:
																								lowerCAmelCase__  :       List[str]				=  '''weight_g'''
																				elif "weight_v" in name:
																								lowerCAmelCase__  :       Tuple				=  '''weight_v'''
																				elif "bias" in name:
																								lowerCAmelCase__  :       Any				=  '''bias'''
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								lowerCAmelCase__  :       Union[str, Any]				=  '''weight'''
																				else:
																								lowerCAmelCase__  :       Dict				=  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 			lowerCAmelCase__	(					lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	Dict ,lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  full_name.split('''conv_layers.''')[-1]
				lowerCAmelCase__  :       Tuple				=  name.split('''.''')
				lowerCAmelCase__  :       Any				=  int(items[0])
				lowerCAmelCase__  :       int				=  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."""
												)
												lowerCAmelCase__  :       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."""
												)
												lowerCAmelCase__  :       List[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."
												)
												lowerCAmelCase__  :       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."""
												)
												lowerCAmelCase__  :       str				=  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 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Union[str, Any] ,lowerCamelCase_	:	str=None ,lowerCamelCase_	:	List[Any]=None ,lowerCamelCase_	:	List[str]=True):





				'''simple docstring'''

				if config_path is not None:
								lowerCAmelCase__  :       Optional[Any]				=  UniSpeechConfig.from_pretrained(lowerCamelCase_)
				else:
								lowerCAmelCase__  :       List[str]				=  UniSpeechConfig()

				if is_finetuned:
								if dict_path:
												lowerCAmelCase__  :       Dict				=  Dictionary.load_from_json(lowerCamelCase_)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												lowerCAmelCase__  :       Any				=  target_dict.pad_index
												lowerCAmelCase__  :       str				=  target_dict.bos_index
												lowerCAmelCase__  :       Any				=  target_dict.eos_index
												lowerCAmelCase__  :       List[Any]				=  len(target_dict.symbols)
												lowerCAmelCase__  :       Union[str, Any]				=  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_)
												lowerCAmelCase__  :       str				=  target_dict.indices

												# fairseq has the <pad> and <s> switched
												lowerCAmelCase__  :       Optional[int]				=  42
												lowerCAmelCase__  :       List[Any]				=  43
												with open(lowerCamelCase_ ,'''w''' ,encoding='''utf-8''') as vocab_handle:
																json.dump(lowerCamelCase_ ,lowerCamelCase_)
												lowerCAmelCase__  :       Dict				=  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_ ,)
												lowerCAmelCase__  :       Optional[Any]				=  True if config.feat_extract_norm == '''layer''' else False
												lowerCAmelCase__  :       Tuple				=  WavaVecaFeatureExtractor(
												    feature_size=1 ,sampling_rate=16000 ,padding_value=0 ,do_normalize=lowerCamelCase_ ,return_attention_mask=lowerCamelCase_ ,)
												lowerCAmelCase__  :       Any				=  WavaVecaProcessor(feature_extractor=lowerCamelCase_ ,tokenizer=lowerCamelCase_)
												processor.save_pretrained(lowerCamelCase_)

								lowerCAmelCase__  :       Union[str, Any]				=  UniSpeechForCTC(lowerCamelCase_)
				else:
								lowerCAmelCase__  :       List[Any]				=  UniSpeechForPreTraining(lowerCamelCase_)

				if is_finetuned:
								lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       List[str]				=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path] ,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''')[:-1]), '''w2v_path''': checkpoint_path})
				else:
								lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       Any				=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])

				lowerCAmelCase__  :       Dict				=  model[0].eval()

				recursively_load_weights(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_)

				hf_unispeech.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
							__snake_case       :	Optional[Any]            =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'
							)
							__snake_case       :	List[Any]            =parser.parse_args()
							convert_unispeech_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)

