



"""simple docstring"""







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()
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': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
lowerCAmelCase_   						=   [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      Tuple:
		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
								lowercase__   :    Tuple				=			'''lm_head'''

				lowercase__   :    Tuple				=			getattr(__lowerCamelCase						,     __lowerCamelCase	)

		if weight_type is not None:
				lowercase__   :    List[Any]				=			getattr(__lowerCamelCase						,     __lowerCamelCase	).shape
		else:
				lowercase__   :    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[int]				=			value
		elif weight_type == "weight_g":
				lowercase__   :    Tuple				=			value
		elif weight_type == "weight_v":
				lowercase__   :    Any				=			value
		elif weight_type == "bias":
				lowercase__   :    int				=			value
		else:
				lowercase__   :    Tuple				=			value

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



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      Dict:
		lowercase__   :    Tuple				=			[]
		lowercase__   :    int				=			fairseq_model.state_dict()

		lowercase__   :    str				=			hf_model.unispeech.feature_extractor

		for name, value in fairseq_dict.items():
				lowercase__   :    Optional[int]				=			False
				if "conv_layers" in name:
						load_conv_layer(
						    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     hf_model.config.feat_extract_norm == '''group'''						,     )
						lowercase__   :    int				=			True
				else:
						for key, mapped_key in MAPPING.items():
								lowercase__   :    Union[str, 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]:
										lowercase__   :    List[str]				=			True
										if "*" in mapped_key:
												lowercase__   :    Tuple				=			name.split(__lowerCamelCase	)[0].split('''.'''	)[-2]
												lowercase__   :    Union[str, Any]				=			mapped_key.replace('''*'''						,     __lowerCamelCase	)
										if "weight_g" in name:
												lowercase__   :    int				=			'''weight_g'''
										elif "weight_v" in name:
												lowercase__   :    Tuple				=			'''weight_v'''
										elif "bias" in name:
												lowercase__   :    Tuple				=			'''bias'''
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												lowercase__   :    List[str]				=			'''weight'''
										else:
												lowercase__   :    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      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      Dict:
		lowercase__   :    Dict				=			full_name.split('''conv_layers.'''	)[-1]
		lowercase__   :    int				=			name.split('''.'''	)
		lowercase__   :    str				=			int(items[0]	)
		lowercase__   :    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."""
						)
						lowercase__   :    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."""
						)
						lowercase__   :    int				=			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__   :    Any				=			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__   :    List[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      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     __lowerCamelCase=True	)							->      List[Any]:
		if config_path is not None:
				lowercase__   :    Union[str, Any]				=			UniSpeechConfig.from_pretrained(__lowerCamelCase	)
		else:
				lowercase__   :    Optional[int]				=			UniSpeechConfig()

		if is_finetuned:
				if dict_path:
						lowercase__   :    Union[str, Any]				=			Dictionary.load_from_json(__lowerCamelCase	)

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						lowercase__   :    Optional[int]				=			target_dict.pad_index
						lowercase__   :    Optional[Any]				=			target_dict.bos_index
						lowercase__   :    Optional[int]				=			target_dict.eos_index
						lowercase__   :    Tuple				=			len(target_dict.symbols	)
						lowercase__   :    Optional[int]				=			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	)
						lowercase__   :    Tuple				=			target_dict.indices

						# fairseq has the <pad> and <s> switched
						lowercase__   :    Any				=			42
						lowercase__   :    Union[str, Any]				=			43
						with open(__lowerCamelCase						,     '''w'''						,     encoding='''utf-8'''	) as vocab_handle:
								json.dump(__lowerCamelCase						,     __lowerCamelCase	)
						lowercase__   :    Tuple				=			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						,     )
						lowercase__   :    str				=			True if config.feat_extract_norm == '''layer''' else False
						lowercase__   :    Union[str, Any]				=			WavaVecaFeatureExtractor(
						    feature_size=1						,     sampling_rate=1_60_00						,     padding_value=0						,     do_normalize=__lowerCamelCase						,     return_attention_mask=__lowerCamelCase						,     )
						lowercase__   :    Tuple				=			WavaVecaProcessor(feature_extractor=__lowerCamelCase						,     tokenizer=__lowerCamelCase	)
						processor.save_pretrained(__lowerCamelCase	)

				lowercase__   :    List[str]				=			UniSpeechForCTC(__lowerCamelCase	)
		else:
				lowercase__   :    List[Any]				=			UniSpeechForPreTraining(__lowerCamelCase	)

		if is_finetuned:
				lowercase__		,			lowercase__		,			lowercase__   :    Dict				=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]						,     arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''	)[:-1]	), '''w2v_path''': checkpoint_path}	)
		else:
				lowercase__		,			lowercase__		,			lowercase__   :    Tuple				=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

		lowercase__   :    Union[str, Any]				=			model[0].eval()

		recursively_load_weights(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)

		hf_unispeech.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('--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'
							)
							lowerCAmelCase_   						=   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"""







from ..utils import DummyObject, requires_backends
class        __A       (	metaclass=A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					List[str]   	=				["torch", "torchsde"]







		def __init__(	self						:  Tuple			,*_snake_case						:  Union[str, Any]			,**_snake_case						:  Any						)					->		Union[str, Any]:






				"""simple docstring"""



				requires_backends(self			,['''torch''', '''torchsde''']						)







		@classmethod
		def 					UpperCAmelCase    (	cls						:  List[str]			,*_snake_case						:  int			,**_snake_case						:  Union[str, Any]						)					->		str:






				"""simple docstring"""



				requires_backends(cls			,['''torch''', '''torchsde''']						)







		@classmethod
		def 					UpperCAmelCase    (	cls						:  List[Any]			,*_snake_case						:  List[Any]			,**_snake_case						:  List[str]						)					->		List[Any]:






				"""simple docstring"""



				requires_backends(cls			,['''torch''', '''torchsde''']						)
