



"""simple docstring"""






import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_lowercase : Tuple      						=						logging.get_logger(__name__)

_lowercase : List[str]      						=						{
    "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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "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",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
_lowercase : List[Any]      						=						[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]






def      snake_case__   (		__lowerCamelCase				:    Dict							,		__lowerCamelCase				:    Optional[Any]							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    Dict							,		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			for attribute in key.split('''.'''	):
						lowerCamelCase__		:		Dict			=getattr(__lowerCamelCase							,		__lowerCamelCase	)

			if weight_type is not None:
						lowerCamelCase__		:		List[Any]			=getattr(__lowerCamelCase							,		__lowerCamelCase	).shape
			else:
						lowerCamelCase__		:		Optional[int]			=hf_pointer.shape

			if hf_shape != value.shape:
						raise ValueError(
						    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__		:		Optional[int]			=value
			elif weight_type == "weight_g":
						lowerCamelCase__		:		Dict			=value
			elif weight_type == "weight_v":
						lowerCamelCase__		:		str			=value
			elif weight_type == "bias":
						lowerCamelCase__		:		Tuple			=value
			else:
						lowerCamelCase__		:		Optional[int]			=value

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






def      snake_case__   (		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    List[str]	):
			"""simple docstring"""
			lowerCamelCase__		:		Dict			=[]
			lowerCamelCase__		:		Dict			=fairseq_model.state_dict()

			lowerCamelCase__		:		Dict			=hf_model.unispeech_sat.feature_extractor

			for name, value in fairseq_dict.items():
						lowerCamelCase__		:		int			=False
						if "conv_layers" in name:
									load_conv_layer(
									    __lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		hf_model.config.feat_extract_norm == '''group'''							,		)
									lowerCamelCase__		:		Union[str, Any]			=True
						else:
									for key, mapped_key in MAPPING.items():
												lowerCamelCase__		:		Tuple			='''unispeech_sat.''' + 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]:
															if "layer_norm_for_extract" in name and (".".join(name.split('''.'''	)[:-1]	) != key):
																		# special case since naming is very similar
																		continue
															lowerCamelCase__		:		Union[str, Any]			=True
															if "*" in mapped_key:
																		lowerCamelCase__		:		Optional[int]			=name.split(__lowerCamelCase	)[0].split('''.'''	)[-2]
																		lowerCamelCase__		:		Dict			=mapped_key.replace('''*'''							,		__lowerCamelCase	)
															if "weight_g" in name:
																		lowerCamelCase__		:		List[str]			='''weight_g'''
															elif "weight_v" in name:
																		lowerCamelCase__		:		Optional[Any]			='''weight_v'''
															elif "bias" in name:
																		lowerCamelCase__		:		List[str]			='''bias'''
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		lowerCamelCase__		:		int			='''weight'''
															else:
																		lowerCamelCase__		:		int			=None
															set_recursively(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)
												continue
						if not is_used:
									unused_weights.append(__lowerCamelCase	)

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






def      snake_case__   (		__lowerCamelCase				:    Optional[Any]							,		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    str							,		__lowerCamelCase				:    Optional[int]	):
			"""simple docstring"""
			lowerCamelCase__		:		Optional[Any]			=full_name.split('''conv_layers.'''	)[-1]
			lowerCamelCase__		:		str			=name.split('''.'''	)
			lowerCamelCase__		:		Optional[Any]			=int(items[0]	)
			lowerCamelCase__		:		str			=int(items[1]	)

			if type_id == 0:
						if "bias" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
												raise ValueError(
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''	)
									lowerCamelCase__		:		Optional[int]			=value
									logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
												raise ValueError(
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''	)
									lowerCamelCase__		:		Optional[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:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
												raise ValueError(
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'''	)
									lowerCamelCase__		:		Tuple			=value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
												raise ValueError(
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''	)
									lowerCamelCase__		:		Dict			=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      snake_case__   (		__lowerCamelCase				:    Optional[int]							,		__lowerCamelCase				:    Union[str, Any]							,		__lowerCamelCase				:    Any=None							,		__lowerCamelCase				:    Dict=None							,		__lowerCamelCase				:    Optional[int]=True	):
			"""simple docstring"""
			if config_path is not None:
						lowerCamelCase__		:		Optional[Any]			=UniSpeechSatConfig.from_pretrained(__lowerCamelCase	)
			else:
						lowerCamelCase__		:		Union[str, Any]			=UniSpeechSatConfig()

			lowerCamelCase__		:		Union[str, Any]			=''''''

			if is_finetuned:
						lowerCamelCase__		:		Any			=UniSpeechSatForCTC(__lowerCamelCase	)
			else:
						lowerCamelCase__		:		Any			=UniSpeechSatForPreTraining(__lowerCamelCase	)

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

			recursively_load_weights(__lowerCamelCase							,		__lowerCamelCase	)

			hf_wavavec.save_pretrained(__lowerCamelCase	)


if __name__ == "__main__":
		_lowercase : str      						=						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 : Union[str, Any]      						=						parser.parse_args()
		convert_unispeech_sat_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)









"""simple docstring"""






class        __SCREAMING_SNAKE_CASE	:


					'''simple docstring'''





					def __init__(       self							:  Dict,  lowerCamelCase							:  list     )->					None:
								lowerCamelCase__		:		Tuple			=set_counts
								lowerCamelCase__		:		Dict			=max(lowerCamelCase     )
								lowerCamelCase__		:		Union[str, Any]			=len(lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=[1] * num_sets
								lowerCamelCase__		:		str			=list(range(lowerCamelCase     )     )



					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  int,  lowerCamelCase							:  int     )->					bool:
								lowerCamelCase__		:		List[Any]			=self.get_parent(lowerCamelCase     )
								lowerCamelCase__		:		str			=self.get_parent(lowerCamelCase     )

								if src_parent == dst_parent:
											return False

								if self.ranks[dst_parent] >= self.ranks[src_parent]:
											self.set_counts[dst_parent] += self.set_counts[src_parent]
											lowerCamelCase__		:		Tuple			=0
											lowerCamelCase__		:		int			=dst_parent
											if self.ranks[dst_parent] == self.ranks[src_parent]:
														self.ranks[dst_parent] += 1
											lowerCamelCase__		:		str			=self.set_counts[dst_parent]
								else:
											self.set_counts[src_parent] += self.set_counts[dst_parent]
											lowerCamelCase__		:		List[Any]			=0
											lowerCamelCase__		:		List[str]			=src_parent
											lowerCamelCase__		:		Dict			=self.set_counts[src_parent]

								lowerCamelCase__		:		str			=max(self.max_set,  lowerCamelCase     )
								return True




					def 		snake_case       (       self							:  Union[str, Any],  lowerCamelCase							:  int     )->					int:
								if self.parents[disj_set] == disj_set:
											return disj_set
								lowerCamelCase__		:		Tuple			=self.get_parent(self.parents[disj_set]     )
								return self.parents[disj_set]




