"""simple docstring"""





import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
__A	=					logging.get_logger(__name__)

__A	=					{
    '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',
}
__A	=					[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]


def  lowercase_ ( _lowerCamelCase:						Union[str, Any]    , _lowerCamelCase:						int    , _lowerCamelCase:						Optional[int]    , _lowerCamelCase:						str    , _lowerCamelCase:						Any   )			->   int:



							'''simple docstring'''

							for attribute in key.split("."   ):
														__lowerCamelCase       :		Tuple		 =  getattr(_lowerCamelCase    , _lowerCamelCase   )

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

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


def  lowercase_ ( _lowerCamelCase:						str    , _lowerCamelCase:						str   )			->   Optional[int]:



							'''simple docstring'''

							__lowerCamelCase       :		Any		 =  []
							__lowerCamelCase       :		List[Any]		 =  fairseq_model.state_dict()

							__lowerCamelCase       :		Any		 =  hf_model.unispeech_sat.feature_extractor

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



							'''simple docstring'''

							__lowerCamelCase       :		Tuple		 =  full_name.split("conv_layers."   )[-1]
							__lowerCamelCase       :		int		 =  name.split("."   )
							__lowerCamelCase       :		int		 =  int(items[0]   )
							__lowerCamelCase       :		List[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       :		Any		 =  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[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:
																					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       :		Optional[int]		 =  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       :		List[Any]		 =  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  lowercase_ ( _lowerCamelCase:						List[str]    , _lowerCamelCase:						Tuple    , _lowerCamelCase:						Any=None    , _lowerCamelCase:						Optional[int]=None    , _lowerCamelCase:						Optional[Any]=True   )			->   List[Any]:



							'''simple docstring'''

							if config_path is not None:
														__lowerCamelCase       :		Optional[int]		 =  UniSpeechSatConfig.from_pretrained(_lowerCamelCase   )
							else:
														__lowerCamelCase       :		Any		 =  UniSpeechSatConfig()

							__lowerCamelCase       :		Tuple		 =  ""

							if is_finetuned:
														__lowerCamelCase       :		Union[str, Any]		 =  UniSpeechSatForCTC(_lowerCamelCase   )
							else:
														__lowerCamelCase       :		int		 =  UniSpeechSatForPreTraining(_lowerCamelCase   )

							__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase       :		List[Any]		 =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
							    [checkpoint_path]    , arg_overrides={"data": "/".join(dict_path.split("/"   )[:-1]   )}   )
							__lowerCamelCase       :		Dict		 =  model[0].eval()

							recursively_load_weights(_lowerCamelCase    , _lowerCamelCase   )

							hf_wavavec.save_pretrained(_lowerCamelCase   )


if __name__ == "__main__":
				__A	=					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'''
				)
				__A	=					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
				)



from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					def __init__(self       :		Any ,   _UpperCAmelCase       :		NestedDataStructureLike[PathLike] ,   _UpperCAmelCase       :		Optional[NamedSplit] = None ,   _UpperCAmelCase       :		Optional[Features] = None ,   _UpperCAmelCase       :		str = None ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		Optional[int] = None ,   **_UpperCAmelCase       :		Optional[int] ,   )		->  List[str]:




							"""simple docstring"""


							super().__init__(
							    _UpperCAmelCase ,   split=_UpperCAmelCase ,   features=_UpperCAmelCase ,   cache_dir=_UpperCAmelCase ,   keep_in_memory=_UpperCAmelCase ,   streaming=_UpperCAmelCase ,   num_proc=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
							lowercase__	     =						path_or_paths if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else {self.split: path_or_paths}
							lowercase__	     =						Text(
							    cache_dir=_UpperCAmelCase ,   data_files=_UpperCAmelCase ,   features=_UpperCAmelCase ,   **_UpperCAmelCase ,   )


					def     lowerCamelCase__	(self       :		Tuple						)		->  Any:




							"""simple docstring"""


							if self.streaming:
									lowercase__	     =						self.builder.as_streaming_dataset(split=self.split						)
							# Build regular (map-style) dataset
							else:
									lowercase__	     =						None
									lowercase__	     =						None
									lowercase__	     =						None
									lowercase__	     =						None

									self.builder.download_and_prepare(
									    download_config=_UpperCAmelCase ,   download_mode=_UpperCAmelCase ,   verification_mode=_UpperCAmelCase ,   base_path=_UpperCAmelCase ,   num_proc=self.num_proc ,   )
									lowercase__	     =						self.builder.as_dataset(
									    split=self.split ,   verification_mode=_UpperCAmelCase ,   in_memory=self.keep_in_memory						)
							return dataset

