






"""simple docstring"""





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    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.linear_k': 'encoder.layers.*.self_attn.linear_k',
    'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
    'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
    'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
    'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
    'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
    'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
    'self_attn.rotary_emb': 'encoder.embed_positions',
    'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
    'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
    'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
    'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
    'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
    'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
    'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
    'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
    'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
    'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
    'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
    'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
    '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': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
__lowerCamelCase							=  [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]



def   a    (     __UpperCAmelCase      :    Optional[Any]      ,   __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    List[Any]      ,   __UpperCAmelCase      :    Tuple      ,   __UpperCAmelCase      :    Optional[int]  )    ->							Any:
						for attribute in key.split("""."""  ):
												__magic_name__:	Union[str, Any]							=       getattr(__UpperCAmelCase      ,   __UpperCAmelCase  )

						if weight_type is not None:
												__magic_name__:	List[str]							=       getattr(__UpperCAmelCase      ,   __UpperCAmelCase  ).shape
						else:
												__magic_name__:	str							=       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":
												__magic_name__:	Tuple							=       value
						elif weight_type == "weight_g":
												__magic_name__:	Dict							=       value
						elif weight_type == "weight_v":
												__magic_name__:	str							=       value
						elif weight_type == "bias":
												__magic_name__:	Dict							=       value
						elif weight_type == "running_mean":
												__magic_name__:	List[Any]							=       value
						elif weight_type == "running_var":
												__magic_name__:	int							=       value
						elif weight_type == "num_batches_tracked":
												__magic_name__:	Any							=       value
						elif weight_type == "inv_freq":
												__magic_name__:	List[Any]							=       value
						else:
												__magic_name__:	List[Any]							=       value

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



def   a    (     __UpperCAmelCase      :    List[Any]      ,   __UpperCAmelCase      :    Optional[Any]      ,   __UpperCAmelCase      :    Optional[int]  )    ->							Any:
						__magic_name__:	Optional[Any]							=       []
						__magic_name__:	Optional[int]							=       fairseq_model.state_dict()

						__magic_name__:	Union[str, Any]							=       hf_model.wavaveca_conformer.feature_extractor

						for name, value in fairseq_dict.items():
												__magic_name__:	int							=       False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   hf_model.config.feat_extract_norm == """group"""      ,   )
																		__magic_name__:	Dict							=       True
												else:
																		for key, mapped_key in MAPPING.items():
																								__magic_name__:	str							=       """wav2vec2_conformer.""" + 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]:
																														__magic_name__:	List[str]							=       True
																														if "*" in mapped_key:
																																				__magic_name__:	List[str]							=       name.split(__UpperCAmelCase  )[0].split("""."""  )[-2]
																																				__magic_name__:	List[str]							=       mapped_key.replace("""*"""      ,   __UpperCAmelCase  )
																														if "pos_bias_u" in name:
																																				__magic_name__:	Optional[Any]							=       None
																														elif "pos_bias_v" in name:
																																				__magic_name__:	Tuple							=       None
																														elif "weight_g" in name:
																																				__magic_name__:	str							=       """weight_g"""
																														elif "weight_v" in name:
																																				__magic_name__:	Union[str, Any]							=       """weight_v"""
																														elif "bias" in name:
																																				__magic_name__:	Optional[Any]							=       """bias"""
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				__magic_name__:	int							=       """weight"""
																														elif "running_mean" in name:
																																				__magic_name__:	str							=       """running_mean"""
																														elif "inv_freq" in name:
																																				__magic_name__:	Union[str, Any]							=       """inv_freq"""
																														elif "running_var" in name:
																																				__magic_name__:	Union[str, Any]							=       """running_var"""
																														elif "num_batches_tracked" in name:
																																				__magic_name__:	Tuple							=       """num_batches_tracked"""
																														else:
																																				__magic_name__:	Dict							=       None
																														set_recursively(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )
																								continue
												if not is_used:
																		unused_weights.append(__UpperCAmelCase  )

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



def   a    (     __UpperCAmelCase      :    Dict      ,   __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    List[Any]      ,   __UpperCAmelCase      :    Dict      ,   __UpperCAmelCase      :    str  )    ->							List[Any]:
						__magic_name__:	str							=       full_name.split("""conv_layers."""  )[-1]
						__magic_name__:	Dict							=       name.split("""."""  )
						__magic_name__:	Dict							=       int(items[0]  )
						__magic_name__:	Optional[Any]							=       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.'  )
																		__magic_name__:	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.'  )
																		__magic_name__:	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:
																		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.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'  )
																		__magic_name__:	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.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'  )
																		__magic_name__:	Dict							=       value
																		logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'  )
						else:
												unused_weights.append(__UpperCAmelCase  )



@torch.no_grad()
def   a    (     __UpperCAmelCase      :    Optional[Any]      ,   __UpperCAmelCase      :    List[Any]      ,   __UpperCAmelCase      :    int=None      ,   __UpperCAmelCase      :    List[Any]=None      ,   __UpperCAmelCase      :    Dict=True  )    ->							Optional[Any]:
						if config_path is not None:
												__magic_name__:	int							=       WavaVecaConformerConfig.from_pretrained(__UpperCAmelCase      ,   hidden_act="""swish"""  )
						else:
												__magic_name__:	str							=       WavaVecaConformerConfig()

						if "rope" in checkpoint_path:
												__magic_name__:	str							=       """rotary"""

						if is_finetuned:
												if dict_path:
																		__magic_name__:	Any							=       Dictionary.load(__UpperCAmelCase  )

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

																		# fairseq has the <pad> and <s> switched
																		__magic_name__:	str							=       0
																		__magic_name__:	Optional[Any]							=       1
																		with open(__UpperCAmelCase      ,   """w"""      ,   encoding="""utf-8"""  ) as vocab_handle:
																								json.dump(__UpperCAmelCase      ,   __UpperCAmelCase  )
																		__magic_name__:	Tuple							=       WavaVecaCTCTokenizer(
																		    __UpperCAmelCase      ,   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=__UpperCAmelCase      ,   )
																		__magic_name__:	int							=       True if config.feat_extract_norm == """layer""" else False
																		__magic_name__:	int							=       WavaVecaFeatureExtractor(
																		    feature_size=1      ,   sampling_rate=1_6_0_0_0      ,   padding_value=0      ,   do_normalize=__UpperCAmelCase      ,   return_attention_mask=__UpperCAmelCase      ,   )
																		__magic_name__:	Optional[Any]							=       WavaVecaProcessor(feature_extractor=__UpperCAmelCase      ,   tokenizer=__UpperCAmelCase  )
																		processor.save_pretrained(__UpperCAmelCase  )

												__magic_name__:	Optional[int]							=       WavaVecaConformerForCTC(__UpperCAmelCase  )
						else:
												__magic_name__:	Dict							=       WavaVecaConformerForPreTraining(__UpperCAmelCase  )

						if is_finetuned:
												__magic_name__,			__magic_name__,			__magic_name__:	Tuple							=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path]      ,   arg_overrides={"""data""": """/""".join(dict_path.split("""/"""  )[:-1]  )}  )
						else:
												__magic_name__:	Tuple							=       argparse.Namespace(task="""audio_pretraining"""  )
												__magic_name__:	Any							=       fairseq.tasks.setup_task(__UpperCAmelCase  )

												__magic_name__,			__magic_name__,			__magic_name__:	List[str]							=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      ,   task=__UpperCAmelCase  )

						__magic_name__:	Tuple							=       model[0].eval()

						recursively_load_weights(__UpperCAmelCase      ,   __UpperCAmelCase      ,   not is_finetuned  )

						hf_wavavec.save_pretrained(__UpperCAmelCase  )


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_wavaveca_conformer_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase       		=    logging.get_logger(__name__)

lowerCAmelCase       		=    {
    """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						'''gpt_neox_japanese'''


	def __init__(    self				,     lowercase__=3_2_0_0_0				,     lowercase__=2_5_6_0				,     lowercase__=3_2				,     lowercase__=3_2				,     lowercase__=4				,     lowercase__="gelu"				,     lowercase__=1.0_0				,     lowercase__=1_0_0_0_0				,     lowercase__=2_0_4_8				,     lowercase__=0.0_2				,     lowercase__=1e-5				,     lowercase__=True				,     lowercase__=3_1_9_9_6				,     lowercase__=3_1_9_9_9				,     lowercase__=0.1				,     lowercase__=0.0				,     **lowercase__				,     ):
							super().__init__(bos_token_id=lowercase__				,     eos_token_id=lowercase__				,     **lowercase__)
							__UpperCAmelCase			:							Optional[Any]	    =	vocab_size
							__UpperCAmelCase			:							Dict	    =	max_position_embeddings
							__UpperCAmelCase			:							int	    =	hidden_size
							__UpperCAmelCase			:							List[Any]	    =	num_hidden_layers
							__UpperCAmelCase			:							Union[str, Any]	    =	num_attention_heads
							__UpperCAmelCase			:							Optional[int]	    =	intermediate_multiple_size
							__UpperCAmelCase			:							Any	    =	hidden_act
							__UpperCAmelCase			:							str	    =	rotary_pct
							__UpperCAmelCase			:							Any	    =	rotary_emb_base
							__UpperCAmelCase			:							Dict	    =	initializer_range
							__UpperCAmelCase			:							Tuple	    =	layer_norm_eps
							__UpperCAmelCase			:							Optional[int]	    =	use_cache
							__UpperCAmelCase			:							str	    =	attention_dropout
							__UpperCAmelCase			:							Any	    =	hidden_dropout

