



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
snake_case_			:     str  	=  logging.get_logger(__name__)

snake_case_			:     Optional[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',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						for attribute in key.split('.'							):
												_UpperCamelCase :		Optional[int]			    =       getattr(_A  ,  _A							)

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

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






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase :		str			    =       []
						_UpperCamelCase :		int			    =       fairseq_model.state_dict()

						_UpperCamelCase :		int			    =       hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												_UpperCamelCase :		Tuple			    =       False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _A  ,  _A  ,  _A  ,  _A  ,  hf_model.config.feat_extract_norm == 'group'  ,  )
																		_UpperCamelCase :		Any			    =       True
												else:
																		for key, mapped_key in MAPPING.items():
																								_UpperCamelCase :		Optional[Any]			    =       'hubert.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

																								if key in name or (key.split('w2v_model.'							)[-1] == name.split('.'							)[0] and not is_finetuned):
																														_UpperCamelCase :		Tuple			    =       True
																														if "*" in mapped_key:
																																				_UpperCamelCase :		Optional[int]			    =       name.split(_A							)[0].split('.'							)[-2]
																																				_UpperCamelCase :		List[Any]			    =       mapped_key.replace('*'  ,  _A							)
																														if "weight_g" in name:
																																				_UpperCamelCase :		Dict			    =       'weight_g'
																														elif "weight_v" in name:
																																				_UpperCamelCase :		str			    =       'weight_v'
																														elif "weight" in name:
																																				_UpperCamelCase :		int			    =       'weight'
																														elif "bias" in name:
																																				_UpperCamelCase :		Optional[Any]			    =       'bias'
																														else:
																																				_UpperCamelCase :		Union[str, Any]			    =       None
																														set_recursively(_A  ,  _A  ,  _A  ,  _A  ,  _A							)
																								continue
												if not is_used:
																		unused_weights.append(_A							)

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






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase :		int			    =       full_name.split('conv_layers.'							)[-1]
						_UpperCamelCase :		Tuple			    =       name.split('.'							)
						_UpperCamelCase :		List[str]			    =       int(items[0]							)
						_UpperCamelCase :		Tuple			    =       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.'
																		)
																		_UpperCamelCase :		int			    =       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.'
																		)
																		_UpperCamelCase :		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."
																		)
																		_UpperCamelCase :		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.'
																		)
																		_UpperCamelCase :		List[Any]			    =       value
																		logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
						else:
												unused_weights.append(_A							)






@torch.no_grad()
def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_=None  ,  UpperCAmelCase_=None  ,  UpperCAmelCase_=True							):
						if config_path is not None:
												_UpperCamelCase :		Optional[int]			    =       HubertConfig.from_pretrained(_A							)
						else:
												_UpperCamelCase :		int			    =       HubertConfig()

						if is_finetuned:
												if dict_path:
																		_UpperCamelCase :		str			    =       Dictionary.load(_A							)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		_UpperCamelCase :		Any			    =       target_dict.pad_index
																		_UpperCamelCase :		List[str]			    =       target_dict.bos_index
																		_UpperCamelCase :		str			    =       target_dict.eos_index
																		_UpperCamelCase :		Optional[int]			    =       len(target_dict.symbols							)
																		_UpperCamelCase :		Union[str, Any]			    =       os.path.join(_A  ,  'vocab.json'							)
																		if not os.path.isdir(_A							):
																								logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_A							)							)
																								return
																		os.makedirs(_A  ,  exist_ok=_A							)
																		with open(_A  ,  'w'  ,  encoding='utf-8'							) as vocab_handle:
																								json.dump(target_dict.indices  ,  _A							)
																		_UpperCamelCase :		Tuple			    =       WavaVecaCTCTokenizer(
																		    _A  ,  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=_A  ,  )
																		_UpperCamelCase :		List[str]			    =       True if config.feat_extract_norm == 'layer' else False
																		_UpperCamelCase :		Tuple			    =       WavaVecaFeatureExtractor(
																		    feature_size=1  ,  sampling_rate=1_6_0_0_0  ,  padding_value=0  ,  do_normalize=_A  ,  return_attention_mask=_A  ,  )
																		_UpperCamelCase :		Optional[Any]			    =       WavaVecaProcessor(feature_extractor=_A  ,  tokenizer=_A							)
																		processor.save_pretrained(_A							)

												_UpperCamelCase :		Optional[int]			    =       HubertForCTC(_A							)
						else:
												_UpperCamelCase :		Union[str, Any]			    =       HubertModel(_A							)

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

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

						recursively_load_weights(_A  ,  _A  ,  _A							)

						hf_wavavec.save_pretrained(_A							)


if __name__ == "__main__":
					snake_case_			:     Optional[int]  	=  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_			:     Tuple  	=  parser.parse_args()
					convert_hubert_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)







'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


snake_case_			:     int  	=  logging.get_logger(__name__)

snake_case_			:     List[str]  	=  {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class 							lowercase__     (							lowercase       ):
	lowercase__      =      """gptj"""
	lowercase__      =      {
	    """max_position_embeddings""": """n_positions""",
	    """hidden_size""": """n_embd""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}





	def __init__(			self :    Any			,lowerCamelCase__ :    Optional[Any]=50400			,lowerCamelCase__ :    Tuple=2048			,lowerCamelCase__ :    Tuple=4096			,lowerCamelCase__ :    int=28			,lowerCamelCase__ :    Optional[Any]=16			,lowerCamelCase__ :    Optional[Any]=64			,lowerCamelCase__ :    List[Any]=None			,lowerCamelCase__ :    List[Any]="gelu_new"			,lowerCamelCase__ :    Optional[Any]=0.0			,lowerCamelCase__ :    List[str]=0.0			,lowerCamelCase__ :    List[Any]=0.0			,lowerCamelCase__ :    Tuple=1E-5			,lowerCamelCase__ :    int=0.0_2			,lowerCamelCase__ :    Any=True			,lowerCamelCase__ :    str=50256			,lowerCamelCase__ :    Any=50256			,lowerCamelCase__ :    Tuple=False			,**lowerCamelCase__ :    Optional[Any]			,):







							'''simple docstring'''
							_UpperCamelCase :		Optional[Any]			    =       vocab_size
							_UpperCamelCase :		Optional[Any]			    =       n_positions
							_UpperCamelCase :		Union[str, Any]			    =       n_embd
							_UpperCamelCase :		Any			    =       n_layer
							_UpperCamelCase :		Optional[int]			    =       n_head
							_UpperCamelCase :		List[str]			    =       n_inner
							_UpperCamelCase :		List[Any]			    =       rotary_dim
							_UpperCamelCase :		int			    =       activation_function
							_UpperCamelCase :		Dict			    =       resid_pdrop
							_UpperCamelCase :		Any			    =       embd_pdrop
							_UpperCamelCase :		Union[str, Any]			    =       attn_pdrop
							_UpperCamelCase :		Union[str, Any]			    =       layer_norm_epsilon
							_UpperCamelCase :		Optional[Any]			    =       initializer_range
							_UpperCamelCase :		str			    =       use_cache

							_UpperCamelCase :		Union[str, Any]			    =       bos_token_id
							_UpperCamelCase :		Any			    =       eos_token_id

							super().__init__(
							    bos_token_id=lowerCamelCase__			,eos_token_id=lowerCamelCase__			,tie_word_embeddings=lowerCamelCase__			,**lowerCamelCase__			)






class 							lowercase__     (							lowercase       ):





	def __init__(			self :    Tuple			,lowerCamelCase__ :    PretrainedConfig			,lowerCamelCase__ :    str = "default"			,lowerCamelCase__ :    List[PatchingSpec] = None			,lowerCamelCase__ :    bool = False			,):







							'''simple docstring'''
							super().__init__(lowerCamelCase__			,task=lowerCamelCase__			,patching_specs=lowerCamelCase__			,use_past=lowerCamelCase__			)
							if not getattr(self._config			,'pad_token_id'			,lowerCamelCase__			):
													# TODO: how to do that better?
													_UpperCamelCase :		int			    =       0





	@property
	def      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		List[str]			    =       OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}			)
							if self.use_past:
													self.fill_with_past_key_values_(lowerCamelCase__			,direction='inputs'			)
													_UpperCamelCase :		Tuple			    =       {0: 'batch', 1: 'past_sequence + sequence'}
							else:
													_UpperCamelCase :		Any			    =       {0: 'batch', 1: 'sequence'}

							return common_inputs





	@property
	def      UpperCamelCase_  (			self :    Tuple			):







							'''simple docstring'''
							return self._config.n_layer





	@property
	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							return self._config.n_head





	def      UpperCamelCase_  (			self :    List[str]			,lowerCamelCase__ :    PreTrainedTokenizer			,lowerCamelCase__ :    int = -1			,lowerCamelCase__ :    int = -1			,lowerCamelCase__ :    bool = False			,lowerCamelCase__ :    Optional[TensorType] = None			,):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       super(lowerCamelCase__			,self			).generate_dummy_inputs(
							    lowerCamelCase__			,batch_size=lowerCamelCase__			,seq_length=lowerCamelCase__			,is_pair=lowerCamelCase__			,framework=lowerCamelCase__			)

							# We need to order the input in the way they appears in the forward()
							_UpperCamelCase :		Tuple			    =       OrderedDict({'input_ids': common_inputs['input_ids']}			)

							# Need to add the past_keys
							if self.use_past:
													if not is_torch_available():
																			raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'			)
													else:
																			import torch

																			_UpperCamelCase    ,      _UpperCamelCase :		str			    =       common_inputs['input_ids'].shape
																			# Not using the same length for past_key_values
																			_UpperCamelCase :		Optional[int]			    =       seqlen + 2
																			_UpperCamelCase :		List[Any]			    =       (
																			    batch,
																			    self.num_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // self.num_attention_heads,
																			)
																			_UpperCamelCase :		Optional[Any]			    =       [
																			    (torch.zeros(lowerCamelCase__			), torch.zeros(lowerCamelCase__			)) for _ in range(self.num_layers			)
																			]

							_UpperCamelCase :		Union[str, Any]			    =       common_inputs['attention_mask']
							if self.use_past:
													_UpperCamelCase :		Any			    =       ordered_inputs['attention_mask'].dtype
													_UpperCamelCase :		List[str]			    =       torch.cat(
													    [ordered_inputs['attention_mask'], torch.ones(lowerCamelCase__			,lowerCamelCase__			,dtype=lowerCamelCase__			)]			,dim=1			)

							return ordered_inputs





	@property
	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							return 13


