



'''simple docstring'''


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowercase_      :				int   					=    logging.get_logger(__name__)

lowercase_      :				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''',
    '''adapter_layer''': '''encoder.layers.*.adapter_layer''',
    '''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''',
    '''pooling_layer.linear''': '''projector''',
    '''pooling_layer.projection''': '''classifier''',
}
lowercase_      :				Dict   					=    [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[Any]						):
						lowercase        =							{}
						with open(lowercase_ ,  """r"""						) as file:
												for line_number, line in enumerate(lowercase_						):
																		lowercase        =							line.strip()
																		if line:
																								lowercase        =							line.split()
																								lowercase        =							line_number
																								lowercase        =							words[0]
																								lowercase        =							value
						return result







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[Any] ,  lowercase_    :      str ,  lowercase_    :      Tuple ,  lowercase_    :      Dict ,  lowercase_    :      List[str]						):
						for attribute in key.split("""."""						):
												lowercase        =							getattr(lowercase_ ,  lowercase_						)

						lowercase        =							None
						for param_key in PARAM_MAPPING.keys():
												if full_name.endswith(lowercase_						):
																		lowercase        =							PARAM_MAPPING[full_name.split("""."""						)[-1]]
																		lowercase        =							"""param"""

						if weight_type is not None and weight_type != "param":
												lowercase        =							getattr(lowercase_ ,  lowercase_						).shape
						elif weight_type is not None and weight_type == "param":
												lowercase        =							hf_pointer
												for attribute in hf_param_name.split("""."""						):
																		lowercase        =							getattr(lowercase_ ,  lowercase_						)
												lowercase        =							shape_pointer.shape

												# let's reduce dimension
												lowercase        =							value[0]
						else:
												lowercase        =							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":
												lowercase        =							value
						elif weight_type == "weight_g":
												lowercase        =							value
						elif weight_type == "weight_v":
												lowercase        =							value
						elif weight_type == "bias":
												lowercase        =							value
						elif weight_type == "param":
												for attribute in hf_param_name.split("""."""						):
																		lowercase        =							getattr(lowercase_ ,  lowercase_						)
												lowercase        =							value
						else:
												lowercase        =							value

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







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[str] ,  lowercase_    :      List[Any] ,  lowercase_    :      Dict ,  lowercase_    :      str ,  lowercase_    :      Optional[Any]						):
						lowercase        =							None
						for param_key in PARAM_MAPPING.keys():
												if full_name.endswith(lowercase_						):
																		lowercase        =							PARAM_MAPPING[full_name.split("""."""						)[-1]]
																		lowercase        =							"""param"""

						if weight_type is not None and weight_type != "param":
												lowercase        =							""".""".join([key, weight_type]						)
						elif weight_type is not None and weight_type == "param":
												lowercase        =							""".""".join([key, hf_param_name]						)
						else:
												lowercase        =							key

						lowercase        =							value if """lm_head""" in full_key else value[0]


lowercase_      :				Tuple   					=    {
    '''W_a''': '''linear_1.weight''',
    '''W_b''': '''linear_2.weight''',
    '''b_a''': '''linear_1.bias''',
    '''b_b''': '''linear_2.bias''',
    '''ln_W''': '''norm.weight''',
    '''ln_b''': '''norm.bias''',
}







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[int] ,  lowercase_    :      str ,  lowercase_    :      List[str]=None ,  lowercase_    :      Optional[int]=None						):
						lowercase        =							False
						for key, mapped_key in MAPPING.items():
												lowercase        =							"""wav2vec2.""" + 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        =							True
																		if "*" in mapped_key:
																								lowercase        =							name.split(lowercase_						)[0].split("""."""						)[-2]
																								lowercase        =							mapped_key.replace("""*""" ,  lowercase_						)
																		if "weight_g" in name:
																								lowercase        =							"""weight_g"""
																		elif "weight_v" in name:
																								lowercase        =							"""weight_v"""
																		elif "bias" in name:
																								lowercase        =							"""bias"""
																		elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								lowercase        =							"""weight"""
																		else:
																								lowercase        =							None
																		if hf_dict is not None:
																								rename_dict(lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_						)
																		else:
																								set_recursively(lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_						)
																		return is_used
						return is_used







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[Any] ,  lowercase_    :      int ,  lowercase_    :      Optional[int]						):
						lowercase        =							[]
						lowercase        =							fairseq_model.state_dict()

						lowercase        =							hf_model.wavaveca.feature_extractor

						for name, value in fairseq_dict.items():
												lowercase        =							False
												if "conv_layers" in name:
																		load_conv_layer(
																		    lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_ ,  hf_model.config.feat_extract_norm == """group""" ,  )
																		lowercase        =							True
												else:
																		lowercase        =							load_wavaveca_layer(lowercase_ ,  lowercase_ ,  lowercase_						)
												if not is_used:
																		unused_weights.append(lowercase_						)

						logger.warning(F"""Unused weights: {unused_weights}"""						)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[int] ,  lowercase_    :      Union[str, Any] ,  lowercase_    :      Dict ,  lowercase_    :      Optional[int] ,  lowercase_    :      Optional[Any]						):
						lowercase        =							full_name.split("""conv_layers."""						)[-1]
						lowercase        =							name.split("""."""						)
						lowercase        =							int(items[0]						)
						lowercase        =							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."""						)
																		lowercase        =							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."""						)
																		lowercase        =							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."""						)
																		lowercase        =							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."""						)
																		lowercase        =							value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
						else:
												unused_weights.append(lowercase_						)







@torch.no_grad()
def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Tuple ,  lowercase_    :      List[Any] ,  lowercase_    :      List[str]=None ,  lowercase_    :      str=None ,  lowercase_    :      List[str]=True ,  lowercase_    :      str=False						):
						if config_path is not None:
												lowercase        =							WavaVecaConfig.from_pretrained(lowercase_						)
						else:
												lowercase        =							WavaVecaConfig()

						if is_seq_class:
												lowercase        =							read_txt_into_dict(lowercase_						)
												lowercase        =							idalabel
												lowercase        =							WavaVecaForSequenceClassification(lowercase_						)
												lowercase        =							WavaVecaFeatureExtractor(
												    feature_size=1 ,  sampling_rate=1_6000 ,  padding_value=0 ,  do_normalize=lowercase_ ,  return_attention_mask=lowercase_ ,  )
												feature_extractor.save_pretrained(lowercase_						)

						elif is_finetuned:
												if dict_path:
																		lowercase        =							Dictionary.load(lowercase_						)

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

																		# fairseq has the <pad> and <s> switched
																		lowercase        =							0
																		lowercase        =							1
																		with open(lowercase_ ,  """w""" ,  encoding="""utf-8"""						) as vocab_handle:
																								json.dump(lowercase_ ,  lowercase_						)
																		lowercase        =							WavaVecaCTCTokenizer(
																		    lowercase_ ,  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=lowercase_ ,  )
																		lowercase        =							True if config.feat_extract_norm == """layer""" else False
																		lowercase        =							WavaVecaFeatureExtractor(
																		    feature_size=1 ,  sampling_rate=1_6000 ,  padding_value=0 ,  do_normalize=lowercase_ ,  return_attention_mask=lowercase_ ,  )
																		lowercase        =							WavaVecaProcessor(feature_extractor=lowercase_ ,  tokenizer=lowercase_						)
																		processor.save_pretrained(lowercase_						)

												lowercase        =							WavaVecaForCTC(lowercase_						)
						else:
												lowercase        =							WavaVecaForPreTraining(lowercase_						)

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

												lowercase     ,   lowercase     ,   lowercase        =							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,  task=lowercase_						)

						lowercase        =							model[0].eval()

						recursively_load_weights(lowercase_ ,  lowercase_ ,  not is_finetuned						)

						hf_wavavec.save_pretrained(lowercase_						)


if __name__ == "__main__":
						lowercase_      :				Dict   					=    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'''
						)
						parser.add_argument(
						    '''--is_seq_class''',
						    action='''store_true''',
						    help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
						)
						lowercase_      :				Union[str, Any]   					=    parser.parse_args()

						lowercase_      :				Optional[int]   					=    not args.not_finetuned and not args.is_seq_class
						convert_wavaveca_checkpoint(
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.config_path,
						    args.dict_path,
						    is_finetuned,
						    args.is_seq_class,
						)







'''simple docstring'''


from ... import PretrainedConfig


lowercase_      :				int   					=    {
    '''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
	__A			  =					'''nezha'''







	def __init__(    self ,				_lowerCAmelCase=2_1128 ,				_lowerCAmelCase=768 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=3072 ,				_lowerCAmelCase="gelu" ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=512 ,				_lowerCAmelCase=64 ,				_lowerCAmelCase=2 ,				_lowerCAmelCase=0.02 ,				_lowerCAmelCase=1E-12 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0 ,				_lowerCAmelCase=2 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=True ,				**_lowerCAmelCase ,				)    ->    int:
							'''simple docstring'''






							super().__init__(pad_token_id=_lowerCAmelCase ,				bos_token_id=_lowerCAmelCase ,				eos_token_id=_lowerCAmelCase ,				**_lowerCAmelCase						)

							lowercase        =							vocab_size
							lowercase        =							hidden_size
							lowercase        =							num_hidden_layers
							lowercase        =							num_attention_heads
							lowercase        =							hidden_act
							lowercase        =							intermediate_size
							lowercase        =							hidden_dropout_prob
							lowercase        =							attention_probs_dropout_prob
							lowercase        =							max_position_embeddings
							lowercase        =							max_relative_position
							lowercase        =							type_vocab_size
							lowercase        =							initializer_range
							lowercase        =							layer_norm_eps
							lowercase        =							classifier_dropout
							lowercase        =							use_cache


