





'''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()
snake_case_     :       Tuple						=  logging.get_logger(__name__)

snake_case_     :       List[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",
    "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",
}
snake_case_     :       List[str]						=  [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[Any]   ) ->	Any:
						UpperCAmelCase_			:      Any     						=  {}
						with open(SCREAMING_SNAKE_CASE__,			'''r'''   ) as file:
												for line_number, line in enumerate(SCREAMING_SNAKE_CASE__   ):
																		UpperCAmelCase_			:      Dict     						=  line.strip()
																		if line:
																								UpperCAmelCase_			:      Tuple     						=  line.split()
																								UpperCAmelCase_			:      Any     						=  line_number
																								UpperCAmelCase_			:      Any     						=  words[0]
																								UpperCAmelCase_			:      Any     						=  value
						return result





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Optional[int],			SCREAMING_SNAKE_CASE__      :			Any,			SCREAMING_SNAKE_CASE__      :			List[str],			SCREAMING_SNAKE_CASE__      :			Any,			SCREAMING_SNAKE_CASE__      :			int   ) ->	List[Any]:
						for attribute in key.split('''.'''   ):
												UpperCAmelCase_			:      int     						=  getattr(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )

						UpperCAmelCase_			:      Optional[Any]     						=  None
						for param_key in PARAM_MAPPING.keys():
												if full_name.endswith(SCREAMING_SNAKE_CASE__   ):
																		UpperCAmelCase_			:      List[Any]     						=  PARAM_MAPPING[full_name.split('''.'''   )[-1]]
																		UpperCAmelCase_			:      str     						=  '''param'''

						if weight_type is not None and weight_type != "param":
												UpperCAmelCase_			:      str     						=  getattr(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ).shape
						elif weight_type is not None and weight_type == "param":
												UpperCAmelCase_			:      Tuple     						=  hf_pointer
												for attribute in hf_param_name.split('''.'''   ):
																		UpperCAmelCase_			:      Optional[int]     						=  getattr(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      List[str]     						=  shape_pointer.shape

												# let's reduce dimension
												UpperCAmelCase_			:      str     						=  value[0]
						else:
												UpperCAmelCase_			:      Optional[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":
												UpperCAmelCase_			:      int     						=  value
						elif weight_type == "weight_g":
												UpperCAmelCase_			:      str     						=  value
						elif weight_type == "weight_v":
												UpperCAmelCase_			:      List[Any]     						=  value
						elif weight_type == "bias":
												UpperCAmelCase_			:      List[str]     						=  value
						elif weight_type == "param":
												for attribute in hf_param_name.split('''.'''   ):
																		UpperCAmelCase_			:      Dict     						=  getattr(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      List[Any]     						=  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        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			List[Any],			SCREAMING_SNAKE_CASE__      :			Any,			SCREAMING_SNAKE_CASE__      :			Dict,			SCREAMING_SNAKE_CASE__      :			str   ) ->	int:
						UpperCAmelCase_			:      Union[str, Any]     						=  None
						for param_key in PARAM_MAPPING.keys():
												if full_name.endswith(SCREAMING_SNAKE_CASE__   ):
																		UpperCAmelCase_			:      Tuple     						=  PARAM_MAPPING[full_name.split('''.'''   )[-1]]
																		UpperCAmelCase_			:      Optional[int]     						=  '''param'''

						if weight_type is not None and weight_type != "param":
												UpperCAmelCase_			:      Optional[Any]     						=  '''.'''.join([key, weight_type]   )
						elif weight_type is not None and weight_type == "param":
												UpperCAmelCase_			:      Union[str, Any]     						=  '''.'''.join([key, hf_param_name]   )
						else:
												UpperCAmelCase_			:      str     						=  key

						UpperCAmelCase_			:      Any     						=  value if '''lm_head''' in full_key else value[0]


snake_case_     :       int						=  {
    "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        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Optional[int],			SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			Any=None,			SCREAMING_SNAKE_CASE__      :			Dict=None   ) ->	Optional[int]:
						UpperCAmelCase_			:      Tuple     						=  False
						for key, mapped_key in MAPPING.items():
												UpperCAmelCase_			:      Union[str, Any]     						=  '''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]:
																		UpperCAmelCase_			:      List[Any]     						=  True
																		if "*" in mapped_key:
																								UpperCAmelCase_			:      List[Any]     						=  name.split(SCREAMING_SNAKE_CASE__   )[0].split('''.'''   )[-2]
																								UpperCAmelCase_			:      Dict     						=  mapped_key.replace('''*''',			SCREAMING_SNAKE_CASE__   )
																		if "weight_g" in name:
																								UpperCAmelCase_			:      List[str]     						=  '''weight_g'''
																		elif "weight_v" in name:
																								UpperCAmelCase_			:      Optional[Any]     						=  '''weight_v'''
																		elif "bias" in name:
																								UpperCAmelCase_			:      Union[str, Any]     						=  '''bias'''
																		elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								UpperCAmelCase_			:      Optional[Any]     						=  '''weight'''
																		else:
																								UpperCAmelCase_			:      Optional[Any]     						=  None
																		if hf_dict is not None:
																								rename_dict(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
																		else:
																								set_recursively(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
																		return is_used
						return is_used





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			str,			SCREAMING_SNAKE_CASE__      :			Any,			SCREAMING_SNAKE_CASE__      :			Optional[int]   ) ->	List[str]:
						UpperCAmelCase_			:      str     						=  []
						UpperCAmelCase_			:      Dict     						=  fairseq_model.state_dict()

						UpperCAmelCase_			:      List[str]     						=  hf_model.wavaveca.feature_extractor

						for name, value in fairseq_dict.items():
												UpperCAmelCase_			:      int     						=  False
												if "conv_layers" in name:
																		load_conv_layer(
																		    SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			hf_model.config.feat_extract_norm == '''group''',			)
																		UpperCAmelCase_			:      Any     						=  True
												else:
																		UpperCAmelCase_			:      List[str]     						=  load_wavaveca_layer(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
												if not is_used:
																		unused_weights.append(SCREAMING_SNAKE_CASE__   )

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





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





@torch.no_grad()
def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			str,			SCREAMING_SNAKE_CASE__      :			Any,			SCREAMING_SNAKE_CASE__      :			Optional[Any]=None,			SCREAMING_SNAKE_CASE__      :			Tuple=None,			SCREAMING_SNAKE_CASE__      :			List[str]=True,			SCREAMING_SNAKE_CASE__      :			List[Any]=False   ) ->	str:
						if config_path is not None:
												UpperCAmelCase_			:      Optional[int]     						=  WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__   )
						else:
												UpperCAmelCase_			:      str     						=  WavaVecaConfig()

						if is_seq_class:
												UpperCAmelCase_			:      List[str]     						=  read_txt_into_dict(SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      Tuple     						=  idalabel
												UpperCAmelCase_			:      int     						=  WavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      Optional[Any]     						=  WavaVecaFeatureExtractor(
												    feature_size=1,			sampling_rate=16000,			padding_value=0,			do_normalize=SCREAMING_SNAKE_CASE__,			return_attention_mask=SCREAMING_SNAKE_CASE__,			)
												feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__   )

						elif is_finetuned:
												if dict_path:
																		UpperCAmelCase_			:      Any     						=  Dictionary.load(SCREAMING_SNAKE_CASE__   )

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

																		# fairseq has the <pad> and <s> switched
																		UpperCAmelCase_			:      Optional[int]     						=  0
																		UpperCAmelCase_			:      List[str]     						=  1
																		with open(SCREAMING_SNAKE_CASE__,			'''w''',			encoding='''utf-8'''   ) as vocab_handle:
																								json.dump(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
																		UpperCAmelCase_			:      Dict     						=  WavaVecaCTCTokenizer(
																		    SCREAMING_SNAKE_CASE__,			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=SCREAMING_SNAKE_CASE__,			)
																		UpperCAmelCase_			:      List[str]     						=  True if config.feat_extract_norm == '''layer''' else False
																		UpperCAmelCase_			:      int     						=  WavaVecaFeatureExtractor(
																		    feature_size=1,			sampling_rate=16000,			padding_value=0,			do_normalize=SCREAMING_SNAKE_CASE__,			return_attention_mask=SCREAMING_SNAKE_CASE__,			)
																		UpperCAmelCase_			:      Optional[Any]     						=  WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__,			tokenizer=SCREAMING_SNAKE_CASE__   )
																		processor.save_pretrained(SCREAMING_SNAKE_CASE__   )

												UpperCAmelCase_			:      List[Any]     						=  WavaVecaForCTC(SCREAMING_SNAKE_CASE__   )
						else:
												UpperCAmelCase_			:      Optional[int]     						=  WavaVecaForPreTraining(SCREAMING_SNAKE_CASE__   )

						if is_finetuned or is_seq_class:
												UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Union[str, Any]     						=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path],			arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''   )[:-1]   )}   )
						else:
												UpperCAmelCase_			:      Union[str, Any]     						=  argparse.Namespace(task='''audio_pretraining'''   )
												UpperCAmelCase_			:      List[str]     						=  fairseq.tasks.setup_task(SCREAMING_SNAKE_CASE__   )

												UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path],			task=SCREAMING_SNAKE_CASE__   )

						UpperCAmelCase_			:      Any     						=  model[0].eval()

						recursively_load_weights(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			not is_finetuned   )

						hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__   )


if __name__ == "__main__":
					snake_case_     :       Any						=  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",
					)
					snake_case_     :       int						=  parser.parse_args()

					snake_case_     :       List[str]						=  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'''







def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int   ) ->	str:
						if number > 0:
												raise ValueError('''input must be a negative integer'''   )
						UpperCAmelCase_			:      Union[str, Any]     						=  len(bin(SCREAMING_SNAKE_CASE__   )[3:]   )
						UpperCAmelCase_			:      Union[str, Any]     						=  bin(abs(SCREAMING_SNAKE_CASE__   ) - (1 << binary_number_length)   )[3:]
						UpperCAmelCase_			:      Optional[Any]     						=  (
						    (
						        '''1'''
						        + '''0''' * (binary_number_length - len(SCREAMING_SNAKE_CASE__   ))
						        + twos_complement_number
						    )
						    if number < 0
						    else '''0'''
						)
						return "0b" + twos_complement_number


if __name__ == "__main__":
					import doctest

					doctest.testmod()





