

'''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__			    =			logging.get_logger(__name__)

lowercase__			    =			{
    """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__			    =			[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]




def       UpperCamelCase(     UpperCAmelCase_	):
 UpperCAmelCase       :	int											= {}
 with open(_snake_case  ,       'r'	) as file:
  for line_number, line in enumerate(_snake_case	):
   UpperCAmelCase       :	Union[str, Any]											= line.strip()
   if line:
    UpperCAmelCase       :	List[Any]											= line.split()
    UpperCAmelCase       :	Optional[int]											= line_number
    UpperCAmelCase       :	Tuple											= words[0]
    UpperCAmelCase       :	Dict											= value
 return result




def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_	):





 for attribute in key.split('.'	):
  UpperCAmelCase       :	int											= getattr(_snake_case  ,       _snake_case	)

 UpperCAmelCase       :	str											= None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(_snake_case	):
   UpperCAmelCase       :	Tuple											= PARAM_MAPPING[full_name.split('.'	)[-1]]
   UpperCAmelCase       :	Union[str, Any]											= '''param'''

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

  # let's reduce dimension
  UpperCAmelCase       :	Tuple											= value[0]
 else:
  UpperCAmelCase       :	Tuple											= 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       :	Any											= value
 elif weight_type == "weight_g":
  UpperCAmelCase       :	List[str]											= value
 elif weight_type == "weight_v":
  UpperCAmelCase       :	int											= value
 elif weight_type == "bias":
  UpperCAmelCase       :	List[Any]											= value
 elif weight_type == "param":
  for attribute in hf_param_name.split('.'	):
   UpperCAmelCase       :	Optional[int]											= getattr(_snake_case  ,       _snake_case	)
  UpperCAmelCase       :	List[Any]											= value
 else:
  UpperCAmelCase       :	Optional[Any]											= value

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




def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_	):
 UpperCAmelCase       :	Optional[int]											= None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(_snake_case	):
   UpperCAmelCase       :	List[str]											= PARAM_MAPPING[full_name.split('.'	)[-1]]
   UpperCAmelCase       :	str											= '''param'''

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

 UpperCAmelCase       :	Optional[int]											= value if '''lm_head''' in full_key else value[0]


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




def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_	):
 UpperCAmelCase       :	List[Any]											= []
 UpperCAmelCase       :	Union[str, Any]											= fairseq_model.state_dict()

 UpperCAmelCase       :	Optional[int]											= hf_model.wavaveca.feature_extractor

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

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




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




@torch.no_grad()
def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_=None  ,       UpperCAmelCase_=None  ,       UpperCAmelCase_=True  ,       UpperCAmelCase_=False	):





 if config_path is not None:
  UpperCAmelCase       :	Tuple											= WavaVecaConfig.from_pretrained(_snake_case	)
 else:
  UpperCAmelCase       :	Optional[int]											= WavaVecaConfig()

 if is_seq_class:
  UpperCAmelCase       :	Optional[int]											= read_txt_into_dict(_snake_case	)
  UpperCAmelCase       :	Any											= idalabel
  UpperCAmelCase       :	List[str]											= WavaVecaForSequenceClassification(_snake_case	)
  UpperCAmelCase       :	Dict											= WavaVecaFeatureExtractor(
      feature_size=1  ,       sampling_rate=1_60_00  ,       padding_value=0  ,       do_normalize=_snake_case  ,       return_attention_mask=_snake_case  ,       )
  feature_extractor.save_pretrained(_snake_case	)

 elif is_finetuned:
  if dict_path:
   UpperCAmelCase       :	Optional[int]											= Dictionary.load(_snake_case	)

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

   # fairseq has the <pad> and <s> switched
   UpperCAmelCase       :	Dict											= 0
   UpperCAmelCase       :	str											= 1
   with open(_snake_case  ,       'w'  ,       encoding='utf-8'	) as vocab_handle:
    json.dump(_snake_case  ,       _snake_case	)
   UpperCAmelCase       :	str											= WavaVecaCTCTokenizer(
       _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=_snake_case  ,       )
   UpperCAmelCase       :	Optional[Any]											= True if config.feat_extract_norm == '''layer''' else False
   UpperCAmelCase       :	Dict											= WavaVecaFeatureExtractor(
       feature_size=1  ,       sampling_rate=1_60_00  ,       padding_value=0  ,       do_normalize=_snake_case  ,       return_attention_mask=_snake_case  ,       )
   UpperCAmelCase       :	Optional[int]											= WavaVecaProcessor(feature_extractor=_snake_case  ,       tokenizer=_snake_case	)
   processor.save_pretrained(_snake_case	)

  UpperCAmelCase       :	Any											= WavaVecaForCTC(_snake_case	)
 else:
  UpperCAmelCase       :	Dict											= WavaVecaForPreTraining(_snake_case	)

 if is_finetuned or is_seq_class:
  UpperCAmelCase       :	Optional[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       :	int											= fairseq.tasks.setup_task(_snake_case	)

  UpperCAmelCase       :	Optional[Any]											= fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  ,       task=_snake_case	)

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

 recursively_load_weights(_snake_case  ,       _snake_case  ,       not is_finetuned	)

 hf_wavavec.save_pretrained(_snake_case	)


if __name__ == "__main__":
 lowercase__			    =			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__			    =			parser.parse_args()

 lowercase__			    =			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"""
import numpy as np






def 		lowercase							(     _snake_case						:      int     ,   _snake_case						:      Optional[Any]     ,   _snake_case						:      Optional[int]     ,   _snake_case						:      int     ,   _snake_case						:      Union[str, Any]     )     ->Dict:
	"""simple docstring"""





	__snake_case	:						Union[str, Any]						=				int(np.ceil((x_end - xa) / h     )     )
	__snake_case	:						Dict						=				np.zeros((n + 1,)     )
	__snake_case	:						List[Any]						=				ya
	__snake_case	:						int						=				xa

	for k in range(_snake_case     ):
		__snake_case	:						Any						=				f(_snake_case     ,   y[k]     )
		__snake_case	:						List[Any]						=				f(x + 0.5 * h     ,   y[k] + 0.5 * h * ka     )
		__snake_case	:						int						=				f(x + 0.5 * h     ,   y[k] + 0.5 * h * ka     )
		__snake_case	:						Optional[int]						=				f(x + h     ,   y[k] + h * ka     )
		__snake_case	:						Optional[int]						=				y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
		x += h

	return y


if __name__ == "__main__":
			import doctest

			doctest.testmod()





