


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__UpperCamelCase       :       List[str]						       =				logging.get_logger(__name__)

__UpperCamelCase       :       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",
    "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": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
__UpperCamelCase       :       List[str]						       =				[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def 			_a  (				SCREAMING_SNAKE_CASE	:				List[Any]    , SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				Optional[int]    , SCREAMING_SNAKE_CASE	:				Tuple    , SCREAMING_SNAKE_CASE	:				Tuple    , SCREAMING_SNAKE_CASE	:				Dict     ):







 """simple docstring"""



 for attribute in key.split('''.'''     ):
  if is_finetuned:
   if attribute in ["quantizer", "project_q", "project_hid"]:
    # those layers are only relevant for pretraining and should be dropped
    return

   if attribute == "ctc_proj":
    # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
    UpperCamelCase__					:    int          =   '''lm_head'''

  UpperCamelCase__					:    List[str]          =   getattr(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 if weight_type is not None:
  UpperCamelCase__					:    Tuple          =   getattr(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     ).shape
 else:
  UpperCamelCase__					:    Any          =   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__					:    Tuple          =   value
 elif weight_type == "weight_v":
  UpperCamelCase__					:    Optional[Any]          =   value
 elif weight_type == "bias":
  UpperCamelCase__					:    str          =   value
 else:
  UpperCamelCase__					:    Any          =   value

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

def 			_a  (				SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				int     ):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   []
 UpperCamelCase__					:    Optional[int]          =   fairseq_model.state_dict()

 UpperCamelCase__					:    int          =   hf_model.unispeech.feature_extractor

 for name, value in fairseq_dict.items():
  UpperCamelCase__					:    Optional[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__					:    str          =   True
  else:
   for key, mapped_key in MAPPING.items():
    UpperCamelCase__					:    Any          =   '''unispeech.''' + 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__					:    Dict          =   True
     if "*" in mapped_key:
      UpperCamelCase__					:    Tuple          =   name.split(SCREAMING_SNAKE_CASE     )[0].split('''.'''     )[-2]
      UpperCamelCase__					:    int          =   mapped_key.replace('''*'''    , SCREAMING_SNAKE_CASE     )
     if "weight_g" in name:
      UpperCamelCase__					:    Optional[Any]          =   '''weight_g'''
     elif "weight_v" in name:
      UpperCamelCase__					:    List[str]          =   '''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[int]          =   None
     set_recursively(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
    continue
  if not is_used:
   unused_weights.append(SCREAMING_SNAKE_CASE     )

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

def 			_a  (				SCREAMING_SNAKE_CASE	:				List[Any]    , SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				Optional[int]    , SCREAMING_SNAKE_CASE	:				List[str]    , SCREAMING_SNAKE_CASE	:				List[str]     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[int]          =   full_name.split('''conv_layers.'''     )[-1]
 UpperCamelCase__					:    Optional[int]          =   name.split('''.'''     )
 UpperCamelCase__					:    List[Any]          =   int(items[0]     )
 UpperCamelCase__					:    Optional[Any]          =   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__					:    List[str]          =   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__					:    Tuple          =   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__					:    Optional[int]          =   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__					:    Optional[int]          =   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 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				Any    , SCREAMING_SNAKE_CASE	:				str=None    , SCREAMING_SNAKE_CASE	:				Optional[Any]=None    , SCREAMING_SNAKE_CASE	:				List[Any]=True     ):







 """simple docstring"""



 if config_path is not None:
  UpperCamelCase__					:    List[str]          =   UniSpeechConfig.from_pretrained(SCREAMING_SNAKE_CASE     )
 else:
  UpperCamelCase__					:    Optional[int]          =   UniSpeechConfig()

 if is_finetuned:
  if dict_path:
   UpperCamelCase__					:    str          =   Dictionary.load_from_json(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__					:    str          =   target_dict.bos_index
   UpperCamelCase__					:    List[Any]          =   target_dict.eos_index
   UpperCamelCase__					:    int          =   len(target_dict.symbols     )
   UpperCamelCase__					:    str          =   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[Any]          =   target_dict.indices

   # fairseq has the <pad> and <s> switched
   UpperCamelCase__					:    str          =   42
   UpperCamelCase__					:    List[str]          =   43
   with open(SCREAMING_SNAKE_CASE    , '''w'''    , encoding='''utf-8'''     ) as vocab_handle:
    json.dump(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
   UpperCamelCase__					:    Dict          =   WavaVecaPhonemeCTCTokenizer(
       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__					:    int          =   True if config.feat_extract_norm == '''layer''' else False
   UpperCamelCase__					:    Union[str, Any]          =   WavaVecaFeatureExtractor(
       feature_size=1    , sampling_rate=16000    , padding_value=0    , do_normalize=SCREAMING_SNAKE_CASE    , return_attention_mask=SCREAMING_SNAKE_CASE    , )
   UpperCamelCase__					:    int          =   WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE    , tokenizer=SCREAMING_SNAKE_CASE     )
   processor.save_pretrained(SCREAMING_SNAKE_CASE     )

  UpperCamelCase__					:    Dict          =   UniSpeechForCTC(SCREAMING_SNAKE_CASE     )
 else:
  UpperCamelCase__					:    Dict          =   UniSpeechForPreTraining(SCREAMING_SNAKE_CASE     )

 if is_finetuned:
  UpperCamelCase__       ,				UpperCamelCase__       ,				UpperCamelCase__					:    Dict          =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]    , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''     )[:-1]     ), '''w2v_path''': checkpoint_path}     )
 else:
  UpperCamelCase__       ,				UpperCamelCase__       ,				UpperCamelCase__					:    Tuple          =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     )

 UpperCamelCase__					:    Optional[int]          =   model[0].eval()

 recursively_load_weights(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 hf_unispeech.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       __UpperCamelCase       :       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"
       )
       __UpperCamelCase       :       int						       =				parser.parse_args()
       convert_unispeech_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )






from math import factorial, radians

def 			_a  (				SCREAMING_SNAKE_CASE	:				float    , SCREAMING_SNAKE_CASE	:				int = 18    , SCREAMING_SNAKE_CASE	:				int = 10     ):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

 # Converting from degrees to radians
 UpperCamelCase__					:    int          =   radians(SCREAMING_SNAKE_CASE     )

 UpperCamelCase__					:    Tuple          =   angle_in_radians
 UpperCamelCase__					:    str          =   3
 UpperCamelCase__					:    int          =   -1

 for _ in range(SCREAMING_SNAKE_CASE     ):
  result += (b * (angle_in_radians**a)) / factorial(SCREAMING_SNAKE_CASE     )

  UpperCamelCase__					:    List[Any]          =   -b  # One positive term and the next will be negative and so on...
  a += 2  # Increased by 2 for every term.

 return round(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       __import__("doctest").testmod()


