



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()
A_		:     int        =     logging.get_logger(__name__)

A_		:     Dict        =     {
    '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 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[int] ,					UpperCAmelCase__ :Optional[Any] ,					UpperCAmelCase__ :Union[str, Any] ,					UpperCAmelCase__ :Optional[Any] ,					UpperCAmelCase__ :int					):






 '''simple docstring'''

 for attribute in key.split("."					):
  a						=		getattr(A_ ,					A_					)

 if weight_type is not None:
  a						=		getattr(A_ ,					A_					).shape
 else:
  a						=		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":
  a						=		value
 elif weight_type == "weight_g":
  a						=		value
 elif weight_type == "weight_v":
  a						=		value
 elif weight_type == "bias":
  a						=		value
 else:
  a						=		value

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


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[Any] ,					UpperCAmelCase__ :Any ,					UpperCAmelCase__ :Optional[Any]					):






 '''simple docstring'''

 a						=		[]
 a						=		fairseq_model.state_dict()

 a						=		hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  a						=		False
  if "conv_layers" in name:
   load_conv_layer(
       A_ ,					A_ ,					A_ ,					A_ ,					hf_model.config.feat_extract_norm == "group" ,					)
   a						=		True
  else:
   for key, mapped_key in MAPPING.items():
    a						=		"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):
     a						=		True
     if "*" in mapped_key:
      a						=		name.split(A_					)[0].split("."					)[-2]
      a						=		mapped_key.replace("*" ,					A_					)
     if "weight_g" in name:
      a						=		"weight_g"
     elif "weight_v" in name:
      a						=		"weight_v"
     elif "weight" in name:
      a						=		"weight"
     elif "bias" in name:
      a						=		"bias"
     else:
      a						=		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 	UpperCAmelCase__      (      UpperCAmelCase__ :Union[str, Any] ,					UpperCAmelCase__ :int ,					UpperCAmelCase__ :List[Any] ,					UpperCAmelCase__ :Dict ,					UpperCAmelCase__ :str					):






 '''simple docstring'''

 a						=		full_name.split("conv_layers."					)[-1]
 a						=		name.split("."					)
 a						=		int(items[0]					)
 a						=		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."""
   )
   a						=		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."""
   )
   a						=		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."
   )
   a						=		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."""
   )
   a						=		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 	UpperCAmelCase__      (      UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :str ,					UpperCAmelCase__ :List[str]=None ,					UpperCAmelCase__ :Optional[int]=None ,					UpperCAmelCase__ :int=True					):






 '''simple docstring'''

 if config_path is not None:
  a						=		HubertConfig.from_pretrained(A_					)
 else:
  a						=		HubertConfig()

 if is_finetuned:
  if dict_path:
   a						=		Dictionary.load(A_					)

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   a						=		target_dict.pad_index
   a						=		target_dict.bos_index
   a						=		target_dict.eos_index
   a						=		len(target_dict.symbols					)
   a						=		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_					)
   a						=		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_ ,					)
   a						=		True if config.feat_extract_norm == "layer" else False
   a						=		WavaVecaFeatureExtractor(
       feature_size=1 ,					sampling_rate=1_60_00 ,					padding_value=0 ,					do_normalize=A_ ,					return_attention_mask=A_ ,					)
   a						=		WavaVecaProcessor(feature_extractor=A_ ,					tokenizer=A_					)
   processor.save_pretrained(A_					)

  a						=		HubertForCTC(A_					)
 else:
  a						=		HubertModel(A_					)

 if is_finetuned:
  a		,  a		,  a						=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path] ,					arg_overrides={"data": "/".join(dict_path.split("/"					)[:-1]					)}					)
 else:
  a		,  a		,  a						=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					)

 a						=		model[0].eval()

 recursively_load_weights(A_ ,					A_ ,					A_					)

 hf_wavavec.save_pretrained(A_					)


if __name__ == "__main__":
     A_		:     Union[str, 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'''
     )
     A_		:     Dict        =     parser.parse_args()
     convert_hubert_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )






import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class 		_lowercase		(       UpperCAmelCase__	):

   def 		A							(      self			:       Optional[int]							,   __lowerCAmelCase			:       str							)       ->				Union[str, Any]:


    """simple docstring"""






    with open(__lowerCAmelCase							,   encoding="utf-8"							) as input_file:
     a						=		re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"							)
     a						=		input_file.read()
     a						=		regexp.search(__lowerCAmelCase							)

    return match

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       str							)       ->				Dict:


    """simple docstring"""






    with open(__lowerCAmelCase							,   encoding="utf-8"							) as input_file:
     a						=		re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"							,   re.DOTALL							)
     a						=		input_file.read()
     # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
     a						=		regexp.finditer(__lowerCAmelCase							)

    a						=		[match for match in matches if match is not None and match.group(1							) is not None]
    return matches[0] if matches else None

   def 		A							(      self			:       List[str]							)       ->				List[Any]:


    """simple docstring"""






    a						=		Path("./datasets"							)
    a						=		list(dataset_paths.absolute().glob("**/*.py"							)							)

    for dataset in dataset_files:
     if self._no_encoding_on_file_open(str(__lowerCAmelCase							)							):
      raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}"""							)




   def 		A							(      self			:       Tuple							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		Path("./datasets"							)
    a						=		list(dataset_paths.absolute().glob("**/*.py"							)							)

    for dataset in dataset_files:
     if self._no_print_statements(str(__lowerCAmelCase							)							):
      raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead."""							)

