
'''simple docstring'''







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()
__lowerCAmelCase         =    logging.get_logger(__name__)

__lowerCAmelCase         =    {
    """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""",
}
__lowerCAmelCase         =    [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]





def 					UpperCAmelCase_		(__a      :    Dict ,	__a      :    List[Any] ,	__a      :    Any ,	__a      :    Optional[int] ,	__a      :    Tuple ,	__a      :    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
            _a				:	Any			=   'lm_head'

      _a				:	Any			=   getattr(__a ,	__a )

   if weight_type is not None:
      _a				:	List[Any]			=   getattr(__a ,	__a ).shape
   else:
      _a				:	Optional[int]			=   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				:	List[Any]			=   value
   elif weight_type == "weight_g":
      _a				:	Union[str, Any]			=   value
   elif weight_type == "weight_v":
      _a				:	Dict			=   value
   elif weight_type == "bias":
      _a				:	Optional[Any]			=   value
   else:
      _a				:	Any			=   value

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





def 					UpperCAmelCase_		(__a      :    int ,	__a      :    Optional[Any] ,	__a      :    Optional[int] ):






   """simple docstring"""

   _a				:	int			=   []
   _a				:	List[str]			=   fairseq_model.state_dict()

   _a				:	List[Any]			=   hf_model.unispeech.feature_extractor

   for name, value in fairseq_dict.items():
      _a				:	Any			=   False
      if "conv_layers" in name:
         load_conv_layer(
             __a ,	__a ,	__a ,	__a ,	hf_model.config.feat_extract_norm == 'group' ,	)
         _a				:	List[str]			=   True
      else:
         for key, mapped_key in MAPPING.items():
            _a				:	Optional[int]			=   '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]:
               _a				:	Union[str, Any]			=   True
               if "*" in mapped_key:
                  _a				:	Dict			=   name.split(__a )[0].split('.' )[-2]
                  _a				:	Optional[int]			=   mapped_key.replace('*' ,	__a )
               if "weight_g" in name:
                  _a				:	List[Any]			=   'weight_g'
               elif "weight_v" in name:
                  _a				:	str			=   'weight_v'
               elif "bias" in name:
                  _a				:	Dict			=   'bias'
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  _a				:	str			=   'weight'
               else:
                  _a				:	List[str]			=   None
               set_recursively(__a ,	__a ,	__a ,	__a ,	__a ,	__a )
            continue
      if not is_used:
         unused_weights.append(__a )

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





def 					UpperCAmelCase_		(__a      :    Union[str, Any] ,	__a      :    List[Any] ,	__a      :    List[Any] ,	__a      :    Dict ,	__a      :    int ):






   """simple docstring"""

   _a				:	Any			=   full_name.split('conv_layers.' )[-1]
   _a				:	int			=   name.split('.' )
   _a				:	Optional[Any]			=   int(items[0] )
   _a				:	Union[str, 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."""
         )
         _a				:	int			=   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				:	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."
         )
         _a				:	List[str]			=   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				:	Optional[Any]			=   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_		(__a      :    Union[str, Any] ,	__a      :    Union[str, Any] ,	__a      :    Any=None ,	__a      :    Optional[Any]=None ,	__a      :    Union[str, Any]=True ):






   """simple docstring"""

   if config_path is not None:
      _a				:	Optional[Any]			=   UniSpeechConfig.from_pretrained(__a )
   else:
      _a				:	str			=   UniSpeechConfig()

   if is_finetuned:
      if dict_path:
         _a				:	int			=   Dictionary.load_from_json(__a )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         _a				:	Optional[int]			=   target_dict.pad_index
         _a				:	Union[str, Any]			=   target_dict.bos_index
         _a				:	List[str]			=   target_dict.eos_index
         _a				:	List[str]			=   len(target_dict.symbols )
         _a				:	Optional[Any]			=   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 )
         _a				:	Optional[Any]			=   target_dict.indices

         # fairseq has the <pad> and <s> switched
         _a				:	Optional[Any]			=   4_2
         _a				:	Any			=   4_3
         with open(__a ,	'w' ,	encoding='utf-8' ) as vocab_handle:
            json.dump(__a ,	__a )
         _a				:	Tuple			=   WavaVecaPhonemeCTCTokenizer(
             __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				:	str			=   True if config.feat_extract_norm == 'layer' else False
         _a				:	Tuple			=   WavaVecaFeatureExtractor(
             feature_size=1 ,	sampling_rate=1_6_0_0_0 ,	padding_value=0 ,	do_normalize=__a ,	return_attention_mask=__a ,	)
         _a				:	int			=   WavaVecaProcessor(feature_extractor=__a ,	tokenizer=__a )
         processor.save_pretrained(__a )

      _a				:	str			=   UniSpeechForCTC(__a )
   else:
      _a				:	str			=   UniSpeechForPreTraining(__a )

   if is_finetuned:
      _a,  _a,  _a				:	str			=   fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path] ,	arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] ), 'w2v_path': checkpoint_path} )
   else:
      _a,  _a,  _a				:	Union[str, Any]			=   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )

   _a				:	Union[str, Any]			=   model[0].eval()

   recursively_load_weights(__a ,	__a ,	__a )

   hf_unispeech.save_pretrained(__a )


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





'''simple docstring'''







import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


__lowerCAmelCase         =    re.compile(r"""\s+""")





def 					UpperCAmelCase_		(__a      :    Any ):






   """simple docstring"""

   return {"hash": hashlib.mda(re.sub(__a ,	'' ,	example['content'] ).encode('utf-8' ) ).hexdigest()}





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   _a				:	List[str]			=   [len(__a ) for line in example['content'].splitlines()]
   return {"line_mean": np.mean(__a ), "line_max": max(__a )}





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   _a				:	Union[str, Any]			=   np.mean([c.isalnum() for c in example['content']] )
   return {"alpha_frac": alpha_frac}





def 					UpperCAmelCase_		(__a      :    Optional[int] ,	__a      :    Any ):






   """simple docstring"""

   if example["hash"] in uniques:
      uniques.remove(example['hash'] )
      return True
   else:
      return False





def 					UpperCAmelCase_		(__a      :    int ,	__a      :    Union[str, Any]=5 ):






   """simple docstring"""

   _a				:	Optional[int]			=   ['auto-generated', 'autogenerated', 'automatically generated']
   _a				:	List[str]			=   example['content'].splitlines()
   for _, line in zip(range(__a ) ,	__a ):
      for keyword in keywords:
         if keyword in line.lower():
            return {"autogenerated": True}
   else:
      return {"autogenerated": False}





def 					UpperCAmelCase_		(__a      :    List[str] ,	__a      :    Dict=5 ,	__a      :    Tuple=0.05 ):






   """simple docstring"""

   _a				:	Optional[int]			=   ['unit tests', 'test file', 'configuration file']
   _a				:	int			=   example['content'].splitlines()
   _a				:	int			=   0
   _a				:	Dict			=   0
   # first test
   for _, line in zip(range(__a ) ,	__a ):
      for keyword in keywords:
         if keyword in line.lower():
            return {"config_or_test": True}
    # second test
   _a				:	int			=   example['content'].count('\n' )
   _a				:	int			=   int(coeff * nlines )
   for line in lines:
      count_config += line.lower().count('config' )
      count_test += line.lower().count('test' )
      if count_config > threshold or count_test > threshold:
         return {"config_or_test": True}
   return {"config_or_test": False}





def 					UpperCAmelCase_		(__a      :    Optional[int] ):






   """simple docstring"""

   _a				:	List[str]			=   ['def ', 'class ', 'for ', 'while ']
   _a				:	str			=   example['content'].splitlines()
   for line in lines:
      for keyword in keywords:
         if keyword in line.lower():
            return {"has_no_keywords": False}
   return {"has_no_keywords": True}





def 					UpperCAmelCase_		(__a      :    int ,	__a      :    Any=4 ):






   """simple docstring"""

   _a				:	List[str]			=   example['content'].splitlines()
   _a				:	Dict			=   0
   for line in lines:
      counter += line.lower().count('=' )
      if counter > minimum:
         return {"has_few_assignments": False}
   return {"has_few_assignments": True}





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   _a				:	Optional[Any]			=   tokenizer(example['content'] ,	truncation=__a )['input_ids']
   _a				:	Optional[int]			=   len(example['content'] ) / len(__a )
   return {"ratio": ratio}





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   _a				:	Dict			=   {}
   results.update(get_hash(__a ) )
   results.update(line_stats(__a ) )
   results.update(alpha_stats(__a ) )
   results.update(char_token_ratio(__a ) )
   results.update(is_autogenerated(__a ) )
   results.update(is_config_or_test(__a ) )
   results.update(has_no_keywords(__a ) )
   results.update(has_few_assignments(__a ) )
   return results





def 					UpperCAmelCase_		(__a      :    Any ,	__a      :    Any ,	__a      :    str ):






   """simple docstring"""

   if not check_uniques(__a ,	__a ):
      return False
   elif example["autogenerated"]:
      return False
   elif example["line_max"] > args.line_max:
      return False
   elif example["line_mean"] > args.line_mean:
      return False
   elif example["alpha_frac"] < args.alpha_frac:
      return False
   elif example["ratio"] < args.min_token_ratio:
      return False
   elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
      return False
   elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
      return False
   elif example["has_few_assignments"]:
      return False
   else:
      return True





def 					UpperCAmelCase_		(__a      :    Union[str, Any] ):






   """simple docstring"""

   with open(__a ,	'rb' ) as f_in:
      with gzip.open(str(__a ) + '.gz' ,	'wb' ,	compresslevel=6 ) as f_out:
         shutil.copyfileobj(__a ,	__a )
   os.unlink(__a )


# Settings
__lowerCAmelCase         =    HfArgumentParser(PreprocessingArguments)
__lowerCAmelCase         =    parser.parse_args()
if args.num_workers is None:
  __lowerCAmelCase         =    multiprocessing.cpu_count()
__lowerCAmelCase         =    AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
__lowerCAmelCase         =    time.time()
__lowerCAmelCase         =    load_dataset(args.dataset_name, split="""train""")
print(f'''Time to load dataset: {time.time()-t_start:.2f}''')

# Run preprocessing
__lowerCAmelCase         =    time.time()
__lowerCAmelCase         =    ds.map(preprocess, num_proc=args.num_workers)
print(f'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

# Deduplicate hashes
__lowerCAmelCase         =    set(ds.unique("""hash"""))
__lowerCAmelCase         =    len(uniques) / len(ds)
print(f'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
__lowerCAmelCase         =    time.time()
__lowerCAmelCase         =    ds.filter(filter, fn_kwargs={"""uniques""": uniques, """args""": args})
print(f'''Time to filter dataset: {time.time()-t_start:.2f}''')
print(f'''Size of filtered dataset: {len(ds_filter)}''')

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
  __lowerCAmelCase         =    time.time()
  __lowerCAmelCase   ,				__lowerCAmelCase         =    deduplicate_dataset(ds_filter, args.jaccard_threshold)
  print(f'''Time to deduplicate dataset: {time.time()-t_start:.2f}''')
  print(f'''Size of deduplicate dataset: {len(ds_filter)}''')

# Save data in batches of samples_per_file
__lowerCAmelCase         =    Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
  with open(output_dir / """duplicate_clusters.json""", """w""") as f:
    json.dump(duplicate_clusters, f)

__lowerCAmelCase         =    output_dir / """data"""
data_dir.mkdir(exist_ok=True)

__lowerCAmelCase         =    time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
  __lowerCAmelCase         =    str(data_dir / f'''file-{file_number+1:012}.json''')
  __lowerCAmelCase         =    min(len(ds_filter), index + args.samples_per_file)
  ds_filter.select(list(range(index, end_index))).to_json(file_path)
  compress_file(file_path)
print(f'''Time to save dataset: {time.time()-t_start:.2f}''')



