





"""simple docstring"""




import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase_     :						Dict						=					{
    'facebook/mask2former-swin-small-coco-instance': (
        'https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

lowerCamelCase_     :						List[Any]						=					logging.get_logger(__name__)

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : Optional[int]	    =   """mask2former"""
      lowercase_      : Union[str, Any]	    =   ["""swin"""]
      lowercase_      : Optional[Any]	    =   {"""hidden_size""": """hidden_dim"""}

      def __init__(     self					,      snake_case_ = None					,      snake_case_ = 2_5_6					,      snake_case_ = 2_5_6					,      snake_case_ = 2_5_6					,      snake_case_ = 1_0_2_4					,      snake_case_ = "relu"					,      snake_case_ = 6					,      snake_case_ = 1_0					,      snake_case_ = 8					,      snake_case_ = 0.0					,      snake_case_ = 2_0_4_8					,      snake_case_ = False					,      snake_case_ = False					,      snake_case_ = 4					,      snake_case_ = 2_5_5					,      snake_case_ = 1_0_0					,      snake_case_ = 0.1					,      snake_case_ = 2.0					,      snake_case_ = 5.0					,      snake_case_ = 5.0					,      snake_case_ = 1_2_5_4_4					,      snake_case_ = 3.0					,      snake_case_ = 0.75					,      snake_case_ = 0.02					,      snake_case_ = 1.0					,      snake_case_ = True					,      snake_case_ = [4, 8, 1_6, 3_2]					,      snake_case_ = None					,      **snake_case_					,      ):

        """simple docstring"""

        if backbone_config is None:
          logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'  )
          A_  :       Tuple										=  CONFIG_MAPPING['swin'](
              image_size=2_2_4					,      in_channels=3					,      patch_size=4					,      embed_dim=9_6					,      depths=[2, 2, 1_8, 2]					,      num_heads=[3, 6, 1_2, 2_4]					,      window_size=7					,      drop_path_rate=0.3					,      use_absolute_embeddings=snake_case_					,      out_features=['stage1', 'stage2', 'stage3', 'stage4']					,      )

        if isinstance(snake_case_					,      snake_case_  ):
          A_  :       Optional[int]										=  backbone_config.pop('model_type'  )
          A_  :       List[str]										=  CONFIG_MAPPING[backbone_model_type]
          A_  :       List[str]										=  config_class.from_dict(snake_case_  )

        # verify that the backbone is supported
        if backbone_config.model_type not in self.backbones_supported:
          logger.warning_once(
              F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
              F"""Supported model types: {",".join(self.backbones_supported  )}"""  )

        A_  :       Tuple										=  backbone_config
        A_  :       Any										=  feature_size
        A_  :       int										=  mask_feature_size
        A_  :       Tuple										=  hidden_dim
        A_  :       int										=  encoder_feedforward_dim
        A_  :       Optional[Any]										=  activation_function
        A_  :       Optional[Any]										=  encoder_layers
        A_  :       Optional[Any]										=  decoder_layers
        A_  :       List[str]										=  num_attention_heads
        A_  :       Dict										=  dropout
        A_  :       List[str]										=  dim_feedforward
        A_  :       Tuple										=  pre_norm
        A_  :       List[str]										=  enforce_input_projection
        A_  :       str										=  common_stride
        A_  :       int										=  ignore_value
        A_  :       Optional[int]										=  num_queries
        A_  :       List[Any]										=  no_object_weight
        A_  :       str										=  class_weight
        A_  :       List[str]										=  mask_weight
        A_  :       Optional[Any]										=  dice_weight
        A_  :       List[str]										=  train_num_points
        A_  :       Any										=  oversample_ratio
        A_  :       List[str]										=  importance_sample_ratio
        A_  :       int										=  init_std
        A_  :       Tuple										=  init_xavier_std
        A_  :       int										=  use_auxiliary_loss
        A_  :       List[str]										=  feature_strides
        A_  :       List[str]										=  output_auxiliary_logits
        A_  :       Tuple										=  decoder_layers

        super().__init__(**snake_case_  )

      @classmethod
      def 					lowerCamelCase_			(     cls					,      snake_case_					,      **snake_case_  ):

        """simple docstring"""

        return cls(
            backbone_config=snake_case_					,      **snake_case_					,      )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Tuple										=  copy.deepcopy(self.__dict__  )
        A_  :       Union[str, Any]										=  self.backbone_config.to_dict()
        A_  :       Optional[int]										=  self.__class__.model_type
        return output






"""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_     :						Any						=					re.compile(r'\s+')





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase=5					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase=5 ,					_UpperCAmelCase=0.05					):






  """simple docstring"""





  A_  :       Any										=  ['unit tests', 'test file', 'configuration file']
  A_  :       Dict										=  example['content'].splitlines()
  A_  :       List[Any]										=  0
  A_  :       str										=  0
  # first test
  for _, line in zip(range(_UpperCAmelCase					) ,					_UpperCAmelCase					):
    for keyword in keywords:
      if keyword in line.lower():
        return {"config_or_test": True}
    # second test
  A_  :       Tuple										=  example['content'].count('\n'					)
  A_  :       Tuple										=  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__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[Any]										=  ['def ', 'class ', 'for ', 'while ']
  A_  :       Tuple										=  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__		(     _UpperCAmelCase ,					_UpperCAmelCase=4					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[Any]										=  tokenizer(example['content'] ,					truncation=_UpperCAmelCase					)['input_ids']
  A_  :       Dict										=  len(example['content']					) / len(_UpperCAmelCase					)
  return {"ratio": ratio}





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Any										=  {}
  results.update(get_hash(_UpperCAmelCase					)					)
  results.update(line_stats(_UpperCAmelCase					)					)
  results.update(alpha_stats(_UpperCAmelCase					)					)
  results.update(char_token_ratio(_UpperCAmelCase					)					)
  results.update(is_autogenerated(_UpperCAmelCase					)					)
  results.update(is_config_or_test(_UpperCAmelCase					)					)
  results.update(has_no_keywords(_UpperCAmelCase					)					)
  results.update(has_few_assignments(_UpperCAmelCase					)					)
  return results





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  if not check_uniques(_UpperCAmelCase ,					_UpperCAmelCase					):
    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__		(     _UpperCAmelCase					):






  """simple docstring"""





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


# Settings
lowerCamelCase_     :						Optional[int]						=					HfArgumentParser(PreprocessingArguments)
lowerCamelCase_     :						Optional[Any]						=					parser.parse_args()
if args.num_workers is None:
   lowerCamelCase_     :						int						=					multiprocessing.cpu_count()
lowerCamelCase_     :						Tuple						=					AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

# Run preprocessing
lowerCamelCase_     :						List[str]						=					time.time()
lowerCamelCase_     :						Optional[int]						=					ds.map(preprocess, num_proc=args.num_workers)
print(F"Time to preprocess dataset: {time.time()-t_start:.2f}")

# Deduplicate hashes
lowerCamelCase_     :						int						=					set(ds.unique('hash'))
lowerCamelCase_     :						Union[str, Any]						=					len(uniques) / len(ds)
print(F"Fraction of duplicates: {1-frac:.2%}")

# Deduplicate data and apply heuristics
lowerCamelCase_     :						Optional[int]						=					time.time()
lowerCamelCase_     :						Tuple						=					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_     :						Union[str, Any]						=					time.time()
   lowerCamelCase_					,					lowerCamelCase_     :						str						=					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_     :						Tuple						=					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_     :						Optional[Any]						=					output_dir / 'data'
data_dir.mkdir(exist_ok=True)

lowerCamelCase_     :						List[str]						=					time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
   lowerCamelCase_     :						Optional[int]						=					str(data_dir / F"file-{file_number+1:012}.json")
   lowerCamelCase_     :						List[str]						=					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}")