
'''simple docstring'''







import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


__lowerCAmelCase         =    logging.get_logger(__name__)





def 					UpperCAmelCase_		(__a      :    nn.ModuleList ,	__a      :    nn.ModuleList ,	__a      :    List[int] ):






   """simple docstring"""

   _a				:	Optional[int]			=   nn.ModuleList([src_layers[i] for i in layers_to_copy] )
   assert len(__a ) == len(__a ), f"""{len(__a )} != {len(__a )}"""
   dest_layers.load_state_dict(layers_to_copy.state_dict() )


__lowerCAmelCase         =    {
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    1_2: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 1_1],
        4: [0, 4, 8, 1_1],
        6: [0, 2, 4, 7, 9, 1_1],
        9: [0, 1, 2, 4, 5, 7, 9, 1_0, 1_1],
        1_2: list(range(1_2)),
    },
    1_6: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 1_5],
        3: [0, 8, 1_5],
        4: [0, 5, 1_0, 1_5],
        6: [0, 3, 6, 9, 1_2, 1_5],
        8: [0, 2, 4, 6, 8, 1_0, 1_2, 1_5],
        9: [0, 1, 3, 5, 7, 9, 1_1, 1_3, 1_5],
        1_2: [0, 1, 2, 3, 4, 5, 6, 7, 9, 1_1, 1_3, 1_5],
        1_6: list(range(1_6)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
__lowerCAmelCase         =    {
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    1_2: {1: [1_1], 2: [5, 1_1], 3: [3, 7, 1_1], 6: [1, 3, 5, 8, 1_0, 1_1]},
    1_6: {1: [1_5], 4: [4, 9, 1_2, 1_5], 8: [1, 3, 5, 7, 9, 1_1, 1_3, 1_5]},
}





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






   """simple docstring"""

   try:
      _a				:	Tuple			=   LAYERS_TO_COPY[n_teacher][n_student]
      return val
   except KeyError:
      if n_student != n_teacher:
         warnings.warn(
             f"""no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"""
             f""" {n_student}""" )
      return list(range(__a ) )





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






   """simple docstring"""

   if n_student > n_teacher:
      raise ValueError(f"""Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}""" )
   elif n_teacher == n_student:
      return list(range(__a ) )
   elif n_student == 1:
      return [n_teacher - 1]
   else:
      return LAYERS_TO_SUPERVISE[n_teacher][n_student]





def 					UpperCAmelCase_		(__a      :    Union[str, PreTrainedModel] ,	__a      :    Union[str, Path] = "student" ,	__a      :    Union[int, None] = None ,	__a      :    Union[int, None] = None ,	__a      :    str=False ,	__a      :    Union[str, Any]=None ,	__a      :    int=None ,	**__a      :    Optional[Any] ,	):






   """simple docstring"""

   _a				:	str			=   'encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'
   assert (e is not None) or (d is not None), _msg
   if isinstance(__a ,	__a ):
      AutoTokenizer.from_pretrained(__a ).save_pretrained(__a )  # purely for convenience
      _a				:	str			=   AutoModelForSeqaSeqLM.from_pretrained(__a ).eval()
   else:
      assert isinstance(__a ,	__a ), f"""teacher must be a model or string got type {type(__a )}"""
   _a				:	Any			=   teacher.config.to_diff_dict()

   try:
      _a,  _a				:	Optional[Any]			=   teacher.config.encoder_layers, teacher.config.decoder_layers
      if e is None:
         _a				:	str			=   teacher_e
      if d is None:
         _a				:	Union[str, Any]			=   teacher_d
      init_kwargs.update({'encoder_layers': e, 'decoder_layers': d} )
   except AttributeError:  # T5
      if hasattr(teacher.config ,	'num_encoder_layers' ):
         _a,  _a				:	int			=   teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
      else:
         _a,  _a				:	Any			=   teacher.config.num_layers, teacher.config.num_decoder_layers
      if e is None:
         _a				:	int			=   teacher_e
      if d is None:
         _a				:	Tuple			=   teacher_d
      if hasattr(teacher.config ,	'num_encoder_layers' ):
         init_kwargs.update({'num_encoder_layers': e, 'num_decoder_layers': d} )
      else:
         init_kwargs.update({'num_layers': e, 'num_decoder_layers': d} )

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
   init_kwargs.update(__a )

   # Copy weights
   _a				:	Tuple			=   teacher.config_class(**__a )
   _a				:	Tuple			=   AutoModelForSeqaSeqLM.from_config(__a )
   # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
   _a				:	Optional[Any]			=   student.load_state_dict(teacher.state_dict() ,	strict=__a )
   assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

   if copy_first_teacher_layers:  # Our copying is done. We just log and save
      _a,  _a				:	List[str]			=   list(range(__a ) ), list(range(__a ) )
      logger.info(
          f"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"""
          f""" {save_path}""" )
      student.save_pretrained(__a )
      return student, e_layers_to_copy, d_layers_to_copy

   # Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
   if e_layers_to_copy is None:
      _a				:	List[int]			=   pick_layers_to_copy(__a ,	__a )
   if d_layers_to_copy is None:
      _a				:	List[int]			=   pick_layers_to_copy(__a ,	__a )

   try:
      if hasattr(
          __a ,	'prophetnet' ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
         copy_layers(teacher.prophetnet.encoder.layers ,	student.prophetnet.encoder.layers ,	__a )
         copy_layers(teacher.prophetnet.decoder.layers ,	student.prophetnet.decoder.layers ,	__a )
      else:
         copy_layers(teacher.model.encoder.layers ,	student.model.encoder.layers ,	__a )
         copy_layers(teacher.model.decoder.layers ,	student.model.decoder.layers ,	__a )
   except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
      copy_layers(teacher.encoder.block ,	student.encoder.block ,	__a )
      copy_layers(teacher.decoder.block ,	student.decoder.block ,	__a )
   logger.info(
       f"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}""" )
   _a				:	Dict			=   {
       'teacher_type': teacher.config.model_type,
       'copied_encoder_layers': e_layers_to_copy,
       'copied_decoder_layers': d_layers_to_copy,
   }
   student.save_pretrained(__a )
   # Save information about copying for easier reproducibility

   return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
  fire.Fire(create_student_by_copying_alternating_layers)





'''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}''')



