




"""simple docstring"""


import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''1.0.0a'''):
     raise Exception('''requires fairseq >= 1.0.0a''')

logging.set_verbosity_info()
A_						=				logging.get_logger(__name__)

A_						=				'''Hello world! cécé herlolip'''


def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					str		,   snake_case__							:					bool    ):
  """simple docstring"""

  _snake_case							:		Dict								=  FairseqRobertaModel.from_pretrained(snake_case__    )
  roberta.eval()  # disable dropout
  _snake_case							:		Union[str, Any]								=  roberta.model.encoder.sentence_encoder
  _snake_case							:		Optional[int]								=  XLMRobertaConfig(
      vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings		,   hidden_size=roberta.cfg.model.encoder_embed_dim		,   num_hidden_layers=roberta.cfg.model.encoder_layers		,   num_attention_heads=roberta.cfg.model.encoder_attention_heads		,   intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim		,   max_position_embeddings=5_14		,   type_vocab_size=1		,   layer_norm_eps=1e-5		,   )
  if classification_head:
    _snake_case							:		Optional[int]								=  roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

  print("""Our RoBERTa config:"""		,   snake_case__    )

  _snake_case							:		Any								=  XLMRobertaXLForSequenceClassification(snake_case__    ) if classification_head else XLMRobertaXLForMaskedLM(snake_case__    )
  model.eval()

  # Now let's copy all the weights.
  # Embeddings
  _snake_case							:		str								=  roberta_sent_encoder.embed_tokens.weight
  _snake_case							:		Union[str, Any]								=  roberta_sent_encoder.embed_positions.weight
  _snake_case							:		str								=  torch.zeros_like(
      model.roberta.embeddings.token_type_embeddings.weight    )  # just zero them out b/c RoBERTa doesn't use them.

  _snake_case							:		Any								=  roberta_sent_encoder.layer_norm.weight
  _snake_case							:		List[Any]								=  roberta_sent_encoder.layer_norm.bias

  for i in range(config.num_hidden_layers    ):
    # Encoder: start of layer
    _snake_case							:		BertLayer								=  model.roberta.encoder.layer[i]
    _snake_case							:		TransformerSentenceEncoderLayer								=  roberta_sent_encoder.layers[i]

    _snake_case							:		RobertaAttention								=  layer.attention
    _snake_case							:		Any								=  roberta_layer.self_attn_layer_norm.weight
    _snake_case							:		List[str]								=  roberta_layer.self_attn_layer_norm.bias

    # self attention
    _snake_case							:		BertSelfAttention								=  layer.attention.self
    assert (
        roberta_layer.self_attn.k_proj.weight.data.shape
        == roberta_layer.self_attn.q_proj.weight.data.shape
        == roberta_layer.self_attn.v_proj.weight.data.shape
        == torch.Size((config.hidden_size, config.hidden_size)    )
    )

    _snake_case							:		int								=  roberta_layer.self_attn.q_proj.weight
    _snake_case							:		int								=  roberta_layer.self_attn.q_proj.bias
    _snake_case							:		Dict								=  roberta_layer.self_attn.k_proj.weight
    _snake_case							:		Union[str, Any]								=  roberta_layer.self_attn.k_proj.bias
    _snake_case							:		Any								=  roberta_layer.self_attn.v_proj.weight
    _snake_case							:		int								=  roberta_layer.self_attn.v_proj.bias

    # self-attention output
    _snake_case							:		BertSelfOutput								=  layer.attention.output
    assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
    _snake_case							:		Dict								=  roberta_layer.self_attn.out_proj.weight
    _snake_case							:		Dict								=  roberta_layer.self_attn.out_proj.bias

    # this one is final layer norm
    _snake_case							:		Union[str, Any]								=  roberta_layer.final_layer_norm.weight
    _snake_case							:		Optional[int]								=  roberta_layer.final_layer_norm.bias

    # intermediate
    _snake_case							:		BertIntermediate								=  layer.intermediate
    assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
    _snake_case							:		Tuple								=  roberta_layer.fca.weight
    _snake_case							:		Optional[Any]								=  roberta_layer.fca.bias

    # output
    _snake_case							:		BertOutput								=  layer.output
    assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
    _snake_case							:		Optional[int]								=  roberta_layer.fca.weight
    _snake_case							:		Tuple								=  roberta_layer.fca.bias
    # end of layer

  if classification_head:
    _snake_case							:		str								=  roberta.model.classification_heads["""mnli"""].dense.weight
    _snake_case							:		Optional[Any]								=  roberta.model.classification_heads["""mnli"""].dense.bias
    _snake_case							:		Optional[int]								=  roberta.model.classification_heads["""mnli"""].out_proj.weight
    _snake_case							:		Optional[int]								=  roberta.model.classification_heads["""mnli"""].out_proj.bias
  else:
    # LM Head
    _snake_case							:		List[Any]								=  roberta.model.encoder.lm_head.dense.weight
    _snake_case							:		Dict								=  roberta.model.encoder.lm_head.dense.bias
    _snake_case							:		Any								=  roberta.model.encoder.lm_head.layer_norm.weight
    _snake_case							:		Dict								=  roberta.model.encoder.lm_head.layer_norm.bias
    _snake_case							:		Dict								=  roberta.model.encoder.lm_head.weight
    _snake_case							:		str								=  roberta.model.encoder.lm_head.bias

  # Let's check that we get the same results.
  _snake_case							:		torch.Tensor								=  roberta.encode(snake_case__    ).unsqueeze(0    )  # batch of size 1

  _snake_case							:		int								=  model(snake_case__    )[0]
  if classification_head:
    _snake_case							:		Optional[Any]								=  roberta.model.classification_heads["""mnli"""](roberta.extract_features(snake_case__    )    )
  else:
    _snake_case							:		Optional[int]								=  roberta.model(snake_case__    )[0]
  print(our_output.shape		,   their_output.shape    )
  _snake_case							:		List[str]								=  torch.max(torch.abs(our_output - their_output    )    ).item()
  print(F"max_absolute_diff = {max_absolute_diff}"    )  # ~ 1e-7
  _snake_case							:		Any								=  torch.allclose(snake_case__		,   snake_case__		,   atol=1e-3    )
  print("""Do both models output the same tensors?"""		,   """🔥""" if success else """💩"""    )
  if not success:
    raise Exception("""Something went wRoNg"""    )

  pathlib.Path(snake_case__    ).mkdir(parents=snake_case__		,   exist_ok=snake_case__    )
  print(F"Saving model to {pytorch_dump_folder_path}"    )
  model.save_pretrained(snake_case__    )


if __name__ == "__main__":
     A_						=				argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--roberta_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     parser.add_argument(
         '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
     )
     A_						=				parser.parse_args()
     convert_xlm_roberta_xl_checkpoint_to_pytorch(
         args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )








"""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


A_						=				re.compile(r'''\s+''')


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					Dict    ):
  """simple docstring"""

  _snake_case							:		Any								=  [len(snake_case__    ) for line in example["""content"""].splitlines()]
  return {"line_mean": np.mean(snake_case__    ), "line_max": max(snake_case__    )}


def 					UpperCAmelCase__       (snake_case__							:					List[Any]    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					List[Any]    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					List[str]=5    ):
  """simple docstring"""

  _snake_case							:		Any								=  ["""auto-generated""", """autogenerated""", """automatically generated"""]
  _snake_case							:		Tuple								=  example["""content"""].splitlines()
  for _, line in zip(range(snake_case__    )		,   snake_case__    ):
    for keyword in keywords:
      if keyword in line.lower():
        return {"autogenerated": True}
  else:
    return {"autogenerated": False}


def 					UpperCAmelCase__       (snake_case__							:					Any		,   snake_case__							:					Union[str, Any]=5		,   snake_case__							:					Any=0.05    ):
  """simple docstring"""

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

  _snake_case							:		Optional[int]								=  ["""def """, """class """, """for """, """while """]
  _snake_case							:		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__       (snake_case__							:					List[str]		,   snake_case__							:					List[str]=4    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					List[str]    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  tokenizer(example["""content"""]		,   truncation=snake_case__    )["""input_ids"""]
  _snake_case							:		Optional[Any]								=  len(example["""content"""]    ) / len(snake_case__    )
  return {"ratio": ratio}


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]    ):
  """simple docstring"""

  _snake_case							:		Optional[int]								=  {}
  results.update(get_hash(snake_case__    )    )
  results.update(line_stats(snake_case__    )    )
  results.update(alpha_stats(snake_case__    )    )
  results.update(char_token_ratio(snake_case__    )    )
  results.update(is_autogenerated(snake_case__    )    )
  results.update(is_config_or_test(snake_case__    )    )
  results.update(has_no_keywords(snake_case__    )    )
  results.update(has_few_assignments(snake_case__    )    )
  return results


def 					UpperCAmelCase__       (snake_case__							:					Tuple		,   snake_case__							:					List[Any]		,   snake_case__							:					List[str]    ):
  """simple docstring"""

  if not check_uniques(snake_case__		,   snake_case__    ):
    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__       (snake_case__							:					Optional[Any]    ):
  """simple docstring"""

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


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

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

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

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

# Deduplicate data and apply heuristics
A_						=				time.time()
A_						=				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:
     A_						=				time.time()
     A_	,    A_						=				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
A_						=				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)

A_						=				output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

A_						=				time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
     A_						=				str(data_dir / F'''file-{file_number+1:012}.json''')
     A_						=				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}''')


