





"""simple docstring"""




import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       XLMForMultipleChoice,
       XLMForQuestionAnswering,
       XLMForQuestionAnsweringSimple,
       XLMForSequenceClassification,
       XLMForTokenClassification,
       XLMModel,
       XLMWithLMHeadModel,
   )
   from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST

class  _UpperCAmelCase  :



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_=1_3					,      snake_case_=7					,      snake_case_=True					,      snake_case_=True					,      snake_case_=True					,      snake_case_=True					,      snake_case_=True					,      snake_case_=False					,      snake_case_=False					,      snake_case_=False					,      snake_case_=2					,      snake_case_=9_9					,      snake_case_=0					,      snake_case_=3_2					,      snake_case_=5					,      snake_case_=4					,      snake_case_=0.1					,      snake_case_=0.1					,      snake_case_=5_1_2					,      snake_case_=2					,      snake_case_=0.02					,      snake_case_=2					,      snake_case_=4					,      snake_case_="last"					,      snake_case_=True					,      snake_case_=None					,      snake_case_=0					,      ):

        """simple docstring"""

        A_  :       List[str]										=  parent
        A_  :       Any										=  batch_size
        A_  :       Any										=  seq_length
        A_  :       Union[str, Any]										=  is_training
        A_  :       Dict										=  use_input_lengths
        A_  :       str										=  use_token_type_ids
        A_  :       Union[str, Any]										=  use_labels
        A_  :       Union[str, Any]										=  gelu_activation
        A_  :       Optional[int]										=  sinusoidal_embeddings
        A_  :       Tuple										=  causal
        A_  :       Dict										=  asm
        A_  :       List[Any]										=  n_langs
        A_  :       Dict										=  vocab_size
        A_  :       Tuple										=  n_special
        A_  :       Dict										=  hidden_size
        A_  :       Union[str, Any]										=  num_hidden_layers
        A_  :       List[str]										=  num_attention_heads
        A_  :       Optional[Any]										=  hidden_dropout_prob
        A_  :       List[Any]										=  attention_probs_dropout_prob
        A_  :       List[Any]										=  max_position_embeddings
        A_  :       Union[str, Any]										=  type_sequence_label_size
        A_  :       List[str]										=  initializer_range
        A_  :       List[str]										=  num_labels
        A_  :       Union[str, Any]										=  num_choices
        A_  :       int										=  summary_type
        A_  :       Optional[int]										=  use_proj
        A_  :       List[Any]										=  scope
        A_  :       str										=  bos_token_id

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       List[Any]										=  ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size  )
        A_  :       int										=  random_attention_mask([self.batch_size, self.seq_length]  )

        A_  :       List[str]										=  None
        if self.use_input_lengths:
          A_  :       List[Any]										=  (
              ids_tensor([self.batch_size]					,      vocab_size=2  ) + self.seq_length - 2
          )  # small variation of seq_length

        A_  :       str										=  None
        if self.use_token_type_ids:
          A_  :       int										=  ids_tensor([self.batch_size, self.seq_length]					,      self.n_langs  )

        A_  :       Union[str, Any]										=  None
        A_  :       Optional[Any]										=  None
        A_  :       Dict										=  None
        if self.use_labels:
          A_  :       Optional[int]										=  ids_tensor([self.batch_size]					,      self.type_sequence_label_size  )
          A_  :       List[str]										=  ids_tensor([self.batch_size, self.seq_length]					,      self.num_labels  )
          A_  :       int										=  ids_tensor([self.batch_size]					,      2  ).float()
          A_  :       Tuple										=  ids_tensor([self.batch_size]					,      self.num_choices  )

        A_  :       Optional[int]										=  self.get_config()

        return (
            config,
            input_ids,
            token_type_ids,
            input_lengths,
            sequence_labels,
            token_labels,
            is_impossible_labels,
            choice_labels,
            input_mask,
        )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        return XLMConfig(
            vocab_size=self.vocab_size					,      n_special=self.n_special					,      emb_dim=self.hidden_size					,      n_layers=self.num_hidden_layers					,      n_heads=self.num_attention_heads					,      dropout=self.hidden_dropout_prob					,      attention_dropout=self.attention_probs_dropout_prob					,      gelu_activation=self.gelu_activation					,      sinusoidal_embeddings=self.sinusoidal_embeddings					,      asm=self.asm					,      causal=self.causal					,      n_langs=self.n_langs					,      max_position_embeddings=self.max_position_embeddings					,      initializer_range=self.initializer_range					,      summary_type=self.summary_type					,      use_proj=self.use_proj					,      num_labels=self.num_labels					,      bos_token_id=self.bos_token_id					,      )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      ):

        """simple docstring"""

        A_  :       int										=  XLMModel(config=snake_case_  )
        model.to(snake_case_  )
        model.eval()
        A_  :       Tuple										=  model(snake_case_					,      lengths=snake_case_					,      langs=snake_case_  )
        A_  :       Union[str, Any]										=  model(snake_case_					,      langs=snake_case_  )
        A_  :       Optional[int]										=  model(snake_case_  )
        self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      ):

        """simple docstring"""

        A_  :       List[str]										=  XLMWithLMHeadModel(snake_case_  )
        model.to(snake_case_  )
        model.eval()

        A_  :       Tuple										=  model(snake_case_					,      token_type_ids=snake_case_					,      labels=snake_case_  )
        self.parent.assertEqual(result.loss.shape					,      ()  )
        self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      ):

        """simple docstring"""

        A_  :       List[str]										=  XLMForQuestionAnsweringSimple(snake_case_  )
        model.to(snake_case_  )
        model.eval()

        A_  :       Tuple										=  model(snake_case_  )

        A_  :       str										=  model(snake_case_					,      start_positions=snake_case_					,      end_positions=snake_case_  )
        A_  :       Union[str, Any]										=  outputs
        self.parent.assertEqual(result.start_logits.shape					,      (self.batch_size, self.seq_length)  )
        self.parent.assertEqual(result.end_logits.shape					,      (self.batch_size, self.seq_length)  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      ):

        """simple docstring"""

        A_  :       Optional[int]										=  XLMForQuestionAnswering(snake_case_  )
        model.to(snake_case_  )
        model.eval()

        A_  :       Union[str, Any]										=  model(snake_case_  )

        A_  :       int										=  model(
            snake_case_					,      start_positions=snake_case_					,      end_positions=snake_case_					,      cls_index=snake_case_					,      is_impossible=snake_case_					,      p_mask=snake_case_					,      )

        A_  :       Optional[Any]										=  model(
            snake_case_					,      start_positions=snake_case_					,      end_positions=snake_case_					,      cls_index=snake_case_					,      is_impossible=snake_case_					,      )

        ((A_)						,       )  :       Optional[int]										=  result_with_labels.to_tuple()

        A_  :       Dict										=  model(snake_case_					,      start_positions=snake_case_					,      end_positions=snake_case_  )

        ((A_)						,       )  :       int										=  result_with_labels.to_tuple()

        self.parent.assertEqual(result_with_labels.loss.shape					,      ()  )
        self.parent.assertEqual(result.start_top_log_probs.shape					,      (self.batch_size, model.config.start_n_top)  )
        self.parent.assertEqual(result.start_top_index.shape					,      (self.batch_size, model.config.start_n_top)  )
        self.parent.assertEqual(
            result.end_top_log_probs.shape					,      (self.batch_size, model.config.start_n_top * model.config.end_n_top)  )
        self.parent.assertEqual(
            result.end_top_index.shape					,      (self.batch_size, model.config.start_n_top * model.config.end_n_top)  )
        self.parent.assertEqual(result.cls_logits.shape					,      (self.batch_size,)  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      ):

        """simple docstring"""

        A_  :       Tuple										=  XLMForSequenceClassification(snake_case_  )
        model.to(snake_case_  )
        model.eval()

        A_  :       str										=  model(snake_case_  )
        A_  :       Tuple										=  model(snake_case_					,      labels=snake_case_  )
        self.parent.assertEqual(result.loss.shape					,      ()  )
        self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      ):

        """simple docstring"""

        A_  :       Tuple										=  self.num_labels
        A_  :       List[Any]										=  XLMForTokenClassification(snake_case_  )
        model.to(snake_case_  )
        model.eval()

        A_  :       List[Any]										=  model(snake_case_					,      attention_mask=snake_case_					,      labels=snake_case_  )
        self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.num_labels)  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      ):

        """simple docstring"""

        A_  :       List[str]										=  self.num_choices
        A_  :       Any										=  XLMForMultipleChoice(config=snake_case_  )
        model.to(snake_case_  )
        model.eval()
        A_  :       Tuple										=  input_ids.unsqueeze(1  ).expand(-1					,      self.num_choices					,      -1  ).contiguous()
        A_  :       str										=  token_type_ids.unsqueeze(1  ).expand(-1					,      self.num_choices					,      -1  ).contiguous()
        A_  :       str										=  input_mask.unsqueeze(1  ).expand(-1					,      self.num_choices					,      -1  ).contiguous()
        A_  :       Union[str, Any]										=  model(
            snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_					,      labels=snake_case_					,      )
        self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_choices)  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       List[str]										=  self.prepare_config_and_inputs()
        (
            (
            A_
        )						,       (
            A_
        )						,       (
            A_
        )						,       (
            A_
        )						,       (
            A_
        )						,       (
            A_
        )						,       (
            A_
        )						,       (
            A_
        )						,       (
            A_
        )						,       
        )  :       Tuple										=  config_and_inputs
        A_  :       Dict										=  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
        return config, inputs_dict


@require_torch
class  _UpperCAmelCase  (   UpperCAmelCase__			,   UpperCAmelCase__			,   UpperCAmelCase__			,   unittest.TestCase		):



      '''simple docstring'''
      lowercase_      : Optional[Any]	    =   (
          (
              XLMModel,
              XLMWithLMHeadModel,
              XLMForQuestionAnswering,
              XLMForSequenceClassification,
              XLMForQuestionAnsweringSimple,
              XLMForTokenClassification,
              XLMForMultipleChoice,
          )
          if is_torch_available()
          else ()
      )
      lowercase_      : List[str]	    =   (
          (XLMWithLMHeadModel,) if is_torch_available() else ()
      )  # TODO (PVP): Check other models whether language generation is also applicable
      lowercase_      : int	    =   (
          {
              """feature-extraction""": XLMModel,
              """fill-mask""": XLMWithLMHeadModel,
              """question-answering""": XLMForQuestionAnsweringSimple,
              """text-classification""": XLMForSequenceClassification,
              """text-generation""": XLMWithLMHeadModel,
              """token-classification""": XLMForTokenClassification,
              """zero-shot""": XLMForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        if (
            pipeline_test_casse_name == "QAPipelineTests"
            and tokenizer_name is not None
            and not tokenizer_name.endswith('Fast'  )
        ):
          # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
          # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
          # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
          return True

        return False

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_=False  ):

        """simple docstring"""

        A_  :       Tuple										=  super()._prepare_for_class(snake_case_					,      snake_case_					,      return_labels=snake_case_  )

        if return_labels:
          if model_class.__name__ == "XLMForQuestionAnswering":
            A_  :       Any										=  torch.zeros(
                self.model_tester.batch_size					,      dtype=torch.long					,      device=snake_case_  )
            A_  :       Union[str, Any]										=  torch.zeros(
                self.model_tester.batch_size					,      dtype=torch.long					,      device=snake_case_  )

        return inputs_dict

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       List[Any]										=  XLMModelTester(self  )
        A_  :       Dict										=  ConfigTester(self					,      config_class=snake_case_					,      emb_dim=3_7  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        self.config_tester.run_common_tests()

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Optional[int]										=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_model(*snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_lm_head(*snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Tuple										=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_simple_qa(*snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Optional[Any]										=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_qa(*snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Any										=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_sequence_classif(*snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Dict										=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_token_classif(*snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       List[str]										=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_for_multiple_choice(*snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_=False					,      snake_case_=1  ):

        """simple docstring"""

        self.assertIsInstance(snake_case_					,      snake_case_  )
        self.assertListEqual(
            [isinstance(snake_case_					,      snake_case_  ) for iter_attentions in attentions]					,      [True] * len(snake_case_  )  )
        self.assertEqual(len(snake_case_  )					,      (max_length - min_length) * num_beam_groups  )

        for idx, iter_attentions in enumerate(snake_case_  ):
          # adds PAD dummy token
          A_  :       Optional[int]										=  min_length + idx + 1
          A_  :       List[str]										=  min_length + idx + 1

          A_  :       Any										=  (
              batch_size * num_beam_groups,
              config.num_attention_heads,
              tgt_len,
              src_len,
          )
          # check attn size
          self.assertListEqual(
              [layer_attention.shape for layer_attention in iter_attentions]					,      [expected_shape] * len(snake_case_  )  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_=False					,      snake_case_=1  ):

        """simple docstring"""

        self.assertIsInstance(snake_case_					,      snake_case_  )
        self.assertListEqual(
            [isinstance(snake_case_					,      snake_case_  ) for iter_hidden_states in hidden_states]					,      [True] * len(snake_case_  )					,      )
        self.assertEqual(len(snake_case_  )					,      (max_length - min_length) * num_beam_groups  )

        for idx, iter_hidden_states in enumerate(snake_case_  ):
          # adds PAD dummy token
          A_  :       List[str]										=  min_length + idx + 1
          A_  :       List[str]										=  (batch_size * num_beam_groups, seq_len, config.hidden_size)
          # check hidden size
          self.assertListEqual(
              [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]					,      [expected_shape] * len(snake_case_  )					,      )
        pass

      @slow
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          A_  :       Union[str, Any]										=  XLMModel.from_pretrained(snake_case_  )
          self.assertIsNotNone(snake_case_  )


@require_torch
class  _UpperCAmelCase  (   unittest.TestCase		):



      '''simple docstring'''

      @slow
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       int										=  XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'  )
        model.to(snake_case_  )
        A_  :       Any										=  torch.tensor([[1_4, 4_4_7]]					,      dtype=torch.long					,      device=snake_case_  )  # the president
        A_  :       Dict										=  [
            1_4,
            4_4_7,
            1_4,
            4_4_7,
            1_4,
            4_4_7,
            1_4,
            4_4_7,
            1_4,
            4_4_7,
            1_4,
            4_4_7,
            1_4,
            4_4_7,
            1_4,
            4_4_7,
            1_4,
            4_4_7,
            1_4,
            4_4_7,
        ]  # the president the president the president the president the president the president the president the president the president the president
        # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
        A_  :       int										=  model.generate(snake_case_					,      do_sample=snake_case_  )
        self.assertListEqual(output_ids[0].cpu().numpy().tolist()					,      snake_case_  )






"""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}")