

import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
    import torch

    from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
    from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder







def 						lowerCamelCase__      ( __A    :Optional[Any]	,__A    :Any	,__A    :Union[str, Any]	,__A    :List[Any]=None	,__A    :Optional[int]=None	,__A    :List[str]=None	,__A    :int=None	,__A    :Dict=None	,):


       """simple docstring"""




       if attention_mask is None:
              __snake_case       	=							input_ids.ne(config.pad_token_id		)
       if decoder_attention_mask is None:
              __snake_case       	=							decoder_input_ids.ne(config.pad_token_id		)
       if head_mask is None:
              __snake_case       	=							torch.ones(config.encoder_layers	,config.encoder_attention_heads	,device=__A		)
       if decoder_head_mask is None:
              __snake_case       	=							torch.ones(config.decoder_layers	,config.decoder_attention_heads	,device=__A		)
       if cross_attn_head_mask is None:
              __snake_case       	=							torch.ones(config.decoder_layers	,config.decoder_attention_heads	,device=__A		)
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }

class     __snake_case    :
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase=13      , _UpperCamelCase=7      , _UpperCamelCase=True      , _UpperCamelCase=False      , _UpperCamelCase=99      , _UpperCamelCase=16      , _UpperCamelCase=2      , _UpperCamelCase=4      , _UpperCamelCase=4      , _UpperCamelCase="relu"      , _UpperCamelCase=0.1      , _UpperCamelCase=0.1      , _UpperCamelCase=0.0      , _UpperCamelCase=0.0      , _UpperCamelCase=20      , _UpperCamelCase=2      , _UpperCamelCase=1      , _UpperCamelCase=0      , )      ->							List[str]:






              """simple docstring"""


              __snake_case       	=							parent
              __snake_case       	=							batch_size
              __snake_case       	=							seq_length
              __snake_case       	=							is_training
              __snake_case       	=							use_labels
              __snake_case       	=							vocab_size
              __snake_case       	=							hidden_size
              __snake_case       	=							num_hidden_layers
              __snake_case       	=							num_attention_heads
              __snake_case       	=							intermediate_size
              __snake_case       	=							hidden_act
              __snake_case       	=							hidden_dropout_prob
              __snake_case       	=							attention_probs_dropout_prob
              __snake_case       	=							encoder_layerdrop
              __snake_case       	=							decoder_layerdrop
              __snake_case       	=							max_position_embeddings
              __snake_case       	=							eos_token_id
              __snake_case       	=							pad_token_id
              __snake_case       	=							bos_token_id


       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


              __snake_case       	=							ids_tensor([self.batch_size, self.seq_length]      , self.vocab_size   )
              __snake_case       	=							self.eos_token_id  # Eos Token
              __snake_case       	=							ids_tensor([self.batch_size, self.seq_length]      , self.vocab_size   )

              # we need to clamp the input ids here to avoid having pad token in between
              # this is because for M2M100 the position_ids are prepared such that
              # all pad tokens have pos id = 2 and rest are between 2..seq_length
              # and the seq_length here is seq_length - num_pad_tokens
              # but when using past, there is no way of knowing if the past input ids had
              # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
              # position_ids being off by num_pad_tokens in past input
              __snake_case       	=							input_ids.clamp(self.pad_token_id + 1   )
              __snake_case       	=							decoder_input_ids.clamp(self.pad_token_id + 1   )

              __snake_case       	=							self.get_config()
              __snake_case       	=							prepare_mam_aaa_inputs_dict(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )
              return config, inputs_dict


       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              return MaMaaaConfig(
                  vocab_size=self.vocab_size      , d_model=self.hidden_size      , encoder_layers=self.num_hidden_layers      , decoder_layers=self.num_hidden_layers      , encoder_attention_heads=self.num_attention_heads      , decoder_attention_heads=self.num_attention_heads      , encoder_ffn_dim=self.intermediate_size      , decoder_ffn_dim=self.intermediate_size      , dropout=self.hidden_dropout_prob      , attention_dropout=self.attention_probs_dropout_prob      , encoder_layerdrop=self.encoder_layerdrop      , decoder_layerdrop=self.decoder_layerdrop      , max_position_embeddings=self.max_position_embeddings      , eos_token_id=self.eos_token_id      , bos_token_id=self.bos_token_id      , pad_token_id=self.pad_token_id      , )


       def 			a		(		self   )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case	,    __snake_case       	=							self.prepare_config_and_inputs()
              return config, inputs_dict


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							MaMaaaModel(config=_UpperCamelCase   ).get_decoder().to(_UpperCamelCase   ).eval()
              __snake_case       	=							inputs_dict["""input_ids"""]
              __snake_case       	=							inputs_dict["""attention_mask"""]
              __snake_case       	=							inputs_dict["""head_mask"""]

              # first forward pass
              __snake_case       	=							model(_UpperCamelCase      , attention_mask=_UpperCamelCase      , head_mask=_UpperCamelCase      , use_cache=_UpperCamelCase   )

              __snake_case	,    __snake_case       	=							outputs.to_tuple()

              # create hypothetical multiple next token and extent to next_input_ids
              __snake_case       	=							ids_tensor((self.batch_size, 3)      , config.vocab_size   )
              __snake_case       	=							ids_tensor((self.batch_size, 3)      , 2   )

              # append to next input_ids and
              __snake_case       	=							torch.cat([input_ids, next_tokens]      , dim=-1   )
              __snake_case       	=							torch.cat([attention_mask, next_attn_mask]      , dim=-1   )

              __snake_case       	=							model(_UpperCamelCase      , attention_mask=_UpperCamelCase   )["""last_hidden_state"""]
              __snake_case       	=							model(_UpperCamelCase      , attention_mask=_UpperCamelCase      , past_key_values=_UpperCamelCase   )[
                  """last_hidden_state"""
              ]

              # select random slice
              __snake_case       	=							ids_tensor((1,)      , output_from_past.shape[-1]   ).item()
              __snake_case       	=							output_from_no_past[:, -3:, random_slice_idx].detach()
              __snake_case       	=							output_from_past[:, :, random_slice_idx].detach()

              self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]   )

              # test that outputs are equal for slice
              self.parent.assertTrue(torch.allclose(_UpperCamelCase      , _UpperCamelCase      , atol=1E-2   )   )






       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							MaMaaaModel(config=_UpperCamelCase   ).to(_UpperCamelCase   ).eval()
              __snake_case       	=							model(**_UpperCamelCase   )

              __snake_case       	=							outputs.encoder_last_hidden_state
              __snake_case       	=							outputs.last_hidden_state

              with tempfile.TemporaryDirectory() as tmpdirname:
                     __snake_case       	=							model.get_encoder()
                     encoder.save_pretrained(_UpperCamelCase   )
                     __snake_case       	=							MaMaaaEncoder.from_pretrained(_UpperCamelCase   ).to(_UpperCamelCase   )

              __snake_case       	=							encoder(inputs_dict["""input_ids"""]      , attention_mask=inputs_dict["""attention_mask"""]   )[
                  0
              ]

              self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3   )

              with tempfile.TemporaryDirectory() as tmpdirname:
                     __snake_case       	=							model.get_decoder()
                     decoder.save_pretrained(_UpperCamelCase   )
                     __snake_case       	=							MaMaaaDecoder.from_pretrained(_UpperCamelCase   ).to(_UpperCamelCase   )

              __snake_case       	=							decoder(
                  input_ids=inputs_dict["""decoder_input_ids"""]      , attention_mask=inputs_dict["""decoder_attention_mask"""]      , encoder_hidden_states=_UpperCamelCase      , encoder_attention_mask=inputs_dict["""attention_mask"""]      , )[0]

              self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3   )






@require_torch
class     __snake_case    (				snake_case__			,	snake_case__			,	snake_case__			,	unittest.TestCase  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							(
           (
               MaMaaaModel,
               MaMaaaForConditionalGeneration,
           )
           if is_torch_available()
           else ()
       )
       __SCREAMING_SNAKE_CASE				    =							(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
       __SCREAMING_SNAKE_CASE				    =							(
           {
               "conversational": MaMaaaForConditionalGeneration,
               "feature-extraction": MaMaaaModel,
               "summarization": MaMaaaForConditionalGeneration,
               "text2text-generation": MaMaaaForConditionalGeneration,
               "translation": MaMaaaForConditionalGeneration,
           }
           if is_torch_available()
           else {}
       )
       __SCREAMING_SNAKE_CASE				    =							True
       __SCREAMING_SNAKE_CASE				    =							True
       __SCREAMING_SNAKE_CASE				    =							False
       __SCREAMING_SNAKE_CASE				    =							False


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							str:






              """simple docstring"""


              if pipeline_test_casse_name == "TranslationPipelineTests":
                     # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
                     # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
                     return True

              return False


       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


              __snake_case       	=							MaMaaaModelTester(self   )
              __snake_case       	=							ConfigTester(self      , config_class=_UpperCamelCase   )


       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


              self.config_tester.run_common_tests()


       def 			a		(		self   )      ->							str:






              """simple docstring"""


              __snake_case	,    __snake_case       	=							self.model_tester.prepare_config_and_inputs()
              for model_class in self.all_model_classes:
                     __snake_case       	=							model_class(_UpperCamelCase   )

                     with tempfile.TemporaryDirectory() as tmpdirname:
                            model.save_pretrained(_UpperCamelCase   )
                            __snake_case	,    __snake_case       	=							model_class.from_pretrained(_UpperCamelCase      , output_loading_info=_UpperCamelCase   )
                     self.assertEqual(info["""missing_keys"""]      , []   )


       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_decoder_model_past_large_inputs(*_UpperCamelCase   )


       def 			a		(		self   )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							self.model_tester.prepare_config_and_inputs_for_common()
              self.model_tester.check_encoder_decoder_model_standalone(*_UpperCamelCase   )


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case	,    __snake_case       	=							self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
                     __snake_case       	=							model_class(_UpperCamelCase   )
                     model.to(_UpperCamelCase   )
                     model.eval()

                     __snake_case       	=							copy.deepcopy(self._prepare_for_class(_UpperCamelCase      , _UpperCamelCase   )   )

                     if not self.is_encoder_decoder:
                            __snake_case       	=							inputs["""input_ids"""]
                            del inputs["input_ids"]
                     else:
                            __snake_case       	=							inputs["""input_ids"""]
                            __snake_case       	=							inputs.get("""decoder_input_ids"""      , _UpperCamelCase   )
                            del inputs["input_ids"]
                            inputs.pop("""decoder_input_ids"""      , _UpperCamelCase   )

                     __snake_case       	=							model.get_input_embeddings()
                     if not self.is_encoder_decoder:
                            __snake_case       	=							wte(_UpperCamelCase   )
                     else:
                            __snake_case       	=							wte(_UpperCamelCase   )
                            __snake_case       	=							wte(_UpperCamelCase   )

                     with torch.no_grad():
                            model(**_UpperCamelCase   )[0]






       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


              __snake_case	,    __snake_case       	=							self.model_tester.prepare_config_and_inputs()
              __snake_case       	=							input_dict["""input_ids"""]
              __snake_case       	=							input_ids.ne(1   ).to(_UpperCamelCase   )
              __snake_case       	=							MaMaaaForConditionalGeneration(_UpperCamelCase   ).eval().to(_UpperCamelCase   )
              if torch_device == "cuda":
                     model.half()
              model.generate(_UpperCamelCase      , attention_mask=_UpperCamelCase   )
              model.generate(num_beams=4      , do_sample=_UpperCamelCase      , early_stopping=_UpperCamelCase      , num_return_sequences=3   )







def 						lowerCamelCase__      ( __A    :Any		):


       """simple docstring"""




       return torch.tensor(__A	,dtype=torch.long	,device=__A		)


UpperCamelCase__		 =     1E-4




@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class     __snake_case    (				unittest.TestCase  ):
       """simple docstring"""







       @cached_property
       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              return MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""   )


       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							MaMaaaModel.from_pretrained("""facebook/m2m100_418M"""   ).to(_UpperCamelCase   )
              __snake_case       	=							_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]   )
              __snake_case       	=							_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]   )
              __snake_case       	=							prepare_mam_aaa_inputs_dict(model.config      , _UpperCamelCase      , _UpperCamelCase   )
              with torch.no_grad():
                     __snake_case       	=							model(**_UpperCamelCase   )[0]
              __snake_case       	=							torch.Size((1, 11, 10_24)   )
              self.assertEqual(output.shape      , _UpperCamelCase   )
              # change to expected output here
              __snake_case       	=							torch.tensor(
                  [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]      , device=_UpperCamelCase   )
              self.assertTrue(torch.allclose(output[:, :3, :3]      , _UpperCamelCase      , atol=_UpperCamelCase   )   )


       def 			a		(		self   )      ->							str:






              """simple docstring"""


              __snake_case       	=							MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""   ).to(_UpperCamelCase   )

              # change to intended input
              __snake_case       	=							_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]   )
              __snake_case       	=							_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]   )
              __snake_case       	=							prepare_mam_aaa_inputs_dict(model.config      , _UpperCamelCase      , _UpperCamelCase   )
              with torch.no_grad():
                     __snake_case       	=							model(**_UpperCamelCase   )[0]
              __snake_case       	=							torch.Size((1, 11, model.config.vocab_size)   )
              self.assertEqual(output.shape      , _UpperCamelCase   )
              # change to expected output here
              __snake_case       	=							torch.tensor(
                  [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]      , device=_UpperCamelCase   )
              self.assertTrue(torch.allclose(output[:, :3, :3]      , _UpperCamelCase      , atol=_UpperCamelCase   )   )






       def 			a		(		self   )      ->							str:






              """simple docstring"""


              __snake_case       	=							MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""   ).to(_UpperCamelCase   )
              __snake_case       	=							MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""      , src_lang="""fr"""      , tgt_lang="""en"""   )

              __snake_case       	=							[
                  """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
                  """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
                  """Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"""
                  """ Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"""
                  """ l'ampleur de la surveillance américaine sur l'ensemble des communications en France.""",
              ]

              # The below article tests that we don't add any hypotheses outside of the top n_beams
              __snake_case       	=							tokenizer(_UpperCamelCase      , padding=_UpperCamelCase      , return_tensors="""pt"""   )

              __snake_case       	=							model.generate(
                  input_ids=dct["""input_ids"""].to(_UpperCamelCase   )      , attention_mask=dct["""attention_mask"""].to(_UpperCamelCase   )      , num_beams=5      , forced_bos_token_id=tokenizer.get_lang_id("""en"""   )      , )

              __snake_case       	=							[
                  """The NSA case highlights the total absence of intelligence debate""",
                  """I think there are two levels of response from the French government.""",
                  """When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."""
                  """ Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"""
                  """ communications in France.""",
              ]

              __snake_case       	=							tokenizer.batch_decode(
                  hypotheses_batch.tolist()      , clean_up_tokenization_spaces=_UpperCamelCase      , skip_special_tokens=_UpperCamelCase   )
              assert generated == expected_en




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


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







def 						lowerCamelCase__      ( __A    :int		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :Any		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :Any		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :str	,__A    :int		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :Optional[int]	,__A    :int=5		):


       """simple docstring"""




       __snake_case       	=							["""auto-generated""", """autogenerated""", """automatically generated"""]
       __snake_case       	=							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 						lowerCamelCase__      ( __A    :int	,__A    :int=5	,__A    :str=0.05		):


       """simple docstring"""




       __snake_case       	=							["""unit tests""", """test file""", """configuration file"""]
       __snake_case       	=							example["""content"""].splitlines()
       __snake_case       	=							0
       __snake_case       	=							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
       __snake_case       	=							example["""content"""].count("""\n"""		)
       __snake_case       	=							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 						lowerCamelCase__      ( __A    :Dict		):


       """simple docstring"""




       __snake_case       	=							["""def """, """class """, """for """, """while """]
       __snake_case       	=							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 						lowerCamelCase__      ( __A    :Dict	,__A    :List[str]=4		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :str		):


       """simple docstring"""




       __snake_case       	=							tokenizer(example["""content"""]	,truncation=__A		)["""input_ids"""]
       __snake_case       	=							len(example["""content"""]		) / len(__A		)
       return {"ratio": ratio}







def 						lowerCamelCase__      ( __A    :Optional[Any]		):


       """simple docstring"""




       __snake_case       	=							{}
       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 						lowerCamelCase__      ( __A    :int	,__A    :str	,__A    :Dict		):


       """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 						lowerCamelCase__      ( __A    :str		):


       """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
UpperCamelCase__		 =     HfArgumentParser(PreprocessingArguments)
UpperCamelCase__		 =     parser.parse_args()
if args.num_workers is None:
    UpperCamelCase__		 =     multiprocessing.cpu_count()
UpperCamelCase__		 =     AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

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

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

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

