






import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
		import faiss



@require_faiss
class 				__UpperCAmelCase		(_UpperCAmelCase   ):







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  tempfile.mkdtemp()
									_SCREAMING_SNAKE_CASE     				=  8

									# DPR tok
									_SCREAMING_SNAKE_CASE     				=  [
									    """[UNK]""",
									    """[CLS]""",
									    """[SEP]""",
									    """[PAD]""",
									    """[MASK]""",
									    """want""",
									    """##want""",
									    """##ed""",
									    """wa""",
									    """un""",
									    """runn""",
									    """##ing""",
									    """,""",
									    """low""",
									    """lowest""",
									]
									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     """dpr_tokenizer"""					)
									os.makedirs(UpperCAmelCase_				,     exist_ok=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  os.path.join(UpperCAmelCase_				,     DPR_VOCAB_FILES_NAMES["""vocab_file"""]					)
									with open(self.vocab_file				,     """w"""				,     encoding="""utf-8"""					) as vocab_writer:
															vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]					)					)

									# BART tok
									_SCREAMING_SNAKE_CASE     				=  [
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """\u0120""",
									    """\u0120l""",
									    """\u0120n""",
									    """\u0120lo""",
									    """\u0120low""",
									    """er""",
									    """\u0120lowest""",
									    """\u0120newer""",
									    """\u0120wider""",
									    """<unk>""",
									]
									_SCREAMING_SNAKE_CASE     				=  dict(zip(UpperCAmelCase_				,     range(len(UpperCAmelCase_					)					)					)					)
									_SCREAMING_SNAKE_CASE     				=  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
									_SCREAMING_SNAKE_CASE     				=  {"""unk_token""": """<unk>"""}

									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     """bart_tokenizer"""					)
									os.makedirs(UpperCAmelCase_				,     exist_ok=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  os.path.join(UpperCAmelCase_				,     BART_VOCAB_FILES_NAMES["""vocab_file"""]					)
									_SCREAMING_SNAKE_CASE     				=  os.path.join(UpperCAmelCase_				,     BART_VOCAB_FILES_NAMES["""merges_file"""]					)
									with open(self.vocab_file				,     """w"""				,     encoding="""utf-8"""					) as fp:
															fp.write(json.dumps(UpperCAmelCase_					) + """\n"""					)
									with open(self.merges_file				,     """w"""				,     encoding="""utf-8"""					) as fp:
															fp.write("""\n""".join(UpperCAmelCase_					)					)







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname				,     """dpr_tokenizer"""					)					)







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname				,     """dpr_tokenizer"""					)					)







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname				,     """bart_tokenizer"""					)					)







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									shutil.rmtree(self.tmpdirname					)







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  Dataset.from_dict(
									    {
									        """id""": ["""0""", """1"""],
									        """text""": ["""foo""", """bar"""],
									        """title""": ["""Foo""", """Bar"""],
									        """embeddings""": [np.ones(self.retrieval_vector_size					), 2 * np.ones(self.retrieval_vector_size					)],
									    }					)
									dataset.add_faiss_index("""embeddings"""				,     string_factory="""Flat"""				,     metric_type=faiss.METRIC_INNER_PRODUCT					)
									return dataset







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_dataset()
									_SCREAMING_SNAKE_CASE     				=  RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size				,     question_encoder=DPRConfig().to_dict()				,     generator=BartConfig().to_dict()				,     )
									with patch("""transformers.models.rag.retrieval_rag.load_dataset"""					) as mock_load_dataset:
															_SCREAMING_SNAKE_CASE     				=  dataset
															_SCREAMING_SNAKE_CASE     				=  RagRetriever(
															    UpperCAmelCase_				,     question_encoder_tokenizer=self.get_dpr_tokenizer()				,     generator_tokenizer=self.get_bart_tokenizer()				,     )
									return retriever







			def 							UpperCamelCase				(     self:  int				,     UpperCAmelCase_:  bool					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_dataset()
									_SCREAMING_SNAKE_CASE     				=  RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size				,     question_encoder=DPRConfig().to_dict()				,     generator=BartConfig().to_dict()				,     index_name="""custom"""				,     )
									if from_disk:
															_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     """dataset"""					)
															_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     """index.faiss"""					)
															dataset.get_index("""embeddings"""					).save(os.path.join(self.tmpdirname				,     """index.faiss"""					)					)
															dataset.drop_index("""embeddings"""					)
															dataset.save_to_disk(os.path.join(self.tmpdirname				,     """dataset"""					)					)
															del dataset
															_SCREAMING_SNAKE_CASE     				=  RagRetriever(
															    UpperCAmelCase_				,     question_encoder_tokenizer=self.get_dpr_tokenizer()				,     generator_tokenizer=self.get_bart_tokenizer()				,     )
									else:
															_SCREAMING_SNAKE_CASE     				=  RagRetriever(
															    UpperCAmelCase_				,     question_encoder_tokenizer=self.get_dpr_tokenizer()				,     generator_tokenizer=self.get_bart_tokenizer()				,     index=CustomHFIndex(config.retrieval_vector_size				,     UpperCAmelCase_					)				,     )
									return retriever







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  Dataset.from_dict(
									    {
									        """id""": ["""0""", """1"""],
									        """text""": ["""foo""", """bar"""],
									        """title""": ["""Foo""", """Bar"""],
									        """embeddings""": [np.ones(self.retrieval_vector_size + 1					), 2 * np.ones(self.retrieval_vector_size + 1					)],
									    }					)
									dataset.add_faiss_index("""embeddings"""				,     string_factory="""Flat"""				,     metric_type=faiss.METRIC_INNER_PRODUCT					)

									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     """hf_bert_base.hnswSQ8_correct_phi_128.c_index"""					)
									dataset.save_faiss_index("""embeddings"""				,     index_file_name + """.index.dpr"""					)
									pickle.dump(dataset["""id"""]				,     open(index_file_name + """.index_meta.dpr"""				,     """wb"""					)					)

									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     """psgs_w100.tsv.pkl"""					)
									_SCREAMING_SNAKE_CASE     				=  {sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
									pickle.dump(UpperCAmelCase_				,     open(UpperCAmelCase_				,     """wb"""					)					)

									_SCREAMING_SNAKE_CASE     				=  RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size				,     question_encoder=DPRConfig().to_dict()				,     generator=BartConfig().to_dict()				,     index_name="""legacy"""				,     index_path=self.tmpdirname				,     )
									_SCREAMING_SNAKE_CASE     				=  RagRetriever(
									    UpperCAmelCase_				,     question_encoder_tokenizer=self.get_dpr_tokenizer()				,     generator_tokenizer=self.get_bart_tokenizer()					)
									return retriever







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  1
									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_canonical_hf_index_retriever()
									_SCREAMING_SNAKE_CASE     				=  np.array(
									    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,     dtype=np.floataa					)
									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  retriever.retrieve(UpperCAmelCase_				,     n_docs=UpperCAmelCase_					)
									self.assertEqual(retrieved_doc_embeds.shape				,     (2, n_docs, self.retrieval_vector_size)					)
									self.assertEqual(len(UpperCAmelCase_					)				,     2					)
									self.assertEqual(sorted(doc_dicts[0]					)				,     ["""embeddings""", """id""", """text""", """title"""]					)
									self.assertEqual(len(doc_dicts[0]["""id"""]					)				,     UpperCAmelCase_					)
									self.assertEqual(doc_dicts[0]["""id"""][0]				,     """1"""					)  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]["""id"""][0]				,     """0"""					)  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist()				,     [[1], [0]]					)







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_canonical_hf_index_retriever()
									with tempfile.TemporaryDirectory() as tmp_dirname:
															with patch("""transformers.models.rag.retrieval_rag.load_dataset"""					) as mock_load_dataset:
																					_SCREAMING_SNAKE_CASE     				=  self.get_dummy_dataset()
																					retriever.save_pretrained(UpperCAmelCase_					)
																					_SCREAMING_SNAKE_CASE     				=  RagRetriever.from_pretrained(UpperCAmelCase_					)
																					self.assertIsInstance(UpperCAmelCase_				,     UpperCAmelCase_					)
																					_SCREAMING_SNAKE_CASE     				=  np.array(
																					    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,     dtype=np.floataa					)
																					_SCREAMING_SNAKE_CASE     				=  retriever.retrieve(UpperCAmelCase_				,     n_docs=1					)
																					self.assertTrue(out is not None					)







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  1
									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  np.array(
									    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,     dtype=np.floataa					)
									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  retriever.retrieve(UpperCAmelCase_				,     n_docs=UpperCAmelCase_					)
									self.assertEqual(retrieved_doc_embeds.shape				,     (2, n_docs, self.retrieval_vector_size)					)
									self.assertEqual(len(UpperCAmelCase_					)				,     2					)
									self.assertEqual(sorted(doc_dicts[0]					)				,     ["""embeddings""", """id""", """text""", """title"""]					)
									self.assertEqual(len(doc_dicts[0]["""id"""]					)				,     UpperCAmelCase_					)
									self.assertEqual(doc_dicts[0]["""id"""][0]				,     """1"""					)  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]["""id"""][0]				,     """0"""					)  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist()				,     [[1], [0]]					)







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase_					)
									with tempfile.TemporaryDirectory() as tmp_dirname:
															retriever.save_pretrained(UpperCAmelCase_					)
															_SCREAMING_SNAKE_CASE     				=  RagRetriever.from_pretrained(UpperCAmelCase_					)
															self.assertIsInstance(UpperCAmelCase_				,     UpperCAmelCase_					)
															_SCREAMING_SNAKE_CASE     				=  np.array(
															    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,     dtype=np.floataa					)
															_SCREAMING_SNAKE_CASE     				=  retriever.retrieve(UpperCAmelCase_				,     n_docs=1					)
															self.assertTrue(out is not None					)







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  1
									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  np.array(
									    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,     dtype=np.floataa					)
									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  retriever.retrieve(UpperCAmelCase_				,     n_docs=UpperCAmelCase_					)
									self.assertEqual(retrieved_doc_embeds.shape				,     (2, n_docs, self.retrieval_vector_size)					)
									self.assertEqual(len(UpperCAmelCase_					)				,     2					)
									self.assertEqual(sorted(doc_dicts[0]					)				,     ["""embeddings""", """id""", """text""", """title"""]					)
									self.assertEqual(len(doc_dicts[0]["""id"""]					)				,     UpperCAmelCase_					)
									self.assertEqual(doc_dicts[0]["""id"""][0]				,     """1"""					)  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]["""id"""][0]				,     """0"""					)  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist()				,     [[1], [0]]					)







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase_					)
									with tempfile.TemporaryDirectory() as tmp_dirname:
															retriever.save_pretrained(UpperCAmelCase_					)
															_SCREAMING_SNAKE_CASE     				=  RagRetriever.from_pretrained(UpperCAmelCase_					)
															self.assertIsInstance(UpperCAmelCase_				,     UpperCAmelCase_					)
															_SCREAMING_SNAKE_CASE     				=  np.array(
															    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,     dtype=np.floataa					)
															_SCREAMING_SNAKE_CASE     				=  retriever.retrieve(UpperCAmelCase_				,     n_docs=1					)
															self.assertTrue(out is not None					)







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  1
									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_legacy_index_retriever()
									_SCREAMING_SNAKE_CASE     				=  np.array(
									    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,     dtype=np.floataa					)
									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  retriever.retrieve(UpperCAmelCase_				,     n_docs=UpperCAmelCase_					)
									self.assertEqual(retrieved_doc_embeds.shape				,     (2, n_docs, self.retrieval_vector_size)					)
									self.assertEqual(len(UpperCAmelCase_					)				,     2					)
									self.assertEqual(sorted(doc_dicts[0]					)				,     ["""text""", """title"""]					)
									self.assertEqual(len(doc_dicts[0]["""text"""]					)				,     UpperCAmelCase_					)
									self.assertEqual(doc_dicts[0]["""text"""][0]				,     """bar"""					)  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]["""text"""][0]				,     """foo"""					)  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist()				,     [[1], [0]]					)







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_legacy_index_retriever()
									with tempfile.TemporaryDirectory() as tmp_dirname:
															retriever.save_pretrained(UpperCAmelCase_					)
															_SCREAMING_SNAKE_CASE     				=  RagRetriever.from_pretrained(UpperCAmelCase_					)
															self.assertIsInstance(UpperCAmelCase_				,     UpperCAmelCase_					)
															_SCREAMING_SNAKE_CASE     				=  np.array(
															    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,     dtype=np.floataa					)
															_SCREAMING_SNAKE_CASE     				=  retriever.retrieve(UpperCAmelCase_				,     n_docs=1					)
															self.assertTrue(out is not None					)







			@require_torch
			@require_tokenizers
			@require_sentencepiece
			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									import torch

									_SCREAMING_SNAKE_CASE     				=  1
									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_canonical_hf_index_retriever()
									_SCREAMING_SNAKE_CASE     				=  [[5, 7], [10, 11]]
									_SCREAMING_SNAKE_CASE     				=  np.array(
									    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,     dtype=np.floataa					)
									_SCREAMING_SNAKE_CASE     				=  retriever(UpperCAmelCase_				,     UpperCAmelCase_				,     prefix=retriever.config.generator.prefix				,     n_docs=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  (
									    out["""context_input_ids"""],
									    out["""context_attention_mask"""],
									    out["""retrieved_doc_embeds"""],
									)
									self.assertEqual(retrieved_doc_embeds.shape				,     (2, n_docs, self.retrieval_vector_size)					)
									self.assertIsInstance(UpperCAmelCase_				,     UpperCAmelCase_					)
									self.assertIsInstance(UpperCAmelCase_				,     UpperCAmelCase_					)
									self.assertIsInstance(UpperCAmelCase_				,     np.ndarray					)

									_SCREAMING_SNAKE_CASE     				=  retriever(
									    UpperCAmelCase_				,     UpperCAmelCase_				,     prefix=retriever.config.generator.prefix				,     n_docs=UpperCAmelCase_				,     return_tensors="""pt"""				,     )
									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  (  # noqa: F841
									    out["""context_input_ids"""],
									    out["""context_attention_mask"""],
									    out["""retrieved_doc_embeds"""],
									    out["""doc_ids"""],
									)
									self.assertEqual(retrieved_doc_embeds.shape				,     (2, n_docs, self.retrieval_vector_size)					)
									self.assertIsInstance(UpperCAmelCase_				,     torch.Tensor					)
									self.assertIsInstance(UpperCAmelCase_				,     torch.Tensor					)
									self.assertIsInstance(UpperCAmelCase_				,     torch.Tensor					)







			@require_torch
			@require_tokenizers
			@require_sentencepiece
			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.get_dpr_ctx_encoder_tokenizer()
									_SCREAMING_SNAKE_CASE     				=  1
									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase_					)
									retriever.set_ctx_encoder_tokenizer(UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  [[5, 7], [10, 11]]
									_SCREAMING_SNAKE_CASE     				=  np.array(
									    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,     dtype=np.floataa					)
									_SCREAMING_SNAKE_CASE     				=  retriever(UpperCAmelCase_				,     UpperCAmelCase_				,     prefix=retriever.config.generator.prefix				,     n_docs=UpperCAmelCase_					)

									self.assertEqual(
									    len(UpperCAmelCase_					)				,     6					)  # check whether the retriever output consist of 6 attributes including tokenized docs
									self.assertEqual(
									    all(k in out for k in ("""tokenized_doc_ids""", """tokenized_doc_attention_mask""")					)				,     UpperCAmelCase_					)  # check for doc token related keys in dictionary.









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      (		snake_case__		)							->  List[str]:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__		)							->  Optional[Any]:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__		)							->  int:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  Tuple:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__	,snake_case__=5		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  ["""auto-generated""", """autogenerated""", """automatically generated"""]
						_SCREAMING_SNAKE_CASE     				=  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 	__lowerCamelCase      (		snake_case__	,snake_case__=5	,snake_case__=0.05		)							->  Tuple:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  ["""unit tests""", """test file""", """configuration file"""]
						_SCREAMING_SNAKE_CASE     				=  example["""content"""].splitlines()
						_SCREAMING_SNAKE_CASE     				=  0
						_SCREAMING_SNAKE_CASE     				=  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
						_SCREAMING_SNAKE_CASE     				=  example["""content"""].count("""\n"""		)
						_SCREAMING_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      (		snake_case__		)							->  str:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  ["""def """, """class """, """for """, """while """]
						_SCREAMING_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      (		snake_case__	,snake_case__=4		)							->  Union[str, Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  example["""content"""].splitlines()
						_SCREAMING_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      (		snake_case__		)							->  Optional[Any]:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  {}
						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 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  int:






						"""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 	__lowerCamelCase      (		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
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}")

