'''simple docstring'''





import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


lowerCAmelCase_	:		Tuple			=     [
    {"""dataset""": """wikipedia""", """config_name""": """20220301.de"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.en"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.fr"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.frr"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.it"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.simple"""},
    {"""dataset""": """snli""", """config_name""": """plain_text"""},
    {"""dataset""": """eli5""", """config_name""": """LFQA_reddit"""},
    {"""dataset""": """wiki40b""", """config_name""": """en"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.compressed"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.no_index"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.multiset.no_index"""},
    {"""dataset""": """natural_questions""", """config_name""": """default"""},
]
def 						__A							(     UpperCAmelCase=True					)     ->			Tuple:




						'''simple docstring'''
						if with_config:
												return [
												    {
												        "testcase_name": d["dataset"] + "/" + d["config_name"],
												        "dataset": d["dataset"],
												        "config_name": d["config_name"],
												    }
												    for d in DATASETS_ON_HF_GCP
												]
						else:
												return [
												    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
												]




@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=SCREAMING_SNAKE_CASE	)	)
class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							None
				UpperCAmelCase__							=							None





				def  snake_case__				(						self					:						Union[str, Any]       ,		lowercase__					:						Optional[int]       ,		lowercase__					:						Union[str, Any]					)							->int:


										'''simple docstring'''




										with TemporaryDirectory() as tmp_dir:
																_UpperCamelCase			:			List[str]   			=						dataset_module_factory(_UpperCAmelCase       ,		cache_dir=_UpperCAmelCase					)

																_UpperCamelCase			:			Dict   			=						import_main_class(dataset_module.module_path       ,		dataset=_UpperCAmelCase					)

																_UpperCamelCase			:			Optional[int]   			=						builder_cls(
																    cache_dir=_UpperCAmelCase       ,		config_name=_UpperCAmelCase       ,		hash=dataset_module.hash       ,		)

																_UpperCamelCase			:			List[Any]   			=						"/".join(
																    [
																        HF_GCP_BASE_URL,
																        builder_instance._relative_data_dir(with_hash=_UpperCAmelCase					).replace(os.sep       ,		"/"					),
																        config.DATASET_INFO_FILENAME,
																    ]					)
																_UpperCamelCase			:			Tuple   			=						cached_path(_UpperCAmelCase       ,		cache_dir=_UpperCAmelCase					)
																self.assertTrue(os.path.exists(_UpperCAmelCase					)					)





@pytest.mark.integration
def 						__A							(     UpperCAmelCase					)     ->			Dict:




						'''simple docstring'''
						_UpperCamelCase			:			Optional[int]   			=						tmp_path_factory.mktemp("test_hf_gcp"					) / "test_wikipedia_simple"
						_UpperCamelCase			:			str   			=						dataset_module_factory("wikipedia"     ,cache_dir=lowerCAmelCase__					)
						_UpperCamelCase			:			Dict   			=						import_main_class(dataset_module.module_path					)
						_UpperCamelCase			:			List[Any]   			=						builder_cls(
						    cache_dir=lowerCAmelCase__     ,config_name="20220301.frr"     ,hash=dataset_module.hash     ,)
						# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
						_UpperCamelCase			:			List[str]   			=						None
						builder_instance.download_and_prepare()
						_UpperCamelCase			:			Any   			=						builder_instance.as_dataset()
						assert ds






@pytest.mark.integration
def 						__A							(     UpperCAmelCase					)     ->			Any:




						'''simple docstring'''
						_UpperCamelCase			:			Optional[Any]   			=						dataset_module_factory("wikipedia"     ,cache_dir=lowerCAmelCase__					)
						_UpperCamelCase			:			Dict   			=						import_main_class(dataset_module.module_path     ,dataset=lowerCAmelCase__					)
						_UpperCamelCase			:			Optional[int]   			=						builder_cls(
						    cache_dir=lowerCAmelCase__     ,config_name="20220301.frr"     ,hash=dataset_module.hash     ,)
						_UpperCamelCase			:			Optional[Any]   			=						builder_instance.as_streaming_dataset()
						assert ds
						assert isinstance(lowerCAmelCase__     ,lowerCAmelCase__					)
						assert "train" in ds
						assert isinstance(ds["train"]     ,lowerCAmelCase__					)
						assert next(iter(ds["train"]					)					)



'''simple docstring'''





import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowerCAmelCase_	:		Optional[int]			=     logging.getLogger(__name__)

if __name__ == "__main__":
			lowerCAmelCase_	:		Optional[Any]			=     argparse.ArgumentParser(
			    description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
			)
			parser.add_argument(
			    """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
			)
			parser.add_argument(
			    """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
			)
			parser.add_argument("""--vocab_size""", default=3_0522, type=int)
			lowerCAmelCase_	:		str			=     parser.parse_args()

			logger.info(f"""Loading data from {args.data_file}""")
			with open(args.data_file, """rb""") as fp:
						lowerCAmelCase_	:		Tuple			=     pickle.load(fp)

			logger.info("""Counting occurrences for MLM.""")
			lowerCAmelCase_	:		str			=     Counter()
			for tk_ids in data:
						counter.update(tk_ids)
			lowerCAmelCase_	:		Any			=     [0] * args.vocab_size
			for k, v in counter.items():
						lowerCAmelCase_	:		Optional[Any]			=     v

			logger.info(f"""Dump to {args.token_counts_dump}""")
			with open(args.token_counts_dump, """wb""") as handle:
						pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)


