import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam


class   lowercase      (       datasets.BeamBasedBuilder       ):




							def 		__snake_case(  self				:   str )   ->			int:



												'''simple docstring'''

												return datasets.DatasetInfo(
												    features=datasets.Features({"content": datasets.Value("string" )} )		,							supervised_keys=_UpperCamelCase		,							)




							def 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   List[str] )   ->			Union[str, Any]:



												'''simple docstring'''

												return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,							gen_kwargs={"examples": get_test_dummy_examples()} )]





							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   str )   ->			Optional[int]:



												'''simple docstring'''

												import apache_beam as beam

												return pipeline | "Load Examples" >> beam.Create(_UpperCamelCase )



class   lowercase      (       datasets.BeamBasedBuilder       ):




							def 		__snake_case(  self				:   List[Any] )   ->			int:



												'''simple docstring'''

												return datasets.DatasetInfo(
												    features=datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string" )} )} )		,							supervised_keys=_UpperCamelCase		,							)




							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   int )   ->			List[str]:



												'''simple docstring'''

												return [
												    datasets.SplitGenerator(name=datasets.Split.TRAIN		,							gen_kwargs={"examples": get_test_nested_examples()} )
												]





							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   Union[str, Any] )   ->			Dict:



												'''simple docstring'''

												import apache_beam as beam

												return pipeline | "Load Examples" >> beam.Create(_UpperCamelCase )





def 		__lowerCamelCase							():
					return [(i, {"content": content}) for i, content in enumerate(["foo", "bar", "foobar"]					)]





def 		__lowerCamelCase							():
					return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["foo", "bar", "foobar"]					)]


class   lowercase      (       a       ):




							@require_beam
							def 		__snake_case(  self				:   Dict )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    len(get_test_dummy_examples() )
												with tempfile.TemporaryDirectory() as tmp_cache_dir:
																	SCREAMING_SNAKE_CASE           =    DummyBeamDataset(cache_dir=_UpperCamelCase		,							beam_runner="DirectRunner" )
																	builder.download_and_prepare()
																	self.assertTrue(
																	    os.path.exists(
																	        os.path.join(_UpperCamelCase		,							builder.name		,							"default"		,							"0.0.0"		,							F"{builder.name}-train.arrow" ) ) )
																	self.assertDictEqual(builder.info.features		,							datasets.Features({"content": datasets.Value("string" )} ) )
																	SCREAMING_SNAKE_CASE           =    builder.as_dataset()
																	self.assertEqual(dset["train"].num_rows		,							_UpperCamelCase )
																	self.assertEqual(dset["train"].info.splits["train"].num_examples		,							_UpperCamelCase )
																	self.assertDictEqual(dset["train"][0]		,							get_test_dummy_examples()[0][1] )
																	self.assertDictEqual(
																	    dset["train"][expected_num_examples - 1]		,							get_test_dummy_examples()[expected_num_examples - 1][1] )
																	self.assertTrue(
																	    os.path.exists(os.path.join(_UpperCamelCase		,							builder.name		,							"default"		,							"0.0.0"		,							"dataset_info.json" ) ) )
																	del dset




							@require_beam
							def 		__snake_case(  self				:   Union[str, Any] )   ->			Any:



												'''simple docstring'''

												import apache_beam as beam

												SCREAMING_SNAKE_CASE           =    beam.io.parquetio.WriteToParquet

												SCREAMING_SNAKE_CASE           =    len(get_test_dummy_examples() )
												with tempfile.TemporaryDirectory() as tmp_cache_dir:
																	SCREAMING_SNAKE_CASE           =    DummyBeamDataset(cache_dir=_UpperCamelCase		,							beam_runner="DirectRunner" )
																	with patch("apache_beam.io.parquetio.WriteToParquet" ) as write_parquet_mock:
																						SCREAMING_SNAKE_CASE           =    partial(_UpperCamelCase		,							num_shards=2 )
																						builder.download_and_prepare()
																	self.assertTrue(
																	    os.path.exists(
																	        os.path.join(
																	            _UpperCamelCase		,							builder.name		,							"default"		,							"0.0.0"		,							F"{builder.name}-train-00000-of-00002.arrow" ) ) )
																	self.assertTrue(
																	    os.path.exists(
																	        os.path.join(
																	            _UpperCamelCase		,							builder.name		,							"default"		,							"0.0.0"		,							F"{builder.name}-train-00000-of-00002.arrow" ) ) )
																	self.assertDictEqual(builder.info.features		,							datasets.Features({"content": datasets.Value("string" )} ) )
																	SCREAMING_SNAKE_CASE           =    builder.as_dataset()
																	self.assertEqual(dset["train"].num_rows		,							_UpperCamelCase )
																	self.assertEqual(dset["train"].info.splits["train"].num_examples		,							_UpperCamelCase )
																	# Order is not preserved when sharding, so we just check that all the elements are there
																	self.assertListEqual(sorted(dset["train"]["content"] )		,							sorted(["foo", "bar", "foobar"] ) )
																	self.assertTrue(
																	    os.path.exists(os.path.join(_UpperCamelCase		,							builder.name		,							"default"		,							"0.0.0"		,							"dataset_info.json" ) ) )
																	del dset




							@require_beam
							def 		__snake_case(  self				:   int )   ->			Optional[Any]:



												'''simple docstring'''

												with tempfile.TemporaryDirectory() as tmp_cache_dir:
																	SCREAMING_SNAKE_CASE           =    DummyBeamDataset(cache_dir=_UpperCamelCase )
																	self.assertRaises(datasets.builder.MissingBeamOptions		,							builder.download_and_prepare )





							@require_beam
							def 		__snake_case(  self				:   Any )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    len(get_test_nested_examples() )
												with tempfile.TemporaryDirectory() as tmp_cache_dir:
																	SCREAMING_SNAKE_CASE           =    NestedBeamDataset(cache_dir=_UpperCamelCase		,							beam_runner="DirectRunner" )
																	builder.download_and_prepare()
																	self.assertTrue(
																	    os.path.exists(
																	        os.path.join(_UpperCamelCase		,							builder.name		,							"default"		,							"0.0.0"		,							F"{builder.name}-train.arrow" ) ) )
																	self.assertDictEqual(
																	    builder.info.features		,							datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string" )} )} ) )
																	SCREAMING_SNAKE_CASE           =    builder.as_dataset()
																	self.assertEqual(dset["train"].num_rows		,							_UpperCamelCase )
																	self.assertEqual(dset["train"].info.splits["train"].num_examples		,							_UpperCamelCase )
																	self.assertDictEqual(dset["train"][0]		,							get_test_nested_examples()[0][1] )
																	self.assertDictEqual(
																	    dset["train"][expected_num_examples - 1]		,							get_test_nested_examples()[expected_num_examples - 1][1] )
																	self.assertTrue(
																	    os.path.exists(os.path.join(_UpperCamelCase		,							builder.name		,							"default"		,							"0.0.0"		,							"dataset_info.json" ) ) )
																	del dset





from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


_lowerCamelCase   :				Optional[Any]     			=						logging.get_logger(__name__)

_lowerCamelCase   :				Any     			=						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase   :				int     			=						{
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

_lowerCamelCase   :				Tuple     			=						{'''allegro/herbert-base-cased''': 5_14}
_lowerCamelCase   :				Optional[int]     			=						{}


class   lowercase      (       a       ):
							lowercase__  :    List[str]									=							VOCAB_FILES_NAMES
							lowercase__  :    str									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Tuple									=							PRETRAINED_INIT_CONFIGURATION
							lowercase__  :    Optional[int]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :    str									=							HerbertTokenizer




							def __init__(  self				:   Dict		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   Optional[int]=None		,							_UpperCamelCase				:   Optional[int]="<s>"		,							_UpperCamelCase				:   Union[str, Any]="<unk>"		,							_UpperCamelCase				:   List[str]="<pad>"		,							_UpperCamelCase				:   List[str]="<mask>"		,							_UpperCamelCase				:   Tuple="</s>"		,							**_UpperCamelCase				:   Any		,							)   ->			str:



												'''simple docstring'''

												super().__init__(
												    _UpperCamelCase		,							_UpperCamelCase		,							tokenizer_file=_UpperCamelCase		,							cls_token=_UpperCamelCase		,							unk_token=_UpperCamelCase		,							pad_token=_UpperCamelCase		,							mask_token=_UpperCamelCase		,							sep_token=_UpperCamelCase		,							**_UpperCamelCase		,							)




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.cls_token_id]
												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												if token_ids_a is None:
																	return cls + token_ids_a + sep

												return cls + token_ids_a + sep + token_ids_a + sep




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None		,							_UpperCamelCase				:   bool = False )   ->			List[int]:



												'''simple docstring'''

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=_UpperCamelCase		,							token_ids_a=_UpperCamelCase		,							already_has_special_tokens=_UpperCamelCase )

												if token_ids_a is None:
																	return [1] + ([0] * len(_UpperCamelCase )) + [1]
												return [1] + ([0] * len(_UpperCamelCase )) + [1] + ([0] * len(_UpperCamelCase )) + [1]




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												SCREAMING_SNAKE_CASE           =    [self.cls_token_id]

												if token_ids_a is None:
																	return len(cls + token_ids_a + sep ) * [0]
												return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]





							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self._tokenizer.model.save(_UpperCamelCase		,							name=_UpperCamelCase )
												return tuple(_UpperCamelCase )




