
import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






def 							_lowercase							(     UpperCamelCase_		)		->		Optional[int]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= np.inf

					def set_batch_size(UpperCamelCase_		) -> None:
										nonlocal batch_size
										if isinstance(UpperCamelCase_	,	UpperCamelCase_		):
															SCREAMING_SNAKE_CASE__													= min(UpperCamelCase_	,	config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS		)
										elif isinstance(UpperCamelCase_	,	UpperCamelCase_		):
															SCREAMING_SNAKE_CASE__													= min(UpperCamelCase_	,	config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS		)
										elif isinstance(UpperCamelCase_	,	UpperCamelCase_		) and feature.dtype == "binary":
															SCREAMING_SNAKE_CASE__													= min(UpperCamelCase_	,	config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS		)

					_visit(UpperCamelCase_	,	UpperCamelCase_		)

					return None if batch_size is np.inf else batch_size




class 						lowercase__  (						_UpperCAmelCase  ):
	def __init__(			self							:   Dict       ,   UpperCAmelCase_							:   NestedDataStructureLike[PathLike]       ,   UpperCAmelCase_							:   Optional[NamedSplit] = None       ,   UpperCAmelCase_							:   Optional[Features] = None       ,   UpperCAmelCase_							:   str = None       ,   UpperCAmelCase_							:   bool = False       ,   UpperCAmelCase_							:   bool = False       ,   UpperCAmelCase_							:   Optional[int] = None       ,   **UpperCAmelCase_							:   List[Any]       ,   ):
						super().__init__(
						    UpperCAmelCase_       ,   split=UpperCAmelCase_       ,   features=UpperCAmelCase_       ,   cache_dir=UpperCAmelCase_       ,   keep_in_memory=UpperCAmelCase_       ,   streaming=UpperCAmelCase_       ,   num_proc=UpperCAmelCase_       ,   **UpperCAmelCase_       ,   )
						SCREAMING_SNAKE_CASE__													= path_or_paths if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							) else {self.split: path_or_paths}
						SCREAMING_SNAKE_CASE__													= _PACKAGED_DATASETS_MODULES['parquet'][1]
						SCREAMING_SNAKE_CASE__													= Parquet(
						    cache_dir=UpperCAmelCase_       ,   data_files=UpperCAmelCase_       ,   features=UpperCAmelCase_       ,   hash=UpperCAmelCase_       ,   **UpperCAmelCase_       ,   )





	def      A_				(			self							:   Tuple							):
						# Build iterable dataset
						if self.streaming:
											SCREAMING_SNAKE_CASE__													= self.builder.as_streaming_dataset(split=self.split							)
						# Build regular (map-style) dataset
						else:
											SCREAMING_SNAKE_CASE__													= None
											SCREAMING_SNAKE_CASE__													= None
											SCREAMING_SNAKE_CASE__													= None
											SCREAMING_SNAKE_CASE__													= None

											self.builder.download_and_prepare(
											    download_config=UpperCAmelCase_       ,   download_mode=UpperCAmelCase_       ,   verification_mode=UpperCAmelCase_       ,   base_path=UpperCAmelCase_       ,   num_proc=self.num_proc       ,   )
											SCREAMING_SNAKE_CASE__													= self.builder.as_dataset(
											    split=self.split       ,   verification_mode=UpperCAmelCase_       ,   in_memory=self.keep_in_memory							)
						return dataset





class 						lowercase__  :
	def __init__(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   Dataset       ,   UpperCAmelCase_							:   Union[PathLike, BinaryIO]       ,   UpperCAmelCase_							:   Optional[int] = None       ,   **UpperCAmelCase_							:   str       ,   ):
						SCREAMING_SNAKE_CASE__													= dataset
						SCREAMING_SNAKE_CASE__													= path_or_buf
						SCREAMING_SNAKE_CASE__													= batch_size or get_writer_batch_size(dataset.features							)
						SCREAMING_SNAKE_CASE__													= parquet_writer_kwargs
	def      A_				(			self							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

						if isinstance(self.path_or_buf       ,   (str, bytes, os.PathLike)							):
											with open(self.path_or_buf       ,   'wb+'							) as buffer:
																SCREAMING_SNAKE_CASE__													= self._write(file_obj=UpperCAmelCase_       ,   batch_size=UpperCAmelCase_       ,   **self.parquet_writer_kwargs							)
						else:
											SCREAMING_SNAKE_CASE__													= self._write(file_obj=self.path_or_buf       ,   batch_size=UpperCAmelCase_       ,   **self.parquet_writer_kwargs							)
						return written





	def      A_				(			self							:   List[Any]       ,   UpperCAmelCase_							:   BinaryIO       ,   UpperCAmelCase_							:   int       ,   **UpperCAmelCase_							:   Any							):
						SCREAMING_SNAKE_CASE__													= 0
						SCREAMING_SNAKE_CASE__													= parquet_writer_kwargs.pop('path_or_buf'       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= self.dataset.features.arrow_schema

						SCREAMING_SNAKE_CASE__													= pq.ParquetWriter(UpperCAmelCase_       ,   schema=UpperCAmelCase_       ,   **UpperCAmelCase_							)

						for offset in logging.tqdm(
						    range(0       ,   len(self.dataset							)       ,   UpperCAmelCase_							)       ,   unit='ba'       ,   disable=not logging.is_progress_bar_enabled()       ,   desc='Creating parquet from Arrow format'       ,   ):
											SCREAMING_SNAKE_CASE__													= query_table(
											    table=self.dataset._data       ,   key=slice(UpperCAmelCase_       ,   offset + batch_size							)       ,   indices=self.dataset._indices if self.dataset._indices is not None else None       ,   )
											writer.write_table(UpperCAmelCase_							)
											written += batch.nbytes
						writer.close()
						return written







import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Dict				       =LxmertTokenizer
	A__    :   List[Any]				       =LxmertTokenizerFast
	A__    :   Any				       =True
	A__    :   List[Any]				       =True
	def      A_				(			self							:   Optional[Any]							):
						super().setUp()

						SCREAMING_SNAKE_CASE__													= [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						SCREAMING_SNAKE_CASE__													= os.path.join(self.tmpdirname       ,   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]							)							)
	def      A_				(			self							:   int       ,   UpperCAmelCase_							:   int							):
						SCREAMING_SNAKE_CASE__													= 'UNwant\u00E9d,running'
						SCREAMING_SNAKE_CASE__													= 'unwanted, running'
						return input_text, output_text
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= self.tokenizer_class(self.vocab_file							)

						SCREAMING_SNAKE_CASE__													= tokenizer.tokenize('UNwant\u00E9d,running'							)
						self.assertListEqual(UpperCAmelCase_       ,   ['un', '##want', '##ed', ',', 'runn', '##ing']							)
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)       ,   [7, 4, 5, 10, 8, 9]							)





	def      A_				(			self							:   List[str]							):
						if not self.test_rust_tokenizer:
											return

						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()
						SCREAMING_SNAKE_CASE__													= self.get_rust_tokenizer()

						SCREAMING_SNAKE_CASE__													= 'I was born in 92000, and this is falsé.'

						SCREAMING_SNAKE_CASE__													= tokenizer.tokenize(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= rust_tokenizer.tokenize(UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= tokenizer.encode(UpperCAmelCase_       ,   add_special_tokens=UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= rust_tokenizer.encode(UpperCAmelCase_       ,   add_special_tokens=UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE__													= tokenizer.encode(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= rust_tokenizer.encode(UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)





