






"""simple docstring"""






import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
    assert isinstance(lowerCAmelCase__       ,lowerCAmelCase__				)
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
        assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('keep_in_memory'       ,[False, True]				)
def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
    A__							 =       tmp_path / 'cache'
    A__							 =       {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        A__							 =       ParquetDatasetReader(lowerCAmelCase__       ,cache_dir=lowerCAmelCase__       ,keep_in_memory=lowerCAmelCase__				).read()
    _check_parquet_dataset(lowerCAmelCase__       ,lowerCAmelCase__				)



@pytest.mark.parametrize(
    'features'       ,[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]       ,)
def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
    A__							 =       tmp_path / 'cache'
    A__							 =       {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
    A__							 =       features.copy() if features else default_expected_features
    A__							 =       (
        Features({feature: Value(lowerCAmelCase__				) for feature, dtype in features.items()}				) if features is not None else None
    )
    A__							 =       ParquetDatasetReader(lowerCAmelCase__       ,features=lowerCAmelCase__       ,cache_dir=lowerCAmelCase__				).read()
    _check_parquet_dataset(lowerCAmelCase__       ,lowerCAmelCase__				)



@pytest.mark.parametrize('split'       ,[None, NamedSplit('train'				), 'train', 'test']				)
def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
    A__							 =       tmp_path / 'cache'
    A__							 =       {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
    A__							 =       ParquetDatasetReader(lowerCAmelCase__       ,cache_dir=lowerCAmelCase__       ,split=lowerCAmelCase__				).read()
    _check_parquet_dataset(lowerCAmelCase__       ,lowerCAmelCase__				)
    assert dataset.split == split if split else "train"



@pytest.mark.parametrize('path_type'       ,[str, list]				)
def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):






    if issubclass(lowerCAmelCase__       ,lowerCAmelCase__				):
        A__							 =       parquet_path
    elif issubclass(lowerCAmelCase__       ,lowerCAmelCase__				):
        A__							 =       [parquet_path]
    A__							 =       tmp_path / 'cache'
    A__							 =       {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
    A__							 =       ParquetDatasetReader(lowerCAmelCase__       ,cache_dir=lowerCAmelCase__				).read()
    _check_parquet_dataset(lowerCAmelCase__       ,lowerCAmelCase__				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__=("train",)				):
    assert isinstance(lowerCAmelCase__       ,lowerCAmelCase__				)
    for split in splits:
        A__							 =       dataset_dict[split]
        assert dataset.num_rows == 4
        assert dataset.num_columns == 3
        assert dataset.column_names == ["col_1", "col_2", "col_3"]
        for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('keep_in_memory'       ,[False, True]				)
def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
    A__							 =       tmp_path / 'cache'
    A__							 =       {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        A__							 =       ParquetDatasetReader(
            {'train': parquet_path}       ,cache_dir=lowerCAmelCase__       ,keep_in_memory=lowerCAmelCase__				).read()
    _check_parquet_datasetdict(lowerCAmelCase__       ,lowerCAmelCase__				)



@pytest.mark.parametrize(
    'features'       ,[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]       ,)
def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
    A__							 =       tmp_path / 'cache'
    A__							 =       {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
    A__							 =       features.copy() if features else default_expected_features
    A__							 =       (
        Features({feature: Value(lowerCAmelCase__				) for feature, dtype in features.items()}				) if features is not None else None
    )
    A__							 =       ParquetDatasetReader({'train': parquet_path}       ,features=lowerCAmelCase__       ,cache_dir=lowerCAmelCase__				).read()
    _check_parquet_datasetdict(lowerCAmelCase__       ,lowerCAmelCase__				)



@pytest.mark.parametrize('split'       ,[None, NamedSplit('train'				), 'train', 'test']				)
def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):






    if split:
        A__							 =       {split: parquet_path}
    else:
        A__							 =       'train'
        A__							 =       {'train': parquet_path, 'test': parquet_path}
    A__							 =       tmp_path / 'cache'
    A__							 =       {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
    A__							 =       ParquetDatasetReader(lowerCAmelCase__       ,cache_dir=lowerCAmelCase__				).read()
    _check_parquet_datasetdict(lowerCAmelCase__       ,lowerCAmelCase__       ,splits=list(path.keys()				)				)
    assert all(dataset[split].split == split for split in path.keys()				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
    A__							 =       ParquetDatasetWriter(lowerCAmelCase__       ,tmp_path / 'foo.parquet'				)
    assert writer.write() > 0
    A__							 =       pq.ParquetFile(tmp_path / 'foo.parquet'				)
    A__							 =       pf.read()
    assert dataset.data.table == output_table



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
    A__							 =       str(shared_datadir / 'test_image_rgb.jpg'				)
    A__							 =       {'image': [image_path]}
    A__							 =       Features({'image': Image()}				)
    A__							 =       Dataset.from_dict(lowerCAmelCase__       ,features=lowerCAmelCase__				)
    A__							 =       ParquetDatasetWriter(lowerCAmelCase__       ,tmp_path / 'foo.parquet'				)
    assert writer.write() > 0

    A__							 =       Dataset.from_parquet(str(tmp_path / 'foo.parquet'				)				)
    assert dataset.features == reloaded_dataset.features

    A__							 =       ParquetDatasetReader(str(tmp_path / 'foo.parquet'				)       ,streaming=lowerCAmelCase__				).read()
    assert dataset.features == reloaded_iterable_dataset.features







@pytest.mark.parametrize(
    'feature, expected'       ,[
        (Features({'foo': Value('int32'				)}				), None),
        (Features({'image': Image(), 'foo': Value('int32'				)}				), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'nested': Sequence(Audio()				)}				), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]       ,)
def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
    assert get_writer_batch_size(lowerCAmelCase__				) == expected










"""simple docstring"""






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE						:						Optional[int]					   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:						List[Any]					   =       {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE						:						List[Any]					   =       {
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE						:						str					   =       {
    '''gpt-neox-20b''': 2_048,
}





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_:							Any   			=					PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_:							List[Any]   			=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_:							Optional[Any]   			=					["""input_ids""", """attention_mask"""]


	def __init__(      self  ,					__a=None  ,					__a=None  ,					__a=None  ,					__a="<|endoftext|>"  ,					__a="<|endoftext|>"  ,					__a="<|endoftext|>"  ,					__a=False  ,					**__a  ,					):


					"""simple docstring"""

					super().__init__(
					    __a  ,					__a  ,					tokenizer_file=__a  ,					unk_token=__a  ,					bos_token=__a  ,					eos_token=__a  ,					add_prefix_space=__a  ,					**__a  ,					)

					A__							 =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
					if pre_tok_state.get('add_prefix_space'  ,					__a						) != add_prefix_space:
									A__							 =       getattr(__a  ,					pre_tok_state.pop('type'						)						)
									A__							 =       add_prefix_space
									A__							 =       pre_tok_class(**__a						)

					A__							 =       add_prefix_space


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None						):


					"""simple docstring"""

					A__							 =       self._tokenizer.model.save(__a  ,					name=__a						)
					return tuple(__a						)






	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       []
					for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(__a  ,					add_special_tokens=__a						) + [self.eos_token_id]						)

					if len(__a						) > self.model_max_length:
									A__							 =       input_ids[-self.model_max_length :]
					return input_ids


