




from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
		from ..models.auto import AutoTokenizer




class     snake_case__							:
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     Dict							)					->     List[Any]:
							raise NotImplementedError()





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     List[Any]:
							raise NotImplementedError()




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				def __init__(      self				:     List[str]					,      __lowerCamelCase				:     "AutoTokenizer"					,      __lowerCamelCase				:     bool = False					,      **__lowerCamelCase				:     str							)					->     Any:
							a    =			tokenizer
							a    =			skip_prompt
							a    =			decode_kwargs

							# variables used in the streaming process
							a    =			[]
							a    =			0
							a    =			True





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     str							)					->     List[Any]:
							if len(value.shape							) > 1 and value.shape[0] > 1:
										raise ValueError("TextStreamer only supports batch size 1"							)
							elif len(value.shape							) > 1:
										a    =			value[0]

							if self.skip_prompt and self.next_tokens_are_prompt:
										a    =			False
										return

							# Add the new token to the cache and decodes the entire thing.
							self.token_cache.extend(value.tolist()							)
							a    =			self.tokenizer.decode(self.token_cache					,      **self.decode_kwargs							)

							# After the symbol for a new line, we flush the cache.
							if text.endswith("\n"							):
										a    =			text[self.print_len :]
										a    =			[]
										a    =			0
							# If the last token is a CJK character, we print the characters.
							elif len(__lowerCamelCase							) > 0 and self._is_chinese_char(ord(text[-1]							)							):
										a    =			text[self.print_len :]
										self.print_len += len(__lowerCamelCase							)
							# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
							# which may change with the subsequent token -- there are probably smarter ways to do this!)
							else:
										a    =			text[self.print_len : text.rfind(" "							) + 1]
										self.print_len += len(__lowerCamelCase							)

							self.on_finalized_text(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     int:
							# Flush the cache, if it exists
							if len(self.token_cache							) > 0:
										a    =			self.tokenizer.decode(self.token_cache					,      **self.decode_kwargs							)
										a    =			text[self.print_len :]
										a    =			[]
										a    =			0
							else:
										a    =			""

							a    =			True
							self.on_finalized_text(__lowerCamelCase					,      stream_end=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     bool = False							)					->     Dict:
							print(__lowerCamelCase					,      flush=__lowerCamelCase					,      end="" if not stream_end else None							)





				def 							__UpperCAmelCase							(      self				:     Any					,      __lowerCamelCase				:     List[Any]							)					->     Any:
							# This defines a "chinese character" as anything in the CJK Unicode block:
							#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
							#
							# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
							# despite its name. The modern Korean Hangul alphabet is a different block,
							# as is Japanese Hiragana and Katakana. Those alphabets are used to write
							# space-separated words, so they are not treated specially and handled
							# like the all of the other languages.
							if (
							    (cp >= 0X4e_00 and cp <= 0X9f_ff)
							    or (cp >= 0X34_00 and cp <= 0X4d_bf)  #
							    or (cp >= 0X2_00_00 and cp <= 0X2_a6_df)  #
							    or (cp >= 0X2_a7_00 and cp <= 0X2_b7_3f)  #
							    or (cp >= 0X2_b7_40 and cp <= 0X2_b8_1f)  #
							    or (cp >= 0X2_b8_20 and cp <= 0X2_ce_af)  #
							    or (cp >= 0Xf9_00 and cp <= 0Xfa_ff)
							    or (cp >= 0X2_f8_00 and cp <= 0X2_fa_1f)  #
							):  #
										return True

							return False




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				def __init__(      self				:     Tuple					,      __lowerCamelCase				:     "AutoTokenizer"					,      __lowerCamelCase				:     bool = False					,      __lowerCamelCase				:     Optional[float] = None					,      **__lowerCamelCase				:     Any							)					->     int:
							super().__init__(__lowerCamelCase					,      __lowerCamelCase					,      **__lowerCamelCase							)
							a    =			Queue()
							a    =			None
							a    =			timeout





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     bool = False							)					->     Tuple:
							self.text_queue.put(__lowerCamelCase					,      timeout=self.timeout							)
							if stream_end:
										self.text_queue.put(self.stop_signal					,      timeout=self.timeout							)





				def __iter__(      self				:     Dict							)					->     str:
							return self





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Union[str, Any]:
							a    =			self.text_queue.get(timeout=self.timeout							)
							if value == self.stop_signal:
										raise StopIteration()
							else:
										return value













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_ (       __a	,    __a		)     ->		Optional[Any]:







  """simple docstring"""





  assert isinstance(__a	,    __a		)
  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_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      Any			  =tmp_path / "cache"
  lowerCamelCase__:      Optional[int]			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowerCamelCase__:      int			  =ParquetDatasetReader(__a	,    cache_dir=__a	,    keep_in_memory=__a		).read()
  _check_parquet_dataset(__a	,    __a		)





@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_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      int			  =tmp_path / "cache"
  lowerCamelCase__:      Tuple			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__:      Union[str, Any]			  =features.copy() if features else default_expected_features
  lowerCamelCase__:      Union[str, Any]			  =(
      Features({feature: Value(__a		) for feature, dtype in features.items()}		) if features is not None else None
  )
  lowerCamelCase__:      int			  =ParquetDatasetReader(__a	,    features=__a	,    cache_dir=__a		).read()
  _check_parquet_dataset(__a	,    __a		)





@pytest.mark.parametrize("split"	,    [None, NamedSplit("train"		), "train", "test"]		)
def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		Any:







  """simple docstring"""





  lowerCamelCase__:      Union[str, Any]			  =tmp_path / "cache"
  lowerCamelCase__:      Dict			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__:      Optional[int]			  =ParquetDatasetReader(__a	,    cache_dir=__a	,    split=__a		).read()
  _check_parquet_dataset(__a	,    __a		)
  assert dataset.split == split if split else "train"





@pytest.mark.parametrize("path_type"	,    [str, list]		)
def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		Dict:







  """simple docstring"""





  if issubclass(__a	,    __a		):
    lowerCamelCase__:      str			  =parquet_path
  elif issubclass(__a	,    __a		):
    lowerCamelCase__:      str			  =[parquet_path]
  lowerCamelCase__:      Optional[Any]			  =tmp_path / "cache"
  lowerCamelCase__:      Any			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__:      Optional[int]			  =ParquetDatasetReader(__a	,    cache_dir=__a		).read()
  _check_parquet_dataset(__a	,    __a		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a=("train",)		)     ->		Union[str, Any]:







  """simple docstring"""





  assert isinstance(__a	,    __a		)
  for split in splits:
    lowerCamelCase__:      Optional[Any]			  =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_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      Any			  =tmp_path / "cache"
  lowerCamelCase__:      str			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowerCamelCase__:      List[str]			  =ParquetDatasetReader(
        {"train": parquet_path}	,    cache_dir=__a	,    keep_in_memory=__a		).read()
  _check_parquet_datasetdict(__a	,    __a		)





@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_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      List[Any]			  =tmp_path / "cache"
  lowerCamelCase__:      Any			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__:      int			  =features.copy() if features else default_expected_features
  lowerCamelCase__:      Union[str, Any]			  =(
      Features({feature: Value(__a		) for feature, dtype in features.items()}		) if features is not None else None
  )
  lowerCamelCase__:      Union[str, Any]			  =ParquetDatasetReader({"train": parquet_path}	,    features=__a	,    cache_dir=__a		).read()
  _check_parquet_datasetdict(__a	,    __a		)





@pytest.mark.parametrize("split"	,    [None, NamedSplit("train"		), "train", "test"]		)
def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		List[str]:







  """simple docstring"""





  if split:
    lowerCamelCase__:      Union[str, Any]			  ={split: parquet_path}
  else:
    lowerCamelCase__:      int			  ="train"
    lowerCamelCase__:      Union[str, Any]			  ={"train": parquet_path, "test": parquet_path}
  lowerCamelCase__:      int			  =tmp_path / "cache"
  lowerCamelCase__:      Union[str, Any]			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__:      Optional[Any]			  =ParquetDatasetReader(__a	,    cache_dir=__a		).read()
  _check_parquet_datasetdict(__a	,    __a	,    splits=list(path.keys()		)		)
  assert all(dataset[split].split == split for split in path.keys()		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		Tuple:







  """simple docstring"""





  lowerCamelCase__:      Tuple			  =ParquetDatasetWriter(__a	,    tmp_path / "foo.parquet"		)
  assert writer.write() > 0
  lowerCamelCase__:      Tuple			  =pq.ParquetFile(tmp_path / "foo.parquet"		)
  lowerCamelCase__:      Optional[int]			  =pf.read()
  assert dataset.data.table == output_table





def      lowerCAmelCase_ (       __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      List[str]			  =str(shared_datadir / "test_image_rgb.jpg"		)
  lowerCamelCase__:      Union[str, Any]			  ={"image": [image_path]}
  lowerCamelCase__:      int			  =Features({"image": Image()}		)
  lowerCamelCase__:      Tuple			  =Dataset.from_dict(__a	,    features=__a		)
  lowerCamelCase__:      Optional[int]			  =ParquetDatasetWriter(__a	,    tmp_path / "foo.parquet"		)
  assert writer.write() > 0

  lowerCamelCase__:      Optional[Any]			  =Dataset.from_parquet(str(tmp_path / "foo.parquet"		)		)
  assert dataset.features == reloaded_dataset.features

  lowerCamelCase__:      List[str]			  =ParquetDatasetReader(str(tmp_path / "foo.parquet"		)	,    streaming=__a		).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_ (       __a	,    __a		)     ->		Any:







  """simple docstring"""





  assert get_writer_batch_size(__a		) == expected

