

import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Any:



    """simple docstring"""






    assert isinstance(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
    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(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						List[str]:



    """simple docstring"""






    UpperCamelCase_			  =						tmp_path / "cache"
    UpperCamelCase_			  =						{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        UpperCamelCase_			  =						JsonDatasetReader(SCREAMING_SNAKE_CASE_			,						cache_dir=SCREAMING_SNAKE_CASE_			,						keep_in_memory=SCREAMING_SNAKE_CASE_			).read()
    _check_json_dataset(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)




@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(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						List[Any]:



    """simple docstring"""






    UpperCamelCase_			  =						tmp_path / "cache"
    UpperCamelCase_			  =						{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    UpperCamelCase_			  =						features.copy() if features else default_expected_features
    UpperCamelCase_			  =						(
        Features({feature: Value(SCREAMING_SNAKE_CASE_			) for feature, dtype in features.items()}			) if features is not None else None
    )
    UpperCamelCase_			  =						JsonDatasetReader(SCREAMING_SNAKE_CASE_			,						features=SCREAMING_SNAKE_CASE_			,						cache_dir=SCREAMING_SNAKE_CASE_			).read()
    _check_json_dataset(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)




@pytest.mark.parametrize(
    "features"			,						[
        None,
        {"col_3": "float64", "col_1": "string", "col_2": "int64"},
    ]			,						)
def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						str:



    """simple docstring"""






    UpperCamelCase_			  =						tmp_path / "cache"
    UpperCamelCase_			  =						{"col_3": "float64", "col_1": "string", "col_2": "int64"}
    UpperCamelCase_			  =						features.copy() if features else default_expected_features
    UpperCamelCase_			  =						(
        Features({feature: Value(SCREAMING_SNAKE_CASE_			) for feature, dtype in features.items()}			) if features is not None else None
    )
    UpperCamelCase_			  =						JsonDatasetReader(SCREAMING_SNAKE_CASE_			,						features=SCREAMING_SNAKE_CASE_			,						cache_dir=SCREAMING_SNAKE_CASE_			).read()
    assert isinstance(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
    assert dataset.num_rows == 2
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_3", "col_1", "col_2"]
    for feature, expected_dtype in expected_features.items():
        assert dataset.features[feature].dtype == expected_dtype




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Optional[Any]:



    """simple docstring"""






    UpperCamelCase_			  =						{"col_2": "int64", "col_3": "float64", "col_1": "string"}
    UpperCamelCase_			  =						features.copy()
    UpperCamelCase_			  =						(
        Features({feature: Value(SCREAMING_SNAKE_CASE_			) for feature, dtype in features.items()}			) if features is not None else None
    )
    UpperCamelCase_			  =						tmp_path / "cache"
    UpperCamelCase_			  =						JsonDatasetReader(SCREAMING_SNAKE_CASE_			,						features=SCREAMING_SNAKE_CASE_			,						cache_dir=SCREAMING_SNAKE_CASE_			).read()
    assert isinstance(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
    assert dataset.num_rows == 2
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_2", "col_3", "col_1"]
    for feature, expected_dtype in expected_features.items():
        assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("split"			,						[None, NamedSplit("train"			), "train", "test"]			)
def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Tuple:



    """simple docstring"""






    UpperCamelCase_			  =						tmp_path / "cache"
    UpperCamelCase_			  =						{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    UpperCamelCase_			  =						JsonDatasetReader(SCREAMING_SNAKE_CASE_			,						cache_dir=SCREAMING_SNAKE_CASE_			,						split=SCREAMING_SNAKE_CASE_			).read()
    _check_json_dataset(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
    assert dataset.split == split if split else "train"




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



    """simple docstring"""






    if issubclass(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			):
        UpperCamelCase_			  =						jsonl_path
    elif issubclass(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			):
        UpperCamelCase_			  =						[jsonl_path]
    UpperCamelCase_			  =						tmp_path / "cache"
    UpperCamelCase_			  =						{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    UpperCamelCase_			  =						JsonDatasetReader(SCREAMING_SNAKE_CASE_			,						cache_dir=SCREAMING_SNAKE_CASE_			).read()
    _check_json_dataset(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=("train",)			)->						Optional[Any]:



    """simple docstring"""






    assert isinstance(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
    for split in splits:
        UpperCamelCase_			  =						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(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Union[str, Any]:



    """simple docstring"""






    UpperCamelCase_			  =						tmp_path / "cache"
    UpperCamelCase_			  =						{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        UpperCamelCase_			  =						JsonDatasetReader({"train": jsonl_path}			,						cache_dir=SCREAMING_SNAKE_CASE_			,						keep_in_memory=SCREAMING_SNAKE_CASE_			).read()
    _check_json_datasetdict(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)




@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(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Any:



    """simple docstring"""






    UpperCamelCase_			  =						tmp_path / "cache"
    UpperCamelCase_			  =						{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    UpperCamelCase_			  =						features.copy() if features else default_expected_features
    UpperCamelCase_			  =						(
        Features({feature: Value(SCREAMING_SNAKE_CASE_			) for feature, dtype in features.items()}			) if features is not None else None
    )
    UpperCamelCase_			  =						JsonDatasetReader({"train": jsonl_path}			,						features=SCREAMING_SNAKE_CASE_			,						cache_dir=SCREAMING_SNAKE_CASE_			).read()
    _check_json_datasetdict(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)




@pytest.mark.parametrize("split"			,						[None, NamedSplit("train"			), "train", "test"]			)
def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Union[str, Any]:



    """simple docstring"""






    if split:
        UpperCamelCase_			  =						{split: jsonl_path}
    else:
        UpperCamelCase_			  =						"train"
        UpperCamelCase_			  =						{"train": jsonl_path, "test": jsonl_path}
    UpperCamelCase_			  =						tmp_path / "cache"
    UpperCamelCase_			  =						{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    UpperCamelCase_			  =						JsonDatasetReader(SCREAMING_SNAKE_CASE_			,						cache_dir=SCREAMING_SNAKE_CASE_			).read()
    _check_json_datasetdict(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						splits=list(path.keys()			)			)
    assert all(dataset[split].split == split for split in path.keys()			)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Dict:



    """simple docstring"""






    return json.load(SCREAMING_SNAKE_CASE_			)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						str:



    """simple docstring"""






    return [json.loads(SCREAMING_SNAKE_CASE_			) for line in buffer]






class 						__magic_name__					:






     @pytest.mark.parametrize("lines, load_json_function"		,						[(True, load_json_lines), (False, load_json)]	)
     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase	)->     Any:
         with io.BytesIO() as buffer:
             JsonDatasetWriter(_lowercase		,						_lowercase		,						lines=_lowercase	).write()
             buffer.seek(0	)
             UpperCamelCase_			  =						load_json_function(_lowercase	)
         assert isinstance(_lowercase		,						_lowercase	)
         assert isinstance(exported_content[0]		,						_lowercase	)
         assert len(_lowercase	) == 10






     @pytest.mark.parametrize(
         "orient, container, keys, len_at"		,						[
             ("records", list, {"tokens", "labels", "answers", "id"}, None),
             ("split", dict, {"columns", "data"}, "data"),
             ("index", dict, set("0123456789"	), None),
             ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
             ("values", list, None, None),
             ("table", dict, {"schema", "data"}, "data"),
         ]		,						)
     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)->     Any:
         with io.BytesIO() as buffer:
             JsonDatasetWriter(_lowercase		,						_lowercase		,						lines=_lowercase		,						orient=_lowercase	).write()
             buffer.seek(0	)
             UpperCamelCase_			  =						load_json(_lowercase	)
         assert isinstance(_lowercase		,						_lowercase	)
         if keys:
             if container is dict:
                 assert exported_content.keys() == keys
             else:
                 assert exported_content[0].keys() == keys
         else:
             assert not hasattr(_lowercase		,						"keys"	) and not hasattr(exported_content[0]		,						"keys"	)
         if len_at:
             assert len(exported_content[len_at]	) == 10
         else:
             assert len(_lowercase	) == 10






     @pytest.mark.parametrize("lines, load_json_function"		,						[(True, load_json_lines), (False, load_json)]	)
     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase	)->     Any:
         with io.BytesIO() as buffer:
             JsonDatasetWriter(_lowercase		,						_lowercase		,						lines=_lowercase		,						num_proc=2	).write()
             buffer.seek(0	)
             UpperCamelCase_			  =						load_json_function(_lowercase	)
         assert isinstance(_lowercase		,						_lowercase	)
         assert isinstance(exported_content[0]		,						_lowercase	)
         assert len(_lowercase	) == 10






     @pytest.mark.parametrize(
         "orient, container, keys, len_at"		,						[
             ("records", list, {"tokens", "labels", "answers", "id"}, None),
             ("split", dict, {"columns", "data"}, "data"),
             ("index", dict, set("0123456789"	), None),
             ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
             ("values", list, None, None),
             ("table", dict, {"schema", "data"}, "data"),
         ]		,						)
     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)->     str:
         with io.BytesIO() as buffer:
             JsonDatasetWriter(_lowercase		,						_lowercase		,						lines=_lowercase		,						orient=_lowercase		,						num_proc=2	).write()
             buffer.seek(0	)
             UpperCamelCase_			  =						load_json(_lowercase	)
         assert isinstance(_lowercase		,						_lowercase	)
         if keys:
             if container is dict:
                 assert exported_content.keys() == keys
             else:
                 assert exported_content[0].keys() == keys
         else:
             assert not hasattr(_lowercase		,						"keys"	) and not hasattr(exported_content[0]		,						"keys"	)
         if len_at:
             assert len(exported_content[len_at]	) == 10
         else:
             assert len(_lowercase	) == 10






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     Any:
         with pytest.raises(_lowercase	):
             with io.BytesIO() as buffer:
                 JsonDatasetWriter(_lowercase		,						_lowercase		,						num_proc=0	)






     @pytest.mark.parametrize("compression, extension"		,						[("gzip", "gz"), ("bz2", "bz2"), ("xz", "xz")]	)
     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)->     int:
         UpperCamelCase_			  =						tmp_path_factory.mktemp("data"	) / F"test.json.{extension}"
         UpperCamelCase_			  =						str(shared_datadir / F"test_file.json.{extension}"	)
         JsonDatasetWriter(_lowercase		,						_lowercase		,						compression=_lowercase	).write()

         with fsspec.open(_lowercase		,						"rb"		,						compression="infer"	) as f:
             UpperCamelCase_			  =						f.read()
         with fsspec.open(_lowercase		,						"rb"		,						compression="infer"	) as f:
             UpperCamelCase_			  =						f.read()
         assert exported_content == original_content



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE		:Union[str, Any]						=     {
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     ["""SpeechT5Tokenizer"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Any						=     [
     """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """SpeechT5ForSpeechToText""",
     """SpeechT5ForSpeechToSpeech""",
     """SpeechT5ForTextToSpeech""",
     """SpeechT5Model""",
     """SpeechT5PreTrainedModel""",
     """SpeechT5HifiGan""",
 ]

if TYPE_CHECKING:
 from .configuration_speechta import (
     SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
     SpeechTaConfig,
     SpeechTaHifiGanConfig,
 )
 from .feature_extraction_speechta import SpeechTaFeatureExtractor
 from .processing_speechta import SpeechTaProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speechta import SpeechTaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speechta import (
      SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechTaForSpeechToSpeech,
      SpeechTaForSpeechToText,
      SpeechTaForTextToSpeech,
      SpeechTaHifiGan,
      SpeechTaModel,
      SpeechTaPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE		:List[str]						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
