
import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


a__				:      str    		=     (30, 487)
a__				:      Union[str, Any]    		=     (36, 1_024)
a__				:      Optional[Any]    		=     (100, 100)
a__				:      Union[str, Any]    		=     100

a__				:      Any    		=     datasets.Features(
    {'''text''': ArrayaD(SHAPE_TEST_1, dtype='''float32'''), '''image''': ArrayaD(SHAPE_TEST_2, dtype='''float32''')}
)

a__				:      Any    		=     os.path.split(__file__)
a__				:      Union[str, Any]    		=     os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))







@get_duration
def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    with ArrowWriter(features=a__ ,						path=os.path.join(a__ ,						'''beta.arrow'''   )   ) as writer:
        for key, record in dummy_data:
            SCREAMING_SNAKE_CASE				:				Tuple				      = my_features.encode_example(a__   )
            writer.write(a__   )
        SCREAMING_SNAKE_CASE				:				int				      = writer.finalize()







@get_duration
def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = datasets.Dataset.from_file(
        filename=os.path.join(a__ ,						'''beta.arrow'''   ) ,						info=datasets.DatasetInfo(features=a__   )   )
    for _ in dataset:
        pass







@get_duration
def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[Any]				      = datasets.Dataset.from_file(
        filename=os.path.join(a__ ,						'''beta.arrow'''   ) ,						info=datasets.DatasetInfo(features=a__   )   )
    dataset.set_format('''numpy'''   )
    for _ in dataset:
        pass







@get_duration
def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				int				      = 10
    SCREAMING_SNAKE_CASE				:				List[Any]				      = datasets.Dataset.from_file(
        filename=os.path.join(a__ ,						'''beta.arrow'''   ) ,						info=datasets.DatasetInfo(features=a__   )   )
    for i in range(0 ,						len(a__   ) ,						a__   ):
        SCREAMING_SNAKE_CASE				:				Any				      = dataset[i : i + batch_size]







@get_duration
def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				int				      = 10
    SCREAMING_SNAKE_CASE				:				int				      = datasets.Dataset.from_file(
        filename=os.path.join(a__ ,						'''beta.arrow'''   ) ,						info=datasets.DatasetInfo(features=a__   )   )
    dataset.set_format('''numpy'''   )
    for i in range(0 ,						len(a__   ) ,						a__   ):
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataset[i : i + batch_size]







@get_duration
def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				str				      = datasets.Dataset.from_file(
        filename=os.path.join(a__ ,						'''beta.arrow'''   ) ,						info=datasets.DatasetInfo(features=a__   )   )
    for col in feats:
        SCREAMING_SNAKE_CASE				:				int				      = dataset[col]







@get_duration
def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				int				      = datasets.Dataset.from_file(
        filename=os.path.join(a__ ,						'''beta.arrow'''   ) ,						info=datasets.DatasetInfo(features=a__   )   )
    dataset.set_format('''numpy'''   )
    for col in feats:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataset[col]







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[int]				      = {}
    SCREAMING_SNAKE_CASE				:				str				      = (
        read_unformated,
        read_formatted_as_numpy,
        read_batch_unformated,
        read_batch_formatted_as_numpy,
        read_col_unformated,
        read_col_formatted_as_numpy,
    )
    with tempfile.TemporaryDirectory() as tmp_dir:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = datasets.Features({'''image''': ArrayaD(a__ ,						dtype='''float32'''   )}   )
        SCREAMING_SNAKE_CASE				:				Tuple				      = generate_examples(features=a__ ,						num_examples=a__   )
        SCREAMING_SNAKE_CASE				:				List[str]				      = write(a__ ,						a__ ,						a__   )
        for read_func in read_functions:
            SCREAMING_SNAKE_CASE				:				str				      = read_func(a__ ,						a__   )

    with tempfile.TemporaryDirectory() as tmp_dir:
        # don't use fixed length for fair comparison
        # feats = datasets.Features(
        #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
        # )
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''   )   )   )}   )
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = generate_examples(
            features=a__ ,						num_examples=a__ ,						seq_shapes={'''image''': SPEED_TEST_SHAPE}   )
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = write(a__ ,						a__ ,						a__   )
        for read_func in read_functions:
            SCREAMING_SNAKE_CASE				:				List[Any]				      = read_func(a__ ,						a__   )

    with tempfile.TemporaryDirectory() as tmp_dir:
        # don't use fixed length for fair comparison
        # feats = datasets.Features(
        #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
        # )
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''   )   )}   )
        SCREAMING_SNAKE_CASE				:				str				      = generate_examples(
            features=a__ ,						num_examples=a__ ,						seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]} ,						)
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = write(a__ ,						a__ ,						a__   )
        for read_func in read_functions:
            SCREAMING_SNAKE_CASE				:				Optional[int]				      = read_func(a__ ,						a__   )

    with open(a__ ,						'''wb'''   ) as f:
        f.write(json.dumps(a__   ).encode('''utf-8'''   )   )


if __name__ == "__main__":  # useful to run the profiler
      benchmark_array_xd()






'''simple docstring'''






from typing import TYPE_CHECKING

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


lowercase						:   int      						=							{
    '''configuration_xlm_prophetnet''': ['''XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMProphetNetConfig'''],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase						:   Any      						=							['''XLMProphetNetTokenizer''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase						:   Tuple      						=							[
          '''XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''XLMProphetNetDecoder''',
          '''XLMProphetNetEncoder''',
          '''XLMProphetNetForCausalLM''',
          '''XLMProphetNetForConditionalGeneration''',
          '''XLMProphetNetModel''',
          '''XLMProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_prophetnet import (
                XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMProphetNetDecoder,
                XLMProphetNetEncoder,
                XLMProphetNetForCausalLM,
                XLMProphetNetForConditionalGeneration,
                XLMProphetNetModel,
                XLMProphetNetPreTrainedModel,
            )

else:
      import sys

      lowercase						:   Optional[Any]      						=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




