




'''simple docstring'''







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 :    Union[str, Any]			   =(30, 4_87)
A :    Optional[int]			   =(36, 10_24)
A :    str			   =(1_00, 1_00)
A :    Optional[Any]			   =1_00

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

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





@get_duration
def 						snake_case_							(_a     :							List[str]   ,       _a     :							Optional[Any]   ,       _a     :							Tuple					):
    with ArrowWriter(features=SCREAMING_SNAKE_CASE_   ,       path=os.path.join(SCREAMING_SNAKE_CASE_   ,       '''beta.arrow'''					)					) as writer:
        for key, record in dummy_data:
            UpperCAmelCase      	=		my_features.encode_example(SCREAMING_SNAKE_CASE_					)
            writer.write(SCREAMING_SNAKE_CASE_					)
        UpperCAmelCase	,					UpperCAmelCase      	=		writer.finalize()





@get_duration
def 						snake_case_							(_a     :							int   ,       _a     :							Tuple					):
    UpperCAmelCase      	=		datasets.Dataset.from_file(
        filename=os.path.join(SCREAMING_SNAKE_CASE_   ,       '''beta.arrow'''					)   ,       info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_					)					)
    for _ in dataset:
        pass





@get_duration
def 						snake_case_							(_a     :							Optional[int]   ,       _a     :							Tuple					):
    UpperCAmelCase      	=		datasets.Dataset.from_file(
        filename=os.path.join(SCREAMING_SNAKE_CASE_   ,       '''beta.arrow'''					)   ,       info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_					)					)
    dataset.set_format('''numpy'''					)
    for _ in dataset:
        pass





@get_duration
def 						snake_case_							(_a     :							Optional[Any]   ,       _a     :							Union[str, Any]					):
    UpperCAmelCase      	=		1_0
    UpperCAmelCase      	=		datasets.Dataset.from_file(
        filename=os.path.join(SCREAMING_SNAKE_CASE_   ,       '''beta.arrow'''					)   ,       info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_					)					)
    for i in range(0   ,       len(SCREAMING_SNAKE_CASE_					)   ,       SCREAMING_SNAKE_CASE_					):
        UpperCAmelCase      	=		dataset[i : i + batch_size]





@get_duration
def 						snake_case_							(_a     :							int   ,       _a     :							Union[str, Any]					):
    UpperCAmelCase      	=		1_0
    UpperCAmelCase      	=		datasets.Dataset.from_file(
        filename=os.path.join(SCREAMING_SNAKE_CASE_   ,       '''beta.arrow'''					)   ,       info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_					)					)
    dataset.set_format('''numpy'''					)
    for i in range(0   ,       len(SCREAMING_SNAKE_CASE_					)   ,       SCREAMING_SNAKE_CASE_					):
        UpperCAmelCase      	=		dataset[i : i + batch_size]





@get_duration
def 						snake_case_							(_a     :							List[str]   ,       _a     :							str					):
    UpperCAmelCase      	=		datasets.Dataset.from_file(
        filename=os.path.join(SCREAMING_SNAKE_CASE_   ,       '''beta.arrow'''					)   ,       info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_					)					)
    for col in feats:
        UpperCAmelCase      	=		dataset[col]





@get_duration
def 						snake_case_							(_a     :							Union[str, Any]   ,       _a     :							List[str]					):
    UpperCAmelCase      	=		datasets.Dataset.from_file(
        filename=os.path.join(SCREAMING_SNAKE_CASE_   ,       '''beta.arrow'''					)   ,       info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_					)					)
    dataset.set_format('''numpy'''					)
    for col in feats:
        UpperCAmelCase      	=		dataset[col]





def 						snake_case_							():
    UpperCAmelCase      	=		{}
    UpperCAmelCase      	=		(
        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:
        UpperCAmelCase      	=		datasets.Features({'''image''': ArrayaD(SCREAMING_SNAKE_CASE_   ,       dtype='''float32'''					)}					)
        UpperCAmelCase      	=		generate_examples(features=SCREAMING_SNAKE_CASE_   ,       num_examples=SCREAMING_SNAKE_CASE_					)
        UpperCAmelCase      	=		write(SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_					)
        for read_func in read_functions:
            UpperCAmelCase      	=		read_func(SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_					)

    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])}
        # )
        UpperCAmelCase      	=		datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''					)					)					)}					)
        UpperCAmelCase      	=		generate_examples(
            features=SCREAMING_SNAKE_CASE_   ,       num_examples=SCREAMING_SNAKE_CASE_   ,       seq_shapes={'''image''': SPEED_TEST_SHAPE}					)
        UpperCAmelCase      	=		write(SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_					)
        for read_func in read_functions:
            UpperCAmelCase      	=		read_func(SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_					)

    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])}
        # )
        UpperCAmelCase      	=		datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''					)					)}					)
        UpperCAmelCase      	=		generate_examples(
            features=SCREAMING_SNAKE_CASE_   ,       num_examples=SCREAMING_SNAKE_CASE_   ,       seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}   ,       )
        UpperCAmelCase      	=		write(SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_					)
        for read_func in read_functions:
            UpperCAmelCase      	=		read_func(SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_					)

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


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










'''simple docstring'''







import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
   import torch


class       _a  (     unittest.TestCase							,							__a			):
       def    A				(     self						:     str    ):



           '''simple docstring'''

           UpperCAmelCase      	=		load_tool('''speech-to-text'''    )
           self.tool.setup()
       def    A				(     self						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tool(torch.ones(3_000    )    )
           self.assertEqual(lowercase       ,    ''' you'''    )







       def    A				(     self						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tool(audio=torch.ones(3_000    )    )
           self.assertEqual(lowercase       ,    ''' you'''    )




