


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowerCAmelCase__      		= 5_00_00
lowerCAmelCase__      		= 50_00

lowerCAmelCase__		,lowerCAmelCase__      		= os.path.split(__file__)
lowerCAmelCase__      		= os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 							_UpperCAmelCase				(UpperCamelCase__     :  datasets.Dataset				,							UpperCamelCase__     :  Optional[Any]						):
       for i in range(UpperCamelCase__						):
              _A      :       Optional[int]				    = dataset[i]
@get_duration
def 							_UpperCAmelCase				(UpperCamelCase__     :  datasets.Dataset				,							UpperCamelCase__     :  List[Any]				,							UpperCamelCase__     :  Dict						):
       for i in range(0				,							len(UpperCamelCase__						)				,							UpperCamelCase__						):
              _A      :       int				    = dataset[i : i + batch_size]
@get_duration
def 							_UpperCAmelCase				(UpperCamelCase__     :  datasets.Dataset				,							UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  int						):
       with dataset.formatted_as(type=UpperCamelCase__						):
              for i in range(UpperCamelCase__						):
                     _A      :       Optional[int]				    = dataset[i]
@get_duration
def 							_UpperCAmelCase				(UpperCamelCase__     :  datasets.Dataset				,							UpperCamelCase__     :  Union[str, Any]				,							UpperCamelCase__     :  List[Any]				,							UpperCamelCase__     :  List[Any]						):
       with dataset.formatted_as(type=UpperCamelCase__						):
              for i in range(0				,							UpperCamelCase__				,							UpperCamelCase__						):
                     _A      :       Dict				    = dataset[i : i + batch_size]
def 							_UpperCAmelCase				():
       _A      :       Optional[int]				    = {"num examples": SPEED_TEST_N_EXAMPLES}
       _A      :       List[Any]				    = [
           (read, {"length": SMALL_TEST}),
           (read, {"length": SPEED_TEST_N_EXAMPLES}),
           (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
           (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
           (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
           (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
           (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
           (read_formatted, {"type": "torch", "length": SMALL_TEST}),
           (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
           (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
           (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
       ]

       _A      :       str				    = [
           (read, {"length": SMALL_TEST}),
           (read, {"length": SPEED_TEST_N_EXAMPLES}),
           (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
           (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
           (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
           (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
           (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
           (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
       ]
       with tempfile.TemporaryDirectory() as tmp_dir:
              print("generating dataset"						)
              _A      :       List[Any]				    = datasets.Features(
                  {"list": datasets.Sequence(datasets.Value("float32"						)						), "numbers": datasets.Value("float32"						)}						)
              _A      :       Dict				    = generate_example_dataset(
                  os.path.join(UpperCamelCase__				,							"dataset.arrow"						)				,							UpperCamelCase__				,							num_examples=UpperCamelCase__				,							seq_shapes={"list": (100,)}				,							)
              print("first set of iterations"						)
              for func, kwargs in functions:
                     print(func.__name__				,							str(UpperCamelCase__						)						)
                     _A      :       List[str]				    = func(UpperCamelCase__				,							**UpperCamelCase__						)

              print("shuffling dataset"						)
              _A      :       Union[str, Any]				    = dataset.shuffle()
              print("Second set of iterations (after shuffling"						)
              for func, kwargs in functions_shuffled:
                     print("shuffled "				,							func.__name__				,							str(UpperCamelCase__						)						)
                     _A      :       Dict				    = func(
                         UpperCamelCase__				,							**UpperCamelCase__						)

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


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




from typing import TYPE_CHECKING

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


lowerCAmelCase__      		= {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= ['SpeechEncoderDecoderModel']

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= ['FlaxSpeechEncoderDecoderModel']

if TYPE_CHECKING:
    from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
    import sys

    lowerCAmelCase__      		= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
