


from typing import TYPE_CHECKING

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


_snake_case   = {"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = [
          "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "HubertForCTC",
          "HubertForSequenceClassification",
          "HubertModel",
          "HubertPreTrainedModel",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = [
          "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFHubertForCTC",
          "TFHubertModel",
          "TFHubertPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_hubert import (
                HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                HubertForCTC,
                HubertForSequenceClassification,
                HubertModel,
                HubertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_hubert import (
                TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFHubertForCTC,
                TFHubertModel,
                TFHubertPreTrainedModel,
            )


else:
      import sys

      _snake_case   = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







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


_snake_case   = (3_0, 4_8_7)
_snake_case   = (3_6, 1_0_2_4)
_snake_case   = (1_0_0, 1_0_0)
_snake_case   = 1_0_0

_snake_case   = datasets.Features(
    {"text": ArrayaD(SHAPE_TEST_1, dtype="float32"), "image": ArrayaD(SHAPE_TEST_2, dtype="float32")}
)

_snake_case ,						_snake_case   = os.path.split(__file__)
_snake_case   = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))







@get_duration
def   A   (		A__		,		A__		,		A__ ):
     with ArrowWriter(features=A__		,		path=os.path.join(A__		,		"beta.arrow" ) ) as writer:
          for key, record in dummy_data:
               UpperCAmelCase						:				Union[str, Any]		  =			my_features.encode_example(A__ )
               writer.write(A__ )
          UpperCAmelCase  , UpperCAmelCase						:				List[str]		  =			writer.finalize()







@get_duration
def   A   (		A__		,		A__ ):
     UpperCAmelCase						:				List[Any]		  =			datasets.Dataset.from_file(
         filename=os.path.join(A__		,		"beta.arrow" )		,		info=datasets.DatasetInfo(features=A__ ) )
     for _ in dataset:
          pass







@get_duration
def   A   (		A__		,		A__ ):
     UpperCAmelCase						:				Tuple		  =			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   A   (		A__		,		A__ ):
     UpperCAmelCase						:				Union[str, Any]		  =			1_0
     UpperCAmelCase						:				str		  =			datasets.Dataset.from_file(
         filename=os.path.join(A__		,		"beta.arrow" )		,		info=datasets.DatasetInfo(features=A__ ) )
     for i in range(0		,		len(A__ )		,		A__ ):
          UpperCAmelCase						:				Dict		  =			dataset[i : i + batch_size]







@get_duration
def   A   (		A__		,		A__ ):
     UpperCAmelCase						:				Any		  =			1_0
     UpperCAmelCase						:				List[str]		  =			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__ ):
          UpperCAmelCase						:				List[str]		  =			dataset[i : i + batch_size]







@get_duration
def   A   (		A__		,		A__ ):
     UpperCAmelCase						:				Tuple		  =			datasets.Dataset.from_file(
         filename=os.path.join(A__		,		"beta.arrow" )		,		info=datasets.DatasetInfo(features=A__ ) )
     for col in feats:
          UpperCAmelCase						:				Any		  =			dataset[col]







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







def   A   (		):
     UpperCAmelCase						:				Tuple		  =			{}
     UpperCAmelCase						:				int		  =			(
         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						:				Optional[Any]		  =			datasets.Features({"image": ArrayaD(A__		,		dtype="float32" )} )
          UpperCAmelCase						:				List[Any]		  =			generate_examples(features=A__		,		num_examples=A__ )
          UpperCAmelCase						:				Optional[Any]		  =			write(A__		,		A__		,		A__ )
          for read_func in read_functions:
               UpperCAmelCase						:				Optional[int]		  =			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])}
          # )
          UpperCAmelCase						:				Any		  =			datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32" ) ) )} )
          UpperCAmelCase						:				Optional[Any]		  =			generate_examples(
              features=A__		,		num_examples=A__		,		seq_shapes={"image": SPEED_TEST_SHAPE} )
          UpperCAmelCase						:				List[str]		  =			write(A__		,		A__		,		A__ )
          for read_func in read_functions:
               UpperCAmelCase						:				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])}
          # )
          UpperCAmelCase						:				Any		  =			datasets.Features({"image": datasets.Sequence(datasets.Value("float32" ) )} )
          UpperCAmelCase						:				Any		  =			generate_examples(
              features=A__		,		num_examples=A__		,		seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}		,		)
          UpperCAmelCase						:				Optional[int]		  =			write(A__		,		A__		,		A__ )
          for read_func in read_functions:
               UpperCAmelCase						:				Optional[Any]		  =			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()



