


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				=			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=__UpperCamelCase		,		path=os.path.join(__UpperCamelCase		,		"beta.arrow"			)			) as writer:
          for key, record in dummy_data:
               UpperCAmelCase    :					Optional[Any]			=      my_features.encode_example(__UpperCamelCase			)
               writer.write(__UpperCamelCase			)
          UpperCAmelCase	,			UpperCAmelCase    :					int			=      writer.finalize()







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







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







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







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







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







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







def 	A (		):
     UpperCAmelCase    :					Union[str, Any]			=      {}
     UpperCAmelCase    :					Union[str, Any]			=      (
         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    :					str			=      datasets.Features({"image": ArrayaD(__UpperCamelCase		,		dtype="float32"			)}			)
          UpperCAmelCase    :					Dict			=      generate_examples(features=__UpperCamelCase		,		num_examples=__UpperCamelCase			)
          UpperCAmelCase    :					str			=      write(__UpperCamelCase		,		__UpperCamelCase		,		__UpperCamelCase			)
          for read_func in read_functions:
               UpperCAmelCase    :					List[str]			=      read_func(__UpperCamelCase		,		__UpperCamelCase			)

     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    :					str			=      datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"			)			)			)}			)
          UpperCAmelCase    :					Dict			=      generate_examples(
              features=__UpperCamelCase		,		num_examples=__UpperCamelCase		,		seq_shapes={"image": SPEED_TEST_SHAPE}			)
          UpperCAmelCase    :					Union[str, Any]			=      write(__UpperCamelCase		,		__UpperCamelCase		,		__UpperCamelCase			)
          for read_func in read_functions:
               UpperCAmelCase    :					str			=      read_func(__UpperCamelCase		,		__UpperCamelCase			)

     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    :					int			=      datasets.Features({"image": datasets.Sequence(datasets.Value("float32"			)			)}			)
          UpperCAmelCase    :					Dict			=      generate_examples(
              features=__UpperCamelCase		,		num_examples=__UpperCamelCase		,		seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}		,		)
          UpperCAmelCase    :					List[str]			=      write(__UpperCamelCase		,		__UpperCamelCase		,		__UpperCamelCase			)
          for read_func in read_functions:
               UpperCAmelCase    :					List[Any]			=      read_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_array_xd()




from typing import TYPE_CHECKING

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


__lowerCamelCase     :					Optional[int]				  =						{'''configuration_vilt''': ['''VILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViltConfig''']}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase     :					Dict				  =						['''ViltFeatureExtractor''']
   __lowerCamelCase     :					Dict				  =						['''ViltImageProcessor''']
   __lowerCamelCase     :					str				  =						['''ViltProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase     :					Any				  =						[
       '''VILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ViltForImageAndTextRetrieval''',
       '''ViltForImagesAndTextClassification''',
       '''ViltForTokenClassification''',
       '''ViltForMaskedLM''',
       '''ViltForQuestionAnswering''',
       '''ViltLayer''',
       '''ViltModel''',
       '''ViltPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_vilt import ViltFeatureExtractor
      from .image_processing_vilt import ViltImageProcessor
      from .processing_vilt import ViltProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vilt import (
          VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViltForImageAndTextRetrieval,
          ViltForImagesAndTextClassification,
          ViltForMaskedLM,
          ViltForQuestionAnswering,
          ViltForTokenClassification,
          ViltLayer,
          ViltModel,
          ViltPreTrainedModel,
      )


else:
   import sys

   __lowerCamelCase     :					List[str]				  =						_LazyModule(__name__, globals()['''__file__'''], _import_structure)
