


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_A			=						50_0000

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

@get_duration
def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : datasets.Dataset      ,					**SCREAMING_SNAKE_CASE__      : Optional[int]    ):
     __UpperCamelCase   							=dataset.map(**SCREAMING_SNAKE_CASE__    )

@get_duration
def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : datasets.Dataset      ,					**SCREAMING_SNAKE_CASE__      : Any    ):
     __UpperCamelCase   							=dataset.filter(**SCREAMING_SNAKE_CASE__    )

def 						_UpperCAmelCase    (      ):
     __UpperCamelCase   							={'num examples': SPEED_TEST_N_EXAMPLES}
     with tempfile.TemporaryDirectory() as tmp_dir:
          __UpperCamelCase   							=datasets.Features({'text': datasets.Value('string'    ), 'numbers': datasets.Value('float32'    )}    )
          __UpperCamelCase   							=generate_example_dataset(
              os.path.join(SCREAMING_SNAKE_CASE__      ,					'dataset.arrow'    )      ,					SCREAMING_SNAKE_CASE__      ,					num_examples=SCREAMING_SNAKE_CASE__    )

          __UpperCamelCase   							=transformers.AutoTokenizer.from_pretrained('bert-base-cased'      ,					use_fast=SCREAMING_SNAKE_CASE__    )

          def tokenize(SCREAMING_SNAKE_CASE__      : List[Any]    ):
               return tokenizer(examples['text']    )

          __UpperCamelCase   							=map(SCREAMING_SNAKE_CASE__    )

          __UpperCamelCase   							=map(SCREAMING_SNAKE_CASE__      ,					batched=SCREAMING_SNAKE_CASE__    )

          __UpperCamelCase   							=map(SCREAMING_SNAKE_CASE__      ,					function=lambda SCREAMING_SNAKE_CASE__    : None      ,					batched=SCREAMING_SNAKE_CASE__    )

          with dataset.formatted_as(type='numpy'    ):
               __UpperCamelCase   							=map(SCREAMING_SNAKE_CASE__      ,					function=lambda SCREAMING_SNAKE_CASE__    : None      ,					batched=SCREAMING_SNAKE_CASE__    )

          with dataset.formatted_as(type='pandas'    ):
               __UpperCamelCase   							=map(SCREAMING_SNAKE_CASE__      ,					function=lambda SCREAMING_SNAKE_CASE__    : None      ,					batched=SCREAMING_SNAKE_CASE__    )

          with dataset.formatted_as(type='torch'      ,					columns='numbers'    ):
               __UpperCamelCase   							=map(SCREAMING_SNAKE_CASE__      ,					function=lambda SCREAMING_SNAKE_CASE__    : None      ,					batched=SCREAMING_SNAKE_CASE__    )

          with dataset.formatted_as(type='tensorflow'      ,					columns='numbers'    ):
               __UpperCamelCase   							=map(SCREAMING_SNAKE_CASE__      ,					function=lambda SCREAMING_SNAKE_CASE__    : None      ,					batched=SCREAMING_SNAKE_CASE__    )

          __UpperCamelCase   							=map(SCREAMING_SNAKE_CASE__      ,					function=SCREAMING_SNAKE_CASE__      ,					batched=SCREAMING_SNAKE_CASE__    )

          __UpperCamelCase   							=filter(SCREAMING_SNAKE_CASE__    )

          # Activate later when tokenizer support batched inputs
          # with dataset.formatted_as(type='numpy'):
          #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

     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_map_filter()









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_A			=						{'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTConfig', 'ViTOnnxConfig']}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						['ViTFeatureExtractor']
   _A			=						['ViTImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'VIT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ViTForImageClassification',
       'ViTForMaskedImageModeling',
       'ViTModel',
       'ViTPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'TFViTForImageClassification',
       'TFViTModel',
       'TFViTPreTrainedModel',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'FlaxViTForImageClassification',
       'FlaxViTModel',
       'FlaxViTPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_vit import ViTFeatureExtractor
      from .image_processing_vit import ViTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit import (
          VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTForImageClassification,
          ViTForMaskedImageModeling,
          ViTModel,
          ViTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
   import sys

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





