
"""simple docstring"""


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


SCREAMING_SNAKE_CASE      =     500_000

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



@get_duration
def 	lowerCamelCase__			(							UpperCAmelCase_		,      **UpperCAmelCase_				)->							Any:







       """simple docstring"""
       UpperCamelCase					 =		dataset.map(**UpperCAmelCase_				)



@get_duration
def 	lowerCamelCase__			(							UpperCAmelCase_		,      **UpperCAmelCase_				)->							int:







       """simple docstring"""
       UpperCamelCase					 =		dataset.filter(**UpperCAmelCase_				)



def 	lowerCamelCase__			(							)->							Dict:







       """simple docstring"""
       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(UpperCAmelCase_		,      "dataset.arrow"				)		,      UpperCAmelCase_		,      num_examples=UpperCAmelCase_				)

              UpperCamelCase					 =		transformers.AutoTokenizer.from_pretrained("bert-base-cased"		,      use_fast=UpperCAmelCase_				)

              def tokenize(UpperCAmelCase_				):
                     return tokenizer(examples["text"]				)

              UpperCamelCase					 =		map(UpperCAmelCase_				)

              UpperCamelCase					 =		map(UpperCAmelCase_		,      batched=UpperCAmelCase_				)

              UpperCamelCase					 =		map(UpperCAmelCase_		,      function=lambda UpperCAmelCase_				: None		,      batched=UpperCAmelCase_				)

              with dataset.formatted_as(type="numpy"				):
                     UpperCamelCase					 =		map(UpperCAmelCase_		,      function=lambda UpperCAmelCase_				: None		,      batched=UpperCAmelCase_				)

              with dataset.formatted_as(type="pandas"				):
                     UpperCamelCase					 =		map(UpperCAmelCase_		,      function=lambda UpperCAmelCase_				: None		,      batched=UpperCAmelCase_				)

              with dataset.formatted_as(type="torch"		,      columns="numbers"				):
                     UpperCamelCase					 =		map(UpperCAmelCase_		,      function=lambda UpperCAmelCase_				: None		,      batched=UpperCAmelCase_				)

              with dataset.formatted_as(type="tensorflow"		,      columns="numbers"				):
                     UpperCamelCase					 =		map(UpperCAmelCase_		,      function=lambda UpperCAmelCase_				: None		,      batched=UpperCAmelCase_				)

              UpperCamelCase					 =		map(UpperCAmelCase_		,      function=UpperCAmelCase_		,      batched=UpperCAmelCase_				)

              UpperCamelCase					 =		filter(UpperCAmelCase_				)

              # 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(UpperCAmelCase_		,      "wb"				) as f:
              f.write(json.dumps(UpperCAmelCase_				).encode("utf-8"				)				)


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







"""simple docstring"""


from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE      =     {
    """configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GraphormerForGraphClassification""",
          """GraphormerModel""",
          """GraphormerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_graphormer import (
                GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                GraphormerForGraphClassification,
                GraphormerModel,
                GraphormerPreTrainedModel,
            )


else:
      import sys

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





