






import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase__							=     50_0000

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






@get_duration
def   UpperCamelCase  (       snake_case__     :   datasets.Dataset	,**snake_case__     :   int  ):




     '''simple docstring'''






     __snake_case			:List[str]          =						dataset.map(**snake_case__  )






@get_duration
def   UpperCamelCase  (       snake_case__     :   datasets.Dataset	,**snake_case__     :   Any  ):




     '''simple docstring'''






     __snake_case			:str          =						dataset.filter(**snake_case__  )






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:Union[str, Any]          =						{"""num examples""": SPEED_TEST_N_EXAMPLES}
     with tempfile.TemporaryDirectory() as tmp_dir:
          __snake_case			:int          =						datasets.Features({"""text""": datasets.Value("""string"""  ), """numbers""": datasets.Value("""float32"""  )}  )
          __snake_case			:Any          =						generate_example_dataset(
              os.path.join(snake_case__	,"""dataset.arrow"""  )	,snake_case__	,num_examples=snake_case__  )

          __snake_case			:Union[str, Any]          =						transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""	,use_fast=snake_case__  )

          def tokenize(snake_case__     :   Union[str, Any]  ):
               return tokenizer(examples["""text"""]  )

          __snake_case			:Optional[Any]          =						map(snake_case__  )

          __snake_case			:Tuple          =						map(snake_case__	,batched=snake_case__  )

          __snake_case			:Optional[Any]          =						map(snake_case__	,function=lambda snake_case__  : None	,batched=snake_case__  )

          with dataset.formatted_as(type="""numpy"""  ):
               __snake_case			:Tuple          =						map(snake_case__	,function=lambda snake_case__  : None	,batched=snake_case__  )

          with dataset.formatted_as(type="""pandas"""  ):
               __snake_case			:int          =						map(snake_case__	,function=lambda snake_case__  : None	,batched=snake_case__  )

          with dataset.formatted_as(type="""torch"""	,columns="""numbers"""  ):
               __snake_case			:List[Any]          =						map(snake_case__	,function=lambda snake_case__  : None	,batched=snake_case__  )

          with dataset.formatted_as(type="""tensorflow"""	,columns="""numbers"""  ):
               __snake_case			:Tuple          =						map(snake_case__	,function=lambda snake_case__  : None	,batched=snake_case__  )

          __snake_case			:List[Any]          =						map(snake_case__	,function=snake_case__	,batched=snake_case__  )

          __snake_case			:List[Any]          =						filter(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(snake_case__	,"""wb"""  ) as f:
          f.write(json.dumps(snake_case__  ).encode("""utf-8"""  )  )


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







from typing import TYPE_CHECKING

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


lowerCamelCase__							=     {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     ["""DPTFeatureExtractor"""]
     lowerCamelCase__							=     ["""DPTImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     [
         """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """DPTForDepthEstimation""",
         """DPTForSemanticSegmentation""",
         """DPTModel""",
         """DPTPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_dpt import DPTFeatureExtractor
          from .image_processing_dpt import DPTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dpt import (
              DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPTForDepthEstimation,
              DPTForSemanticSegmentation,
              DPTModel,
              DPTPreTrainedModel,
          )


else:
     import sys

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


