
import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase					   =5_0_0_0_0_0

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





@get_duration
def     _a					(						lowerCamelCase,	**lowerCamelCase							):
 lowerCamelCase		:	Optional[Any]							=      dataset.map(**lowerCamelCase							)





@get_duration
def     _a					(						lowerCamelCase,	**lowerCamelCase							):
 lowerCamelCase		:	Optional[Any]							=      dataset.filter(**lowerCamelCase							)





def     _a					(						):
 lowerCamelCase		:	Optional[Any]							=      {"""num examples""": SPEED_TEST_N_EXAMPLES}
 with tempfile.TemporaryDirectory() as tmp_dir:
  lowerCamelCase		:	Any							=      datasets.Features({"""text""": datasets.Value("""string"""							), """numbers""": datasets.Value("""float32"""							)}							)
  lowerCamelCase		:	Tuple							=      generate_example_dataset(
      os.path.join(lowerCamelCase,	"""dataset.arrow"""							),	lowerCamelCase,	num_examples=lowerCamelCase							)

  lowerCamelCase		:	Tuple							=      transformers.AutoTokenizer.from_pretrained("""bert-base-cased""",	use_fast=lowerCamelCase							)

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

  lowerCamelCase		:	List[str]							=      map(lowerCamelCase							)

  lowerCamelCase		:	int							=      map(lowerCamelCase,	batched=lowerCamelCase							)

  lowerCamelCase		:	int							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

  with dataset.formatted_as(type="""numpy"""							):
   lowerCamelCase		:	Optional[int]							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

  with dataset.formatted_as(type="""pandas"""							):
   lowerCamelCase		:	List[str]							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

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

  with dataset.formatted_as(type="""tensorflow""",	columns="""numbers"""							):
   lowerCamelCase		:	Optional[int]							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

  lowerCamelCase		:	int							=      map(lowerCamelCase,	function=lowerCamelCase,	batched=lowerCamelCase							)

  lowerCamelCase		:	Union[str, Any]							=      filter(lowerCamelCase							)

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


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






import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """nvidia/segformer-b0-finetuned-ade-512-512""": (
        """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Dict           =			"""segformer"""
      def __init__(      self    ,  __magic_name__=3    ,  __magic_name__=4    ,  __magic_name__=[2, 2, 2, 2]    ,  __magic_name__=[8, 4, 2, 1]    ,  __magic_name__=[3_2, 6_4, 1_6_0, 2_5_6]    ,  __magic_name__=[7, 3, 3, 3]    ,  __magic_name__=[4, 2, 2, 2]    ,  __magic_name__=[1, 2, 5, 8]    ,  __magic_name__=[4, 4, 4, 4]    ,  __magic_name__="gelu"    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.1    ,  __magic_name__=0.02    ,  __magic_name__=0.1    ,  __magic_name__=1e-6    ,  __magic_name__=2_5_6    ,  __magic_name__=2_5_5    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )

       if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
        warnings.warn(
            """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
            """ removed, as the behaviour will default to that of reshape_last_stage = True."""    ,  __magic_name__    ,  )

       lowerCamelCase		:	Optional[Any]							=      num_channels
       lowerCamelCase		:	str							=      num_encoder_blocks
       lowerCamelCase		:	Any							=      depths
       lowerCamelCase		:	List[Any]							=      sr_ratios
       lowerCamelCase		:	int							=      hidden_sizes
       lowerCamelCase		:	Union[str, Any]							=      patch_sizes
       lowerCamelCase		:	Optional[Any]							=      strides
       lowerCamelCase		:	Dict							=      mlp_ratios
       lowerCamelCase		:	str							=      num_attention_heads
       lowerCamelCase		:	Any							=      hidden_act
       lowerCamelCase		:	Tuple							=      hidden_dropout_prob
       lowerCamelCase		:	Union[str, Any]							=      attention_probs_dropout_prob
       lowerCamelCase		:	Dict							=      classifier_dropout_prob
       lowerCamelCase		:	Tuple							=      initializer_range
       lowerCamelCase		:	Dict							=      drop_path_rate
       lowerCamelCase		:	List[str]							=      layer_norm_eps
       lowerCamelCase		:	Any							=      decoder_hidden_size
       lowerCamelCase		:	str							=      kwargs.get("""reshape_last_stage"""    ,  __magic_name__      )
       lowerCamelCase		:	Dict							=      semantic_loss_ignore_index





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    str           =			version.parse("""1.11""")
      @property
      def 						UpperCamelCase__							(      self      ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
           ]      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return 1e-4






      @property
      def 						UpperCamelCase__							(      self      ):
       return 1_2




