





'''simple docstring'''


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowerCAmelCase :					Dict 							=500_000

lowerCAmelCase							,		lowerCAmelCase :					Tuple 							=os.path.split(__file__)
lowerCAmelCase :					int 							=os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def 							UpperCAmelCase_  (						__lowerCamelCase : datasets.Dataset     ,**__lowerCamelCase : int			):
   lowercase_      :List[str]         =			dataset.map(**__lowerCamelCase			)
@get_duration
def 							UpperCAmelCase_  (						__lowerCamelCase : datasets.Dataset     ,**__lowerCamelCase : Dict			):
   lowercase_      :Optional[int]         =			dataset.filter(**__lowerCamelCase			)
def 							UpperCAmelCase_  (						):
   lowercase_      :Dict         =			{"num examples": SPEED_TEST_N_EXAMPLES}
   with tempfile.TemporaryDirectory() as tmp_dir:
      lowercase_      :str         =			datasets.Features({"text": datasets.Value("string"			), "numbers": datasets.Value("float32"			)}			)
      lowercase_      :List[str]         =			generate_example_dataset(
          os.path.join(__lowerCamelCase     ,"dataset.arrow"			)     ,__lowerCamelCase     ,num_examples=__lowerCamelCase			)

      lowercase_      :List[str]         =			transformers.AutoTokenizer.from_pretrained("bert-base-cased"     ,use_fast=__lowerCamelCase			)

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

      lowercase_      :List[str]         =			map(__lowerCamelCase			)

      lowercase_      :Optional[int]         =			map(__lowerCamelCase     ,batched=__lowerCamelCase			)

      lowercase_      :Tuple         =			map(__lowerCamelCase     ,function=lambda __lowerCamelCase			: None     ,batched=__lowerCamelCase			)

      with dataset.formatted_as(type="numpy"			):
         lowercase_      :List[Any]         =			map(__lowerCamelCase     ,function=lambda __lowerCamelCase			: None     ,batched=__lowerCamelCase			)

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

      with dataset.formatted_as(type="torch"     ,columns="numbers"			):
         lowercase_      :Any         =			map(__lowerCamelCase     ,function=lambda __lowerCamelCase			: None     ,batched=__lowerCamelCase			)

      with dataset.formatted_as(type="tensorflow"     ,columns="numbers"			):
         lowercase_      :str         =			map(__lowerCamelCase     ,function=lambda __lowerCamelCase			: None     ,batched=__lowerCamelCase			)

      lowercase_      :int         =			map(__lowerCamelCase     ,function=__lowerCamelCase     ,batched=__lowerCamelCase			)

      lowercase_      :Tuple         =			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()









'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase :					Tuple 							=logging.get_logger(__name__)

lowerCAmelCase :					str 							={
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}
class 				a_       (    _lowerCAmelCase							):
     __A								=      "cvt"





     def __init__(			self		: Tuple     ,				lowercase		: str=3     ,				lowercase		: str=[7, 3, 3]     ,				lowercase		: List[str]=[4, 2, 2]     ,				lowercase		: Dict=[2, 1, 1]     ,				lowercase		: int=[64, 192, 384]     ,				lowercase		: Dict=[1, 3, 6]     ,				lowercase		: Dict=[1, 2, 10]     ,				lowercase		: Any=[4.0, 4.0, 4.0]     ,				lowercase		: Tuple=[0.0, 0.0, 0.0]     ,				lowercase		: List[str]=[0.0, 0.0, 0.0]     ,				lowercase		: List[str]=[0.0, 0.0, 0.1]     ,				lowercase		: Any=[True, True, True]     ,				lowercase		: Any=[False, False, True]     ,				lowercase		: Optional[Any]=["dw_bn", "dw_bn", "dw_bn"]     ,				lowercase		: int=[3, 3, 3]     ,				lowercase		: str=[1, 1, 1]     ,				lowercase		: List[Any]=[2, 2, 2]     ,				lowercase		: Tuple=[1, 1, 1]     ,				lowercase		: Optional[Any]=[1, 1, 1]     ,				lowercase		: str=0.02     ,				lowercase		: str=1e-1_2     ,				**lowercase		: str     ,				):





        """simple docstring"""




        super().__init__(**lowercase						)
        lowercase_      :List[Any]         =			num_channels
        lowercase_      :Union[str, Any]         =			patch_sizes
        lowercase_      :Tuple         =			patch_stride
        lowercase_      :List[Any]         =			patch_padding
        lowercase_      :List[Any]         =			embed_dim
        lowercase_      :Union[str, Any]         =			num_heads
        lowercase_      :Any         =			depth
        lowercase_      :str         =			mlp_ratio
        lowercase_      :List[str]         =			attention_drop_rate
        lowercase_      :List[Any]         =			drop_rate
        lowercase_      :Union[str, Any]         =			drop_path_rate
        lowercase_      :Any         =			qkv_bias
        lowercase_      :Dict         =			cls_token
        lowercase_      :int         =			qkv_projection_method
        lowercase_      :Union[str, Any]         =			kernel_qkv
        lowercase_      :Optional[Any]         =			padding_kv
        lowercase_      :Optional[Any]         =			stride_kv
        lowercase_      :Dict         =			padding_q
        lowercase_      :Any         =			stride_q
        lowercase_      :Dict         =			initializer_range
        lowercase_      :Optional[Any]         =			layer_norm_eps


