






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


__UpperCamelCase   :   List[Any]      =    logging.get_logger(__name__)

__UpperCamelCase   :   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 							__UpperCamelCase     (  _lowerCAmelCase ):
	__snake_case						:Tuple =							'cvt'







	def __init__(	self   :				Dict							,					_lowerCAmelCase   :				Tuple=3							,					_lowerCAmelCase   :				Dict=[7, 3, 3]							,					_lowerCAmelCase   :				List[Any]=[4, 2, 2]							,					_lowerCAmelCase   :				Optional[int]=[2, 1, 1]							,					_lowerCAmelCase   :				Union[str, Any]=[64, 192, 384]							,					_lowerCAmelCase   :				Union[str, Any]=[1, 3, 6]							,					_lowerCAmelCase   :				Union[str, Any]=[1, 2, 10]							,					_lowerCAmelCase   :				Dict=[4.0, 4.0, 4.0]							,					_lowerCAmelCase   :				List[str]=[0.0, 0.0, 0.0]							,					_lowerCAmelCase   :				List[str]=[0.0, 0.0, 0.0]							,					_lowerCAmelCase   :				Optional[int]=[0.0, 0.0, 0.1]							,					_lowerCAmelCase   :				Optional[int]=[True, True, True]							,					_lowerCAmelCase   :				List[Any]=[False, False, True]							,					_lowerCAmelCase   :				List[Any]=["dw_bn", "dw_bn", "dw_bn"]							,					_lowerCAmelCase   :				List[str]=[3, 3, 3]							,					_lowerCAmelCase   :				Union[str, Any]=[1, 1, 1]							,					_lowerCAmelCase   :				Any=[2, 2, 2]							,					_lowerCAmelCase   :				str=[1, 1, 1]							,					_lowerCAmelCase   :				Optional[Any]=[1, 1, 1]							,					_lowerCAmelCase   :				int=0.02							,					_lowerCAmelCase   :				int=1e-12							,					**_lowerCAmelCase   :				List[str]							,					)		->			Any:
		"""simple docstring"""







		super().__init__(**_lowerCAmelCase      )
		__lowercase   							=	num_channels
		__lowercase   							=	patch_sizes
		__lowercase   							=	patch_stride
		__lowercase   							=	patch_padding
		__lowercase   							=	embed_dim
		__lowercase   							=	num_heads
		__lowercase   							=	depth
		__lowercase   							=	mlp_ratio
		__lowercase   							=	attention_drop_rate
		__lowercase   							=	drop_rate
		__lowercase   							=	drop_path_rate
		__lowercase   							=	qkv_bias
		__lowercase   							=	cls_token
		__lowercase   							=	qkv_projection_method
		__lowercase   							=	kernel_qkv
		__lowercase   							=	padding_kv
		__lowercase   							=	stride_kv
		__lowercase   							=	padding_q
		__lowercase   							=	stride_q
		__lowercase   							=	initializer_range
		__lowercase   							=	layer_norm_eps




import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_   							=  5_0_0_0_0_0

lowercase_							,		lowercase_   							=  os.path.split(__file__)
lowercase_   							=  os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[str]	)       -> Optional[Any]:





      """simple docstring"""



      _lowercase      			=dataset.map(**A__	)
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[Any]	)       -> List[str]:





      """simple docstring"""



      _lowercase      			=dataset.filter(**A__	)
def 	a (     )       -> Union[str, Any]:





      """simple docstring"""



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

            _lowercase      			=transformers.AutoTokenizer.from_pretrained('bert-base-cased' ,  use_fast=A__	)

            def tokenize(A__      :  int	):
                  return tokenizer(examples['text']	)

            _lowercase      			=map(A__	)

            _lowercase      			=map(A__ ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

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

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

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

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

            _lowercase      			=map(A__ ,  function=A__ ,  batched=A__	)

            _lowercase      			=filter(A__	)

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


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


