
import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset


@pytest.mark.parametrize('''dataset_size'''						,    [None, 400 * 2**20, 600 * 2**20]  )
@pytest.mark.parametrize('''input_in_memory_max_size'''						,    ['''default''', 0, 100 * 2**20, 900 * 2**20]  )
def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  Tuple:
				if input_in_memory_max_size != "default":
								monkeypatch.setattr(datasets.config						,    '''IN_MEMORY_MAX_SIZE'''						,    a__  )
				__a										=    datasets.config.IN_MEMORY_MAX_SIZE
				if input_in_memory_max_size == "default":
								assert in_memory_max_size == 0
				else:
								assert in_memory_max_size == input_in_memory_max_size
				if dataset_size and in_memory_max_size:
								__a										=    dataset_size < in_memory_max_size
				else:
								__a										=    False
				__a										=    is_small_dataset(a__  )
				assert result == expected

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


A     :	List[str]    = logging.get_logger(__name__)

A     :	Optional[int]    = {
    'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class 			__A( a			):
				snake_case_						   =    '''levit'''




				def __init__(     self		,     _snake_case=224		,     _snake_case=3		,     _snake_case=3		,     _snake_case=2		,     _snake_case=1		,     _snake_case=16		,     _snake_case=[128, 256, 384]		,     _snake_case=[4, 8, 12]		,     _snake_case=[4, 4, 4]		,     _snake_case=[16, 16, 16]		,     _snake_case=0		,     _snake_case=[2, 2, 2]		,     _snake_case=[2, 2, 2]		,     _snake_case=0.02		,     **_snake_case		,     )    ->      Optional[Any]:


								'''simple docstring'''




								super().__init__(**_snake_case				)
								__a										=    image_size
								__a										=    num_channels
								__a										=    kernel_size
								__a										=    stride
								__a										=    padding
								__a										=    hidden_sizes
								__a										=    num_attention_heads
								__a										=    depths
								__a										=    key_dim
								__a										=    drop_path_rate
								__a										=    patch_size
								__a										=    attention_ratio
								__a										=    mlp_ratio
								__a										=    initializer_range
								__a										=    [
								    ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
								    ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
								]







class 			__A( a			):
				snake_case_						   =    version.parse('''1.11'''			)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Mapping[str, Mapping[int, str]]:


								'''simple docstring'''




								return OrderedDict(
								    [
								        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
								    ]				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      float:


								'''simple docstring'''




								return 1E-4