




"""simple docstring"""







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


SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	     =       {
    'google/mobilenet_v1_1.0_224': 'https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json',
    'google/mobilenet_v1_0.75_192': 'https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   A_				      =       '''mobilenet_v1'''







   def __init__(		self       ,					lowerCamelCase_=3       ,					lowerCamelCase_=2_2_4       ,					lowerCamelCase_=1.0       ,					lowerCamelCase_=8       ,					lowerCamelCase_="relu6"       ,					lowerCamelCase_=True       ,					lowerCamelCase_=0.999       ,					lowerCamelCase_=0.02       ,					lowerCamelCase_=0.001       ,					**lowerCamelCase_       ,					)				->   int:
       super().__init__(**lowerCamelCase_)

       if depth_multiplier <= 0:
           raise ValueError('''depth_multiplier must be greater than zero.''')

       UpperCamelCase              =	num_channels
       UpperCamelCase              =	image_size
       UpperCamelCase              =	depth_multiplier
       UpperCamelCase              =	min_depth
       UpperCamelCase              =	hidden_act
       UpperCamelCase              =	tf_padding
       UpperCamelCase              =	classifier_dropout_prob
       UpperCamelCase              =	initializer_range
       UpperCamelCase              =	layer_norm_eps

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   A_				      =       version.parse('''1.11'''			)







   @property
   def   UpperCAmelCase__			(		self)				->   Mapping[str, Mapping[int, str]]:
       return OrderedDict([('''pixel_values''', {0: '''batch'''})])







   @property
   def   UpperCAmelCase__			(		self)				->   Mapping[str, Mapping[int, str]]:
       if self.task == "image-classification":
           return OrderedDict([('''logits''', {0: '''batch'''})])
       else:
           return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})])







   @property
   def   UpperCAmelCase__			(		self)				->   float:
       return 1e-4

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 							a		(				snake_case__:   int		,  snake_case__:   Dict		,  snake_case__:   Dict   ):


				'''simple docstring'''
				if input_in_memory_max_size != "default":
								monkeypatch.setattr(datasets.config		,  '''IN_MEMORY_MAX_SIZE'''		,  snake_case__   )
				lowercase_	    =   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:
								lowercase_	    =   dataset_size < in_memory_max_size
				else:
								lowercase_	    =   False
				lowercase_	    =   is_small_dataset(snake_case__   )
				assert result == expected




