



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


lowercase_													=      logging.get_logger(__name__)

lowercase_													=      {
    '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
    '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
    '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}






class     __a			(  SCREAMING_SNAKE_CASE					):
	SCREAMING_SNAKE_CASE  	=							"mobilenet_v2"



	def __init__(    self				:     List[str]					,						snake_case_				:     Dict=3					,						snake_case_				:     Any=2_24					,						snake_case_				:     int=1.0					,						snake_case_				:     Dict=8					,						snake_case_				:     Dict=8					,						snake_case_				:     str=6					,						snake_case_				:     Optional[Any]=32					,						snake_case_				:     Optional[int]=True					,						snake_case_				:     str=True					,						snake_case_				:     Optional[int]="relu6"					,						snake_case_				:     List[Any]=True					,						snake_case_				:     Dict=0.8					,						snake_case_				:     List[Any]=0.0_2					,						snake_case_				:     Tuple=0.0_0_1					,						snake_case_				:     Union[str, Any]=2_55					,						**snake_case_				:     Union[str, Any]					,						)->				Any:
						super().__init__(**snake_case_)

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

						__lowerCAmelCase						 =num_channels
						__lowerCAmelCase						 =image_size
						__lowerCAmelCase						 =depth_multiplier
						__lowerCAmelCase						 =depth_divisible_by
						__lowerCAmelCase						 =min_depth
						__lowerCAmelCase						 =expand_ratio
						__lowerCAmelCase						 =output_stride
						__lowerCAmelCase						 =first_layer_is_expansion
						__lowerCAmelCase						 =finegrained_output
						__lowerCAmelCase						 =hidden_act
						__lowerCAmelCase						 =tf_padding
						__lowerCAmelCase						 =classifier_dropout_prob
						__lowerCAmelCase						 =initializer_range
						__lowerCAmelCase						 =layer_norm_eps
						__lowerCAmelCase						 =semantic_loss_ignore_index






class     __a			(  SCREAMING_SNAKE_CASE					):
	SCREAMING_SNAKE_CASE  	=							version.parse("1.11"					)



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



	@property
	def 		UpperCamelCase				(    self				:     List[Any])->				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				:     Tuple)->				float:
						return 1e-4









import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def        __lowerCAmelCase		(	__lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Any						)	->    Dict:
					assert isinstance(__lowerCamelCase					, __lowerCamelCase						)
					assert dataset.num_rows == 4
					assert dataset.num_columns == 1
					assert dataset.column_names == ["text"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("""keep_in_memory"""					, [False, True]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     str					, __lowerCamelCase						:     Optional[Any]						)	->    List[Any]:
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, cache_dir=__lowerCamelCase					, keep_in_memory=__lowerCamelCase						).read()
					_check_text_dataset(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize(
    """features"""					, [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]					, )
def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     Any					, __lowerCamelCase						:     Tuple						)	->    List[Any]:
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =features.copy() if features else default_expected_features
					__lowerCAmelCase						 =(
					    Features({feature: Value(__lowerCamelCase						) for feature, dtype in features.items()}						) if features is not None else None
					)
					__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, features=__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_dataset(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize("""split"""					, [None, NamedSplit("""train"""						), """train""", """test"""]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     Union[str, Any]					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     Union[str, Any]						)	->    Any:
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, cache_dir=__lowerCamelCase					, split=__lowerCamelCase						).read()
					_check_text_dataset(__lowerCamelCase					, __lowerCamelCase						)
					assert dataset.split == split if split else "train"






@pytest.mark.parametrize("""path_type"""					, [str, list]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     int					, __lowerCamelCase						:     List[str]						)	->    Any:
					if issubclass(__lowerCamelCase					, __lowerCamelCase						):
										__lowerCAmelCase						 =text_path
					elif issubclass(__lowerCamelCase					, __lowerCamelCase						):
										__lowerCAmelCase						 =[text_path]
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_dataset(__lowerCamelCase					, __lowerCamelCase						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     str=("train",)						)	->    Dict:
					assert isinstance(__lowerCamelCase					, __lowerCamelCase						)
					for split in splits:
										__lowerCAmelCase						 =dataset_dict[split]
										assert dataset.num_rows == 4
										assert dataset.num_columns == 1
										assert dataset.column_names == ["text"]
										for feature, expected_dtype in expected_features.items():
															assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("""keep_in_memory"""					, [False, True]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     List[Any]						)	->    Dict:
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__lowerCAmelCase						 =TextDatasetReader({"""train""": text_path}					, cache_dir=__lowerCamelCase					, keep_in_memory=__lowerCamelCase						).read()
					_check_text_datasetdict(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize(
    """features"""					, [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]					, )
def        __lowerCAmelCase		(	__lowerCamelCase						:     Any					, __lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     Union[str, Any]						)	->    Tuple:
					__lowerCAmelCase						 =tmp_path / """cache"""
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =features.copy() if features else default_expected_features
					__lowerCAmelCase						 =(
					    Features({feature: Value(__lowerCamelCase						) for feature, dtype in features.items()}						) if features is not None else None
					)
					__lowerCAmelCase						 =TextDatasetReader({"""train""": text_path}					, features=__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_datasetdict(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize("""split"""					, [None, NamedSplit("""train"""						), """train""", """test"""]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     int					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     Any						)	->    int:
					if split:
										__lowerCAmelCase						 ={split: text_path}
					else:
										__lowerCAmelCase						 ="""train"""
										__lowerCAmelCase						 ={"""train""": text_path, """test""": text_path}
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_datasetdict(__lowerCamelCase					, __lowerCamelCase					, splits=list(path.keys()						)						)
					assert all(dataset[split].split == split for split in path.keys()						)




