
'''simple docstring'''





def      __lowerCAmelCase						(							UpperCAmelCase__				:      list[list[float]]       )  ->    list[list[float]]:
							lowerCamelCase_							  =					[]
							for data in source_data:
														for i, el in enumerate(UpperCAmelCase__       ):
																					if len(UpperCAmelCase__       ) < i + 1:
																												data_lists.append([]       )
																					data_lists[i].append(float(UpperCAmelCase__       )       )
							return data_lists



def      __lowerCAmelCase						(							UpperCAmelCase__				:      list[list[float]]			,				UpperCAmelCase__				:      list[int]       )  ->    list[list[float]]:
							lowerCamelCase_							  =					[]
							for dlist, weight in zip(UpperCAmelCase__			,				UpperCAmelCase__       ):
														lowerCamelCase_							  =					min(UpperCAmelCase__       )
														lowerCamelCase_							  =					max(UpperCAmelCase__       )

														lowerCamelCase_							  =					[]
														# for weight 0 score is 1 - actual score
														if weight == 0:
																					for item in dlist:
																												try:
																																			score.append(1 - ((item - mind) / (maxd - mind))       )
																												except ZeroDivisionError:
																																			score.append(1       )

														elif weight == 1:
																					for item in dlist:
																												try:
																																			score.append((item - mind) / (maxd - mind)       )
																												except ZeroDivisionError:
																																			score.append(0       )

        # weight not 0 or 1
														else:
																					lowerCamelCase_							  =					F'''Invalid weight of {weight:f} provided'''
																					raise ValueError(UpperCAmelCase__       )

														score_lists.append(UpperCAmelCase__       )

							return score_lists



def      __lowerCAmelCase						(							UpperCAmelCase__				:      list[list[float]]       )  ->    list[float]:
							lowerCamelCase_							  =					[0 for i in range(len(score_lists[0]       )       )]

							for slist in score_lists:
														for j, ele in enumerate(UpperCAmelCase__       ):
																					lowerCamelCase_							  =					final_scores[j] + ele

							return final_scores



def      __lowerCAmelCase						(							UpperCAmelCase__				:      list[list[float]]			,				UpperCAmelCase__				:      list[int]       )  ->    list[list[float]]:
							lowerCamelCase_							  =					get_data(UpperCAmelCase__       )
							lowerCamelCase_							  =					calculate_each_score(UpperCAmelCase__			,				UpperCAmelCase__       )
							lowerCamelCase_							  =					generate_final_scores(UpperCAmelCase__       )

							# append scores to source data
							for i, ele in enumerate(UpperCAmelCase__       ):
														source_data[i].append(UpperCAmelCase__       )

							return source_data







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase        =  {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  ['''DeiTFeatureExtractor''']
		lowercase        =  ['''DeiTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DeiTForImageClassification''',
		    '''DeiTForImageClassificationWithTeacher''',
		    '''DeiTForMaskedImageModeling''',
		    '''DeiTModel''',
		    '''DeiTPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFDeiTForImageClassification''',
		    '''TFDeiTForImageClassificationWithTeacher''',
		    '''TFDeiTForMaskedImageModeling''',
		    '''TFDeiTModel''',
		    '''TFDeiTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

		lowercase        =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





