'''simple docstring'''





class 		SCREAMING_SNAKE_CASE     :







				'''simple docstring'''





				def __init__(						self					:						Optional[Any]       ,		lowercase__					:						Union[str, Any]       ,		lowercase__					:						List[str]       ,		lowercase__					:						int					)							->List[str]:


										'''simple docstring'''




										_UpperCamelCase			:			str   			=						name
										_UpperCamelCase			:			List[str]   			=						value
										_UpperCamelCase			:			Optional[Any]   			=						weight





				def __repr__(						self					:						List[Any]					)							->Optional[Any]:


										'''simple docstring'''




										return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''





				def  snake_case__				(						self					:						Optional[int]					)							->List[str]:


										'''simple docstring'''




										return self.value





				def  snake_case__				(						self					:						Any					)							->List[Any]:


										'''simple docstring'''




										return self.name





				def  snake_case__				(						self					:						str					)							->Dict:


										'''simple docstring'''




										return self.weight







				def  snake_case__				(						self					:						Union[str, Any]					)							->Union[str, Any]:


										'''simple docstring'''




										return self.value / self.weight





def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase					)     ->			str:




						'''simple docstring'''
						_UpperCamelCase			:			str   			=						[]
						for i in range(len(UpperCAmelCase					)					):
												menu.append(Things(name[i]     ,value[i]     ,weight[i]					)					)
						return menu
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase					)     ->			Tuple:




						'''simple docstring'''
						_UpperCamelCase			:			List[Any]   			=						sorted(UpperCAmelCase     ,key=UpperCAmelCase     ,reverse=UpperCAmelCase					)
						_UpperCamelCase			:			int   			=						[]
						_UpperCamelCase			,       _UpperCamelCase			:			Any   			=						0.0, 0.0
						for i in range(len(UpperCAmelCase					)					):
												if (total_cost + items_copy[i].get_weight()) <= max_cost:
																		result.append(items_copy[i]					)
																		total_cost += items_copy[i].get_weight()
																		total_value += items_copy[i].get_value()
						return (result, total_value)
def 						__A							(     )     ->			Union[str, Any]:




						'''simple docstring'''


if __name__ == "__main__":
			import doctest

			doctest.testmod()



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase_	:		Any			=     {
    """configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2TextConfig"""],
    """processing_speech_to_text""": ["""Speech2TextProcessor"""],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		int			=     ["""Speech2TextTokenizer"""]

try:
			if not is_speech_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		Optional[Any]			=     ["""Speech2TextFeatureExtractor"""]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		Optional[int]			=     [
			    """TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFSpeech2TextForConditionalGeneration""",
			    """TFSpeech2TextModel""",
			    """TFSpeech2TextPreTrainedModel""",
			]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		str			=     [
			    """SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """Speech2TextForConditionalGeneration""",
			    """Speech2TextModel""",
			    """Speech2TextPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
			from .processing_speech_to_text import SpeechaTextProcessor

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_speech_to_text import SpeechaTextTokenizer

			try:
						if not is_speech_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_speech_to_text import (
						    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFSpeechaTextForConditionalGeneration,
						    TFSpeechaTextModel,
						    TFSpeechaTextPreTrainedModel,
						)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_speech_to_text import (
						    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SpeechaTextForConditionalGeneration,
						    SpeechaTextModel,
						    SpeechaTextPreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase_	:		Union[str, Any]			=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


