

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_							:					List[str]		 =   {
    '''configuration_bert''': ['''BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BertConfig''', '''BertOnnxConfig'''],
    '''tokenization_bert''': ['''BasicTokenizer''', '''BertTokenizer''', '''WordpieceTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					List[Any]		 =   ['''BertTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					Union[str, Any]		 =   [
			    '''BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''BertForMaskedLM''',
			    '''BertForMultipleChoice''',
			    '''BertForNextSentencePrediction''',
			    '''BertForPreTraining''',
			    '''BertForQuestionAnswering''',
			    '''BertForSequenceClassification''',
			    '''BertForTokenClassification''',
			    '''BertLayer''',
			    '''BertLMHeadModel''',
			    '''BertModel''',
			    '''BertPreTrainedModel''',
			    '''load_tf_weights_in_bert''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					Any		 =   [
			    '''TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFBertEmbeddings''',
			    '''TFBertForMaskedLM''',
			    '''TFBertForMultipleChoice''',
			    '''TFBertForNextSentencePrediction''',
			    '''TFBertForPreTraining''',
			    '''TFBertForQuestionAnswering''',
			    '''TFBertForSequenceClassification''',
			    '''TFBertForTokenClassification''',
			    '''TFBertLMHeadModel''',
			    '''TFBertMainLayer''',
			    '''TFBertModel''',
			    '''TFBertPreTrainedModel''',
			]
try:
			if not is_tensorflow_text_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					List[str]		 =   ['''TFBertTokenizer''']

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					int		 =   [
			    '''FlaxBertForCausalLM''',
			    '''FlaxBertForMaskedLM''',
			    '''FlaxBertForMultipleChoice''',
			    '''FlaxBertForNextSentencePrediction''',
			    '''FlaxBertForPreTraining''',
			    '''FlaxBertForQuestionAnswering''',
			    '''FlaxBertForSequenceClassification''',
			    '''FlaxBertForTokenClassification''',
			    '''FlaxBertModel''',
			    '''FlaxBertPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
			from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bert_fast import BertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bert import (
						    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BertForMaskedLM,
						    BertForMultipleChoice,
						    BertForNextSentencePrediction,
						    BertForPreTraining,
						    BertForQuestionAnswering,
						    BertForSequenceClassification,
						    BertForTokenClassification,
						    BertLayer,
						    BertLMHeadModel,
						    BertModel,
						    BertPreTrainedModel,
						    load_tf_weights_in_bert,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_bert import (
						    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFBertEmbeddings,
						    TFBertForMaskedLM,
						    TFBertForMultipleChoice,
						    TFBertForNextSentencePrediction,
						    TFBertForPreTraining,
						    TFBertForQuestionAnswering,
						    TFBertForSequenceClassification,
						    TFBertForTokenClassification,
						    TFBertLMHeadModel,
						    TFBertMainLayer,
						    TFBertModel,
						    TFBertPreTrainedModel,
						)

			try:
						if not is_tensorflow_text_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bert_tf import TFBertTokenizer

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_bert import (
						    FlaxBertForCausalLM,
						    FlaxBertForMaskedLM,
						    FlaxBertForMultipleChoice,
						    FlaxBertForNextSentencePrediction,
						    FlaxBertForPreTraining,
						    FlaxBertForQuestionAnswering,
						    FlaxBertForSequenceClassification,
						    FlaxBertForTokenClassification,
						    FlaxBertModel,
						    FlaxBertPreTrainedModel,
						)

else:
			import sys

			UpperCAmelCase_							:					Optional[Any]		 =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








class      lowerCamelCase_      :




	def __init__(     self						:     Dict					,      __A						:     Tuple					,      __A						:     Optional[int]					,      __A						:     int							):
		__A :			List[str]		    =			name
		__A :			Optional[int]		    =			value
		__A :			Optional[Any]		    =			weight




	def __repr__(     self						:     Any							):
		return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		return self.value




	def        lowerCAmelCase_       (     self						:     str							):
		return self.name




	def        lowerCAmelCase_       (     self						:     str							):
		return self.weight




	def        lowerCAmelCase_       (     self						:     Dict							):
		return self.value / self.weight






def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   Optional[int]			,a__    :   Union[str, Any]       )					->				int:
	__A :			Tuple		    =			[]
	for i in range(len(a__       )       ):
		menu.append(Things(name[i]			,value[i]			,weight[i]       )       )
	return menu
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Tuple			,a__    :   Any			,a__    :   Optional[int]       )					->				Tuple:
	__A :			Optional[int]		    =			sorted(a__			,key=a__			,reverse=a__       )
	__A :			Optional[Any]		    =			[]
	__A    ,				__A :			Tuple		    =			0.0, 0.0
	for i in range(len(a__       )       ):
		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 							__SCREAMING_SNAKE_CASE     (   )					->				List[Any]:
	pass


if __name__ == "__main__":
			import doctest

			doctest.testmod()





