


def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Any   ,    lowercase__	:int					)							->	Union[str, Any]:
						__magic_name__					:				Union[str, Any]				=   len(lowercase__					)
						# Create a stack and push index of fist element to it
						__magic_name__					:				Dict				=   []
						st.append(0					)

						# Span value of first element is always 1
						__magic_name__					:				List[Any]				=   1

						# Calculate span values for rest of the elements
						for i in range(1   ,    lowercase__					):
												# Pop elements from stack while stack is not
												# empty and top of stack is smaller than price[i]
												while len(lowercase__					) > 0 and price[st[0]] <= price[i]:
																		st.pop()

												# If stack becomes empty, then price[i] is greater
												# than all elements on left of it, i.e. price[0],
												# price[1], ..price[i-1]. Else the price[i]  is
												# greater than elements after top of stack
												__magic_name__					:				Optional[int]				=   i + 1 if len(lowercase__					) <= 0 else (i - st[0])

												# Push this element to stack
												st.append(lowercase__					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Optional[Any]   ,    lowercase__	:Optional[int]					)							->	Optional[Any]:
						for i in range(0   ,    lowercase__					):
												print(arr[i]   ,    end=' '					)


# Driver program to test above function
snake_case__  				=			[10, 4, 5, 90, 120, 80]
snake_case__  				=			[0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)

print_array(S, len(price))






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__  				=			{'configuration_qdqbert': ['QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'QDQBertConfig']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			[
			    'QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'QDQBertForMaskedLM',
			    'QDQBertForMultipleChoice',
			    'QDQBertForNextSentencePrediction',
			    'QDQBertForQuestionAnswering',
			    'QDQBertForSequenceClassification',
			    'QDQBertForTokenClassification',
			    'QDQBertLayer',
			    'QDQBertLMHeadModel',
			    'QDQBertModel',
			    'QDQBertPreTrainedModel',
			    'load_tf_weights_in_qdqbert',
			]


if TYPE_CHECKING:
			from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_qdqbert import (
						    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    QDQBertForMaskedLM,
						    QDQBertForMultipleChoice,
						    QDQBertForNextSentencePrediction,
						    QDQBertForQuestionAnswering,
						    QDQBertForSequenceClassification,
						    QDQBertForTokenClassification,
						    QDQBertLayer,
						    QDQBertLMHeadModel,
						    QDQBertModel,
						    QDQBertPreTrainedModel,
						    load_tf_weights_in_qdqbert,
						)


else:
			import sys

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


