



"""simple docstring"""







def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       str    ,		lowerCamelCase_:       Tuple   ):
						"""simple docstring"""






						snake_case      : Dict							=   len(lowerCamelCase_   )
						# Create a stack and push index of fist element to it
						snake_case      : Optional[int]							=   []
						st.append(0   )

						# Span value of first element is always 1
						snake_case      : Tuple							=   1

						# Calculate span values for rest of the elements
						for i in range(1    ,		lowerCamelCase_   ):
												# Pop elements from stack while stack is not
												# empty and top of stack is smaller than price[i]
												while len(lowerCamelCase_   ) > 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
												snake_case      : Optional[Any]							=   i + 1 if len(lowerCamelCase_   ) <= 0 else (i - st[0])

												# Push this element to stack
												st.append(lowerCamelCase_   )

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       int    ,		lowerCamelCase_:       Dict   ):
						"""simple docstring"""






						for i in range(0    ,		lowerCamelCase_   ):
												print(arr[i]    ,		end=" "   )


# Driver program to test above function
A    =	[1_0, 4, 5, 9_0, 1_2_0, 8_0]
A    =	[0 for i in range(len(price) + 1)]

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








"""simple docstring"""







from typing import TYPE_CHECKING

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


A    =	{
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	['CamembertTokenizer']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	['CamembertTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'CamembertForCausalLM',
			    'CamembertForMaskedLM',
			    'CamembertForMultipleChoice',
			    'CamembertForQuestionAnswering',
			    'CamembertForSequenceClassification',
			    'CamembertForTokenClassification',
			    'CamembertModel',
			    'CamembertPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFCamembertForCausalLM',
			    'TFCamembertForMaskedLM',
			    'TFCamembertForMultipleChoice',
			    'TFCamembertForQuestionAnswering',
			    'TFCamembertForSequenceClassification',
			    'TFCamembertForTokenClassification',
			    'TFCamembertModel',
			    'TFCamembertPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_camembert import CamembertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_camembert_fast import CamembertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_camembert import (
						    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CamembertForCausalLM,
						    CamembertForMaskedLM,
						    CamembertForMultipleChoice,
						    CamembertForQuestionAnswering,
						    CamembertForSequenceClassification,
						    CamembertForTokenClassification,
						    CamembertModel,
						    CamembertPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_camembert import (
						    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFCamembertForCausalLM,
						    TFCamembertForMaskedLM,
						    TFCamembertForMultipleChoice,
						    TFCamembertForQuestionAnswering,
						    TFCamembertForSequenceClassification,
						    TFCamembertForTokenClassification,
						    TFCamembertModel,
						    TFCamembertPreTrainedModel,
						)

else:
			import sys

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



