
"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCamelCase__     =						{
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

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

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase__     =						[
							    """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:
							lowerCamelCase__     =						[
							    """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

							lowerCamelCase__     =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
"""simple docstring"""


def 						UpperCamelCase				(	_lowerCAmelCase   :							Dict				, _lowerCAmelCase   :							Tuple						):
    __a			      =						len(_lowerCAmelCase						)
    # Create a stack and push index of fist element to it
    __a			      =						[]
    st.append(0						)

    # Span value of first element is always 1
    __a			      =						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
        __a			      =						i + 1 if len(_lowerCAmelCase						) <= 0 else (i - st[0])

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

def 						UpperCamelCase				(	_lowerCAmelCase   :							Dict				, _lowerCAmelCase   :							Optional[Any]						):
    for i in range(0				, _lowerCAmelCase						):
        print(arr[i]				, end=""" """						)


# Driver program to test above function
__A       			=      [10, 4, 5, 90, 1_20, 80]
__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))
