


def 							UpperCAmelCase_   (				__snake_case	,		__snake_case		)	->     Union[str, Any]:







 """simple docstring"""


 _lowercase				 =len(__snake_case		)
 # Create a stack and push index of fist element to it
 _lowercase				 =[]
 st.append(0		)

 # Span value of first element is always 1
 _lowercase				 =1

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

  # Push this element to stack
  st.append(__snake_case		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case		)	->     Optional[Any]:







 """simple docstring"""


 for i in range(0	,		__snake_case		):
  print(arr[i]	,		end=''' '''		)


# Driver program to test above function
UpperCAmelCase__        =				[10, 4, 5, 90, 120, 80]
UpperCAmelCase__        =				[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_tf_available, is_torch_available


UpperCAmelCase__        =				{
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__        =				[
      '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''FlaubertForMultipleChoice''',
      '''FlaubertForQuestionAnswering''',
      '''FlaubertForQuestionAnsweringSimple''',
      '''FlaubertForSequenceClassification''',
      '''FlaubertForTokenClassification''',
      '''FlaubertModel''',
      '''FlaubertWithLMHeadModel''',
      '''FlaubertPreTrainedModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__        =				[
      '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFFlaubertForMultipleChoice''',
      '''TFFlaubertForQuestionAnsweringSimple''',
      '''TFFlaubertForSequenceClassification''',
      '''TFFlaubertForTokenClassification''',
      '''TFFlaubertModel''',
      '''TFFlaubertPreTrainedModel''',
      '''TFFlaubertWithLMHeadModel''',
  ]


if TYPE_CHECKING:
  from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
  from .tokenization_flaubert import FlaubertTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flaubert import (
        FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaubertForMultipleChoice,
        FlaubertForQuestionAnswering,
        FlaubertForQuestionAnsweringSimple,
        FlaubertForSequenceClassification,
        FlaubertForTokenClassification,
        FlaubertModel,
        FlaubertPreTrainedModel,
        FlaubertWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_flaubert import (
        TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFFlaubertForMultipleChoice,
        TFFlaubertForQuestionAnsweringSimple,
        TFFlaubertForSequenceClassification,
        TFFlaubertForTokenClassification,
        TFFlaubertModel,
        TFFlaubertPreTrainedModel,
        TFFlaubertWithLMHeadModel,
    )

else:
  import sys

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

