



'''simple docstring'''





def  a_  (							_lowerCAmelCase ,_lowerCAmelCase )   ->   Union[str, Any]:
  __lowerCamelCase : Optional[int]					 =				len(_lowerCAmelCase )
  # Create a stack and push index of fist element to it
  __lowerCamelCase : Optional[Any]					 =				[]
  st.append(0 )

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

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

def  a_  (							_lowerCAmelCase ,_lowerCAmelCase )   ->   str:
  for i in range(0 ,_lowerCAmelCase ):
    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))











'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   import torch

   from transformers import CamembertModel






@require_torch
@require_sentencepiece
@require_tokenizers
class       lowerCamelCase_					(       unittest.TestCase  ):
  """simple docstring"""




  @slow
  def 		_lowercase  (				self   :			str     )      ->      List[Any]:
    __lowerCamelCase : str					 =				CamembertModel.from_pretrained('camembert-base'     )
    model.to(_a     )

    __lowerCamelCase : List[Any]					 =				torch.tensor(
        [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]    ,			device=_a    ,			dtype=torch.long    ,			)  # J'aime le camembert !
    with torch.no_grad():
      __lowerCamelCase : int					 =				model(_a     )['last_hidden_state']
    __lowerCamelCase : Optional[int]					 =				torch.Size((1, 10, 768)     )
    self.assertEqual(output.shape    ,			_a     )
    # compare the actual values for a slice.
    __lowerCamelCase : Tuple					 =				torch.tensor(
        [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,			device=_a    ,			dtype=torch.float    ,			)
    # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
    # camembert.eval()
    # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

    self.assertTrue(torch.allclose(output[:, :3, :3]    ,			_a    ,			atol=1e-4     )     )






