"""simple docstring"""



def    _A(		lowerCAmelCase ,     lowerCAmelCase       ):
							A__					:    int														= len(lowerCAmelCase       )
							# Create a stack and push index of fist element to it
							A__					:    Optional[int]														= []
							st.append(0       )

							# Span value of first element is always 1
							A__					:    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
														A__					:    Any														= i + 1 if len(lowerCAmelCase       ) <= 0 else (i - st[0])

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

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


# Driver program to test above function
_UpperCamelCase         =		[10, 4, 5, 90, 1_20, 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"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase         =		logging.get_logger(__name__)

_UpperCamelCase         =		{
    "facebook/opt-125m": "https://huggingface.co/facebook/opt-125m/blob/main/config.json",
    "facebook/opt-350m": "https://huggingface.co/facebook/opt-350m/blob/main/config.json",
    "facebook/opt-1.3b": "https://huggingface.co/facebook/opt-1.3b/blob/main/config.json",
    "facebook/opt-2.7b": "https://huggingface.co/facebook/opt-2.7b/blob/main/config.json",
    "facebook/opt-6.7b": "https://huggingface.co/facebook/opt-6.7b/blob/main/config.json",
    "facebook/opt-13b": "https://huggingface.co/facebook/opt-13b/blob/main/config.json",
}
class 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''
			_UpperCamelCase     :	Dict				    =							'opt'
			_UpperCamelCase     :	Optional[Any]				    =							['past_key_values']





			def __init__( self      ,			snake_case_=50_272      ,			snake_case_=768      ,			snake_case_=12      ,			snake_case_=3_072      ,			snake_case_=2_048      ,			snake_case_=True      ,			snake_case_=False      ,			snake_case_=None      ,			snake_case_=0.1      ,			snake_case_=0.0      ,			snake_case_=12      ,			snake_case_="relu"      ,			snake_case_=0.0      ,			snake_case_=0.02      ,			snake_case_=True      ,			snake_case_=1      ,			snake_case_=2      ,			snake_case_=2      ,			snake_case_=True      ,			snake_case_=True      ,			**snake_case_      ,			):







										'''simple docstring'''


										super().__init__(
										    pad_token_id=snake_case_      ,			bos_token_id=snake_case_      ,			eos_token_id=snake_case_      ,			**snake_case_      ,			)
										A__					:    int														= vocab_size
										A__					:    List[str]														= max_position_embeddings
										A__					:    str														= num_attention_heads
										A__					:    Dict														= word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
										A__					:    Optional[int]														= ffn_dim
										A__					:    Optional[int]														= hidden_size
										A__					:    int														= num_hidden_layers
										A__					:    str														= dropout
										A__					:    Tuple														= attention_dropout
										A__					:    Optional[Any]														= activation_function
										A__					:    Any														= init_std
										A__					:    Optional[int]														= layerdrop
										A__					:    Optional[int]														= use_cache
										A__					:    int														= do_layer_norm_before
										# We keep these variables at `True` for backward compatibility.
										A__					:    str														= enable_bias
										A__					:    List[str]														= layer_norm_elementwise_affine

										# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
										# with checkpoints that have been fine-tuned before transformers v4.20.1
										# see https://github.com/facebookresearch/metaseq/pull/164
										A__					:    str														= _remove_final_layer_norm





