'''simple docstring'''




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_lowercase						: str						=						logging.get_logger(__name__)


_lowercase						: Optional[Any]						=						{
    "Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
    "Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
    "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
    "Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
    "Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
    "Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
    "Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
    "Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
    "Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
    "Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
    "Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
    "Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}





class     __magic_name__						(    _UpperCAmelCase):
			UpperCamelCase__				=       '''codegen'''
			UpperCamelCase__				=       {
			    '''max_position_embeddings''': '''n_positions''',
			    '''hidden_size''': '''n_embd''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}







			def __init__(	self				:  Union[str, Any]				,			lowercase_				:  List[str]=50400				,			lowercase_				:  Dict=2048				,			lowercase_				:  Union[str, Any]=2048				,			lowercase_				:  Dict=4096				,			lowercase_				:  Optional[int]=28				,			lowercase_				:  Any=16				,			lowercase_				:  Tuple=64				,			lowercase_				:  Tuple=None				,			lowercase_				:  List[str]="gelu_new"				,			lowercase_				:  List[Any]=0.0				,			lowercase_				:  Optional[int]=0.0				,			lowercase_				:  Optional[int]=0.0				,			lowercase_				:  List[str]=1E-5				,			lowercase_				:  Optional[int]=0.02				,			lowercase_				:  Optional[int]=True				,			lowercase_				:  Any=50256				,			lowercase_				:  Optional[int]=50256				,			lowercase_				:  str=False				,			**lowercase_				:  str				,			):
										lowercase_					:			Optional[int]										=       vocab_size
										lowercase_					:			Tuple										=       n_ctx
										lowercase_					:			int										=       n_positions
										lowercase_					:			Union[str, Any]										=       n_embd
										lowercase_					:			Optional[int]										=       n_layer
										lowercase_					:			Optional[Any]										=       n_head
										lowercase_					:			Optional[Any]										=       n_inner
										lowercase_					:			Tuple										=       rotary_dim
										lowercase_					:			int										=       activation_function
										lowercase_					:			List[Any]										=       resid_pdrop
										lowercase_					:			List[str]										=       embd_pdrop
										lowercase_					:			Union[str, Any]										=       attn_pdrop
										lowercase_					:			List[str]										=       layer_norm_epsilon
										lowercase_					:			Optional[int]										=       initializer_range
										lowercase_					:			int										=       use_cache

										lowercase_					:			Any										=       bos_token_id
										lowercase_					:			int										=       eos_token_id

										super().__init__(
										    bos_token_id=lowercase_				,			eos_token_id=lowercase_				,			tie_word_embeddings=lowercase_				,			**lowercase_    )





class     __magic_name__						(    _UpperCAmelCase):







			def __init__(	self				:  Union[str, Any]				,			lowercase_				:  PretrainedConfig				,			lowercase_				:  str = "default"				,			lowercase_				:  List[PatchingSpec] = None				,			lowercase_				:  bool = False				,			):
										super().__init__(lowercase_				,			task=lowercase_				,			patching_specs=lowercase_				,			use_past=lowercase_    )
										if not getattr(self._config				,			"""pad_token_id"""				,			lowercase_    ):
																	# TODO: how to do that better?
																	lowercase_					:			Dict										=       0







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			Optional[Any]										=       OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}    )
										if self.use_past:
																	self.fill_with_past_key_values_(lowercase_				,			direction="""inputs"""    )
																	lowercase_					:			Union[str, Any]										=       {0: """batch""", 1: """past_sequence + sequence"""}
										else:
																	lowercase_					:			str										=       {0: """batch""", 1: """sequence"""}

										return common_inputs







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										return self._config.n_layer







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										return self._config.n_head







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str				,			lowercase_				:  PreTrainedTokenizer				,			lowercase_				:  int = -1				,			lowercase_				:  int = -1				,			lowercase_				:  bool = False				,			lowercase_				:  Optional[TensorType] = None				,			):
										lowercase_					:			Dict										=       super(lowercase_				,			self    ).generate_dummy_inputs(
										    lowercase_				,			batch_size=lowercase_				,			seq_length=lowercase_				,			is_pair=lowercase_				,			framework=lowercase_    )

										# We need to order the input in the way they appears in the forward()
										lowercase_					:			List[Any]										=       OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}    )

										# Need to add the past_keys
										if self.use_past:
																	if not is_torch_available():
																								raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""    )
																	else:
																								import torch

																								lowercase_						,						lowercase_					:			Dict										=       common_inputs["""input_ids"""].shape
																								# Not using the same length for past_key_values
																								lowercase_					:			Optional[int]										=       seqlen + 2
																								lowercase_					:			str										=       (
																								    batch,
																								    self.num_attention_heads,
																								    past_key_values_length,
																								    self._config.hidden_size // self.num_attention_heads,
																								)
																								lowercase_					:			Tuple										=       [
																								    (torch.zeros(lowercase_    ), torch.zeros(lowercase_    )) for _ in range(self.num_layers    )
																								]

										lowercase_					:			str										=       common_inputs["""attention_mask"""]
										if self.use_past:
																	lowercase_					:			str										=       ordered_inputs["""attention_mask"""].dtype
																	lowercase_					:			List[str]										=       torch.cat(
																	    [ordered_inputs["""attention_mask"""], torch.ones(lowercase_				,			lowercase_				,			dtype=lowercase_    )]				,			dim=1    )

										return ordered_inputs







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										return 13






'''simple docstring'''




import operator as op




def 							lowerCamelCase	(      UpperCAmelCase__       :					Optional[Any] )			->      int:
							lowercase_					:			Optional[Any]										=       []
							lowercase_					:			str										=       lambda UpperCAmelCase__      ,      UpperCAmelCase__ : int(x / y )  # noqa: E731 integer division operation
							lowercase_					:			Optional[Any]										=       {
							    """^""": op.pow,
							    """*""": op.mul,
							    """/""": div,
							    """+""": op.add,
							    """-""": op.sub,
							}  # operators & their respective operation

							# print table header
							print("""Symbol""".center(8 )      ,      """Action""".center(12 )      ,      """Stack"""      ,      sep=""" | """ )
							print("""-""" * (30 + len(UpperCAmelCase__ )) )

							for x in post_fix:
														if x.isdigit():  # if x in digit
																					stack.append(UpperCAmelCase__ )  # append x to stack
																					# output in tabular format
																					print(x.rjust(8 )      ,      ("""push(""" + x + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """ )
														else:
																					lowercase_					:			str										=       stack.pop()  # pop stack
																					# output in tabular format
																					print("""""".rjust(8 )      ,      ("""pop(""" + b + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """ )

																					lowercase_					:			Optional[int]										=       stack.pop()  # pop stack
																					# output in tabular format
																					print("""""".rjust(8 )      ,      ("""pop(""" + a + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """ )

																					stack.append(
																					    str(opr[x](int(UpperCAmelCase__ )      ,      int(UpperCAmelCase__ ) ) ) )  # evaluate the 2 values popped from stack & push result to stack
																					# output in tabular format
																					print(
																					    x.rjust(8 )      ,      ("""push(""" + a + x + b + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """      ,      )

							return int(stack[0] )


if __name__ == "__main__":
						_lowercase						: Tuple						=						input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
						print("\n\tResult = ", solve(Postfix))





