
"""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


_a									=				logging.get_logger(__name__)

_a									=				{
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}


class A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		List[Any]    	=						"""gpt2"""
  SCREAMING_SNAKE_CASE__     :		Union[str, Any]    	=						["""past_key_values"""]
  SCREAMING_SNAKE_CASE__     :		str    	=						{
      """hidden_size""": """n_embd""",
      """max_position_embeddings""": """n_positions""",
      """num_attention_heads""": """n_head""",
      """num_hidden_layers""": """n_layer""",
  }







  def __init__(						self							,  lowercase_=5_0257							,  lowercase_=1024							,  lowercase_=768							,  lowercase_=12							,  lowercase_=12							,  lowercase_=None							,  lowercase_="gelu_new"							,  lowercase_=0.1							,  lowercase_=0.1							,  lowercase_=0.1							,  lowercase_=1E-5							,  lowercase_=0.02							,  lowercase_="cls_index"							,  lowercase_=True							,  lowercase_=None							,  lowercase_=True							,  lowercase_=0.1							,  lowercase_=True							,  lowercase_=True							,  lowercase_=5_0256							,  lowercase_=5_0256							,  lowercase_=False							,  lowercase_=False							,  **lowercase_							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       vocab_size
     UpperCAmelCase_   :   List[Any]     	=       n_positions
     UpperCAmelCase_   :   str     	=       n_embd
     UpperCAmelCase_   :   str     	=       n_layer
     UpperCAmelCase_   :   Tuple     	=       n_head
     UpperCAmelCase_   :   int     	=       n_inner
     UpperCAmelCase_   :   Any     	=       activation_function
     UpperCAmelCase_   :   Optional[int]     	=       resid_pdrop
     UpperCAmelCase_   :   List[str]     	=       embd_pdrop
     UpperCAmelCase_   :   str     	=       attn_pdrop
     UpperCAmelCase_   :   Optional[Any]     	=       layer_norm_epsilon
     UpperCAmelCase_   :   Optional[int]     	=       initializer_range
     UpperCAmelCase_   :   Union[str, Any]     	=       summary_type
     UpperCAmelCase_   :   Tuple     	=       summary_use_proj
     UpperCAmelCase_   :   Dict     	=       summary_activation
     UpperCAmelCase_   :   List[Any]     	=       summary_first_dropout
     UpperCAmelCase_   :   str     	=       summary_proj_to_labels
     UpperCAmelCase_   :   str     	=       scale_attn_weights
     UpperCAmelCase_   :   List[str]     	=       use_cache
     UpperCAmelCase_   :   Union[str, Any]     	=       scale_attn_by_inverse_layer_idx
     UpperCAmelCase_   :   Tuple     	=       reorder_and_upcast_attn

     UpperCAmelCase_   :   int     	=       bos_token_id
     UpperCAmelCase_   :   Any     	=       eos_token_id

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




class A_      (lowercase__ ):




  '''simple docstring'''







  def __init__(						self							,  lowercase_							,  lowercase_ = "default"							,  lowercase_ = None							,  lowercase_ = False							,  ):

     """simple docstring"""
     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?
        UpperCAmelCase_   :   int     	=       0







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}    )
     if self.use_past:
        self.fill_with_past_key_values_(lowercase_							,  direction="inputs"    )
        UpperCAmelCase_   :   Dict     	=       {0: "batch", 1: "past_sequence + sequence"}
     else:
        UpperCAmelCase_   :   List[Any]     	=       {0: "batch", 1: "sequence"}

     return common_inputs







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self._config.n_layer







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self._config.n_head







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_ = -1							,  lowercase_ = -1							,  lowercase_ = False							,  lowercase_ = None							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   List[Any]     	=       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()
     UpperCAmelCase_   :   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

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

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

     return ordered_inputs







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return 13



"""simple docstring"""







_a	:	Optional[Any]=					{
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}
def     __UpperCAmelCase							(       UpperCAmelCase_       :							List[Any]    )				->     List[Any]:

	'''simple docstring'''





	return c.isdigit()
def     __UpperCAmelCase							(       UpperCAmelCase_       :							int    )				->     List[Any]:

	'''simple docstring'''





	__snake_case     :				Dict 						=					[]

	# iterate over the string in reverse order
	for c in expression.split()[::-1]:
		# push operand to stack
		if is_operand(UpperCAmelCase_    ):
			stack.append(int(UpperCAmelCase_    )    )

		else:
			# pop values from stack can calculate the result
			# push the result onto the stack again
			__snake_case     :				Optional[Any] 						=					stack.pop()
			__snake_case     :				int 						=					stack.pop()
			stack.append(calc[c](UpperCAmelCase_     ,				UpperCAmelCase_    )    )

	return stack.pop()


# Driver code
if __name__ == "__main__":
	_a	:	Union[str, Any]=					"+ 9 * 2 6"
	print(evaluate(test_expression))

	_a	:	List[Any]=					"/ * 10 2 + 4 1 "
	print(evaluate(test_expression))





