


'''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] =			{
    """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 							__UpperCamelCase       (	_a      ):
	A_			    =					"gpt2"
	A_			    =					["past_key_values"]
	A_			    =					{
	    "hidden_size": "n_embd",
	    "max_position_embeddings": "n_positions",
	    "num_attention_heads": "n_head",
	    "num_hidden_layers": "n_layer",
	}




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

						'''simple docstring'''





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

						__a    : Any      					=						bos_token_id
						__a    : Tuple      					=						eos_token_id

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





class 							__UpperCamelCase       (	_a      ):




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

						'''simple docstring'''





						super().__init__(lowerCamelCase__		,       task=lowerCamelCase__		,       patching_specs=lowerCamelCase__		,       use_past=lowerCamelCase__							)
						if not getattr(self._config		,       'pad_token_id'		,       lowerCamelCase__							):
											# TODO: how to do that better?
											__a    : Union[str, Any]      					=						0




	@property
	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						__a    : Optional[Any]      					=						OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}							)
						if self.use_past:
											self.fill_with_past_key_values_(lowerCamelCase__		,       direction='inputs'							)
											__a    : Dict      					=						{0: "batch", 1: "past_sequence + sequence"}
						else:
											__a    : Tuple      					=						{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		,       __a		,       __a = -1		,       __a = -1		,       __a = False		,       __a = None		,       ):

						'''simple docstring'''





						__a    : List[Any]      					=						super(lowerCamelCase__		,       self							).generate_dummy_inputs(
						    lowerCamelCase__		,       batch_size=lowerCamelCase__		,       seq_length=lowerCamelCase__		,       is_pair=lowerCamelCase__		,       framework=lowerCamelCase__							)

						# We need to order the input in the way they appears in the forward()
						__a    : Tuple      					=						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

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

						__a    : Union[str, Any]      					=						common_inputs["attention_mask"]
						if self.use_past:
											__a    : List[Any]      					=						ordered_inputs["attention_mask"].dtype
											__a    : Dict      					=						torch.cat(
											    [ordered_inputs['attention_mask'], torch.ones(lowerCamelCase__		,       lowerCamelCase__		,       dtype=lowerCamelCase__							)]		,       dim=1							)

						return ordered_inputs





	@property
	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						return 13







'''simple docstring'''



def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     int = 30     ):
					__a    : Dict      					=						exponent + 2
					__a    : List[Any]      					=						(1 + 5**0.5) / 2
					__a    : Any      					=						(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

					return int(_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
	print(f'''{solution() = }''')



