
'''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							(       __UpperCAmelCase				):
			lowerCamelCase							:							int       		=  """gpt2"""
			lowerCamelCase							:							Optional[int]       		=  ["""past_key_values"""]
			lowerCamelCase							:							Any       		=  {
			    """hidden_size""": """n_embd""",
			    """max_position_embeddings""": """n_positions""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}



			def __init__(					self       ,     lowerCamelCase__=50_257       ,     lowerCamelCase__=1_024       ,     lowerCamelCase__=768       ,     lowerCamelCase__=12       ,     lowerCamelCase__=12       ,     lowerCamelCase__=None       ,     lowerCamelCase__="gelu_new"       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=1e-5       ,     lowerCamelCase__=0.02       ,     lowerCamelCase__="cls_index"       ,     lowerCamelCase__=True       ,     lowerCamelCase__=None       ,     lowerCamelCase__=True       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=True       ,     lowerCamelCase__=True       ,     lowerCamelCase__=50_256       ,     lowerCamelCase__=50_256       ,     lowerCamelCase__=False       ,     lowerCamelCase__=False       ,     **lowerCamelCase__       ,     )			->		Union[str, Any]:




									'''simple docstring'''



									lowercase__							 =       vocab_size
									lowercase__							 =       n_positions
									lowercase__							 =       n_embd
									lowercase__							 =       n_layer
									lowercase__							 =       n_head
									lowercase__							 =       n_inner
									lowercase__							 =       activation_function
									lowercase__							 =       resid_pdrop
									lowercase__							 =       embd_pdrop
									lowercase__							 =       attn_pdrop
									lowercase__							 =       layer_norm_epsilon
									lowercase__							 =       initializer_range
									lowercase__							 =       summary_type
									lowercase__							 =       summary_use_proj
									lowercase__							 =       summary_activation
									lowercase__							 =       summary_first_dropout
									lowercase__							 =       summary_proj_to_labels
									lowercase__							 =       scale_attn_weights
									lowercase__							 =       use_cache
									lowercase__							 =       scale_attn_by_inverse_layer_idx
									lowercase__							 =       reorder_and_upcast_attn

									lowercase__							 =       bos_token_id
									lowercase__							 =       eos_token_id

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





class 			A							(       __UpperCAmelCase				):



			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__ = "default"       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = False       ,     )			->		Optional[int]:




									'''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?
															lowercase__							 =       0



			@property
			def A__ (					self       )			->		Mapping[str, Mapping[int, str]]:




									'''simple docstring'''



									lowercase__							 =       OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}       )
									if self.use_past:
															self.fill_with_past_key_values_(lowerCamelCase__       ,     direction="""inputs"""       )
															lowercase__							 =       {0: """batch""", 1: """past_sequence + sequence"""}
									else:
															lowercase__							 =       {0: """batch""", 1: """sequence"""}

									return common_inputs



			@property
			def A__ (					self       )			->		int:




									'''simple docstring'''



									return self._config.n_layer



			@property
			def A__ (					self       )			->		int:




									'''simple docstring'''



									return self._config.n_head



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__ = -1       ,     lowerCamelCase__ = -1       ,     lowerCamelCase__ = False       ,     lowerCamelCase__ = None       ,     )			->		Mapping[str, Any]:




									'''simple docstring'''



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

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

									return ordered_inputs



			@property
			def A__ (					self       )			->		int:




									'''simple docstring'''



									return 13








'''simple docstring'''



def  _A		(							lowercase__	):

						# validate:
						if not isinstance(lowercase__		,		lowercase__	) or not all(
						    isinstance(lowercase__		,		lowercase__	) and item >= 0 for item in citations	):
												raise ValueError("""The citations should be a list of non negative integers."""	)

						citations.sort()
						lowercase__							 =       len(lowercase__	)

						for i in range(lowercase__	):
												if citations[len_citations - 1 - i] <= i:
																		return i

						return len_citations


if __name__ == "__main__":
				import doctest

				doctest.testmod()






