
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


SCREAMING_SNAKE_CASE__				=							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__				=							{
    """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__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		Any            =				"gpt2"
							lowerCAmelCase__						:		List[str]            =				["past_key_values"]
							lowerCAmelCase__						:		str            =				{
							    "hidden_size": "n_embd",
							    "max_position_embeddings": "n_positions",
							    "num_attention_heads": "n_head",
							    "num_hidden_layers": "n_layer",
							}





							def __init__(					self    :  Optional[int]	,					_UpperCAmelCase    :  Tuple=5_02_57	,					_UpperCAmelCase    :  Any=10_24	,					_UpperCAmelCase    :  int=7_68	,					_UpperCAmelCase    :  Dict=12	,					_UpperCAmelCase    :  Any=12	,					_UpperCAmelCase    :  Union[str, Any]=None	,					_UpperCAmelCase    :  Optional[Any]="gelu_new"	,					_UpperCAmelCase    :  List[str]=0.1	,					_UpperCAmelCase    :  Dict=0.1	,					_UpperCAmelCase    :  Union[str, Any]=0.1	,					_UpperCAmelCase    :  Union[str, Any]=1e-5	,					_UpperCAmelCase    :  Optional[Any]=0.02	,					_UpperCAmelCase    :  int="cls_index"	,					_UpperCAmelCase    :  str=True	,					_UpperCAmelCase    :  Union[str, Any]=None	,					_UpperCAmelCase    :  Optional[int]=True	,					_UpperCAmelCase    :  Any=0.1	,					_UpperCAmelCase    :  Tuple=True	,					_UpperCAmelCase    :  Optional[Any]=True	,					_UpperCAmelCase    :  str=5_02_56	,					_UpperCAmelCase    :  Optional[int]=5_02_56	,					_UpperCAmelCase    :  Any=False	,					_UpperCAmelCase    :  Tuple=False	,					**_UpperCAmelCase    :  Optional[Any]	,					)		->						List[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=_UpperCAmelCase	,					eos_token_id=_UpperCAmelCase	,					**_UpperCAmelCase							)






class       A__  (		lowerCAmelCase__							):





							def __init__(					self    :  str	,					_UpperCAmelCase    :  PretrainedConfig	,					_UpperCAmelCase    :  str = "default"	,					_UpperCAmelCase    :  List[PatchingSpec] = None	,					_UpperCAmelCase    :  bool = False	,					)		->						str:



											"""simple docstring"""


											super().__init__(_UpperCAmelCase	,					task=_UpperCAmelCase	,					patching_specs=_UpperCAmelCase	,					use_past=_UpperCAmelCase							)
											if not getattr(self._config	,					'pad_token_id'	,					_UpperCAmelCase							):
															# TODO: how to do that better?
															__lowercase								=   0





							@property
							def      a__					(					self    :  Union[str, Any]							)		->						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_(_UpperCAmelCase	,					direction='inputs'							)
															__lowercase								=   {0: 'batch', 1: 'past_sequence + sequence'}
											else:
															__lowercase								=   {0: 'batch', 1: 'sequence'}

											return common_inputs





							@property
							def      a__					(					self    :  List[Any]							)		->						int:



											"""simple docstring"""


											return self._config.n_layer





							@property
							def      a__					(					self    :  List[str]							)		->						int:



											"""simple docstring"""


											return self._config.n_head





							def      a__					(					self    :  Optional[Any]	,					_UpperCAmelCase    :  PreTrainedTokenizer	,					_UpperCAmelCase    :  int = -1	,					_UpperCAmelCase    :  int = -1	,					_UpperCAmelCase    :  bool = False	,					_UpperCAmelCase    :  Optional[TensorType] = None	,					)		->						Mapping[str, Any]:



											"""simple docstring"""


											__lowercase								=   super(_UpperCAmelCase	,					self							).generate_dummy_inputs(
											    _UpperCAmelCase	,					batch_size=_UpperCAmelCase	,					seq_length=_UpperCAmelCase	,					is_pair=_UpperCAmelCase	,					framework=_UpperCAmelCase							)

											# 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(_UpperCAmelCase							), torch.zeros(_UpperCAmelCase							)) 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(_UpperCAmelCase	,					_UpperCAmelCase	,					dtype=_UpperCAmelCase							)]	,					dim=1							)

											return ordered_inputs





							@property
							def      a__					(					self    :  List[Any]							)		->						int:



											"""simple docstring"""


											return 13







def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Union[str, Any]       ,				SCREAMING_SNAKE_CASE			: Any     )	->		Tuple:
				# "Simpson Rule"
				# int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn)
				__lowercase								=   (boundary[1] - boundary[0]) / steps
				__lowercase								=   boundary[0]
				__lowercase								=   boundary[1]
				__lowercase								=   make_points(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
				__lowercase								=   0.0
				y += (h / 3.0) * f(SCREAMING_SNAKE_CASE     )
				__lowercase								=   2
				for i in x_i:
								y += (h / 3) * (4 - 2 * (cnt % 2)) * f(SCREAMING_SNAKE_CASE     )
								cnt += 1
				y += (h / 3.0) * f(SCREAMING_SNAKE_CASE     )
				return y




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]       ,				SCREAMING_SNAKE_CASE			: Dict       ,				SCREAMING_SNAKE_CASE			: int     )	->		Any:
				__lowercase								=   a + h
				while x < (b - h):
								yield x
								__lowercase								=   x + h




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Any     )	->		List[Any]:  # enter your function here
				__lowercase								=   (x - 0) * (x - 0)
				return y




def 				__SCREAMING_SNAKE_CASE  (						)	->		List[str]:
				__lowercase								=   0.0  # Lower bound of integration
				__lowercase								=   1.0  # Upper bound of integration
				__lowercase								=   10.0  # define number of steps or resolution
				__lowercase								=   [a, b]  # define boundary of integration
				__lowercase								=   method_a(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
				print(F"""y = {y}"""     )


if __name__ == "__main__":
							main()





