
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


lowerCamelCase__						=				logging.get_logger(__name__)

lowerCamelCase__						=				{
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}







class 							lowerCAmelCase__   ( __lowercase						):
						UpperCamelCase_				:			Any						=		"gptj"
						UpperCamelCase_				:			Optional[Any]						=		{
						    "max_position_embeddings": "n_positions",
						    "hidden_size": "n_embd",
						    "num_attention_heads": "n_head",
						    "num_hidden_layers": "n_layer",
						}

						def __init__(      self							,				a=5_04_00							,				a=20_48							,				a=40_96							,				a=28							,				a=16							,				a=64							,				a=None							,				a="gelu_new"							,				a=0.0							,				a=0.0							,				a=0.0							,				a=1e-5							,				a=0.02							,				a=True							,				a=5_02_56							,				a=5_02_56							,				a=False							,				**a							,				)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						vocab_size
								_UpperCamelCase         =						n_positions
								_UpperCamelCase         =						n_embd
								_UpperCamelCase         =						n_layer
								_UpperCamelCase         =						n_head
								_UpperCamelCase         =						n_inner
								_UpperCamelCase         =						rotary_dim
								_UpperCamelCase         =						activation_function
								_UpperCamelCase         =						resid_pdrop
								_UpperCamelCase         =						embd_pdrop
								_UpperCamelCase         =						attn_pdrop
								_UpperCamelCase         =						layer_norm_epsilon
								_UpperCamelCase         =						initializer_range
								_UpperCamelCase         =						use_cache

								_UpperCamelCase         =						bos_token_id
								_UpperCamelCase         =						eos_token_id

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







class 							lowerCAmelCase__   ( __lowercase						):

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


								'''simple docstring'''

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

						@property
						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

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

								return common_inputs

						@property
						def 			A_					(      self					)       ->   int:


								'''simple docstring'''

								return self._config.n_layer

						@property
						def 			A_					(      self					)       ->   Dict:


								'''simple docstring'''

								return self._config.n_head

						def 			A_					(      self							,				a							,				a = -1							,				a = -1							,				a = False							,				a = None							,				)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase         =						super(A_							,				self					).generate_dummy_inputs(
								    A_							,				batch_size=A_							,				seq_length=A_							,				is_pair=A_							,				framework=A_					)

								# We need to order the input in the way they appears in the forward()
								_UpperCamelCase         =						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         =						common_inputs["""input_ids"""].shape
												# Not using the same length for past_key_values
												_UpperCamelCase         =						seqlen + 2
												_UpperCamelCase         =						(
												    batch,
												    self.num_attention_heads,
												    past_key_values_length,
												    self._config.hidden_size // self.num_attention_heads,
												)
												_UpperCamelCase         =						[
												    (torch.zeros(A_					), torch.zeros(A_					)) for _ in range(self.num_layers					)
												]

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

								return ordered_inputs







						@property
						def 			A_					(      self					)       ->   int:


								'''simple docstring'''

								return 13







"""simple docstring"""




from collections.abc import Callable





def       a_ (	lowercase__ :Callable[[float], float],			lowercase__ :float,			lowercase__ :float     ):
			__lowerCamelCase          =      a
			__lowerCamelCase          =      b
			if function(lowercase__     ) == 0:  # one of the a or b is a root for the function
						return a
			elif function(lowercase__     ) == 0:
						return b
			elif (
			    function(lowercase__     ) * function(lowercase__     ) > 0
			):  # if none of these are root and they are both positive or negative,
						# then this algorithm can't find the root
						raise ValueError("""could not find root in given interval."""     )
			else:
						__lowerCamelCase          =      start + (end - start) / 2.0
						while abs(start - mid     ) > 10**-7:  # until precisely equals to 10^-7
									if function(lowercase__     ) == 0:
												return mid
									elif function(lowercase__     ) * function(lowercase__     ) < 0:
												__lowerCamelCase          =      mid
									else:
												__lowerCamelCase          =      mid
									__lowerCamelCase          =      start + (end - start) / 2.0
						return mid





def       a_ (	lowercase__ :float     ):
			return x**3 - 2 * x - 5


if __name__ == "__main__":
					print(bisection(f, 1, 1_0_0_0))

					import doctest

					doctest.testmod()




