





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	(      UpperCAmelCase_					):
			lowerCamelCase_					:					Optional[Any]             =				"""gptj"""
			lowerCamelCase_					:					Tuple             =				{
			    """max_position_embeddings""": """n_positions""",
			    """hidden_size""": """n_embd""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}






			def __init__(self ,							__magic_name__=5_0400 ,							__magic_name__=2048 ,							__magic_name__=4096 ,							__magic_name__=28 ,							__magic_name__=16 ,							__magic_name__=64 ,							__magic_name__=None ,							__magic_name__="gelu_new" ,							__magic_name__=0.0 ,							__magic_name__=0.0 ,							__magic_name__=0.0 ,							__magic_name__=1e-5 ,							__magic_name__=0.02 ,							__magic_name__=True ,							__magic_name__=5_0256 ,							__magic_name__=5_0256 ,							__magic_name__=False ,							**__magic_name__ ,							)					->       Tuple:


							'''simple docstring'''



							snake_case_						:   Any           =  vocab_size
							snake_case_						:   Optional[int]           =  n_positions
							snake_case_						:   Tuple           =  n_embd
							snake_case_						:   int           =  n_layer
							snake_case_						:   Any           =  n_head
							snake_case_						:   Any           =  n_inner
							snake_case_						:   Dict           =  rotary_dim
							snake_case_						:   Tuple           =  activation_function
							snake_case_						:   Optional[Any]           =  resid_pdrop
							snake_case_						:   Any           =  embd_pdrop
							snake_case_						:   List[str]           =  attn_pdrop
							snake_case_						:   str           =  layer_norm_epsilon
							snake_case_						:   List[Any]           =  initializer_range
							snake_case_						:   Dict           =  use_cache

							snake_case_						:   List[Any]           =  bos_token_id
							snake_case_						:   Tuple           =  eos_token_id

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




class  __lowerCAmelCase	(      UpperCAmelCase_					):






			def __init__(self ,							__magic_name__ ,							__magic_name__ = "default" ,							__magic_name__ = None ,							__magic_name__ = False ,							)					->       Any:


							'''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?
											snake_case_						:   Tuple           =  0






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


							'''simple docstring'''



							snake_case_						:   Tuple           =  OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}   )
							if self.use_past:
											self.fill_with_past_key_values_(__lowercase ,							direction='''inputs'''   )
											snake_case_						:   str           =  {0: '''batch''', 1: '''past_sequence + sequence'''}
							else:
											snake_case_						:   Any           =  {0: '''batch''', 1: '''sequence'''}

							return common_inputs






			@property
			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							return self._config.n_layer






			@property
			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							return self._config.n_head






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


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  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()
							snake_case_						:   int           =  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

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

							snake_case_						:   Tuple           =  common_inputs['''attention_mask''']
							if self.use_past:
											snake_case_						:   Tuple           =  ordered_inputs['''attention_mask'''].dtype
											snake_case_						:   Optional[Any]           =  torch.cat(
											    [ordered_inputs['''attention_mask'''], torch.ones(__lowercase ,							__lowercase ,							dtype=__lowercase   )] ,							dim=1   )

							return ordered_inputs






			@property
			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							return 13







from math import isclose, sqrt






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						tuple[float, float, float]:






				"""simple docstring"""


				snake_case_						:   Dict           =  point_y / 4 / point_x
				snake_case_						:   List[str]           =  2 * normal_gradient / (1 + normal_gradient * normal_gradient)
				snake_case_						:   Union[str, Any]           =  (1 - normal_gradient * normal_gradient) / (
				    1 + normal_gradient * normal_gradient
				)
				snake_case_						:   Tuple           =  (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

				# to find the next point, solve the simultaeneous equations:
				# y^2 + 4x^2 = 100
				# y - b = m * (x - a)
				# ==> A x^2 + B x + C = 0
				snake_case_						:   Union[str, Any]           =  outgoing_gradient**2 + 4
				snake_case_						:   Tuple           =  2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
				snake_case_						:   Optional[Any]           =  (point_y - outgoing_gradient * point_x) ** 2 - 100

				snake_case_						:   Dict           =  (
				    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term			)
				) / (2 * quadratic_term)
				snake_case_						:   Optional[int]           =  (
				    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term			)
				) / (2 * quadratic_term)

				# two solutions, one of which is our input point
				snake_case_						:   Any           =  x_minus if isclose(_UpperCamelCase			,    _UpperCamelCase			) else x_plus
				snake_case_						:   int           =  point_y + outgoing_gradient * (next_x - point_x)

				return next_x, next_y, outgoing_gradient






def 					lowerCamelCase_						(    _UpperCamelCase = 1.4			,    _UpperCamelCase = -9.6			)		->						int:






				"""simple docstring"""


				snake_case_						:   int           =  0
				snake_case_						:   float           =  first_x_coord
				snake_case_						:   float           =  first_y_coord
				snake_case_						:   float           =  (10.1 - point_y) / (0.0 - point_x)

				while not (-0.01 <= point_x <= 0.01 and point_y > 0):
								snake_case_      ,							snake_case_      ,							snake_case_						:   str           =  next_point(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)
								num_reflections += 1

				return num_reflections


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