





from math import isclose, sqrt






def 		lowerCAmelCase  (	UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float					)      ->    tuple[float, float, float]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Tuple           =   point_y / 4 / point_x
						__SCREAMING_SNAKE_CASE:				Any           =   2 * normal_gradient / (1 + normal_gradient * normal_gradient)
						__SCREAMING_SNAKE_CASE:				int           =   (1 - normal_gradient * normal_gradient) / (
						    1 + normal_gradient * normal_gradient
						)
						__SCREAMING_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
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   outgoing_gradient**2 + 4
						__SCREAMING_SNAKE_CASE:				int           =   2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
						__SCREAMING_SNAKE_CASE:				Any           =   (point_y - outgoing_gradient * point_x) ** 2 - 100

						__SCREAMING_SNAKE_CASE:				List[Any]           =   (
						    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term					)
						) / (2 * quadratic_term)
						__SCREAMING_SNAKE_CASE:				Dict           =   (
						    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term					)
						) / (2 * quadratic_term)

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

						return next_x, next_y, outgoing_gradient






def 		lowerCAmelCase  (	UpperCamelCase__   :   float = 1.4       ,			UpperCamelCase__   :   float = -9.6					)      ->    int:

						"""simple docstring"""





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

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

						return num_reflections


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










import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def 		lowerCAmelCase  (	UpperCamelCase__   :   Union[str, Any]					)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[Any]           =   [
						    '''decoder.version''',
						    '''decoder.output_projection.weight''',
						    '''_float_tensor''',
						    '''decoder.embed_positions._float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(UpperCamelCase__       ,			UpperCamelCase__					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   str					)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				str           =   emb.weight.shape
						__SCREAMING_SNAKE_CASE:				List[str]           =   nn.Linear(UpperCamelCase__       ,			UpperCamelCase__       ,			bias=UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Tuple           =   emb.weight.data
						return lin_layer






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]					)      ->    Optional[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				int           =   torch.load(UpperCamelCase__       ,			map_location='''cpu'''					)
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   Namespace(**checkpoint['''cfg''']['''model''']					)
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   checkpoint['''model''']
						remove_ignore_keys_(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				int           =   state_dict['''decoder.embed_tokens.weight'''].shape[0]

						__SCREAMING_SNAKE_CASE:				List[Any]           =   {key.replace('''decoder'''       ,			'''model'''					): val for key, val in state_dict.items()}

						__SCREAMING_SNAKE_CASE:				Dict           =   XGLMConfig(
						    vocab_size=UpperCamelCase__       ,			max_position_embeddings=args.max_target_positions       ,			num_layers=args.decoder_layers       ,			attention_heads=args.decoder_attention_heads       ,			ffn_dim=args.decoder_ffn_embed_dim       ,			d_model=args.decoder_embed_dim       ,			layerdrop=args.decoder_layerdrop       ,			dropout=args.dropout       ,			attention_dropout=args.attention_dropout       ,			activation_dropout=args.activation_dropout       ,			activation_function='''gelu'''       ,			scale_embedding=not args.no_scale_embedding       ,			tie_word_embeddings=args.share_decoder_input_output_embed       ,			)

						__SCREAMING_SNAKE_CASE:				int           =   XGLMForCausalLM(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   model.load_state_dict(UpperCamelCase__       ,			strict=UpperCamelCase__					)
						print(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				List[str]           =   make_linear_from_emb(model.model.embed_tokens					)

						return model


if __name__ == "__main__":
							lowerCAmelCase							:       Union[str, Any]					 =	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
							parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							lowerCAmelCase							:       List[str]					 =	parser.parse_args()
							lowerCAmelCase							:       Optional[int]					 =	convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
							model.save_pretrained(args.pytorch_dump_folder_path)



