



"""simple docstring"""
import random
class 							lowerCamelCase    :






					'''simple docstring'''





					def __init__(			self:				List[str]		,	snake_case:				list[list[float]]		,	snake_case:				list[int]		,	snake_case:				float = 0.0_1		,	snake_case:				int = 1_000		,	snake_case:				float = -1		,	)  ->      None:
											snake_case_					:List[Any]		=     sample
											if len(self.sample		) == 0:
																	raise ValueError("""Sample data can not be empty"""		)
											snake_case_					:int		=     target
											if len(self.target		) == 0:
																	raise ValueError("""Target data can not be empty"""		)
											if len(self.sample		) != len(self.target		):
																	raise ValueError("""Sample data and Target data do not have matching lengths"""		)
											snake_case_					:Optional[Any]		=     learning_rate
											snake_case_					:Any		=     epoch_number
											snake_case_					:Union[str, Any]		=     bias
											snake_case_					:Any		=     len(snake_case		)
											snake_case_					:Tuple		=     len(sample[0]		)  # number of columns in dataset
											snake_case_					:list		=     []






					def   lowerCAmelCase_							(			self:				Union[str, Any]		)  ->      None:
											for sample in self.sample:
																	sample.insert(0		,	self.bias		)

											for _ in range(self.col_sample		):
																	self.weight.append(random.random()		)

											self.weight.insert(0		,	self.bias		)

											snake_case_					:str		=     0

											while True:
																	snake_case_					:Tuple		=     False
																	for i in range(self.number_sample		):
																							snake_case_					:List[Any]		=     0
																							for j in range(self.col_sample + 1		):
																													snake_case_					:Union[str, Any]		=     u + self.weight[j] * self.sample[i][j]
																							snake_case_					:Union[str, Any]		=     self.sign(snake_case		)
																							if y != self.target[i]:
																													for j in range(self.col_sample + 1		):
																																			snake_case_					:Tuple		=     (
																																			    self.weight[j]
																																			    + self.learning_rate
																																			    * (self.target[i] - y)
																																			    * self.sample[i][j]
																																			)
																													snake_case_					:Any		=     True
            # print('Epoch: \n',epoch_count)
																	snake_case_					:Union[str, Any]		=     epoch_count + 1
																	# if you want control the epoch or just by error
																	if not has_misclassified:
																							print(("""\nEpoch:\n""", epoch_count)		)
																							print("""------------------------\n"""		)
																							# if epoch_count > self.epoch_number or not error:
																							break






					def   lowerCAmelCase_							(			self:				Tuple		,	snake_case:				list[float]		)  ->      None:
											if len(self.sample		) == 0:
																	raise ValueError("""Sample data can not be empty"""		)
											sample.insert(0		,	self.bias		)
											snake_case_					:Union[str, Any]		=     0
											for i in range(self.col_sample + 1		):
																	snake_case_					:str		=     u + self.weight[i] * sample[i]

											snake_case_					:str		=     self.sign(snake_case		)

											if y == -1:
																	print(("""Sample: """, sample)		)
																	print("""classification: P1"""		)
											else:
																	print(("""Sample: """, sample)		)
																	print("""classification: P2"""		)






					def   lowerCAmelCase_							(			self:				str		,	snake_case:				float		)  ->      int:
											return 1 if u >= 0 else -1

__a           =							[
    [-0.65_08, 0.10_97, 4.00_09],
    [-1.44_92, 0.88_96, 4.40_05],
    [2.08_50, 0.68_76, 12.07_10],
    [0.26_26, 1.14_76, 7.79_85],
    [0.64_18, 1.02_34, 7.04_27],
    [0.25_69, 0.67_30, 8.32_65],
    [1.11_55, 0.60_43, 7.44_46],
    [0.09_14, 0.33_99, 7.06_77],
    [0.01_21, 0.52_56, 4.63_16],
    [-0.04_29, 0.46_60, 5.43_23],
    [0.43_40, 0.68_70, 8.22_87],
    [0.27_35, 1.02_87, 7.19_34],
    [0.48_39, 0.48_51, 7.48_50],
    [0.40_89, -0.12_67, 5.50_19],
    [1.43_91, 0.16_14, 8.58_43],
    [-0.91_15, -0.19_73, 2.19_62],
    [0.36_54, 1.04_75, 7.48_58],
    [0.21_44, 0.75_15, 7.16_99],
    [0.20_13, 1.00_14, 6.54_89],
    [0.64_83, 0.21_83, 5.89_91],
    [-0.11_47, 0.22_42, 7.24_35],
    [-0.79_70, 0.87_95, 3.87_62],
    [-1.06_25, 0.63_66, 2.47_07],
    [0.53_07, 0.12_85, 5.68_83],
    [-1.22_00, 0.77_77, 1.72_52],
    [0.39_57, 0.10_76, 5.66_23],
    [-0.10_13, 0.59_89, 7.18_12],
    [2.44_82, 0.94_55, 11.20_95],
    [2.01_49, 0.61_92, 10.92_63],
    [0.20_12, 0.26_11, 5.46_31],
]

__a           =							[
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						__a           =							Perceptron(
						    sample=samples, target=target, learning_rate=0.01, epoch_number=10_00, bias=-1
						)
						network.training()
						print("Finished training perceptron")
						print("Enter values to predict or q to exit")
						while True:
												__a           =							[]
												for i in range(len(samples[0])):
																		__a           =							input("value: ").strip()
																		if user_input == "q":
																								break
																		__a           =							float(user_input)
																		sample.insert(i, observation)
												network.sort(sample)







import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )					->  List[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		TaConfig.from_json_file(_SCREAMING_SNAKE_CASE      )
      print(F"""Building PyTorch model from configuration: {config}"""      )
      SCREAMING_SNAKE_CASE						=		TaForConditionalGeneration(_SCREAMING_SNAKE_CASE      )

      # Load weights from tf checkpoint
      load_tf_weights_in_ta(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )

      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
      model.save_pretrained(_SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_		  =			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      SCREAMING_SNAKE_CASE_		  =			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


