




import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
						from ... import PreTrainedTokenizerBase, TensorType

a													=			logging.get_logger(__name__)




class       _A				(		__lowercase     ):
		__a					    =       """vision-encoder-decoder"""
		__a					    =       True






		def __init__(    self						,		**_SCREAMING_SNAKE_CASE			):
								super().__init__(**_SCREAMING_SNAKE_CASE			)
								if "encoder" not in kwargs or "decoder" not in kwargs:
														raise ValueError(
														    F"A configuraton of type {self.model_type} cannot be instantiated because "
														    F"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"			)

								_UpperCAmelCase      =			kwargs.pop("""encoder"""			)
								_UpperCAmelCase      =			encoder_config.pop("""model_type"""			)
								_UpperCAmelCase      =			kwargs.pop("""decoder"""			)
								_UpperCAmelCase      =			decoder_config.pop("""model_type"""			)

								_UpperCAmelCase      =			AutoConfig.for_model(_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			AutoConfig.for_model(_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			True






		@classmethod
		def 				UpperCAmelCase (    cls						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""			)
								_UpperCAmelCase      =			True
								_UpperCAmelCase      =			True

								return cls(encoder=encoder_config.to_dict()						,		decoder=decoder_config.to_dict()						,		**_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			copy.deepcopy(self.__dict__			)
								_UpperCAmelCase      =			self.encoder.to_dict()
								_UpperCAmelCase      =			self.decoder.to_dict()
								_UpperCAmelCase      =			self.__class__.model_type
								return output




class       _A				(		__lowercase     ):
		__a					    =       version.parse("""1.11"""     )






		@property
		def 				UpperCAmelCase (    self			):
								return OrderedDict(
								    [
								        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
								    ]			)






		@property
		def 				UpperCAmelCase (    self			):
								return 1e-4






		@property
		def 				UpperCAmelCase (    self			):
								return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}}			)




class       _A				(		__lowercase     ):






		@property
		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			OrderedDict()
								_UpperCAmelCase      =			{0: """batch""", 1: """past_decoder_sequence + sequence"""}
								_UpperCAmelCase      =			{0: """batch""", 1: """past_decoder_sequence + sequence"""}
								_UpperCAmelCase      =			{0: """batch""", 1: """encoder_sequence"""}

								return common_inputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = -1						,		_SCREAMING_SNAKE_CASE = -1						,		_SCREAMING_SNAKE_CASE = False						,		_SCREAMING_SNAKE_CASE = None						,		):
								import torch

								_UpperCAmelCase      =			OrderedDict()

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

								_UpperCAmelCase    ,					_UpperCAmelCase      =			dummy_input["""input_ids"""].shape
								_UpperCAmelCase      =			(batch, encoder_sequence, self._config.encoder_hidden_size)
								_UpperCAmelCase      =			dummy_input.pop("""input_ids"""			)
								_UpperCAmelCase      =			dummy_input.pop("""attention_mask"""			)
								_UpperCAmelCase      =			torch.zeros(_SCREAMING_SNAKE_CASE			)

								return common_inputs




class       _A				(		__lowercase     ):






		@property
		def 				UpperCAmelCase (    self			):
								pass






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								return VisionEncoderDecoderEncoderOnnxConfig(_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = "default"			):
								_UpperCAmelCase      =			encoder_config.hidden_size
								return VisionEncoderDecoderDecoderOnnxConfig(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)





# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[str]:
						return 1 / (1 + np.exp(-z       ))




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Tuple:
						return (-y * np.log(snake_case       ) - (1 - y) * np.log(1 - h       )).mean()




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						_UpperCAmelCase      =			np.dot(snake_case			,     snake_case       )
						return np.sum(y * scores - np.log(1 + np.exp(snake_case       )       )       )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case=7_0_0_0_0       )						->				Tuple:
						_UpperCAmelCase      =			np.zeros(x.shape[1]       )

						for iterations in range(snake_case       ):
												_UpperCAmelCase      =			np.dot(snake_case			,     snake_case       )
												_UpperCAmelCase      =			sigmoid_function(snake_case       )
												_UpperCAmelCase      =			np.dot(x.T			,     h - y       ) / y.size
												_UpperCAmelCase      =			theta - alpha * gradient  # updating the weights
												_UpperCAmelCase      =			np.dot(snake_case			,     snake_case       )
												_UpperCAmelCase      =			sigmoid_function(snake_case       )
												_UpperCAmelCase      =			cost_function(snake_case			,     snake_case       )
												if iterations % 1_0_0 == 0:
																		print(f"loss: {j} \t"       )  # printing the loss after every 100 iterations
						return theta


# In[68]:

if __name__ == "__main__":
				a													=			datasets.load_iris()
				a													=			iris.data[:, :2]
				a													=			(iris.target != 0) * 1

				a													=			0.1
				a													=			logistic_reg(alpha, x, y, max_iterations=70000)
				print("theta: ", theta)  # printing the theta i.e our weights vector




				def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[Any]:
										return sigmoid_function(
										    np.dot(snake_case			,     snake_case       )       )  # predicting the value of probability from the logistic regression algorithm

				plt.figure(figsize=(10, 6))
				plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="b", label="0")
				plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="r", label="1")
				((a)							,  (a))													=			(x[:, 0].min(), x[:, 0].max())
				((a)							,  (a))													=			(x[:, 1].min(), x[:, 1].max())
				((a)							,  (a))													=			np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
				a													=			np.c_[xxa.ravel(), xxa.ravel()]
				a													=			predict_prob(grid).reshape(xxa.shape)
				plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="black")

				plt.legend()

				plt.show()