"""simple docstring"""






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 lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		"vision-encoder-decoder"
					a   	=		True



					def __init__(						self			:				List[Any]   , **_snake_case			:				Optional[int]		)							->       Any:
									super().__init__(**_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}"""		)

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

									SCREAMING_SNAKE_CASE__		  =				AutoConfig.for_model(_snake_case   , **_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				AutoConfig.for_model(_snake_case   , **_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				True



					@classmethod
					def 			lowerCAmelCase_					(						cls			:				List[str]   , _snake_case			:				PretrainedConfig   , _snake_case			:				PretrainedConfig   , **_snake_case			:				str		)							->       PretrainedConfig:
									logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"		)
									SCREAMING_SNAKE_CASE__		  =				True
									SCREAMING_SNAKE_CASE__		  =				True

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



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				copy.deepcopy(self.__dict__		)
									SCREAMING_SNAKE_CASE__		  =				self.encoder.to_dict()
									SCREAMING_SNAKE_CASE__		  =				self.decoder.to_dict()
									SCREAMING_SNAKE_CASE__		  =				self.__class__.model_type
									return output



class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		version.parse("1.11"      )



					@property
					def 			lowerCAmelCase_					(						self			:				Any		)							->       Mapping[str, Mapping[int, str]]:
									return OrderedDict(
									    [
									        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
									    ]		)



					@property
					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       float:
									return 1e-4



					@property
					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Mapping[str, Mapping[int, str]]:
									return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}}		)



class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					@property
					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Mapping[str, Mapping[int, str]]:
									SCREAMING_SNAKE_CASE__		  =				OrderedDict()
									SCREAMING_SNAKE_CASE__		  =				{0: "batch", 1: "past_decoder_sequence + sequence"}
									SCREAMING_SNAKE_CASE__		  =				{0: "batch", 1: "past_decoder_sequence + sequence"}
									SCREAMING_SNAKE_CASE__		  =				{0: "batch", 1: "encoder_sequence"}

									return common_inputs



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				"PreTrainedTokenizerBase"   , _snake_case			:				int = -1   , _snake_case			:				int = -1   , _snake_case			:				bool = False   , _snake_case			:				Optional["TensorType"] = None   , )							->       Mapping[str, Any]:
									import torch

									SCREAMING_SNAKE_CASE__		  =				OrderedDict()

									SCREAMING_SNAKE_CASE__		  =				super().generate_dummy_inputs(
									    _snake_case   , batch_size=_snake_case   , seq_length=_snake_case   , is_pair=_snake_case   , framework=_snake_case		)

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

									return common_inputs



class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					@property
					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       None:
									pass



					def 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				PretrainedConfig		)							->       OnnxConfig:
									return VisionEncoderDecoderEncoderOnnxConfig(_snake_case		)



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				PretrainedConfig   , _snake_case			:				PretrainedConfig   , _snake_case			:				str = "default"		)							->       OnnxConfig:
									SCREAMING_SNAKE_CASE__		  =				encoder_config.hidden_size
									return VisionEncoderDecoderDecoderOnnxConfig(_snake_case   , _snake_case		)





"""simple docstring"""






# 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					(  __UpperCAmelCase       )							->   List[str]:
				return 1 / (1 + np.exp(-z       ))




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   Dict:
				return (-y * np.log(__UpperCAmelCase       ) - (1 - y) * np.log(1 - h       )).mean()




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				np.dot(__UpperCAmelCase  ,					__UpperCAmelCase       )
				return np.sum(y * scores - np.log(1 + np.exp(__UpperCAmelCase       )       )       )




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=70_000       )							->   Optional[Any]:
				SCREAMING_SNAKE_CASE__		  =				np.zeros(x.shape[1]       )

				for iterations in range(__UpperCAmelCase       ):
								SCREAMING_SNAKE_CASE__		  =				np.dot(__UpperCAmelCase  ,					__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				sigmoid_function(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				np.dot(x.T  ,					h - y       ) / y.size
								SCREAMING_SNAKE_CASE__		  =				theta - alpha * gradient  # updating the weights
								SCREAMING_SNAKE_CASE__		  =				np.dot(__UpperCAmelCase  ,					__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				sigmoid_function(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				cost_function(__UpperCAmelCase  ,					__UpperCAmelCase       )
								if iterations % 100 == 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=7_0_0_0_0)
						print('theta: ', theta)  # printing the theta i.e our weights vector




						def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Union[str, Any]:
										return sigmoid_function(
										    np.dot(__UpperCAmelCase  ,					__UpperCAmelCase       )       )  # predicting the value of probability from the logistic regression algorithm

						plt.figure(figsize=(1_0, 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()




