





"""simple docstring"""




import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a		:					Dict	      =     logging.get_logger(__name__)

a		:					List[str]	      =     {
    """google/pix2struct-textcaps-base""": (
        """https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
    ),
}


class 			__UpperCamelCase							(		lowerCamelCase__		):
							lowerCamelCase  :   Union[str, Any]    ="""pix2struct_text_model"""
							lowerCamelCase  :   Optional[Any]    =["""past_key_values"""]
							lowerCamelCase  :   Tuple    ={
							    """hidden_size""": """hidden_size""",
							    """num_attention_heads""": """num_heads""",
							    """num_hidden_layers""": """num_layers""",
							}






							def __init__(  self ,   lowerCAmelCase__=5_0244 ,   lowerCAmelCase__=768 ,   lowerCAmelCase__=64 ,   lowerCAmelCase__=2048 ,   lowerCAmelCase__=12 ,   lowerCAmelCase__=12 ,   lowerCAmelCase__=32 ,   lowerCAmelCase__=128 ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=1E-6 ,   lowerCAmelCase__=1.0 ,   lowerCAmelCase__="gelu_new" ,   lowerCAmelCase__=0 ,   lowerCAmelCase__=False ,   lowerCAmelCase__=0 ,   lowerCAmelCase__=1 ,   lowerCAmelCase__=False ,   lowerCAmelCase__=True ,   **lowerCAmelCase__ ,   )      ->					Dict:
								a :						Tuple      =							vocab_size
								a :						Optional[int]      =							hidden_size
								a :						str      =							d_kv
								a :						Optional[int]      =							d_ff
								a :						List[Any]      =							num_layers
								a :						List[str]      =							num_heads
								a :						Union[str, Any]      =							relative_attention_num_buckets
								a :						str      =							relative_attention_max_distance
								a :						Any      =							dropout_rate
								a :						List[Any]      =							layer_norm_epsilon
								a :						List[Any]      =							initializer_factor
								a :						Any      =							use_cache

								a :						Tuple      =							eos_token_id
								a :						Tuple      =							decoder_start_token_id

								# for backwards compatibility
								a :						Tuple      =							dense_act_fn

								super().__init__(
								    pad_token_id=SCREAMING_SNAKE_CASE__ ,   eos_token_id=SCREAMING_SNAKE_CASE__ ,   decoder_start_token_id=SCREAMING_SNAKE_CASE__ ,   tie_word_embeddings=SCREAMING_SNAKE_CASE__ ,   is_decoder=SCREAMING_SNAKE_CASE__ ,   **SCREAMING_SNAKE_CASE__ ,   )






							@classmethod
							def        __a  (  cls ,   lowerCAmelCase__ ,   **lowerCAmelCase__ )      ->					"PretrainedConfig":
								cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__ )

								a :						List[str]      =							cls.get_config_dict(SCREAMING_SNAKE_CASE__ ,   **SCREAMING_SNAKE_CASE__ )

								# get the text config dict if we are loading from Pix2StructConfig
								if config_dict.get("model_type" ) == "pix2struct":
									a :						List[Any]      =							config_dict['''text_config''']

								if "model_type" in config_dict and hasattr(cls ,   "model_type" ) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
									    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )

								return cls.from_dict(SCREAMING_SNAKE_CASE__ ,   **SCREAMING_SNAKE_CASE__ )


class 			__UpperCamelCase							(		lowerCamelCase__		):
							lowerCamelCase  :   int    ="""pix2struct_vision_model"""






							def __init__(  self ,   lowerCAmelCase__=768 ,   lowerCAmelCase__=768 ,   lowerCAmelCase__=2048 ,   lowerCAmelCase__=64 ,   lowerCAmelCase__=12 ,   lowerCAmelCase__=12 ,   lowerCAmelCase__="gelu_new" ,   lowerCAmelCase__=1E-6 ,   lowerCAmelCase__=0.0 ,   lowerCAmelCase__=0.0 ,   lowerCAmelCase__=1E-10 ,   lowerCAmelCase__=1.0 ,   lowerCAmelCase__=4096 ,   lowerCAmelCase__=32 ,   lowerCAmelCase__=128 ,   **lowerCAmelCase__ ,   )      ->					Optional[int]:
								super().__init__(**SCREAMING_SNAKE_CASE__ )

								a :						str      =							hidden_size
								a :						List[Any]      =							patch_embed_hidden_size
								a :						Union[str, Any]      =							d_ff
								a :						Union[str, Any]      =							dropout_rate
								a :						int      =							num_hidden_layers
								a :						List[Any]      =							num_attention_heads
								a :						Any      =							initializer_range
								a :						Tuple      =							initializer_factor
								a :						str      =							attention_dropout
								a :						str      =							layer_norm_eps
								a :						Dict      =							dense_act_fn
								a :						Optional[int]      =							seq_len
								a :						str      =							relative_attention_num_buckets
								a :						Optional[int]      =							relative_attention_max_distance
								a :						List[str]      =							d_kv






							@classmethod
							def        __a  (  cls ,   lowerCAmelCase__ ,   **lowerCAmelCase__ )      ->					"PretrainedConfig":
								cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__ )

								a :						str      =							cls.get_config_dict(SCREAMING_SNAKE_CASE__ ,   **SCREAMING_SNAKE_CASE__ )

								# get the vision config dict if we are loading from Pix2StructConfig
								if config_dict.get("model_type" ) == "pix2struct":
									a :						Union[str, Any]      =							config_dict['''vision_config''']

								if "model_type" in config_dict and hasattr(cls ,   "model_type" ) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
									    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )

								return cls.from_dict(SCREAMING_SNAKE_CASE__ ,   **SCREAMING_SNAKE_CASE__ )


class 			__UpperCamelCase							(		lowerCamelCase__		):
							lowerCamelCase  :   Optional[int]    ="""pix2struct"""
							lowerCamelCase  :   Optional[int]    =True






							def __init__(  self ,   lowerCAmelCase__=None ,   lowerCAmelCase__=None ,   lowerCAmelCase__=1.0 ,   lowerCAmelCase__=0.02 ,   lowerCAmelCase__=False ,   lowerCAmelCase__=False ,   lowerCAmelCase__=True ,   **lowerCAmelCase__ ,   )      ->					int:
								super().__init__(tie_word_embeddings=SCREAMING_SNAKE_CASE__ ,   is_encoder_decoder=SCREAMING_SNAKE_CASE__ ,   **SCREAMING_SNAKE_CASE__ )

								if text_config is None:
									a :						Tuple      =							{}
									logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values." )

								if vision_config is None:
									a :						List[Any]      =							{}
									logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values." )

								a :						str      =							PixaStructTextConfig(**SCREAMING_SNAKE_CASE__ )
								a :						Tuple      =							PixaStructVisionConfig(**SCREAMING_SNAKE_CASE__ )

								a :						List[str]      =							self.text_config.decoder_start_token_id
								a :						int      =							self.text_config.pad_token_id
								a :						Any      =							self.text_config.eos_token_id

								a :						Optional[Any]      =							initializer_factor
								a :						Optional[Any]      =							initializer_range

								a :						str      =							self.initializer_range
								a :						Tuple      =							self.initializer_range

								a :						Optional[Any]      =							is_vqa






							@classmethod
							def        __a  (  cls ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ )      ->					Optional[Any]:
								return cls(text_config=text_config.to_dict() ,   vision_config=vision_config.to_dict() ,   **SCREAMING_SNAKE_CASE__ )






							def        __a  (  self )      ->					Union[str, Any]:
								a :						List[Any]      =							copy.deepcopy(self.__dict__ )
								a :						Tuple      =							self.text_config.to_dict()
								a :						Tuple      =							self.vision_config.to_dict()
								a :						Tuple      =							self.__class__.model_type
								return output






"""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 				A_       ( snake_case__		)					->  str:
			return 1 / (1 + np.exp(-z		))





def 				A_       ( snake_case__       ,					snake_case__		)					->  str:
			return (-y * np.log(snake_case__		) - (1 - y) * np.log(1 - h		)).mean()





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  Union[str, Any]:
			_UpperCamelCase					:Tuple					     =	np.dot(snake_case__       ,					snake_case__		)
			return np.sum(y * scores - np.log(1 + np.exp(snake_case__		)		)		)





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__=7_00_00		)					->  Optional[int]:
			_UpperCamelCase					:Union[str, Any]					     =	np.zeros(x.shape[1]		)

			for iterations in range(snake_case__		):
						_UpperCamelCase					:Optional[int]					     =	np.dot(snake_case__       ,					snake_case__		)
						_UpperCamelCase					:Tuple					     =	sigmoid_function(snake_case__		)
						_UpperCamelCase					:Optional[Any]					     =	np.dot(x.T       ,					h - y		) / y.size
						_UpperCamelCase					:int					     =	theta - alpha * gradient  # updating the weights
						_UpperCamelCase					:Union[str, Any]					     =	np.dot(snake_case__       ,					snake_case__		)
						_UpperCamelCase					:Dict					     =	sigmoid_function(snake_case__		)
						_UpperCamelCase					:Optional[int]					     =	cost_function(snake_case__       ,					snake_case__		)
						if iterations % 1_00 == 0:
									print(f"loss: {j} \t"		)  # printing the loss after every 100 iterations
			return theta


# In[68]:

if __name__ == "__main__":
					UpperCamelCase__				:Union[str, Any]			   =	datasets.load_iris()
					UpperCamelCase__				:Dict			   =	iris.data[:, :2]
					UpperCamelCase__				:Any			   =	(iris.target != 0) * 1

					UpperCamelCase__				:Tuple			   =	0.1
					UpperCamelCase__				:List[str]			   =	logistic_reg(alpha, x, y, max_iterations=70_000)
					print("""theta: """, theta)  # printing the theta i.e our weights vector





					def 				A_       ( snake_case__		)					->  Optional[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""")
					((UpperCamelCase__)   ,			(UpperCamelCase__))				:Union[str, Any]			   =	(x[:, 0].min(), x[:, 0].max())
					((UpperCamelCase__)   ,			(UpperCamelCase__))				:Tuple			   =	(x[:, 1].min(), x[:, 1].max())
					((UpperCamelCase__)   ,			(UpperCamelCase__))				:Tuple			   =	np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
					UpperCamelCase__				:List[Any]			   =	np.c_[xxa.ravel(), xxa.ravel()]
					UpperCamelCase__				:List[Any]			   =	predict_prob(grid).reshape(xxa.shape)
					plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

					plt.legend()




					plt.show()

