


"""simple docstring"""






import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
						import tensorflow as tf

if is_torch_available():
						import torch


_A   					=    logging.get_logger(__name__)

class lowerCamelCase		:






					'''simple docstring'''


					def __init__(						self			:				Optional[Any]   , _snake_case			:				Optional[int] = None   , _snake_case			:				Any = None   , _snake_case			:				int=None   , _snake_case			:				Any=None		)							->       Any:





									if not conversation_id:
													SCREAMING_SNAKE_CASE__		  =				uuid.uuida()
									if past_user_inputs is None:
													SCREAMING_SNAKE_CASE__		  =				[]
									if generated_responses is None:
													SCREAMING_SNAKE_CASE__		  =				[]

									SCREAMING_SNAKE_CASE__		  =				conversation_id
									SCREAMING_SNAKE_CASE__		  =				past_user_inputs
									SCREAMING_SNAKE_CASE__		  =				generated_responses
									SCREAMING_SNAKE_CASE__		  =				text



					def __eq__(						self			:				List[Any]   , _snake_case			:				Optional[Any]		)							->       str:





									if not isinstance(_lowerCamelCase   , _lowerCamelCase		):
													return False
									if self.uuid == other.uuid:
													return True
									return (
									    self.new_user_input == other.new_user_input
									    and self.past_user_inputs == other.past_user_inputs
									    and self.generated_responses == other.generated_responses
									)



					def 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				int   , _snake_case			:				Any = False		)							->       Optional[int]:





									if self.new_user_input:
													if overwrite:
																	logger.warning(
																	    F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
																	    F"""with: \"{text}\"."""		)
																	SCREAMING_SNAKE_CASE__		  =				text
													else:
																	logger.warning(
																	    F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
																	    F"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"""		)
									else:
													SCREAMING_SNAKE_CASE__		  =				text



					def 			lowerCAmelCase_					(						self			:				str		)							->       Optional[Any]:





									if self.new_user_input:
													self.past_user_inputs.append(self.new_user_input		)
									SCREAMING_SNAKE_CASE__		  =				None



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				Optional[Any]		)							->       Any:
									self.generated_responses.append(_lowerCamelCase		)



					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Tuple:





									for user_input, generated_response in zip(self.past_user_inputs   , self.generated_responses		):
													yield True, user_input
													yield False, generated_response
									if self.new_user_input:
													yield True, self.new_user_input



					def __repr__(						self			:				Tuple		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				F"""Conversation id: {self.uuid} \n"""
									for is_user, text in self.iter_texts():
													SCREAMING_SNAKE_CASE__		  =				"user" if is_user else "bot"
													output += F"""{name} >> {text} \n"""
									return output



@add_end_docstrings(
    lowerCAmelCase__    ,    R"\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    "    ,    )
class lowerCamelCase		(lowerCAmelCase__      ):






					'''simple docstring'''


					def __init__(						self			:				Union[str, Any]   , *_snake_case			:				Tuple   , **_snake_case			:				Tuple		)							->       Tuple:
									super().__init__(*_lowerCamelCase   , **_lowerCamelCase		)
									if self.tokenizer.pad_token_id is None:
													SCREAMING_SNAKE_CASE__		  =				self.tokenizer.eos_token



					def 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				List[str]=None   , _snake_case			:				Tuple=None   , _snake_case			:				Dict=None   , **_snake_case			:				Union[str, Any]		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				{}
									SCREAMING_SNAKE_CASE__		  =				{}
									SCREAMING_SNAKE_CASE__		  =				{}

									if min_length_for_response is not None:
													SCREAMING_SNAKE_CASE__		  =				min_length_for_response
									if minimum_tokens is not None:
													SCREAMING_SNAKE_CASE__		  =				minimum_tokens

									if "max_length" in generate_kwargs:
													SCREAMING_SNAKE_CASE__		  =				generate_kwargs["max_length"]
													# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
									if clean_up_tokenization_spaces is not None:
													SCREAMING_SNAKE_CASE__		  =				clean_up_tokenization_spaces

									if generate_kwargs:
													forward_params.update(_lowerCamelCase		)
									return preprocess_params, forward_params, postprocess_params



					def __call__(						self			:				Any   , _snake_case			:				Optional[int]   , _snake_case			:				str=0   , **_snake_case			:				Optional[int]		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				super().__call__(_lowerCamelCase   , num_workers=_lowerCamelCase   , **_lowerCamelCase		)
									if isinstance(_lowerCamelCase   , _lowerCamelCase		) and len(_lowerCamelCase		) == 1:
													return outputs[0]
									return outputs



					def 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				Union[str, Any]   , _snake_case			:				Optional[Any]=32		)							->       Dict[str, Any]:





									if not isinstance(_lowerCamelCase   , _lowerCamelCase		):
													raise ValueError("ConversationalPipeline, expects Conversation as inputs"		)
									if conversation.new_user_input is None:
													raise ValueError(
													    F"""Conversation with UUID {type(conversation.uuid		)} does not contain new user input to process. """
													    "Add user inputs with the conversation\'s `add_user_input` method"		)
									if hasattr(self.tokenizer   , "_build_conversation_input_ids"		):
													SCREAMING_SNAKE_CASE__		  =				self.tokenizer._build_conversation_input_ids(_lowerCamelCase		)
									else:
													# If the tokenizer cannot handle conversations, we default to only the old version
													SCREAMING_SNAKE_CASE__		  =				self._legacy_parse_and_tokenize(_lowerCamelCase		)

									if self.framework == "pt":
													SCREAMING_SNAKE_CASE__		  =				torch.LongTensor([input_ids]		)
									elif self.framework == "tf":
													SCREAMING_SNAKE_CASE__		  =				tf.constant([input_ids]		)
									return {"input_ids": input_ids, "conversation": conversation}



					def 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				Optional[Any]   , _snake_case			:				List[Any]=10   , **_snake_case			:				Optional[Any]		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				generate_kwargs.get("max_length"   , self.model.config.max_length		)

									SCREAMING_SNAKE_CASE__		  =				model_inputs["input_ids"].shape[1]
									if max_length - minimum_tokens < n:
													logger.warning(F"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})"""		)
													SCREAMING_SNAKE_CASE__		  =				max_length - minimum_tokens
													SCREAMING_SNAKE_CASE__		  =				model_inputs["input_ids"][:, -trim:]
													if "attention_mask" in model_inputs:
																	SCREAMING_SNAKE_CASE__		  =				model_inputs["attention_mask"][:, -trim:]
									SCREAMING_SNAKE_CASE__		  =				model_inputs.pop("conversation"		)
									SCREAMING_SNAKE_CASE__		  =				max_length
									SCREAMING_SNAKE_CASE__		  =				self.model.generate(**_lowerCamelCase   , **_lowerCamelCase		)
									if self.model.config.is_encoder_decoder:
													SCREAMING_SNAKE_CASE__		  =				1
									else:
													SCREAMING_SNAKE_CASE__		  =				n
									return {"output_ids": output_ids[:, start_position:], "conversation": conversation}



					def 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				Optional[Any]   , _snake_case			:				Dict=True		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				model_outputs["output_ids"]
									SCREAMING_SNAKE_CASE__		  =				self.tokenizer.decode(
									    output_ids[0]   , skip_special_tokens=_lowerCamelCase   , clean_up_tokenization_spaces=_lowerCamelCase   , )
									SCREAMING_SNAKE_CASE__		  =				model_outputs["conversation"]
									conversation.mark_processed()
									conversation.append_response(_lowerCamelCase		)
									return conversation



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				List[str]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.tokenizer.eos_token_id
									SCREAMING_SNAKE_CASE__		  =				[]
									for is_user, text in conversation.iter_texts():
													if eos_token_id is not None:
																	input_ids.extend(self.tokenizer.encode(_lowerCamelCase   , add_special_tokens=_lowerCamelCase		) + [eos_token_id]		)
													else:
																	input_ids.extend(self.tokenizer.encode(_lowerCamelCase   , add_special_tokens=_lowerCamelCase		)		)

									if len(_lowerCamelCase		) > self.tokenizer.model_max_length:
													SCREAMING_SNAKE_CASE__		  =				input_ids[-self.tokenizer.model_max_length :]
									return input_ids





"""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()




