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


__lowerCamelCase    :				Tuple							     =	logging.get_logger(__name__)







class __snake_case       :




   def __init__(					self    :       str ,			_lowercase    :       str = None ,			_lowercase    :       uuid.UUID = None ,			_lowercase    :       List[Any]=None ,			_lowercase    :       Optional[int]=None   ):



     """simple docstring"""






     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    :       Optional[int] ,			_lowercase    :       str   ):



     """simple docstring"""






     if not isinstance(_lowercase ,			_lowercase   ):
       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 			__a		(					self    :       Optional[Any] ,			_lowercase    :       str ,			_lowercase    :       bool = False   ):



     """simple docstring"""






     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 			__a		(					self    :       List[str]   ):



     """simple docstring"""






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




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       str   ):



     """simple docstring"""






     self.generated_responses.append(_lowercase   )




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     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    :       Optional[Any]   ):



     """simple docstring"""






     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 __snake_case       (		lowerCamelCase_	):




   def __init__(					self    :       Optional[Any] ,			*_lowercase    :       Optional[int] ,			**_lowercase    :       List[Any]   ):



     """simple docstring"""






     super().__init__(*_lowercase ,			**_lowercase   )
     if self.tokenizer.pad_token_id is None:
       SCREAMING_SNAKE_CASE__    							=   self.tokenizer.eos_token




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       Optional[Any]=None ,			_lowercase    :       Optional[int]=None ,			_lowercase    :       Any=None ,			**_lowercase    :       Dict   ):



     """simple docstring"""






     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(_lowercase   )
     return preprocess_params, forward_params, postprocess_params




   def __call__(					self    :       List[str] ,			_lowercase    :       Union[Conversation, List[Conversation]] ,			_lowercase    :       Tuple=0 ,			**_lowercase    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   super().__call__(_lowercase ,			num_workers=_lowercase ,			**_lowercase   )
     if isinstance(_lowercase ,			_lowercase   ) and len(_lowercase   ) == 1:
       return outputs[0]
     return outputs




   def 			__a		(					self    :       Dict ,			_lowercase    :       Conversation ,			_lowercase    :       Optional[Any]=32   ):



     """simple docstring"""






     if not isinstance(_lowercase ,			_lowercase   ):
       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(_lowercase   )
     else:
       # If the tokenizer cannot handle conversations, we default to only the old version
       SCREAMING_SNAKE_CASE__    							=   self._legacy_parse_and_tokenize(_lowercase   )

     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 			__a		(					self    :       str ,			_lowercase    :       Tuple ,			_lowercase    :       Any=10 ,			**_lowercase    :       Optional[int]   ):



     """simple docstring"""






     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(**_lowercase ,			**_lowercase   )
     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 			__a		(					self    :       Optional[Any] ,			_lowercase    :       List[str] ,			_lowercase    :       int=True   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   model_outputs["""output_ids"""]
     SCREAMING_SNAKE_CASE__    							=   self.tokenizer.decode(
         output_ids[0] ,			skip_special_tokens=_lowercase ,			clean_up_tokenization_spaces=_lowercase ,			)
     SCREAMING_SNAKE_CASE__    							=   model_outputs["""conversation"""]
     conversation.mark_processed()
     conversation.append_response(_lowercase   )
     return conversation




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       Conversation   ):



     """simple docstring"""






     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(_lowercase ,			add_special_tokens=_lowercase   ) + [eos_token_id]   )
       else:
         input_ids.extend(self.tokenizer.encode(_lowercase ,			add_special_tokens=_lowercase   )   )

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

# 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					: Tuple			)							->      int:







  """simple docstring"""





  return 1 / (1 + np.exp(-z			))



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      Optional[Any]:







  """simple docstring"""





  return (-y * np.log(__UpperCamelCase			) - (1 - y) * np.log(1 - h			)).mean()



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
  return np.sum(y * scores - np.log(1 + np.exp(__UpperCamelCase			)			)			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Any=7_00_00			)							->      Optional[int]:







  """simple docstring"""





  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 % 1_00 == 0:
      print(f"""loss: {j} \t"""			)  # printing the loss after every 100 iterations
  return theta


# In[68]:

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

   __lowerCamelCase    :				List[str]							     =	0.1
   __lowerCamelCase    :				str							     =	logistic_reg(alpha, x, y, max_iterations=7_0000)
   print('''theta: ''', theta)  # printing the theta i.e our weights vector



   def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      Tuple:







     """simple docstring"""





     return sigmoid_function(
         np.dot(__UpperCamelCase				,  __UpperCamelCase			)			)  # 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''')
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				int							     =	(x[:, 0].min(), x[:, 0].max())
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				List[Any]							     =	(x[:, 1].min(), x[:, 1].max())
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				List[Any]							     =	np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
   __lowerCamelCase    :				Optional[int]							     =	np.c_[xxa.ravel(), xxa.ravel()]
   __lowerCamelCase    :				Optional[int]							     =	predict_prob(grid).reshape(xxa.shape)
   plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

   plt.legend()






   plt.show()
