



'''simple docstring'''





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


_lowerCAmelCase										=     logging.get_logger(__name__)

_lowerCAmelCase										=     {'''tokenizer_file''': '''tokenizer.json'''}

_lowerCAmelCase										=     {
    '''tokenizer_file''': {
        '''bigscience/tokenizer''': '''https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json''',
        '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json''',
        '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json''',
        '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json''',
        '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json''',
    },
}






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				List[str]           =				VOCAB_FILES_NAMES
  __lowercase							:				Union[str, Any]           =				PRETRAINED_VOCAB_FILES_MAP
  __lowercase							:				str           =				['''input_ids''', '''attention_mask''']
  __lowercase							:				Dict           =				None




  def __init__(   self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase="<unk>"  ,__UpperCAmelCase="<s>"  ,__UpperCAmelCase="</s>"  ,__UpperCAmelCase="<pad>"  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,**__UpperCAmelCase  ,)   ->		List[str]:
      super().__init__(
          __UpperCAmelCase  ,__UpperCAmelCase  ,tokenizer_file=__UpperCAmelCase  ,unk_token=__UpperCAmelCase  ,bos_token=__UpperCAmelCase  ,eos_token=__UpperCAmelCase  ,pad_token=__UpperCAmelCase  ,add_prefix_space=__UpperCAmelCase  ,clean_up_tokenization_spaces=__UpperCAmelCase  ,**__UpperCAmelCase  ,)
      lowerCAmelCase__			:  str											= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
      if pre_tok_state.get("""add_prefix_space"""  ,__UpperCAmelCase				) != add_prefix_space:
          lowerCAmelCase__			:  Dict											= getattr(__UpperCAmelCase  ,pre_tok_state.pop("""type"""				)				)
          lowerCAmelCase__			:  Optional[int]											= add_prefix_space
          lowerCAmelCase__			:  Any											= pre_tok_class(**__UpperCAmelCase				)

      lowerCAmelCase__			:  int											= add_prefix_space




  def 							UpperCAmelCase_     (   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		BatchEncoding:
      lowerCAmelCase__			:  Optional[Any]											= kwargs.get("""is_split_into_words"""  ,__UpperCAmelCase				)
      if not (self.add_prefix_space or not is_split_into_words):
          raise Exception(
              F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
              """ pretokenized inputs."""				)

      return super()._batch_encode_plus(*__UpperCAmelCase  ,**__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		BatchEncoding:
      lowerCAmelCase__			:  Dict											= kwargs.get("""is_split_into_words"""  ,__UpperCAmelCase				)

      if not (self.add_prefix_space or not is_split_into_words):
          raise Exception(
              F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
              """ pretokenized inputs."""				)

      return super()._encode_plus(*__UpperCAmelCase  ,**__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase = None				)   ->		Tuple[str]:
      lowerCAmelCase__			:  int											= self._tokenizer.model.save(__UpperCAmelCase  ,name=__UpperCAmelCase				)
      return tuple(__UpperCAmelCase				)






  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		List[int]:
      lowerCAmelCase__			:  Dict											= []
      for is_user, text in conversation.iter_texts():
          input_ids.extend(self.encode(__UpperCAmelCase  ,add_special_tokens=__UpperCAmelCase				) + [self.eos_token_id]				)

      if len(__UpperCAmelCase				) > self.model_max_length:
          lowerCAmelCase__			:  Optional[int]											= input_ids[-self.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      ):



    """simple docstring"""





    return 1 / (1 + np.exp(-z      ))
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





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



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= np.dot(UpperCamelCase							,   UpperCamelCase      )
    return np.sum(y * scores - np.log(1 + np.exp(UpperCamelCase      )      )      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase=70000      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= np.zeros(x.shape[1]      )

    for iterations in range(UpperCamelCase      ):
        lowerCAmelCase__			:  str											= np.dot(UpperCamelCase							,   UpperCamelCase      )
        lowerCAmelCase__			:  Tuple											= sigmoid_function(UpperCamelCase      )
        lowerCAmelCase__			:  List[str]											= np.dot(x.T							,   h - y      ) / y.size
        lowerCAmelCase__			:  List[Any]											= theta - alpha * gradient  # updating the weights
        lowerCAmelCase__			:  List[Any]											= np.dot(UpperCamelCase							,   UpperCamelCase      )
        lowerCAmelCase__			:  List[Any]											= sigmoid_function(UpperCamelCase      )
        lowerCAmelCase__			:  List[Any]											= 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__":
 _lowerCAmelCase										=     datasets.load_iris()
 _lowerCAmelCase										=     iris.data[:, :2]
 _lowerCAmelCase										=     (iris.target != 0) * 1

 _lowerCAmelCase										=     0.1
 _lowerCAmelCase										=     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      ):



     """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))										=     (x[:, 0].min(), x[:, 0].max())
 ((_lowerCAmelCase)						,      (_lowerCAmelCase))										=     (x[:, 1].min(), x[:, 1].max())
 ((_lowerCAmelCase)						,      (_lowerCAmelCase))										=     np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
 _lowerCAmelCase										=     np.c_[xxa.ravel(), xxa.ravel()]
 _lowerCAmelCase										=     predict_prob(grid).reshape(xxa.shape)
 plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

 plt.legend()




 plt.show()


