
import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
					import torch







class      A__							(     A_	):
				lowercase			    =  'char'
				lowercase			    =  'bpe'
				lowercase			    =  'wp'






lowerCamelCase__          =		(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)







class      A__							(     A_	):
				lowercase			    =  ['image_processor', 'char_tokenizer']
				lowercase			    =  'ViTImageProcessor'
				lowercase			    =  'MgpstrTokenizer'






				def __init__(						self					:      str    ,     a					:      List[str]=None    ,     a					:      Any=None    ,     **a					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		None
							if "feature_extractor" in kwargs:
										warnings.warn(
										    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
										    ' instead.'    ,     _lowerCamelCase    ,     )
										lowerCAmelCase__      :      Optional[Any]         =		kwargs.pop('feature_extractor'   )

							lowerCAmelCase__      :      Optional[int]         =		image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
										raise ValueError('You need to specify an `image_processor`.'   )
							if tokenizer is None:
										raise ValueError('You need to specify a `tokenizer`.'   )

							lowerCAmelCase__      :      Dict         =		tokenizer
							lowerCAmelCase__      :      str         =		AutoTokenizer.from_pretrained('gpt2'   )
							lowerCAmelCase__      :      str         =		AutoTokenizer.from_pretrained('bert-base-uncased'   )

							super().__init__(_lowerCamelCase    ,     _lowerCamelCase   )






				def __call__(						self					:      int    ,     a					:      Any=None    ,     a					:      Any=None    ,     a					:      Optional[int]=None    ,     **a					:      Dict   ):






							'''simple docstring'''







							if images is None and text is None:
										raise ValueError('You need to specify either an `images` or `text` input to process.'   )

							if images is not None:
										lowerCAmelCase__      :      Tuple         =		self.image_processor(_lowerCamelCase    ,     return_tensors=_lowerCamelCase    ,     **_lowerCamelCase   )
							if text is not None:
										lowerCAmelCase__      :      Union[str, Any]         =		self.char_tokenizer(_lowerCamelCase    ,     return_tensors=_lowerCamelCase    ,     **_lowerCamelCase   )

							if text is None:
										return inputs
							elif images is None:
										return encodings
							else:
										lowerCAmelCase__      :      Union[str, Any]         =		encodings['input_ids']
										return inputs






				def 							_lowerCamelCase		(						self					:      Optional[Any]    ,     a					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      :      List[str]         =		sequences
							lowerCAmelCase__      :      Dict         =		char_preds.size(0   )

							lowerCAmelCase__      ,				lowerCAmelCase__      :      Tuple         =		self._decode_helper(_lowerCamelCase    ,     'char'   )
							lowerCAmelCase__      ,				lowerCAmelCase__      :      Dict         =		self._decode_helper(_lowerCamelCase    ,     'bpe'   )
							lowerCAmelCase__      ,				lowerCAmelCase__      :      Optional[int]         =		self._decode_helper(_lowerCamelCase    ,     'wp'   )

							lowerCAmelCase__      :      Any         =		[]
							lowerCAmelCase__      :      List[Any]         =		[]
							for i in range(_lowerCamelCase   ):
										lowerCAmelCase__      :      Any         =		[char_scores[i], bpe_scores[i], wp_scores[i]]
										lowerCAmelCase__      :      Any         =		[char_strs[i], bpe_strs[i], wp_strs[i]]
										lowerCAmelCase__      :      List[Any]         =		scores.index(max(_lowerCamelCase   )   )
										final_strs.append(strs[max_score_index]   )
										final_scores.append(scores[max_score_index]   )

							lowerCAmelCase__      :      Union[str, Any]         =		{}
							lowerCAmelCase__      :      List[str]         =		final_strs
							lowerCAmelCase__      :      int         =		final_scores
							lowerCAmelCase__      :      List[Any]         =		char_strs
							lowerCAmelCase__      :      int         =		bpe_strs
							lowerCAmelCase__      :      List[str]         =		wp_strs
							return out






				def 							_lowerCamelCase		(						self					:      int    ,     a					:      Optional[Any]    ,     a					:      List[str]   ):






							'''simple docstring'''







							if format == DecodeType.CHARACTER:
										lowerCAmelCase__      :      Dict         =		self.char_decode
										lowerCAmelCase__      :      List[str]         =		1
										lowerCAmelCase__      :      Optional[Any]         =		'[s]'
							elif format == DecodeType.BPE:
										lowerCAmelCase__      :      List[str]         =		self.bpe_decode
										lowerCAmelCase__      :      Dict         =		2
										lowerCAmelCase__      :      Optional[Any]         =		'#'
							elif format == DecodeType.WORDPIECE:
										lowerCAmelCase__      :      Optional[Any]         =		self.wp_decode
										lowerCAmelCase__      :      Tuple         =		102
										lowerCAmelCase__      :      List[Any]         =		'[SEP]'
							else:
										raise ValueError(f'''Format {format} is not supported.'''   )

							lowerCAmelCase__      ,				lowerCAmelCase__      :      List[str]         =		[], []
							lowerCAmelCase__      :      int         =		pred_logits.size(0   )
							lowerCAmelCase__      :      int         =		pred_logits.size(1   )
							lowerCAmelCase__      ,				lowerCAmelCase__      :      Optional[int]         =		pred_logits.topk(1    ,     dim=-1    ,     largest=_lowerCamelCase    ,     sorted=_lowerCamelCase   )
							lowerCAmelCase__      :      List[Any]         =		preds_index.view(-1    ,     _lowerCamelCase   )[:, 1:]
							lowerCAmelCase__      :      int         =		decoder(_lowerCamelCase   )
							lowerCAmelCase__      ,				lowerCAmelCase__      :      Optional[Any]         =		torch.nn.functional.softmax(_lowerCamelCase    ,     dim=2   ).max(dim=2   )
							lowerCAmelCase__      :      Union[str, Any]         =		preds_max_prob[:, 1:]

							for index in range(_lowerCamelCase   ):
										lowerCAmelCase__      :      Optional[Any]         =		preds_str[index].find(_lowerCamelCase   )
										lowerCAmelCase__      :      Optional[int]         =		preds_str[index][:pred_eos]
										lowerCAmelCase__      :      Optional[Any]         =		preds_index[index].cpu().tolist()
										lowerCAmelCase__      :      Union[str, Any]         =		pred_index.index(_lowerCamelCase   ) if eos_token in pred_index else -1
										lowerCAmelCase__      :      str         =		preds_max_prob[index][: pred_eos_index + 1]
										lowerCAmelCase__      :      int         =		pred_max_prob.cumprod(dim=0   )[-1] if pred_max_prob.nelement() != 0 else 0.0
										dec_strs.append(_lowerCamelCase   )
										conf_scores.append(_lowerCamelCase   )

							return dec_strs, conf_scores






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[int]         =		[seq.replace(' '    ,     ''   ) for seq in self.char_tokenizer.batch_decode(_lowerCamelCase   )]
							return decode_strs






				def 							_lowerCamelCase		(						self					:      int    ,     a					:      Tuple   ):






							'''simple docstring'''







							return self.bpe_tokenizer.batch_decode(_lowerCamelCase   )






				def 							_lowerCamelCase		(						self					:      List[Any]    ,     a					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		[seq.replace(' '    ,     ''   ) for seq in self.wp_tokenizer.batch_decode(_lowerCamelCase   )]
							return decode_strs

"""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 					_UpperCAmelCase      ( __lowerCamelCase   :       str		)     ->					List[Any]:
  return 1 / (1 + np.exp(-z		))
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       List[str]		)     ->					Optional[Any]:
  return (-y * np.log(__lowerCamelCase		) - (1 - y) * np.log(1 - h		)).mean()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Dict		)     ->					List[str]:
  _snake_case					     =       np.dot(__lowerCamelCase ,					__lowerCamelCase		)
  return np.sum(y * scores - np.log(1 + np.exp(__lowerCamelCase		)		)		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       str=7_00_00		)     ->					Optional[Any]:
  _snake_case					     =       np.zeros(x.shape[1]		)

  for iterations in range(__lowerCamelCase		):
    _snake_case					     =       np.dot(__lowerCamelCase ,					__lowerCamelCase		)
    _snake_case					     =       sigmoid_function(__lowerCamelCase		)
    _snake_case					     =       np.dot(x.T ,					h - y		) / y.size
    _snake_case					     =       theta - alpha * gradient  # updating the weights
    _snake_case					     =       np.dot(__lowerCamelCase ,					__lowerCamelCase		)
    _snake_case					     =       sigmoid_function(__lowerCamelCase		)
    _snake_case					     =       cost_function(__lowerCamelCase ,					__lowerCamelCase		)
    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__          = datasets.load_iris()
 UpperCAmelCase__          = iris.data[:, :2]
 UpperCAmelCase__          = (iris.target != 0) * 1

 UpperCAmelCase__          = 0.1
 UpperCAmelCase__          = logistic_reg(alpha, x, y, max_iterations=70000)
 print('theta: ', theta)  # printing the theta i.e our weights vector
 def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					Union[str, Any]:
   return sigmoid_function(
       np.dot(__lowerCamelCase ,					__lowerCamelCase		)		)  # 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__))          = (x[:, 0].min(), x[:, 0].max())
 ((UpperCAmelCase__)      ,	(UpperCAmelCase__))          = (x[:, 1].min(), x[:, 1].max())
 ((UpperCAmelCase__)      ,	(UpperCAmelCase__))          = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
 UpperCAmelCase__          = np.c_[xxa.ravel(), xxa.ravel()]
 UpperCAmelCase__          = predict_prob(grid).reshape(xxa.shape)
 plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

 plt.legend()

 plt.show()



