




import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
	import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase   :	List[Any]       =    logging.get_logger(__name__)

_lowerCamelCase   :	Optional[int]       =    {"""vocab_file""": """vocab.txt"""}

_lowerCamelCase   :	Dict       =    {
    """vocab_file""": {
        """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt""",
    },
}

_lowerCamelCase   :	Optional[Any]       =    {
    """openbmb/cpm-ant-10b""": 1_0_2_4,
}




def 				_UpperCAmelCase	(UpperCamelCase_			:			int							):
				'''simple docstring'''
				_lowerCAmelCase     :     Tuple              =   collections.OrderedDict()
				with open(UpperCamelCase__   ,     """r"""   ,     encoding="""utf-8"""							) as reader:
								_lowerCAmelCase     :     Tuple              =   reader.readlines()
				for index, token in enumerate(UpperCamelCase__							):
								_lowerCAmelCase     :     int              =   token.rstrip("""\n"""							)
								_lowerCAmelCase     :     List[Any]              =   index
				return vocab
class 							__snake_case	(lowercase_		):



				def __init__(   self   :		Tuple		,						_UpperCAmelCase   :		str		,						_UpperCAmelCase   :		Optional[Any]="<unk>"		,						_UpperCAmelCase   :		List[Any]=200   )	->     List[str]:





								'''simple docstring'''




								_lowerCAmelCase     :     Optional[Any]              =   vocab
								_lowerCAmelCase     :     Tuple              =   unk_token
								_lowerCAmelCase     :     List[str]              =   max_input_chars_per_word



				def    SCREAMING_SNAKE_CASE							(   self   :		str		,						_UpperCAmelCase   :		Union[str, Any]   )	->     List[Any]:





								'''simple docstring'''




								_lowerCAmelCase     :     str              =   list(_UpperCAmelCase   )
								if len(_UpperCAmelCase   ) > self.max_input_chars_per_word:
												return [self.unk_token]

								_lowerCAmelCase     :     Union[str, Any]              =   0
								_lowerCAmelCase     :     List[str]              =   []
								while start < len(_UpperCAmelCase   ):
												_lowerCAmelCase     :     Any              =   len(_UpperCAmelCase   )
												_lowerCAmelCase     :     Optional[int]              =   None
												while start < end:
																_lowerCAmelCase     :     Tuple              =   """""".join(chars[start:end]   )
																if substr in self.vocab:
																				_lowerCAmelCase     :     Tuple              =   substr
																				break
																end -= 1
												if cur_substr is None:
																sub_tokens.append(self.unk_token   )
																start += 1
												else:
																sub_tokens.append(_UpperCAmelCase   )
																_lowerCAmelCase     :     Optional[int]              =   end

								return sub_tokens






class 							__snake_case	(lowercase_		):
				lowerCAmelCase__      						=  VOCAB_FILES_NAMES
				lowerCAmelCase__      						=  PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase__      						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase__      						=  ['''input_ids''', '''attention_mask''']
				lowerCAmelCase__      						=  False



				def __init__(   self   :		Any		,						_UpperCAmelCase   :		Any		,						_UpperCAmelCase   :		Optional[Any]="<d>"		,						_UpperCAmelCase   :		str="</d>"		,						_UpperCAmelCase   :		Any="<s>"		,						_UpperCAmelCase   :		Union[str, Any]="</s>"		,						_UpperCAmelCase   :		Any="<pad>"		,						_UpperCAmelCase   :		Dict="<unk>"		,						_UpperCAmelCase   :		Optional[Any]="</n>"		,						_UpperCAmelCase   :		Optional[Any]="</_>"		,						_UpperCAmelCase   :		Union[str, Any]="left"		,						**_UpperCAmelCase   :		Any		,						)	->     Optional[int]:





								'''simple docstring'''




								requires_backends(self		,						["""jieba"""]   )
								super().__init__(
								    bod_token=_UpperCAmelCase		,						eod_token=_UpperCAmelCase		,						bos_token=_UpperCAmelCase		,						eos_token=_UpperCAmelCase		,						pad_token=_UpperCAmelCase		,						unk_token=_UpperCAmelCase		,						line_token=_UpperCAmelCase		,						space_token=_UpperCAmelCase		,						padding_side=_UpperCAmelCase		,						**_UpperCAmelCase		,						)
								_lowerCAmelCase     :     Union[str, Any]              =   bod_token
								_lowerCAmelCase     :     List[str]              =   eod_token
								_lowerCAmelCase     :     int              =   load_vocab(_UpperCAmelCase   )
								_lowerCAmelCase     :     Dict              =   self.encoder[space_token]
								_lowerCAmelCase     :     List[str]              =   self.encoder[line_token]

								del self.encoder[space_token]
								del self.encoder[line_token]

								_lowerCAmelCase     :     Optional[Any]              =   collections.OrderedDict(sorted(self.encoder.items()		,						key=lambda _UpperCAmelCase   : x[1]   )   )
								_lowerCAmelCase     :     List[str]              =   {v: k for k, v in self.encoder.items()}

								_lowerCAmelCase     :     Optional[Any]              =   WordpieceTokenizer(vocab=self.encoder		,						unk_token=self.unk_token   )



				@property
				def    SCREAMING_SNAKE_CASE							(   self   :		str   )	->     Dict:





								'''simple docstring'''




								return self.encoder[self.bod_token]



				@property
				def    SCREAMING_SNAKE_CASE							(   self   :		Dict   )	->     Union[str, Any]:





								'''simple docstring'''




								return self.encoder[self.eod_token]



				@property
				def    SCREAMING_SNAKE_CASE							(   self   :		str   )	->     List[Any]:





								'''simple docstring'''




								return self.encoder["\n"]



				@property
				def    SCREAMING_SNAKE_CASE							(   self   :		Any   )	->     Any:





								'''simple docstring'''




								return len(self.encoder   )



				def    SCREAMING_SNAKE_CASE							(   self   :		Dict   )	->     str:





								'''simple docstring'''




								return dict(self.encoder		,						**self.added_tokens_encoder   )



				def    SCREAMING_SNAKE_CASE							(   self   :		Union[str, Any]		,						_UpperCAmelCase   :		int   )	->     Any:





								'''simple docstring'''




								_lowerCAmelCase     :     Dict              =   []
								for x in jieba.cut(_UpperCAmelCase		,						cut_all=_UpperCAmelCase   ):
												output_tokens.extend(self.wordpiece_tokenizer.tokenize(_UpperCAmelCase   )   )
								return output_tokens



				def    SCREAMING_SNAKE_CASE							(   self   :		Optional[int]		,						_UpperCAmelCase   :		Optional[Any]		,						**_UpperCAmelCase   :		int   )	->     Dict:





								'''simple docstring'''




								_lowerCAmelCase     :     Any              =   [i for i in token_ids if i >= 0]
								_lowerCAmelCase     :     str              =   [
								    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
								]
								return super()._decode(_UpperCAmelCase		,						**_UpperCAmelCase   )



				def    SCREAMING_SNAKE_CASE							(   self   :		Optional[Any]		,						_UpperCAmelCase   :		Tuple   )	->     List[Any]:





								'''simple docstring'''




								return token in self.encoder



				def    SCREAMING_SNAKE_CASE							(   self   :		Dict		,						_UpperCAmelCase   :		Optional[Any]   )	->     Union[str, Any]:





								'''simple docstring'''




								return "".join(_UpperCAmelCase   )



				def    SCREAMING_SNAKE_CASE							(   self   :		List[str]		,						_UpperCAmelCase   :		str   )	->     List[str]:





								'''simple docstring'''




								return self.encoder.get(_UpperCAmelCase		,						self.encoder.get(self.unk_token   )   )



				def    SCREAMING_SNAKE_CASE							(   self   :		str		,						_UpperCAmelCase   :		Union[str, Any]   )	->     str:





								'''simple docstring'''




								return self.decoder.get(_UpperCAmelCase		,						self.unk_token   )



				def    SCREAMING_SNAKE_CASE							(   self   :		Any		,						_UpperCAmelCase   :		Dict		,						_UpperCAmelCase   :		Optional[int] = None   )	->     Optional[Any]:





								'''simple docstring'''




								if os.path.isdir(_UpperCAmelCase   ):
												_lowerCAmelCase     :     Tuple              =   os.path.join(
												    _UpperCAmelCase		,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
								else:
												_lowerCAmelCase     :     Any              =   (filename_prefix + """-""" if filename_prefix else """""") + save_directory
								_lowerCAmelCase     :     Any              =   0
								if " " in self.encoder:
												_lowerCAmelCase     :     Optional[int]              =   self.encoder[""" """]
												del self.encoder[" "]
								if "\n" in self.encoder:
												_lowerCAmelCase     :     List[str]              =   self.encoder["""\n"""]
												del self.encoder["\n"]
								_lowerCAmelCase     :     List[Any]              =   collections.OrderedDict(sorted(self.encoder.items()		,						key=lambda _UpperCAmelCase   : x[1]   )   )
								with open(_UpperCAmelCase		,						"""w"""		,						encoding="""utf-8"""   ) as writer:
												for token, token_index in self.encoder.items():
																if index != token_index:
																				logger.warning(
																				    f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
																				    """ Please check that the vocabulary is not corrupted!"""   )
																				_lowerCAmelCase     :     Dict              =   token_index
																writer.write(token + """\n"""   )
																index += 1
								return (vocab_file,)



				def    SCREAMING_SNAKE_CASE							(   self   :		Optional[Any]		,						_UpperCAmelCase   :		Optional[int]		,						_UpperCAmelCase   :		Union[str, Any] = None   )	->     str:





								'''simple docstring'''




								if token_ids_a is None:
												return [self.bos_token_id] + token_ids_a
								return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a



				def    SCREAMING_SNAKE_CASE							(   self   :		Optional[int]		,						_UpperCAmelCase   :		List[str]		,						_UpperCAmelCase   :		Optional[int] = None		,						_UpperCAmelCase   :		Any = False   )	->     str:





								'''simple docstring'''




								if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=_UpperCAmelCase		,						token_ids_a=_UpperCAmelCase		,						already_has_special_tokens=_UpperCAmelCase   )

								if token_ids_a is not None:
												return [1] + ([0] * len(_UpperCAmelCase   )) + [1] + ([0] * len(_UpperCAmelCase   ))
								return [1] + ([0] * len(_UpperCAmelCase   ))




# 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    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__=70000 ):







							'''simple docstring'''






							snake_case_     							=       np.zeros(x.shape[1] )

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

					_UpperCAmelCase						:  Optional[int]									=			0.1
					_UpperCAmelCase						:  Dict									=			logistic_reg(alpha, x, y, max_iterations=7_0000)
					print("""theta: """, theta)  # printing the theta i.e our weights vector





					def    __lowerCamelCase      (					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""")
					((_UpperCAmelCase)							,		(_UpperCAmelCase))						:  Any									=			(x[:, 0].min(), x[:, 0].max())
					((_UpperCAmelCase)							,		(_UpperCAmelCase))						:  Union[str, Any]									=			(x[:, 1].min(), x[:, 1].max())
					((_UpperCAmelCase)							,		(_UpperCAmelCase))						:  int									=			np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
					_UpperCAmelCase						:  List[Any]									=			np.c_[xxa.ravel(), xxa.ravel()]
					_UpperCAmelCase						:  Dict									=			predict_prob(grid).reshape(xxa.shape)
					plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

					plt.legend()


					plt.show()




