"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


_A   					=    logging.get_logger(__name__)


_A   					=    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
_A   					=    {
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

_A   					=    {
    'facebook/bart-base': 1_0_2_4,
    'facebook/bart-large': 1_0_2_4,
    'facebook/bart-large-mnli': 1_0_2_4,
    'facebook/bart-large-cnn': 1_0_2_4,
    'facebook/bart-large-xsum': 1_0_2_4,
    'yjernite/bart_eli5': 1_0_2_4,
}

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		VOCAB_FILES_NAMES
					a   	=		PRETRAINED_VOCAB_FILES_MAP
					a   	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a   	=		["input_ids", "attention_mask"]
					a   	=		BartTokenizer



					def __init__(						self			:				List[Any]   , _snake_case			:				Any=None   , _snake_case			:				List[str]=None   , _snake_case			:				Union[str, Any]=None   , _snake_case			:				List[Any]="replace"   , _snake_case			:				Any="<s>"   , _snake_case			:				List[str]="</s>"   , _snake_case			:				Optional[Any]="</s>"   , _snake_case			:				Dict="<s>"   , _snake_case			:				List[Any]="<unk>"   , _snake_case			:				Union[str, Any]="<pad>"   , _snake_case			:				Tuple="<mask>"   , _snake_case			:				str=False   , _snake_case			:				Optional[int]=True   , **_snake_case			:				Optional[int]   , )							->       Dict:
									super().__init__(
									    _snake_case   , _snake_case   , tokenizer_file=_snake_case   , errors=_snake_case   , bos_token=_snake_case   , eos_token=_snake_case   , sep_token=_snake_case   , cls_token=_snake_case   , unk_token=_snake_case   , pad_token=_snake_case   , mask_token=_snake_case   , add_prefix_space=_snake_case   , trim_offsets=_snake_case   , **_snake_case   , )

									SCREAMING_SNAKE_CASE__		  =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
									if pre_tok_state.get("add_prefix_space"   , _snake_case		) != add_prefix_space:
													SCREAMING_SNAKE_CASE__		  =				getattr(_snake_case   , pre_tok_state.pop("type"		)		)
													SCREAMING_SNAKE_CASE__		  =				add_prefix_space
													SCREAMING_SNAKE_CASE__		  =				pre_tok_class(**_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				add_prefix_space

									# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
									SCREAMING_SNAKE_CASE__		  =				"post_processor"
									SCREAMING_SNAKE_CASE__		  =				getattr(self.backend_tokenizer   , _snake_case   , _snake_case		)
									if tokenizer_component_instance:
													SCREAMING_SNAKE_CASE__		  =				json.loads(tokenizer_component_instance.__getstate__()		)

													# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
													if "sep" in state:
																	SCREAMING_SNAKE_CASE__		  =				tuple(state["sep"]		)
													if "cls" in state:
																	SCREAMING_SNAKE_CASE__		  =				tuple(state["cls"]		)

													SCREAMING_SNAKE_CASE__		  =				False

													if state.get("add_prefix_space"   , _snake_case		) != add_prefix_space:
																	SCREAMING_SNAKE_CASE__		  =				add_prefix_space
																	SCREAMING_SNAKE_CASE__		  =				True

													if state.get("trim_offsets"   , _snake_case		) != trim_offsets:
																	SCREAMING_SNAKE_CASE__		  =				trim_offsets
																	SCREAMING_SNAKE_CASE__		  =				True

													if changes_to_apply:
																	SCREAMING_SNAKE_CASE__		  =				getattr(_snake_case   , state.pop("type"		)		)
																	SCREAMING_SNAKE_CASE__		  =				component_class(**_snake_case		)
																	setattr(self.backend_tokenizer   , _snake_case   , _snake_case		)



					@property
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       str:
									if self._mask_token is None:
													if self.verbose:
																	logger.error("Using mask_token, but it is not set yet."		)
													return None
									return str(self._mask_token		)



					@mask_token.setter
					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				Union[str, Any]		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				AddedToken(_snake_case   , lstrip=_snake_case   , rstrip=_snake_case		) if isinstance(_snake_case   , _snake_case		) else value
									SCREAMING_SNAKE_CASE__		  =				value



					def 			lowerCAmelCase_					(						self			:				List[Any]   , *_snake_case			:				str   , **_snake_case			:				str		)							->       BatchEncoding:
									SCREAMING_SNAKE_CASE__		  =				kwargs.get("is_split_into_words"   , _snake_case		)

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

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



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , *_snake_case			:				Tuple   , **_snake_case			:				Optional[int]		)							->       BatchEncoding:
									SCREAMING_SNAKE_CASE__		  =				kwargs.get("is_split_into_words"   , _snake_case		)

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

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



					def 			lowerCAmelCase_					(						self			:				List[Any]   , _snake_case			:				str   , _snake_case			:				Optional[str] = None		)							->       Tuple[str]:
									SCREAMING_SNAKE_CASE__		  =				self._tokenizer.model.save(_snake_case   , name=_snake_case		)
									return tuple(_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				Any   , _snake_case			:				int=None		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
													return output

									return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



					def 			lowerCAmelCase_					(						self			:				List[Any]   , _snake_case			:				List[int]   , _snake_case			:				Optional[List[int]] = None		)							->       List[int]:
									SCREAMING_SNAKE_CASE__		  =				[self.sep_token_id]
									SCREAMING_SNAKE_CASE__		  =				[self.cls_token_id]

									if token_ids_a is None:
													return len(cls + token_ids_a + sep		) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]





"""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()




