





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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation


UpperCamelCase__ :			Optional[int]       	=			logging.get_logger(__name__)

UpperCamelCase__ :			List[Any]       	=			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase__ :			str       	=			{
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

UpperCamelCase__ :			List[str]       	=			{
    """microsoft/deberta-base""": 5_12,
    """microsoft/deberta-large""": 5_12,
    """microsoft/deberta-xlarge""": 5_12,
    """microsoft/deberta-base-mnli""": 5_12,
    """microsoft/deberta-large-mnli""": 5_12,
    """microsoft/deberta-xlarge-mnli""": 5_12,
}

UpperCamelCase__ :			int       	=			{
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}







class 	_UpperCamelCase	(							A_	):







					'''simple docstring'''

					lowerCamelCase  :  Tuple           =							VOCAB_FILES_NAMES
					lowerCamelCase  :  Optional[Any]           =							PRETRAINED_VOCAB_FILES_MAP
					lowerCamelCase  :  Optional[Any]           =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCamelCase  :  Any           =							['input_ids', 'attention_mask', 'token_type_ids']
					lowerCamelCase  :  List[Any]           =							DebertaTokenizer





					def __init__(    self     :	str			,       __lowercase     :	Tuple=None			,       __lowercase     :	List[Any]=None			,       __lowercase     :	Optional[Any]=None			,       __lowercase     :	Dict="replace"			,       __lowercase     :	Optional[int]="[CLS]"			,       __lowercase     :	List[Any]="[SEP]"			,       __lowercase     :	Union[str, Any]="[SEP]"			,       __lowercase     :	str="[CLS]"			,       __lowercase     :	List[Any]="[UNK]"			,       __lowercase     :	Dict="[PAD]"			,       __lowercase     :	Any="[MASK]"			,       __lowercase     :	Any=False			,       **__lowercase     :	Optional[Any]			,       ):






						'''simple docstring'''







						super().__init__(
						    __lowercase			,       __lowercase			,       tokenizer_file=__lowercase			,       errors=__lowercase			,       bos_token=__lowercase			,       eos_token=__lowercase			,       unk_token=__lowercase			,       sep_token=__lowercase			,       cls_token=__lowercase			,       pad_token=__lowercase			,       mask_token=__lowercase			,       add_prefix_space=__lowercase			,       **__lowercase			,       )
						UpperCAmelCase_		    =  kwargs.pop("""add_bos_token"""			,       __lowercase		)

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

						UpperCAmelCase_		    =  add_prefix_space





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






						'''simple docstring'''







						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 							SCREAMING_SNAKE_CASE				(    self     :	Any			,       __lowercase     :	Union[str, Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  AddedToken(__lowercase			,       lstrip=__lowercase			,       rstrip=__lowercase		) if isinstance(__lowercase			,       __lowercase		) else value
						UpperCAmelCase_		    =  value





					def 							SCREAMING_SNAKE_CASE				(    self     :	Tuple			,       __lowercase     :	List[int]			,       __lowercase     :	Optional[List[int]] = None		):






						'''simple docstring'''







						if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						UpperCAmelCase_		    =  [self.cls_token_id]
						UpperCAmelCase_		    =  [self.sep_token_id]
						return cls + token_ids_a + sep + token_ids_a + sep





					def 							SCREAMING_SNAKE_CASE				(    self     :	Any			,       __lowercase     :	List[int]			,       __lowercase     :	Optional[List[int]] = None		):






						'''simple docstring'''







						UpperCAmelCase_		    =  [self.sep_token_id]
						UpperCAmelCase_		    =  [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		) * [0] + len(token_ids_a + sep		) * [1]





					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]			,       *__lowercase     :	Optional[int]			,       **__lowercase     :	Union[str, Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  kwargs.get("""is_split_into_words"""			,       __lowercase		)
						assert self.add_prefix_space or not is_split_into_words, (
						    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
						    "to use it with pretokenized inputs."
						)

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





					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[Any]			,       *__lowercase     :	str			,       **__lowercase     :	Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  kwargs.get("""is_split_into_words"""			,       __lowercase		)

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

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





					def 							SCREAMING_SNAKE_CASE				(    self     :	List[str]			,       __lowercase     :	str			,       __lowercase     :	Optional[str] = None		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self._tokenizer.model.save(__lowercase			,       name=__lowercase		)
						return tuple(__lowercase		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	List[str]			,       __lowercase     :	"Conversation"		):






						'''simple docstring'''







						UpperCAmelCase_		    =  []
						for is_user, text in conversation.iter_texts():
							input_ids.extend(self.encode(__lowercase			,       add_special_tokens=__lowercase		) + [self.eos_token_id]		)

						if len(__lowercase		) > self.model_max_length:
							UpperCAmelCase_		    =  input_ids[-self.model_max_length :]
						return input_ids









import matplotlib.pyplot as plt
import numpy as np




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  len(A )  # Number of training samples
	UpperCAmelCase_		    =  np.eye(A )  # Initialize weights as identity matrix
	for j in range(A ):
		UpperCAmelCase_		    =  point - x_train[j]
		UpperCAmelCase_		    =  np.exp(diff @ diff.T / (-2.0 * tau**2) )

	return weights




def  A_(      A					,   A					,   A					,   A ):
	UpperCAmelCase_		    =  weight_matrix(A					,   A					,   A )
	UpperCAmelCase_		    =  np.linalg.inv(x_train.T @ weight_mat @ x_train ) @ (
	    x_train.T @ weight_mat @ y_train.T
	)

	return weight




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  np.zeros(len(A ) )  # Initialize array of predictions
	for i, item in enumerate(A ):
		UpperCAmelCase_		    =  item @ local_weight(A					,   A					,   A					,   A )

	return y_pred




def  A_(      A					,   A					,   A ):
	import seaborn as sns

	UpperCAmelCase_		    =  sns.load_dataset(A )
	UpperCAmelCase_		    =  np.array(data[x_name] )
	UpperCAmelCase_		    =  np.array(data[y_name] )

	UpperCAmelCase_		    =  np.ones(len(A ) )

	# pairing elements of one and x_data
	UpperCAmelCase_		    =  np.column_stack((one, x_data) )

	return x_train, x_data, y_data




def  A_(      A					,   A					,   A					,   A					,   A					,   A					,   ):
	UpperCAmelCase_		    =  np.sort(A					,   axis=0 )
	plt.scatter(A					,   A					,   color="""blue""" )
	plt.plot(
	    x_train_sorted[:, 1]					,   preds[x_train[:, 1].argsort(0 )]					,   color="""yellow"""					,   linewidth=5					,   )
	plt.title("""Local Weighted Regression""" )
	plt.xlabel(A )
	plt.ylabel(A )
	plt.show()


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# Demo with a dataset from the seaborn module
				UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__ :			Tuple       	=			load_data("""tips""", """total_bill""", """tip""")
				UpperCamelCase__ :			Tuple       	=			local_weight_regression(training_data_x, tip, 5)
				plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")


