



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


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

UpperCAmelCase :				List[str]							={"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase :				str							={
    """vocab_file""": {
        """microsoft/layoutlm-base-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt"""
        ),
        """microsoft/layoutlm-large-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """microsoft/layoutlm-base-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json"""
        ),
        """microsoft/layoutlm-large-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

UpperCAmelCase :				str							={
    """microsoft/layoutlm-base-uncased""": 512,
    """microsoft/layoutlm-large-uncased""": 512,
}

UpperCAmelCase :				Any							={
    """microsoft/layoutlm-base-uncased""": {"""do_lower_case""": True},
    """microsoft/layoutlm-large-uncased""": {"""do_lower_case""": True},
}


class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    VOCAB_FILES_NAMES
				lowercase__     =    PRETRAINED_VOCAB_FILES_MAP
				lowercase__     =    PRETRAINED_INIT_CONFIGURATION
				lowercase__     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__     =    LayoutLMTokenizer

				def __init__(			self  ,			snake_case__=None  ,			snake_case__=None  ,			snake_case__=True  ,			snake_case__="[UNK]"  ,			snake_case__="[SEP]"  ,			snake_case__="[PAD]"  ,			snake_case__="[CLS]"  ,			snake_case__="[MASK]"  ,			snake_case__=True  ,			snake_case__=None  ,			**snake_case__  ,			):

									'''simple docstring'''


									super().__init__(
									    snake_case__  ,			tokenizer_file=snake_case__  ,			do_lower_case=snake_case__  ,			unk_token=snake_case__  ,			sep_token=snake_case__  ,			pad_token=snake_case__  ,			cls_token=snake_case__  ,			mask_token=snake_case__  ,			tokenize_chinese_chars=snake_case__  ,			strip_accents=snake_case__  ,			**snake_case__  ,			)

									UpperCamelCase_			= json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
									if (
									    normalizer_state.get("lowercase"  ,			snake_case__     ) != do_lower_case
									    or normalizer_state.get("strip_accents"  ,			snake_case__     ) != strip_accents
									    or normalizer_state.get("handle_chinese_chars"  ,			snake_case__     ) != tokenize_chinese_chars
									):
														UpperCamelCase_			= getattr(snake_case__  ,			normalizer_state.pop("type"     )     )
														UpperCamelCase_			= do_lower_case
														UpperCamelCase_			= strip_accents
														UpperCamelCase_			= tokenize_chinese_chars
														UpperCamelCase_			= normalizer_class(**snake_case__     )

									UpperCamelCase_			= do_lower_case

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__=None     ):

									'''simple docstring'''


									UpperCamelCase_			= [self.cls_token_id] + token_ids_a + [self.sep_token_id]

									if token_ids_a:
														output += token_ids_a + [self.sep_token_id]

									return output

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__ = 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    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__ = None     ):

									'''simple docstring'''


									UpperCamelCase_			= self._tokenizer.model.save(snake_case__  ,			name=snake_case__     )
									return tuple(snake_case__     )







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""")


