


from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Dict					  =  ["image_processor", "tokenizer"]
							__UpperCAmelCase   :							Any					  =  "Pix2StructImageProcessor"
							__UpperCAmelCase   :							Union[str, Any]					  =  ("T5Tokenizer", "T5TokenizerFast")

							def __init__(							self	:  Dict   ,     lowerCamelCase	:  Union[str, Any]   ,     lowerCamelCase	:  int    )     ->   str:
														__snake_case		:      List[str]										=       False
														super().__init__(lowerCamelCase   ,     lowerCamelCase    )

							def __call__(							self	:  str   ,     lowerCamelCase	:  Dict=None   ,     lowerCamelCase	:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  Union[bool, str, PaddingStrategy] = False   ,     lowerCamelCase	:  Union[bool, str, TruncationStrategy] = None   ,     lowerCamelCase	:  Optional[int] = None   ,     lowerCamelCase	:  Optional[int] = 2048   ,     lowerCamelCase	:  int = 0   ,     lowerCamelCase	:  Optional[int] = None   ,     lowerCamelCase	:  Optional[bool] = None   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  Optional[Union[str, TensorType]] = None   ,     **lowerCamelCase	:  Dict   ,     )     ->   BatchEncoding:
														if images is None and text is None:
																					raise ValueError("You have to specify either images or text."    )

														# Get only text
														if images is None and not self.image_processor.is_vqa:
																					__snake_case		:      Any										=       self.tokenizer
																					__snake_case		:      int										=       self.tokenizer(
																					    text=lowerCamelCase   ,     add_special_tokens=lowerCamelCase   ,     padding=lowerCamelCase   ,     truncation=lowerCamelCase   ,     max_length=lowerCamelCase   ,     stride=lowerCamelCase   ,     pad_to_multiple_of=lowerCamelCase   ,     return_attention_mask=lowerCamelCase   ,     return_overflowing_tokens=lowerCamelCase   ,     return_special_tokens_mask=lowerCamelCase   ,     return_offsets_mapping=lowerCamelCase   ,     return_token_type_ids=lowerCamelCase   ,     return_length=lowerCamelCase   ,     verbose=lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     **lowerCamelCase   ,     )
																					return text_encoding

														if not self.image_processor.is_vqa:
																					# add pixel_values
																					__snake_case		:      Optional[Any]										=       self.image_processor(
																					    lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     max_patches=lowerCamelCase   ,     **lowerCamelCase    )
														else:
																					# add pixel_values and bbox
																					__snake_case		:      List[str]										=       self.image_processor(
																					    lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     max_patches=lowerCamelCase   ,     header_text=lowerCamelCase   ,     **lowerCamelCase    )

														if text is not None and not self.image_processor.is_vqa:
																					__snake_case		:      int										=       self.tokenizer(
																					    text=lowerCamelCase   ,     add_special_tokens=lowerCamelCase   ,     padding=lowerCamelCase   ,     truncation=lowerCamelCase   ,     max_length=lowerCamelCase   ,     stride=lowerCamelCase   ,     pad_to_multiple_of=lowerCamelCase   ,     return_attention_mask=lowerCamelCase   ,     return_overflowing_tokens=lowerCamelCase   ,     return_special_tokens_mask=lowerCamelCase   ,     return_offsets_mapping=lowerCamelCase   ,     return_token_type_ids=lowerCamelCase   ,     return_length=lowerCamelCase   ,     verbose=lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     **lowerCamelCase   ,     )

																					if "attention_mask" in text_encoding:
																												__snake_case		:      Dict										=       text_encoding.pop("attention_mask"    )
																					if "input_ids" in text_encoding:
																												__snake_case		:      List[str]										=       text_encoding.pop("input_ids"    )
														else:
																					__snake_case		:      str										=       None

														if text_encoding is not None:
																					encoding_image_processor.update(lowerCamelCase    )

														return encoding_image_processor

							def    __snake_case (							self	:  str   ,     *lowerCamelCase	:  Any   ,     **lowerCamelCase	:  Optional[Any]    )     ->   Union[str, Any]:
														return self.tokenizer.batch_decode(*lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Dict   ,     *lowerCamelCase	:  int   ,     **lowerCamelCase	:  str    )     ->   Optional[Any]:
														return self.tokenizer.decode(*lowerCamelCase   ,     **lowerCamelCase    )






							@property
							def    __snake_case (							self	:  Any    )     ->   List[Any]:
														__snake_case		:      Optional[int]										=       self.tokenizer.model_input_names
														__snake_case		:      Tuple										=       self.image_processor.model_input_names
														return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )





from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_snake_case					:		Union[str, Any]      =    datasets.load_iris()

_snake_case					:		Tuple      =    np.array(data["data"])
_snake_case					:		int      =    np.array(data["target"])
_snake_case					:		int      =    data["target_names"]

_snake_case ,				_snake_case ,				_snake_case ,				_snake_case					:		Any      =    train_test_split(X, y)







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return np.linalg.norm(np.array(__lowerCamelCase     ) - np.array(__lowerCamelCase     )     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase=5     ):
							__snake_case		:      Optional[Any]										=       zip(__lowerCamelCase							,     __lowerCamelCase     )
							# List of distances of all points from the point to be classified
							__snake_case		:      Optional[int]										=       []
							for data_point in data:
														__snake_case		:      Union[str, Any]										=       euclidean_distance(data_point[0]							,     __lowerCamelCase     )
														distances.append((distance, data_point[1])     )
							# Choosing 'k' points with the least distances.
							__snake_case		:      Dict										=       [i[1] for i in sorted(__lowerCamelCase     )[:k]]
							# Most commonly occurring class among them
							# is the class into which the point is classified
							__snake_case		:      Any										=       Counter(__lowerCamelCase     ).most_common(1     )[0][0]
							return classes[result]


if __name__ == "__main__":
			print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))

