



import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	['image_processor', 'tokenizer']
		SCREAMING_SNAKE_CASE__								=	'OwlViTImageProcessor'
		SCREAMING_SNAKE_CASE__								=	('CLIPTokenizer', 'CLIPTokenizerFast')
		def __init__(							self    ,a_=None    ,a_=None    ,**a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  None
						if "feature_extractor" in kwargs:
										warnings.warn(
										    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
										    ' instead.'    ,a_    ,)
										lowerCAmelCase__		  =  kwargs.pop('feature_extractor'						)

						lowerCAmelCase__		  =  image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
										raise ValueError('You need to specify an `image_processor`.'						)
						if tokenizer is None:
										raise ValueError('You need to specify a `tokenizer`.'						)

						super().__init__(a_    ,a_						)
		def __call__(							self    ,a_=None    ,a_=None    ,a_=None    ,a_="max_length"    ,a_="np"    ,**a_						):







						"""simple docstring"""

						if text is None and query_images is None and images is None:
										raise ValueError(
										    'You have to specify at least one text or query image or image. All three cannot be none.'						)

						if text is not None:
										if isinstance(a_    ,a_						) or (isinstance(a_    ,a_						) and not isinstance(text[0]    ,a_						)):
														lowerCAmelCase__		  =  [self.tokenizer(a_    ,padding=a_    ,return_tensors=a_    ,**a_						)]

										elif isinstance(a_    ,a_						) and isinstance(text[0]    ,a_						):
														lowerCAmelCase__		  =  []

														# Maximum number of queries across batch
														lowerCAmelCase__		  =  max([len(a_						) for t in text]						)

														# Pad all batch samples to max number of text queries
														for t in text:
																		if len(a_						) != max_num_queries:
																						lowerCAmelCase__		  =  t + [' '] * (max_num_queries - len(a_						))

																		lowerCAmelCase__		  =  self.tokenizer(a_    ,padding=a_    ,return_tensors=a_    ,**a_						)
																		encodings.append(a_						)
										else:
														raise TypeError('Input text should be a string, a list of strings or a nested list of strings'						)

										if return_tensors == "np":
														lowerCAmelCase__		  =  np.concatenate([encoding['input_ids'] for encoding in encodings]    ,axis=0						)
														lowerCAmelCase__		  =  np.concatenate([encoding['attention_mask'] for encoding in encodings]    ,axis=0						)

										elif return_tensors == "jax" and is_flax_available():
														import jax.numpy as jnp

														lowerCAmelCase__		  =  jnp.concatenate([encoding['input_ids'] for encoding in encodings]    ,axis=0						)
														lowerCAmelCase__		  =  jnp.concatenate([encoding['attention_mask'] for encoding in encodings]    ,axis=0						)

										elif return_tensors == "pt" and is_torch_available():
														import torch

														lowerCAmelCase__		  =  torch.cat([encoding['input_ids'] for encoding in encodings]    ,dim=0						)
														lowerCAmelCase__		  =  torch.cat([encoding['attention_mask'] for encoding in encodings]    ,dim=0						)

										elif return_tensors == "tf" and is_tf_available():
														import tensorflow as tf

														lowerCAmelCase__		  =  tf.stack([encoding['input_ids'] for encoding in encodings]    ,axis=0						)
														lowerCAmelCase__		  =  tf.stack([encoding['attention_mask'] for encoding in encodings]    ,axis=0						)

										else:
														raise ValueError('Target return tensor type could not be returned'						)

										lowerCAmelCase__		  =  BatchEncoding()
										lowerCAmelCase__		  =  input_ids
										lowerCAmelCase__		  =  attention_mask

						if query_images is not None:
										lowerCAmelCase__		  =  BatchEncoding()
										lowerCAmelCase__		  =  self.image_processor(
										    a_    ,return_tensors=a_    ,**a_						).pixel_values
										lowerCAmelCase__		  =  query_pixel_values

						if images is not None:
										lowerCAmelCase__		  =  self.image_processor(a_    ,return_tensors=a_    ,**a_						)

						if text is not None and images is not None:
										lowerCAmelCase__		  =  image_features.pixel_values
										return encoding
						elif query_images is not None and images is not None:
										lowerCAmelCase__		  =  image_features.pixel_values
										return encoding
						elif text is not None or query_images is not None:
										return encoding
						else:
										return BatchEncoding(data=dict(**a_						)    ,tensor_type=a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,*a_    ,**a_						):







						"""simple docstring"""
						return self.image_processor.post_process(*a_    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,*a_    ,**a_						):







						"""simple docstring"""
						return self.image_processor.post_process_object_detection(*a_    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,*a_    ,**a_						):







						"""simple docstring"""
						return self.image_processor.post_process_image_guided_detection(*a_    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,*a_    ,**a_						):







						"""simple docstring"""
						return self.tokenizer.batch_decode(*a_    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,*a_    ,**a_						):







						"""simple docstring"""
						return self.tokenizer.decode(*a_    ,**a_						)
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						warnings.warn(
						    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,a_    ,)
						return self.image_processor_class



		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						warnings.warn(
						    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,a_    ,)
						return self.image_processor





from collections import Counter

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

_lowerCAmelCase	:   int    =   datasets.load_iris()

_lowerCAmelCase	:   Tuple    =   np.array(data["data"])
_lowerCAmelCase	:   Any    =   np.array(data["target"])
_lowerCAmelCase	:   Union[str, Any]    =   data["target_names"]

_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase	:   Optional[Any]    =   train_test_split(X, y)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Tuple:



				"""simple docstring"""





				return np.linalg.norm(np.array(snake_case__					) - np.array(snake_case__					)					)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__=5					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  zip(snake_case__    ,			snake_case__					)
				# List of distances of all points from the point to be classified
				lowerCAmelCase__		  =  []
				for data_point in data:
								lowerCAmelCase__		  =  euclidean_distance(data_point[0]    ,			snake_case__					)
								distances.append((distance, data_point[1])					)
				# Choosing 'k' points with the least distances.
				lowerCAmelCase__		  =  [i[1] for i in sorted(snake_case__					)[:k]]
				# Most commonly occurring class among them
				# is the class into which the point is classified
				lowerCAmelCase__		  =  Counter(snake_case__					).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]))
