



from collections import Counter

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

lowerCamelCase		       =      datasets.load_iris()

lowerCamelCase		       =      np.array(data["""data"""])
lowerCamelCase		       =      np.array(data["""target"""])
lowerCamelCase		       =      data["""target_names"""]

lowerCamelCase       ,   lowerCamelCase       ,   lowerCamelCase       ,   lowerCamelCase		       =      train_test_split(X, y)






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						Optional[Any]:
				return np.linalg.norm(np.array(__UpperCamelCase ) - np.array(__UpperCamelCase ) )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=5 )	->						Any:
				a__	:							Dict      						= zip(__UpperCamelCase	,				__UpperCamelCase )
				# List of distances of all points from the point to be classified
				a__	:							Optional[Any]      						= []
				for data_point in data:
								a__	:							Tuple      						= euclidean_distance(data_point[0]	,				__UpperCamelCase )
								distances.append((distance, data_point[1]) )
				# Choosing 'k' points with the least distances.
				a__	:							str      						= [i[1] for i in sorted(__UpperCamelCase )[:k]]
				# Most commonly occurring class among them
				# is the class into which the point is classified
				a__	:							str      						= Counter(__UpperCamelCase ).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]))







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase		       =      {
    """configuration_blip""": [
        """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlipConfig""",
        """BlipTextConfig""",
        """BlipVisionConfig""",
    ],
    """processing_blip""": ["""BlipProcessor"""],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      ["""BlipImageProcessor"""]


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """BlipModel""",
						    """BlipPreTrainedModel""",
						    """BlipForConditionalGeneration""",
						    """BlipForQuestionAnswering""",
						    """BlipVisionModel""",
						    """BlipTextModel""",
						    """BlipForImageTextRetrieval""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFBlipModel""",
						    """TFBlipPreTrainedModel""",
						    """TFBlipForConditionalGeneration""",
						    """TFBlipForQuestionAnswering""",
						    """TFBlipVisionModel""",
						    """TFBlipTextModel""",
						    """TFBlipForImageTextRetrieval""",
						]

if TYPE_CHECKING:
						from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
						from .processing_blip import BlipProcessor

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_blip import BlipImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_blip import (
												    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BlipForConditionalGeneration,
												    BlipForImageTextRetrieval,
												    BlipForQuestionAnswering,
												    BlipModel,
												    BlipPreTrainedModel,
												    BlipTextModel,
												    BlipVisionModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_blip import (
												    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFBlipForConditionalGeneration,
												    TFBlipForImageTextRetrieval,
												    TFBlipForQuestionAnswering,
												    TFBlipModel,
												    TFBlipPreTrainedModel,
												    TFBlipTextModel,
												    TFBlipVisionModel,
												)

else:
						import sys

						lowerCamelCase		       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


