
import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
			from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image





class 			_lowerCAmelCase      :




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=13       ,       _UpperCamelCase=64       ,       _UpperCamelCase=2       ,       _UpperCamelCase=3       ,       _UpperCamelCase=True       ,       _UpperCamelCase=True       ,       _UpperCamelCase=32       ,       _UpperCamelCase=5       ,       _UpperCamelCase=4       ,       _UpperCamelCase=37       ,       _UpperCamelCase="gelu"       ,       _UpperCamelCase=0.1       ,       _UpperCamelCase=0.1       ,       _UpperCamelCase=10       ,       _UpperCamelCase=0.02       ,       _UpperCamelCase=[1, 16, 4, 4]       ,       _UpperCamelCase=None       ,       )       ->   Optional[int]:
							lowerCAmelCase_      			=			parent
							lowerCAmelCase_      			=			batch_size
							lowerCAmelCase_      			=			image_size
							lowerCAmelCase_      			=			patch_size
							lowerCAmelCase_      			=			num_channels
							lowerCAmelCase_      			=			is_training
							lowerCAmelCase_      			=			use_labels
							lowerCAmelCase_      			=			hidden_size
							lowerCAmelCase_      			=			num_hidden_layers
							lowerCAmelCase_      			=			num_attention_heads
							lowerCAmelCase_      			=			intermediate_size
							lowerCAmelCase_      			=			hidden_act
							lowerCAmelCase_      			=			hidden_dropout_prob
							lowerCAmelCase_      			=			attention_probs_dropout_prob
							lowerCAmelCase_      			=			type_sequence_label_size
							lowerCAmelCase_      			=			initializer_range
							lowerCAmelCase_      			=			scope
							lowerCAmelCase_      			=			backbone_featmap_shape

							# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							# the number of patches is based on the feature map of the backbone, which by default uses an output stride
							# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
							lowerCAmelCase_      			=			(self.image_size // 32) ** 2
							lowerCAmelCase_      			=			num_patches + 1




				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

							lowerCAmelCase_      			=			None
							if self.use_labels:
										lowerCAmelCase_      			=			ids_tensor([self.batch_size]       ,       self.type_sequence_label_size  )

							lowerCAmelCase_      			=			self.get_config()

							return config, pixel_values, labels




				def   __a		(      self  )       ->   List[str]:
							lowerCAmelCase_      			=			{
							    "global_padding": "same",
							    "layer_type": "bottleneck",
							    "depths": [3, 4, 9],
							    "out_features": ["stage1", "stage2", "stage3"],
							    "embedding_dynamic_padding": True,
							    "hidden_sizes": [4, 8, 16, 32],
							    "num_groups": 2,
							}

							return ViTHybridConfig(
							    image_size=self.image_size       ,       patch_size=self.patch_size       ,       num_channels=self.num_channels       ,       hidden_size=self.hidden_size       ,       num_hidden_layers=self.num_hidden_layers       ,       num_attention_heads=self.num_attention_heads       ,       intermediate_size=self.intermediate_size       ,       hidden_act=self.hidden_act       ,       hidden_dropout_prob=self.hidden_dropout_prob       ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,       is_decoder=UpperCamelCase_       ,       initializer_range=self.initializer_range       ,       backbone_featmap_shape=self.backbone_featmap_shape       ,       backbone_config=UpperCamelCase_       ,       )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Dict:
							lowerCAmelCase_      			=			ViTHybridModel(config=UpperCamelCase_  )
							model.to(UpperCamelCase_  )
							model.eval()
							lowerCAmelCase_      			=			model(UpperCamelCase_  )
							self.parent.assertEqual(result.last_hidden_state.shape       ,       (self.batch_size, self.seq_length, self.hidden_size)  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Tuple:
							lowerCAmelCase_      			=			self.type_sequence_label_size
							lowerCAmelCase_      			=			ViTHybridForImageClassification(UpperCamelCase_  )
							model.to(UpperCamelCase_  )
							model.eval()
							lowerCAmelCase_      			=			model(UpperCamelCase_       ,       labels=UpperCamelCase_  )
							self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.type_sequence_label_size)  )




				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			self.prepare_config_and_inputs()
							lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_      			=			config_and_inputs
							lowerCAmelCase_      			=			{"pixel_values": pixel_values}
							return config, inputs_dict





@require_torch
class 			_lowerCAmelCase      (			__lowerCamelCase      ,   __lowerCamelCase      ,   unittest.TestCase ):
				_lowercase    	=(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
				_lowercase    	=(
				    {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
				    if is_torch_available()
				    else {}
				)
				_lowercase    	=False
				_lowercase    	=False
				_lowercase    	=False




				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			ViTHybridModelTester(self  )
							lowerCAmelCase_      			=			ConfigTester(self       ,       config_class=UpperCamelCase_       ,       has_text_modality=UpperCamelCase_       ,       hidden_size=37  )




				def   __a		(      self  )       ->   int:
							self.config_tester.run_common_tests()




				@unittest.skip(reason="ViT does not use inputs_embeds"  )
				def   __a		(      self  )       ->   List[Any]:
							pass




				def   __a		(      self  )       ->   Dict:
							lowerCAmelCase_			,						lowerCAmelCase_      			=			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										lowerCAmelCase_      			=			model_class(UpperCamelCase_  )
										self.assertIsInstance(model.get_input_embeddings()       ,       (nn.Module)  )
										lowerCAmelCase_      			=			model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(UpperCamelCase_       ,       nn.Linear  )  )




				def   __a		(      self  )       ->   Optional[int]:
							lowerCAmelCase_			,						lowerCAmelCase_      			=			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										lowerCAmelCase_      			=			model_class(UpperCamelCase_  )
										lowerCAmelCase_      			=			inspect.signature(model.forward  )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowerCAmelCase_      			=			[*signature.parameters.keys()]

										lowerCAmelCase_      			=			["pixel_values"]
										self.assertListEqual(arg_names[:1]       ,       UpperCamelCase_  )




				def   __a		(      self  )       ->   Dict:
							lowerCAmelCase_      			=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCamelCase_  )




				def   __a		(      self  )       ->   Dict:
							lowerCAmelCase_      			=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_  )




				def   __a		(      self  )       ->   Optional[int]:
							lowerCAmelCase_			,						lowerCAmelCase_      			=			self.model_tester.prepare_config_and_inputs_for_common()

							lowerCAmelCase_      			=			_config_zero_init(UpperCamelCase_  )
							for model_class in self.all_model_classes:
										lowerCAmelCase_      			=			model_class(config=UpperCamelCase_  )
										# Skip the check for the backbone
										for name, module in model.named_modules():
													if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																lowerCAmelCase_      			=			[f"""{name}.{key}""" for key in module.state_dict().keys()]
																break

										for name, param in model.named_parameters():
													if param.requires_grad:
																if name in backbone_params:
																			continue
																self.assertIn(
																    ((param.data.mean() * 1e9).round() / 1e9).item()       ,       [0.0, 1.0]       ,       msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""       ,       )




				@slow
				def   __a		(      self  )       ->   Union[str, Any]:
							for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase_      			=			ViTHybridModel.from_pretrained(UpperCamelCase_  )
										self.assertIsNotNone(UpperCamelCase_  )


def      lowerCamelCase__       (       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
			return image





@require_torch
@require_vision
class 			_lowerCAmelCase      (			unittest.TestCase ):




				@cached_property
				def   __a		(      self  )       ->   List[Any]:
							return (
							    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
							    if is_vision_available()
							    else None
							)




				@slow
				def   __a		(      self  )       ->   Any:
							lowerCAmelCase_      			=			ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(
							    UpperCamelCase_  )

							lowerCAmelCase_      			=			self.default_image_processor
							lowerCAmelCase_      			=			prepare_img()
							lowerCAmelCase_      			=			image_processor(images=UpperCamelCase_       ,       return_tensors="pt"  ).to(UpperCamelCase_  )

							# forward pass
							with torch.no_grad():
										lowerCAmelCase_      			=			model(**UpperCamelCase_  )

							# verify the logits
							lowerCAmelCase_      			=			torch.Size((1, 1_000)  )
							self.assertEqual(outputs.logits.shape       ,       UpperCamelCase_  )

							lowerCAmelCase_      			=			torch.tensor([-1.9090, -0.4993, -0.2389]  ).to(UpperCamelCase_  )

							self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,       UpperCamelCase_       ,       atol=1e-4  )  )




				@slow
				@require_accelerate
				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"  )
							lowerCAmelCase_      			=			ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"       ,       device_map="auto"  )

							lowerCAmelCase_      			=			prepare_img()

							lowerCAmelCase_      			=			image_processor(images=UpperCamelCase_       ,       return_tensors="pt"  )
							lowerCAmelCase_      			=			model(**UpperCamelCase_  )
							lowerCAmelCase_      			=			outputs.logits
							# model predicts one of the 1000 ImageNet classes
							lowerCAmelCase_      			=			logits.argmax(-1  ).item()

							self.assertTrue(model.config.idalabel[predicted_class_idx]       ,       "tabby, tabby cat"  )





from collections import Counter

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

_A     =  datasets.load_iris()

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

_A,   _A,   _A,   _A     =  train_test_split(X, y)






def      lowerCamelCase__       (       __lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						Union[str, Any]       ):




			"""simple docstring"""


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






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						Any=5       ):




			"""simple docstring"""


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



