




from bisect import bisect
from itertools import accumulate




def 					lowerCamelCase__       (						A__       :					List[Any]  ,			A__       :					Dict  ,			A__       :					str  ,			A__       :					Union[str, Any]	):





       '''simple docstring'''



       __lowerCamelCase   =						sorted(zip(A__  ,			A__	)  ,			key=lambda A__	: x[0] / x[1]  ,			reverse=A__	)
       __lowerCamelCase,   __lowerCamelCase   =						[i[0] for i in r], [i[1] for i in r]
       __lowerCamelCase   =						list(accumulate(A__	)	)
       __lowerCamelCase   =						bisect(A__  ,			A__	)
       return (
           0
           if k == 0
           else sum(vl[:k]	) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
           if k != n
           else sum(vl[:k]	)
       )


if __name__ == "__main__":
       import doctest

       doctest.testmod()




"""simple docstring"""





import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
							import jax

							from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
							from PIL import Image

							from transformers import BeitImageProcessor







class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def __init__(       self			:				Optional[Any]   ,lowercase_			:				List[str]   ,lowercase_			:				Any=1_0_0   ,lowercase_			:				str=1_3   ,lowercase_			:				Any=3_0   ,lowercase_			:				Optional[int]=2   ,lowercase_			:				Dict=3   ,lowercase_			:				Optional[int]=True   ,lowercase_			:				Union[str, Any]=True   ,lowercase_			:				Optional[Any]=3_2   ,lowercase_			:				List[Any]=5   ,lowercase_			:				Any=4   ,lowercase_			:				Optional[int]=3_7   ,lowercase_			:				List[Any]="gelu"   ,lowercase_			:				Dict=0.1   ,lowercase_			:				List[Any]=0.1   ,lowercase_			:				int=1_0   ,lowercase_			:				int=0.02   ,lowercase_			:				List[str]=3   ,):
						lowerCAmelCase__	:						int  				=	parent
						lowerCAmelCase__	:						Dict  				=	vocab_size
						lowerCAmelCase__	:						List[Any]  				=	batch_size
						lowerCAmelCase__	:						Union[str, Any]  				=	image_size
						lowerCAmelCase__	:						Optional[int]  				=	patch_size
						lowerCAmelCase__	:						str  				=	num_channels
						lowerCAmelCase__	:						str  				=	is_training
						lowerCAmelCase__	:						Optional[Any]  				=	use_labels
						lowerCAmelCase__	:						Union[str, Any]  				=	hidden_size
						lowerCAmelCase__	:						Optional[Any]  				=	num_hidden_layers
						lowerCAmelCase__	:						Any  				=	num_attention_heads
						lowerCAmelCase__	:						Union[str, Any]  				=	intermediate_size
						lowerCAmelCase__	:						List[Any]  				=	hidden_act
						lowerCAmelCase__	:						str  				=	hidden_dropout_prob
						lowerCAmelCase__	:						List[Any]  				=	attention_probs_dropout_prob
						lowerCAmelCase__	:						Union[str, Any]  				=	type_sequence_label_size
						lowerCAmelCase__	:						Dict  				=	initializer_range

						# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						lowerCAmelCase__	:						Any  				=	(image_size // patch_size) ** 2
						lowerCAmelCase__	:						str  				=	num_patches + 1







					def        __lowerCAmelCase (       self			:				Tuple		):
						lowerCAmelCase__	:						int  				=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

						lowerCAmelCase__	:						Optional[int]  				=	None
						if self.use_labels:
							lowerCAmelCase__	:						str  				=	ids_tensor([self.batch_size]   ,self.type_sequence_label_size		)

						lowerCAmelCase__	:						List[str]  				=	BeitConfig(
						    vocab_size=self.vocab_size   ,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=lowercase_   ,initializer_range=self.initializer_range   ,)

						return config, pixel_values, labels







					def        __lowerCAmelCase (       self			:				Union[str, Any]   ,lowercase_			:				str   ,lowercase_			:				Dict   ,lowercase_			:				Any		):
						lowerCAmelCase__	:						Any  				=	FlaxBeitModel(config=lowercase_		)
						lowerCAmelCase__	:						str  				=	model(lowercase_		)
						self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)		)







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				Any   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				Any		):
						lowerCAmelCase__	:						Optional[int]  				=	FlaxBeitForMaskedImageModeling(config=lowercase_		)
						lowerCAmelCase__	:						str  				=	model(lowercase_		)
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length - 1, self.vocab_size)		)







					def        __lowerCAmelCase (       self			:				List[str]   ,lowercase_			:				str   ,lowercase_			:				List[str]   ,lowercase_			:				str		):
						lowerCAmelCase__	:						Dict  				=	self.type_sequence_label_size
						lowerCAmelCase__	:						Tuple  				=	FlaxBeitForImageClassification(config=lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	model(lowercase_		)
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.type_sequence_label_size)		)

						# test greyscale images
						lowerCAmelCase__	:						int  				=	1
						lowerCAmelCase__	:						Tuple  				=	FlaxBeitForImageClassification(lowercase_		)

						lowerCAmelCase__	:						str  				=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
						lowerCAmelCase__	:						List[str]  				=	model(lowercase_		)







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						List[str]  				=	self.prepare_config_and_inputs()
						(
						    (
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,
						)	:						Optional[int]  				=	config_and_inputs
						lowerCAmelCase__	:						Dict  				=	{'''pixel_values''': pixel_values}
						return config, inputs_dict







@require_flax
class 				SCREAMING_SNAKE_CASE					(		a_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       (
					    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
					)







					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						Tuple  				=	FlaxBeitModelTester(self		)
						lowerCAmelCase__	:						str  				=	ConfigTester(self   ,config_class=lowercase_   ,has_text_modality=lowercase_   ,hidden_size=3_7		)







					def        __lowerCAmelCase (       self			:				Dict		):
						self.config_tester.run_common_tests()







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							lowerCAmelCase__	:						List[str]  				=	model_class(lowercase_		)
							lowerCAmelCase__	:						Optional[int]  				=	inspect.signature(model.__call__		)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							lowerCAmelCase__	:						Tuple  				=	[*signature.parameters.keys()]

							lowerCAmelCase__	:						Optional[Any]  				=	['''pixel_values''']
							self.assertListEqual(arg_names[:1]   ,lowercase_		)







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__		):
								lowerCAmelCase__	:						Union[str, Any]  				=	self._prepare_for_class(lowercase_   ,lowercase_		)
								lowerCAmelCase__	:						Dict  				=	model_class(lowercase_		)

								@jax.jit
								def model_jitted(lowercase_			:				Dict   ,**lowercase_			:				List[Any]		):
									return model(pixel_values=lowercase_   ,**lowercase_		)

								with self.subTest('''JIT Enabled'''		):
									lowerCAmelCase__	:						Any  				=	model_jitted(**lowercase_		).to_tuple()

								with self.subTest('''JIT Disabled'''		):
									with jax.disable_jit():
										lowerCAmelCase__	:						int  				=	model_jitted(**lowercase_		).to_tuple()

								self.assertEqual(len(lowercase_		)   ,len(lowercase_		)		)
								for jitted_output, output in zip(lowercase_   ,lowercase_		):
									self.assertEqual(jitted_output.shape   ,output.shape		)







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Optional[int]  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowercase_		)







					def        __lowerCAmelCase (       self			:				str		):
						lowerCAmelCase__	:						Optional[Any]  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*lowercase_		)







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Optional[Any]  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*lowercase_		)







					@slow
					def        __lowerCAmelCase (       self			:				List[str]		):
						for model_class_name in self.all_model_classes:
							lowerCAmelCase__	:						Tuple  				=	model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''		)
							lowerCAmelCase__	:						List[Any]  				=	model(np.ones((1, 3, 2_2_4, 2_2_4)		)		)
							self.assertIsNotNone(lowercase_		)

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						List[str]  				=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
	return image







@require_vision
@require_flax
class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					@cached_property
					def        __lowerCAmelCase (       self			:				Dict		):
						return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''		) if is_vision_available() else None







					@slow
					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						Tuple  				=	FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''		)

						lowerCAmelCase__	:						List[str]  				=	self.default_image_processor
						lowerCAmelCase__	:						Optional[int]  				=	prepare_img()
						lowerCAmelCase__	:						Union[str, Any]  				=	image_processor(images=lowercase_   ,return_tensors='''np'''		).pixel_values

						# prepare bool_masked_pos
						lowerCAmelCase__	:						str  				=	np.ones((1, 1_9_6)   ,dtype=lowercase_		)

						# forward pass
						lowerCAmelCase__	:						Dict  				=	model(pixel_values=lowercase_   ,bool_masked_pos=lowercase_		)
						lowerCAmelCase__	:						str  				=	outputs.logits

						# verify the logits
						lowerCAmelCase__	:						Any  				=	(1, 1_9_6, 8_1_9_2)
						self.assertEqual(logits.shape   ,lowercase_		)

						lowerCAmelCase__	:						str  				=	np.array(
						    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]		)

						self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]   ,lowercase_   ,atol=1E-2		)		)







					@slow
					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						List[str]  				=	FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''		)

						lowerCAmelCase__	:						Dict  				=	self.default_image_processor
						lowerCAmelCase__	:						Optional[Any]  				=	prepare_img()
						lowerCAmelCase__	:						Any  				=	image_processor(images=lowercase_   ,return_tensors='''np'''		)

						# forward pass
						lowerCAmelCase__	:						List[Any]  				=	model(**lowercase_		)
						lowerCAmelCase__	:						int  				=	outputs.logits

						# verify the logits
						lowerCAmelCase__	:						Dict  				=	(1, 1_0_0_0)
						self.assertEqual(logits.shape   ,lowercase_		)

						lowerCAmelCase__	:						Dict  				=	np.array([-1.2385, -1.0987, -1.0108]		)

						self.assertTrue(np.allclose(logits[0, :3]   ,lowercase_   ,atol=1E-4		)		)

						lowerCAmelCase__	:						Union[str, Any]  				=	2_8_1
						self.assertEqual(logits.argmax(-1		).item()   ,lowercase_		)







					@slow
					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						Tuple  				=	FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''		)

						lowerCAmelCase__	:						Union[str, Any]  				=	self.default_image_processor
						lowerCAmelCase__	:						str  				=	prepare_img()
						lowerCAmelCase__	:						Union[str, Any]  				=	image_processor(images=lowercase_   ,return_tensors='''np'''		)

						# forward pass
						lowerCAmelCase__	:						Tuple  				=	model(**lowercase_		)
						lowerCAmelCase__	:						int  				=	outputs.logits

						# verify the logits
						lowerCAmelCase__	:						Optional[int]  				=	(1, 2_1_8_4_1)
						self.assertEqual(logits.shape   ,lowercase_		)

						lowerCAmelCase__	:						Union[str, Any]  				=	np.array([1.6881, -0.2787, 0.5901]		)

						self.assertTrue(np.allclose(logits[0, :3]   ,lowercase_   ,atol=1E-4		)		)

						lowerCAmelCase__	:						Optional[int]  				=	2_3_9_6
						self.assertEqual(logits.argmax(-1		).item()   ,lowercase_		)




