

import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

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


if is_flax_available():
			import jax

			from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel

class 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







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

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


							def 				__lowercase				(			self       )    ->    Any:
												_a	:    Optional[Any]         =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

												_a	:    Optional[int]         =		ViTConfig(
												    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=_a       ,							initializer_range=self.initializer_range       ,							)

												return config, pixel_values


							def 				__lowercase				(			self       ,							_a       ,							_a       )    ->    int:
												_a	:    Union[str, Any]         =		FlaxViTModel(config=_a       )
												_a	:    Dict         =		model(_a       )
												# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
												_a	:    Dict         =		(self.image_size, self.image_size)
												_a	:    Optional[Any]         =		(self.patch_size, self.patch_size)
												_a	:    Tuple         =		(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
												self.parent.assertEqual(result.last_hidden_state.shape       ,							(self.batch_size, num_patches + 1, self.hidden_size)       )


							def 				__lowercase				(			self       ,							_a       ,							_a       )    ->    Dict:
												_a	:    str         =		self.type_sequence_label_size
												_a	:    Optional[Any]         =		FlaxViTForImageClassification(config=_a       )
												_a	:    Optional[Any]         =		model(_a       )
												self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.type_sequence_label_size)       )

												# test greyscale images
												_a	:    Dict         =		1
												_a	:    str         =		FlaxViTForImageClassification(_a       )

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




							def 				__lowercase				(			self       )    ->    int:
												_a	:    Any         =		self.prepare_config_and_inputs()
												(
												    (
												    _a
												)      ,					(
												    _a
												)      ,					
												)	:    str         =		config_and_inputs
												_a	:    Union[str, Any]         =		{'''pixel_values''': pixel_values}
												return config, inputs_dict



@require_flax
class 				UpperCAmelCase_		(			__lowercase				,       unittest.TestCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[int]	    =    (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()


							def 				__lowercase				(			self       )    ->    None:
												_a	:    List[str]         =		FlaxViTModelTester(self       )
												_a	:    Union[str, Any]         =		ConfigTester(self       ,							config_class=_a       ,							has_text_modality=_a       ,							hidden_size=3_7       )


							def 				__lowercase				(			self       )    ->    Dict:
												self.config_tester.run_common_tests()


							def 				__lowercase				(			self       )    ->    int:
												_a	:    Tuple         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_a       )


							def 				__lowercase				(			self       )    ->    Dict:
												_a	:    List[Any]         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_a       )


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a      ,					_a	:    List[str]         =		self.model_tester.prepare_config_and_inputs_for_common()

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

																	_a	:    Optional[int]         =		['''pixel_values''']
																	self.assertListEqual(arg_names[:1]       ,							_a       )


							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												_a      ,					_a	:    Tuple         =		self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__       ):
																						_a	:    List[Any]         =		self._prepare_for_class(_a       ,							_a       )
																						_a	:    str         =		model_class(_a       )

																						@jax.jit
																						def model_jitted(_a       ,							**_a       ):
																											return model(pixel_values=_a       ,							**_a       )

																						with self.subTest('''JIT Enabled'''       ):
																											_a	:    Optional[int]         =		model_jitted(**_a       ).to_tuple()

																						with self.subTest('''JIT Disabled'''       ):
																											with jax.disable_jit():
																																_a	:    Optional[Any]         =		model_jitted(**_a       ).to_tuple()

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




							@slow
							def 				__lowercase				(			self       )    ->    Dict:
												for model_class_name in self.all_model_classes:
																	_a	:    Any         =		model_class_name.from_pretrained('''google/vit-base-patch16-224'''       )
																	_a	:    Any         =		model(np.ones((1, 3, 2_2_4, 2_2_4)       )       )
																	self.assertIsNotNone(_a       )






def    __UpperCAmelCase (	__a      :			int		,__a      :			list[int]		,__a      :			int	)			->     int:

					"""simple docstring"""

					def count_of_possible_combinations(__a      :			int	) -> int:
										if target < 0:
															return 0
										if target == 0:
															return 1
										return sum(count_of_possible_combinations(target - item	) for item in array	)

					return count_of_possible_combinations(__a	)



def    __UpperCAmelCase (	__a      :			int		,__a      :			list[int]		,__a      :			int	)			->     int:

					"""simple docstring"""

					def count_of_possible_combinations_with_dp_array(
					    __a      :			int		,__a      :			list[int]	) -> int:
										if target < 0:
															return 0
										if target == 0:
															return 1
										if dp_array[target] != -1:
															return dp_array[target]
										_a	:    Union[str, Any]         =		sum(
										    count_of_possible_combinations_with_dp_array(target - item		,__a	)
										    for item in array	)
										_a	:    Optional[int]         =		answer
										return answer

					_a	:    int         =		[-1] * (target + 1)
					return count_of_possible_combinations_with_dp_array(__a		,__a	)



def    __UpperCAmelCase (	__a      :			int		,__a      :			list[int]		,__a      :			int	)			->     int:

					"""simple docstring"""

					_a	:    str         =		[0] * (target + 1)
					_a	:    Optional[Any]         =		1

					for i in range(1		,target + 1	):
										for j in range(__a	):
															if i - array[j] >= 0:
																				dp_array[i] += dp_array[i - array[j]]

					return dp_array[target]


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			a__				=		3
			a__				=		5
			a__				=		[1, 2, 5]
			print(combination_sum_iv(n, array, target))



