

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 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		def __init__(				self	,       __snake_case	,       __snake_case=13	,       __snake_case=30	,       __snake_case=2	,       __snake_case=3	,       __snake_case=True	,       __snake_case=True	,       __snake_case=32	,       __snake_case=5	,       __snake_case=4	,       __snake_case=37	,       __snake_case="gelu"	,       __snake_case=0.1	,       __snake_case=0.1	,       __snake_case=10	,       __snake_case=0.0_2	,       ):
			_UpperCamelCase   :		List[str]			=						parent
			_UpperCamelCase   :		Tuple			=						batch_size
			_UpperCamelCase   :		Optional[int]			=						image_size
			_UpperCamelCase   :		List[str]			=						patch_size
			_UpperCamelCase   :		str			=						num_channels
			_UpperCamelCase   :		List[str]			=						is_training
			_UpperCamelCase   :		Tuple			=						use_labels
			_UpperCamelCase   :		Dict			=						hidden_size
			_UpperCamelCase   :		List[str]			=						num_hidden_layers
			_UpperCamelCase   :		Optional[Any]			=						num_attention_heads
			_UpperCamelCase   :		str			=						intermediate_size
			_UpperCamelCase   :		Dict			=						hidden_act
			_UpperCamelCase   :		Optional[int]			=						hidden_dropout_prob
			_UpperCamelCase   :		Optional[Any]			=						attention_probs_dropout_prob
			_UpperCamelCase   :		Tuple			=						type_sequence_label_size
			_UpperCamelCase   :		Dict			=						initializer_range

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


		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

			_UpperCamelCase   :		Any			=						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=__snake_case	,       initializer_range=self.initializer_range	,       )

			return config, pixel_values


		def 		A__					(				self	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Dict			=						FlaxViTModel(config=__snake_case)
			_UpperCamelCase   :		Optional[Any]			=						model(__snake_case)
			# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
			_UpperCamelCase   :		Union[str, Any]			=						(self.image_size, self.image_size)
			_UpperCamelCase   :		List[str]			=						(self.patch_size, self.patch_size)
			_UpperCamelCase   :		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 		A__					(				self	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		List[str]			=						self.type_sequence_label_size
			_UpperCamelCase   :		Union[str, Any]			=						FlaxViTForImageClassification(config=__snake_case)
			_UpperCamelCase   :		Union[str, Any]			=						model(__snake_case)
			self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.type_sequence_label_size))

			# test greyscale images
			_UpperCamelCase   :		Any			=						1
			_UpperCamelCase   :		int			=						FlaxViTForImageClassification(__snake_case)

			_UpperCamelCase   :		Optional[Any]			=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
			_UpperCamelCase   :		Any			=						model(__snake_case)






		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						self.prepare_config_and_inputs()
			(
			    (
			    _UpperCamelCase
			)	,		(
			    _UpperCamelCase
			)	,		
			)   :		Dict			=						config_and_inputs
			_UpperCamelCase   :		List[str]			=						{'pixel_values': pixel_values}
			return config, inputs_dict







@require_flax
class 	lowercase    (				_lowercase    ,							unittest.TestCase							):


		"""simple docstring"""




		a__					       =  (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()


		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						FlaxViTModelTester(self)
			_UpperCamelCase   :		Optional[int]			=						ConfigTester(self	,       config_class=__snake_case	,       has_text_modality=__snake_case	,       hidden_size=37)


		def 		A__					(				self):
			self.config_tester.run_common_tests()


		def 		A__					(				self):
			_UpperCamelCase   :		Union[str, Any]			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase   :		int			=						self.model_tester.prepare_config_and_inputs_for_common()

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

				_UpperCamelCase   :		Any			=						['pixel_values']
				self.assertListEqual(arg_names[:1]	,       __snake_case)


		def 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase   :		Any			=						self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				with self.subTest(model_class.__name__):
					_UpperCamelCase   :		Optional[int]			=						self._prepare_for_class(__snake_case	,       __snake_case)
					_UpperCamelCase   :		Optional[int]			=						model_class(__snake_case)

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

					with self.subTest('JIT Enabled'):
						_UpperCamelCase   :		Optional[Any]			=						model_jitted(**__snake_case).to_tuple()

					with self.subTest('JIT Disabled'):
						with jax.disable_jit():
							_UpperCamelCase   :		Optional[int]			=						model_jitted(**__snake_case).to_tuple()

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






		@slow
		def 		A__					(				self):
			for model_class_name in self.all_model_classes:
				_UpperCamelCase   :		Any			=						model_class_name.from_pretrained('google/vit-base-patch16-224')
				_UpperCamelCase   :		List[str]			=						model(np.ones((1, 3, 2_24, 2_24)))
				self.assertIsNotNone(__snake_case)







def 					lowerCamelCase_     (							UpperCAmelCase_							:     list	)						->				list:




	'''simple docstring'''




	if len(UpperCAmelCase_	) <= 1:
		return [tuple(UpperCAmelCase_	)]

	_UpperCamelCase   :		List[Any]			=						[]

	def generate(UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     list	):
		_UpperCamelCase   :		Optional[int]			=						[0] * n
		res.append(tuple(UpperCAmelCase_	)	)

		_UpperCamelCase   :		List[Any]			=						0
		while i < n:
			if c[i] < i:
				if i % 2 == 0:
					_UpperCamelCase	,		_UpperCamelCase   :		List[str]			=						arr[i], arr[0]
				else:
					_UpperCamelCase	,		_UpperCamelCase   :		List[str]			=						arr[i], arr[c[i]]
				res.append(tuple(UpperCAmelCase_	)	)
				c[i] += 1
				_UpperCamelCase   :		Tuple			=						0
			else:
				_UpperCamelCase   :		Tuple			=						0
				i += 1

	generate(len(UpperCAmelCase_	)							,       UpperCAmelCase_	)
	return res


if __name__ == "__main__":
							lowerCAmelCase__								=		input("""Enter numbers separated by a comma:\n""").strip()
							lowerCAmelCase__								=		[int(item) for item in user_input.split(""",""")]
							print(heaps(arr))




