



'''simple docstring'''

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 	a__     (				unittest.TestCase	):


	def __init__(self : List[str],					__UpperCAmelCase : int,					__UpperCAmelCase : List[str]=13,					__UpperCAmelCase : Dict=30,					__UpperCAmelCase : Any=2,					__UpperCAmelCase : str=3,					__UpperCAmelCase : List[Any]=True,					__UpperCAmelCase : str=True,					__UpperCAmelCase : int=32,					__UpperCAmelCase : str=5,					__UpperCAmelCase : List[Any]=4,					__UpperCAmelCase : Dict=37,					__UpperCAmelCase : Tuple="gelu",					__UpperCAmelCase : int=0.1,					__UpperCAmelCase : Dict=0.1,					__UpperCAmelCase : Optional[int]=10,					__UpperCAmelCase : Tuple=0.02,					)     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= parent
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= batch_size
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= image_size
					SCREAMING_SNAKE_CASE  :						List[Any]      					= patch_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= num_channels
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= is_training
					SCREAMING_SNAKE_CASE  :						List[Any]      					= use_labels
					SCREAMING_SNAKE_CASE  :						List[str]      					= hidden_size
					SCREAMING_SNAKE_CASE  :						List[str]      					= num_hidden_layers
					SCREAMING_SNAKE_CASE  :						List[Any]      					= num_attention_heads
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= intermediate_size
					SCREAMING_SNAKE_CASE  :						List[Any]      					= hidden_act
					SCREAMING_SNAKE_CASE  :						str      					= hidden_dropout_prob
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE  :						List[Any]      					= type_sequence_label_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= initializer_range

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


	def 				lowercase__  (self : Union[str, Any]      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

					SCREAMING_SNAKE_CASE  :						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=__UpperCAmelCase,					initializer_range=self.initializer_range,					)

					return config, pixel_values


	def 				lowercase__  (self : Dict,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Any      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= FlaxViTModel(config=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Any      					= model(__UpperCAmelCase      )
					# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= (self.image_size, self.image_size)
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= (self.patch_size, self.patch_size)
					SCREAMING_SNAKE_CASE  :						int      					= (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 : Tuple,					__UpperCAmelCase : List[str],					__UpperCAmelCase : List[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.type_sequence_label_size
					SCREAMING_SNAKE_CASE  :						Any      					= FlaxViTForImageClassification(config=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model(__UpperCAmelCase      )
					self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.type_sequence_label_size)      )

					# test greyscale images
					SCREAMING_SNAKE_CASE  :						Dict      					= 1
					SCREAMING_SNAKE_CASE  :						Any      					= FlaxViTForImageClassification(__UpperCAmelCase      )

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






	def 				lowercase__  (self : Union[str, Any]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= self.prepare_config_and_inputs()
					(
					    (
					    SCREAMING_SNAKE_CASE
					)					,					(
					    SCREAMING_SNAKE_CASE
					)					,					
					)  :						Optional[int]      					= config_and_inputs
					SCREAMING_SNAKE_CASE  :						Tuple      					= {'''pixel_values''': pixel_values}
					return config, inputs_dict



@require_flax
class 	a__     (				_lowercase,			unittest.TestCase	):
	__magic_name__		:    Optional[int]	    =     (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()


	def 				lowercase__  (self : int      )     ->			None:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= FlaxViTModelTester(self      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= ConfigTester(self,					config_class=__UpperCAmelCase,					has_text_modality=__UpperCAmelCase,					hidden_size=37      )


	def 				lowercase__  (self : Tuple      )     ->			Optional[int]:

					"""simple docstring"""

					self.config_tester.run_common_tests()


	def 				lowercase__  (self : Union[str, Any]      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__UpperCAmelCase      )


	def 				lowercase__  (self : Any      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase      )


	def 				lowercase__  (self : Any      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[str]      					= self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model_class(__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						Dict      					= inspect.signature(model.__call__      )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									SCREAMING_SNAKE_CASE  :						List[Any]      					= [*signature.parameters.keys()]

									SCREAMING_SNAKE_CASE  :						Dict      					= ['''pixel_values''']
									self.assertListEqual(arg_names[:1],					__UpperCAmelCase      )


	def 				lowercase__  (self : List[str]      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__      ):
													SCREAMING_SNAKE_CASE  :						Dict      					= self._prepare_for_class(__UpperCAmelCase,					__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						int      					= model_class(__UpperCAmelCase      )

													@jax.jit
													def model_jitted(__UpperCAmelCase : Union[str, Any],					**__UpperCAmelCase : List[str]      ):
																	return model(pixel_values=__UpperCAmelCase,					**__UpperCAmelCase      )

													with self.subTest('''JIT Enabled'''      ):
																	SCREAMING_SNAKE_CASE  :						List[str]      					= model_jitted(**__UpperCAmelCase      ).to_tuple()

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

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






	@slow
	def 				lowercase__  (self : Optional[int]      )     ->			List[str]:

					"""simple docstring"""

					for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= model_class_name.from_pretrained('''google/vit-base-patch16-224'''      )
									SCREAMING_SNAKE_CASE  :						Any      					= model(np.ones((1, 3, 224, 224)      )      )
									self.assertIsNotNone(__UpperCAmelCase      )









'''simple docstring'''

from collections import deque






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]       ):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= len(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						List[str]      					= deque()
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [False for _ in range(_SCREAMING_SNAKE_CASE       )]
				SCREAMING_SNAKE_CASE  :						Any      					= [-1 for _ in range(_SCREAMING_SNAKE_CASE       )]
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= index_of[:]

				def strong_connect(_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :List[str]       ):
								SCREAMING_SNAKE_CASE  :						Any      					= index  # the number when this node is seen
								SCREAMING_SNAKE_CASE  :						str      					= index  # lowest rank node reachable from here
								index += 1
								stack.append(_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= True

								for w in g[v]:
												if index_of[w] == -1:
																SCREAMING_SNAKE_CASE  :						List[str]      					= strong_connect(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
																SCREAMING_SNAKE_CASE  :						Optional[int]      					= (
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)
												elif on_stack[w]:
																SCREAMING_SNAKE_CASE  :						List[str]      					= (
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)

								if lowlink_of[v] == index_of[v]:
												SCREAMING_SNAKE_CASE  :						str      					= []
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= stack.pop()
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= False
												component.append(_SCREAMING_SNAKE_CASE       )
												while w != v:
																SCREAMING_SNAKE_CASE  :						int      					= stack.pop()
																SCREAMING_SNAKE_CASE  :						Tuple      					= False
																component.append(_SCREAMING_SNAKE_CASE       )
												components.append(_SCREAMING_SNAKE_CASE       )
								return index

				SCREAMING_SNAKE_CASE  :						int      					= []
				for v in range(_SCREAMING_SNAKE_CASE       ):
								if index_of[v] == -1:
												strong_connect(_SCREAMING_SNAKE_CASE    ,	0    ,	_SCREAMING_SNAKE_CASE       )

				return components






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				SCREAMING_SNAKE_CASE  :						Tuple      					= [[] for _ in range(_SCREAMING_SNAKE_CASE       )]
				for u, v in edges:
								g[u].append(_SCREAMING_SNAKE_CASE       )
				return g


if __name__ == "__main__":
	# Test
	snake_case_			=     7
	snake_case_			=     [0, 0, 1, 2, 3, 3, 4, 4, 6]
	snake_case_			=     [1, 3, 2, 0, 1, 4, 5, 6, 5]
	snake_case_			=     [(u, v) for u, v in zip(source, target)]
	snake_case_			=     create_graph(n_vertices, edges)

	assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)




