



'''simple docstring'''




from __future__ import annotations

from collections import Counter
from random import random

class     UpperCamelCase_				:





	def __init__(						self					)    ->    str:
								UpperCAmelCase  :	int							  =	{}





	def 				_lowercase(						self	,	A					)    ->    None:
								UpperCAmelCase  :	Any							  =	{}





	def 				_lowercase(						self	,	A	,	A	,	A					)    ->    None:
								if nodea not in self.connections:
															self.add_node(A					)
								if nodea not in self.connections:
															self.add_node(A					)
								UpperCAmelCase  :	List[str]							  =	probability





	def 				_lowercase(						self					)    ->    list[str]:
								return list(self.connections					)






	def 				_lowercase(						self	,	A					)    ->    str:
								UpperCAmelCase  :	List[str]							  =	0
								UpperCAmelCase  :	Dict							  =	random()

								for dest in self.connections[node]:
															current_probability += self.connections[node][dest]
															if current_probability > random_value:
																						return dest
								return ""




def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    dict[str, int]:
							UpperCAmelCase  :	Optional[Any]							  =	MarkovChainGraphUndirectedUnweighted()

							for nodea, nodea, probability in transitions:
														graph.add_transition_probability(_lowercase			,       _lowercase			,       _lowercase	)

							UpperCAmelCase  :	Optional[Any]							  =	Counter(graph.get_nodes()	)
							UpperCAmelCase  :	List[Any]							  =	start

							for _ in range(_lowercase	):
														UpperCAmelCase  :	Tuple							  =	graph.transition(_lowercase	)
														visited[node] += 1

							return visited


if __name__ == "__main__":
							import doctest

							doctest.testmod()






'''simple docstring'''




import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
							from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor

class     UpperCamelCase_				:





	def __init__(						self	,	A	,	A=13	,	A=32	,	A=3	,	A=4	,	A=[10, 20, 30, 40]	,	A=[2, 2, 3, 2]	,	A=True	,	A=True	,	A=37	,	A="gelu"	,	A=10	,	A=0.0_2	,	A=["stage2", "stage3", "stage4"]	,	A=[2, 3, 4]	,	A=None	,	)    ->    int:
								UpperCAmelCase  :	str							  =	parent
								UpperCAmelCase  :	List[Any]							  =	batch_size
								UpperCAmelCase  :	Dict							  =	image_size
								UpperCAmelCase  :	Tuple							  =	num_channels
								UpperCAmelCase  :	Union[str, Any]							  =	num_stages
								UpperCAmelCase  :	Any							  =	hidden_sizes
								UpperCAmelCase  :	str							  =	depths
								UpperCAmelCase  :	Optional[int]							  =	is_training
								UpperCAmelCase  :	Union[str, Any]							  =	use_labels
								UpperCAmelCase  :	Any							  =	intermediate_size
								UpperCAmelCase  :	str							  =	hidden_act
								UpperCAmelCase  :	List[str]							  =	num_labels
								UpperCAmelCase  :	Tuple							  =	initializer_range
								UpperCAmelCase  :	Optional[Any]							  =	out_features
								UpperCAmelCase  :	List[str]							  =	out_indices
								UpperCAmelCase  :	Any							  =	scope





	def 				_lowercase(						self					)    ->    Union[str, Any]:
								UpperCAmelCase  :	Tuple							  =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

								UpperCAmelCase  :	List[Any]							  =	None
								if self.use_labels:
															UpperCAmelCase  :	Dict							  =	ids_tensor([self.batch_size]	,	self.num_labels					)

								UpperCAmelCase  :	List[str]							  =	self.get_config()

								return config, pixel_values, labels





	def 				_lowercase(						self					)    ->    Optional[Any]:
								return ConvNextVaConfig(
								    num_channels=self.num_channels	,	hidden_sizes=self.hidden_sizes	,	depths=self.depths	,	num_stages=self.num_stages	,	hidden_act=self.hidden_act	,	is_decoder=A	,	initializer_range=self.initializer_range	,	out_features=self.out_features	,	out_indices=self.out_indices	,	num_labels=self.num_labels	,	)





	def 				_lowercase(						self	,	A	,	A	,	A					)    ->    Optional[Any]:
								UpperCAmelCase  :	int							  =	ConvNextVaModel(config=A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	List[Any]							  =	model(A					)
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape	,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)	,	)





	def 				_lowercase(						self	,	A	,	A	,	A					)    ->    Any:
								UpperCAmelCase  :	List[str]							  =	ConvNextVaForImageClassification(A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	int							  =	model(A	,	labels=A					)
								self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.num_labels)					)





	def 				_lowercase(						self	,	A	,	A	,	A					)    ->    Any:
								UpperCAmelCase  :	Optional[Any]							  =	ConvNextVaBackbone(config=A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	Any							  =	model(A					)

								# verify hidden states
								self.parent.assertEqual(len(result.feature_maps					)	,	len(config.out_features					)					)
								self.parent.assertListEqual(list(result.feature_maps[0].shape					)	,	[self.batch_size, self.hidden_sizes[1], 4, 4]					)

								# verify channels
								self.parent.assertEqual(len(model.channels					)	,	len(config.out_features					)					)
								self.parent.assertListEqual(model.channels	,	config.hidden_sizes[1:]					)

								# verify backbone works with out_features=None
								UpperCAmelCase  :	Any							  =	None
								UpperCAmelCase  :	Optional[int]							  =	ConvNextVaBackbone(config=A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	int							  =	model(A					)

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps					)	,	1					)
								self.parent.assertListEqual(list(result.feature_maps[0].shape					)	,	[self.batch_size, self.hidden_sizes[-1], 1, 1]					)

								# verify channels
								self.parent.assertEqual(len(model.channels					)	,	1					)
								self.parent.assertListEqual(model.channels	,	[config.hidden_sizes[-1]]					)





	def 				_lowercase(						self					)    ->    List[str]:
								UpperCAmelCase  :	Dict							  =	self.prepare_config_and_inputs()
								UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  :	List[Any]							  =	config_and_inputs
								UpperCAmelCase  :	str							  =	{"""pixel_values""": pixel_values}
								return config, inputs_dict






	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	List[str]							  =	self.prepare_config_and_inputs()
								UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  :	Optional[Any]							  =	config_and_inputs
								UpperCAmelCase  :	List[str]							  =	{"""pixel_values""": pixel_values, """labels""": labels}
								return config, inputs_dict




@require_torch
class     UpperCamelCase_				(							__magic_name__   ,  __magic_name__   ,  unittest.TestCase    ):
	lowercase    =				(
	    (
	        ConvNextVaModel,
	        ConvNextVaForImageClassification,
	        ConvNextVaBackbone,
	    )
	    if is_torch_available()
	    else ()
	)
	lowercase    =				(
	    {'feature-extraction': ConvNextVaModel, 'image-classification': ConvNextVaForImageClassification}
	    if is_torch_available()
	    else {}
	)

	lowercase    =				False
	lowercase    =				False
	lowercase    =				False
	lowercase    =				False
	lowercase    =				False





	def 				_lowercase(						self					)    ->    Optional[int]:
								UpperCAmelCase  :	Dict							  =	ConvNextVaModelTester(self					)
								UpperCAmelCase  :	List[str]							  =	ConfigTester(self	,	config_class=A	,	has_text_modality=A	,	hidden_size=37					)





	def 				_lowercase(						self					)    ->    int:
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()





	def 				_lowercase(						self					)    ->    List[str]:
								return





	@unittest.skip(reason="""ConvNextV2 does not use inputs_embeds"""					)
	def 				_lowercase(						self					)    ->    Dict:
								pass





	@unittest.skip(reason="""ConvNextV2 does not support input and output embeddings"""					)
	def 				_lowercase(						self					)    ->    Any:
								pass





	@unittest.skip(reason="""ConvNextV2 does not use feedforward chunking"""					)
	def 				_lowercase(						self					)    ->    int:
								pass





	def 				_lowercase(						self					)    ->    Dict:
								if not self.model_tester.is_training:
															return

								for model_class in self.all_model_classes:
															UpperCAmelCase  ,  UpperCAmelCase  :	Optional[Any]							  =	self.model_tester.prepare_config_and_inputs_with_labels()
															UpperCAmelCase  :	Optional[int]							  =	True

															if model_class.__name__ in [
															    *get_values(A					),
															    *get_values(A					),
															]:
																						continue

															UpperCAmelCase  :	Any							  =	model_class(A					)
															model.to(A					)
															model.train()
															UpperCAmelCase  :	List[str]							  =	self._prepare_for_class(A	,	A	,	return_labels=A					)
															UpperCAmelCase  :	List[str]							  =	model(**A					).loss
															loss.backward()





	def 				_lowercase(						self					)    ->    Tuple:
								if not self.model_tester.is_training:
															return

								for model_class in self.all_model_classes:
															UpperCAmelCase  ,  UpperCAmelCase  :	Any							  =	self.model_tester.prepare_config_and_inputs_with_labels()
															UpperCAmelCase  :	List[str]							  =	False
															UpperCAmelCase  :	int							  =	True

															if (
															    model_class.__name__
															    in [*get_values(A					), *get_values(A					)]
															    or not model_class.supports_gradient_checkpointing
															):
																						continue

															UpperCAmelCase  :	Dict							  =	model_class(A					)
															model.to(A					)
															model.gradient_checkpointing_enable()
															model.train()
															UpperCAmelCase  :	Any							  =	self._prepare_for_class(A	,	A	,	return_labels=A					)
															UpperCAmelCase  :	Any							  =	model(**A					).loss
															loss.backward()





	def 				_lowercase(						self					)    ->    Tuple:
								UpperCAmelCase  ,  UpperCAmelCase  :	List[Any]							  =	self.model_tester.prepare_config_and_inputs_for_common()

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

															UpperCAmelCase  :	Optional[Any]							  =	["""pixel_values"""]
															self.assertListEqual(arg_names[:1]	,	A					)





	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	Tuple							  =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A					)





	def 				_lowercase(						self					)    ->    List[str]:
								def check_hidden_states_output(A	,	A	,	A					):
															UpperCAmelCase  :	Optional[Any]							  =	model_class(A					)
															model.to(A					)
															model.eval()

															with torch.no_grad():
																						UpperCAmelCase  :	Dict							  =	model(**self._prepare_for_class(A	,	A					)					)

															UpperCAmelCase  :	Tuple							  =	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															UpperCAmelCase  :	Optional[Any]							  =	self.model_tester.num_stages
															self.assertEqual(len(A					)	,	expected_num_stages + 1					)

															# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]					)	,	[self.model_tester.image_size // 4, self.model_tester.image_size // 4]	,	)

								UpperCAmelCase  ,  UpperCAmelCase  :	str							  =	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															UpperCAmelCase  :	str							  =	True
															check_hidden_states_output(A	,	A	,	A					)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															UpperCAmelCase  :	int							  =	True

															check_hidden_states_output(A	,	A	,	A					)





	def 				_lowercase(						self					)    ->    Tuple:
								UpperCAmelCase  :	Dict							  =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*A					)






	@slow
	def 				_lowercase(						self					)    ->    Any:
								for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															UpperCAmelCase  :	Tuple							  =	ConvNextVaModel.from_pretrained(A					)
															self.assertIsNotNone(A					)




def    __lowerCamelCase						(      )   ->    Optional[int]:
							UpperCAmelCase  :	Optional[int]							  =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
							return image


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





	@cached_property
	def 				_lowercase(						self					)    ->    str:
								return AutoImageProcessor.from_pretrained("""facebook/convnextv2-tiny-1k-224"""					) if is_vision_available() else None






	@slow
	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	Any							  =	ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224"""					).to(A					)

								UpperCAmelCase  :	List[Any]							  =	self.default_image_processor
								UpperCAmelCase  :	Any							  =	prepare_img()
								UpperCAmelCase  :	Tuple							  =	preprocessor(images=A	,	return_tensors="""pt"""					).to(A					)

								# forward pass
								with torch.no_grad():
															UpperCAmelCase  :	Optional[Any]							  =	model(**A					)

								# verify the logits
								UpperCAmelCase  :	Dict							  =	torch.Size((1, 1000)					)
								self.assertEqual(outputs.logits.shape	,	A					)

								UpperCAmelCase  :	Dict							  =	torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6]					).to(A					)
								self.assertTrue(torch.allclose(outputs.logits[0, :3]	,	A	,	atol=1e-4					)					)

