

import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import (
					    MODEL_MAPPING,
					    DataaVecVisionForImageClassification,
					    DataaVecVisionForSemanticSegmentation,
					    DataaVecVisionModel,
					)
					from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import BeitImageProcessor

class  a     :


						def __init__(				self  ,    A_  ,    A_=100  ,    A_=13  ,    A_=30  ,    A_=2  ,    A_=3  ,    A_=True  ,    A_=True  ,    A_=32  ,    A_=4  ,    A_=4  ,    A_=37  ,    A_="gelu"  ,    A_=0.1  ,    A_=0.1  ,    A_=10  ,    A_=0.02  ,    A_=3  ,    A_=None  ,    A_=[0, 1, 2, 3]  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						Tuple       						=			parent
										_UpperCAmelCase				:						Any       						=			100
										_UpperCAmelCase				:						List[str]       						=			batch_size
										_UpperCAmelCase				:						List[str]       						=			image_size
										_UpperCAmelCase				:						Tuple       						=			patch_size
										_UpperCAmelCase				:						Tuple       						=			num_channels
										_UpperCAmelCase				:						Any       						=			is_training
										_UpperCAmelCase				:						int       						=			use_labels
										_UpperCAmelCase				:						List[Any]       						=			hidden_size
										_UpperCAmelCase				:						List[str]       						=			num_hidden_layers
										_UpperCAmelCase				:						List[str]       						=			num_attention_heads
										_UpperCAmelCase				:						Union[str, Any]       						=			intermediate_size
										_UpperCAmelCase				:						Any       						=			hidden_act
										_UpperCAmelCase				:						List[Any]       						=			hidden_dropout_prob
										_UpperCAmelCase				:						Union[str, Any]       						=			attention_probs_dropout_prob
										_UpperCAmelCase				:						Tuple       						=			type_sequence_label_size
										_UpperCAmelCase				:						List[Any]       						=			initializer_range
										_UpperCAmelCase				:						Any       						=			scope
										_UpperCAmelCase				:						str       						=			out_indices
										_UpperCAmelCase				:						str       						=			num_labels

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


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[int]       						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

										_UpperCAmelCase				:						Tuple       						=			None
										_UpperCAmelCase				:						str       						=			None
										if self.use_labels:
														_UpperCAmelCase				:						List[str]       						=			ids_tensor([self.batch_size]  ,    self.type_sequence_label_size		)
														_UpperCAmelCase				:						Dict       						=			ids_tensor([self.batch_size, self.image_size, self.image_size]  ,    self.num_labels		)

										_UpperCAmelCase				:						Optional[Any]       						=			self.get_config()

										return config, pixel_values, labels, pixel_labels


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return DataaVecVisionConfig(
										    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=A_  ,    initializer_range=self.initializer_range  ,    out_indices=self.out_indices  ,    )


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			DataaVecVisionModel(config=A_		)
										model.to(A_		)
										model.eval()
										_UpperCAmelCase				:						Optional[int]       						=			model(A_		)
										# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
										_UpperCAmelCase				:						List[str]       						=			(self.image_size // self.patch_size) ** 2
										self.parent.assertEqual(result.last_hidden_state.shape  ,    (self.batch_size, num_patches + 1, self.hidden_size)		)


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[str]       						=			self.type_sequence_label_size
										_UpperCAmelCase				:						Dict       						=			DataaVecVisionForImageClassification(A_		)
										model.to(A_		)
										model.eval()
										_UpperCAmelCase				:						Dict       						=			model(A_  ,    labels=A_		)
										self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.type_sequence_label_size)		)


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Dict       						=			self.num_labels
										_UpperCAmelCase				:						int       						=			DataaVecVisionForSemanticSegmentation(A_		)
										model.to(A_		)
										model.eval()
										_UpperCAmelCase				:						List[Any]       						=			model(A_		)
										self.parent.assertEqual(
										    result.logits.shape  ,    (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)		)
										_UpperCAmelCase				:						Optional[int]       						=			model(A_  ,    labels=A_		)
										self.parent.assertEqual(
										    result.logits.shape  ,    (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)		)





						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			self.prepare_config_and_inputs()
										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase				:						int       						=			config_and_inputs
										_UpperCAmelCase				:						Tuple       						=			{"pixel_values": pixel_values}
										return config, inputs_dict







@require_torch
class  a     (		UpperCAmelCase							,       UpperCAmelCase							,       unittest.TestCase ):
						_lowercase			  =	(
						    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						_lowercase			  =	(
						    {
						        "feature-extraction": DataaVecVisionModel,
						        "image-classification": DataaVecVisionForImageClassification,
						        "image-segmentation": DataaVecVisionForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						_lowercase			  =	False
						_lowercase			  =	False
						_lowercase			  =	False


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[str]       						=			DataaVecVisionModelTester(self		)
										_UpperCAmelCase				:						Union[str, Any]       						=			ConfigTester(
										    self  ,    config_class=A_  ,    has_text_modality=A_  ,    hidden_size=37		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										self.config_tester.run_common_tests()


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										pass


						@require_torch_multi_gpu
						@unittest.skip(
						    reason="Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"		)
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										pass


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						Any       						=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_UpperCAmelCase				:						Union[str, Any]       						=			model_class(A_		)
														self.assertIsInstance(model.get_input_embeddings()  ,    (nn.Module)		)
														_UpperCAmelCase				:						Tuple       						=			model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(A_  ,    nn.Linear		)		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


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

										for model_class in self.all_model_classes:
														_UpperCAmelCase				:						Union[str, Any]       						=			model_class(A_		)
														_UpperCAmelCase				:						List[Any]       						=			inspect.signature(model.forward		)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														_UpperCAmelCase				:						List[str]       						=			[*signature.parameters.keys()]

														_UpperCAmelCase				:						List[str]       						=			["pixel_values"]
														self.assertListEqual(arg_names[:1]  ,    A_		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Tuple       						=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Tuple       						=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_segmentation(*A_		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										if not self.model_tester.is_training:
														return

										_UpperCAmelCase						,							_UpperCAmelCase				:						List[Any]       						=			self.model_tester.prepare_config_and_inputs_for_common()
										_UpperCAmelCase				:						Any       						=			True

										for model_class in self.all_model_classes:
														if model_class in [*get_values(A_		)]:
																		continue

														_UpperCAmelCase				:						str       						=			model_class(A_		)
														model.to(A_		)
														model.train()
														_UpperCAmelCase				:						int       						=			self._prepare_for_class(A_  ,    A_  ,    return_labels=A_		)
														_UpperCAmelCase				:						str       						=			model(**A_		).loss
														loss.backward()


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						List[str]       						=			self.model_tester.prepare_config_and_inputs_for_common()
										if not self.model_tester.is_training:
														return

										_UpperCAmelCase				:						Tuple       						=			False
										_UpperCAmelCase				:						List[Any]       						=			True

										for model_class in self.all_model_classes:
														if model_class in [*get_values(A_		)] or not model_class.supports_gradient_checkpointing:
																		continue
														# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
														# this can then be incorporated into _prepare_for_class in test_modeling_common.py
														elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
																		_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase				:						Union[str, Any]       						=			inputs_dict["pixel_values"].shape
																		_UpperCAmelCase				:						Optional[Any]       						=			torch.zeros(
																		    [self.model_tester.batch_size, height, width]  ,    device=A_		).long()
														_UpperCAmelCase				:						Tuple       						=			model_class(A_		)
														model.gradient_checkpointing_enable()
														model.to(A_		)
														model.train()
														_UpperCAmelCase				:						List[Any]       						=			self._prepare_for_class(A_  ,    A_  ,    return_labels=A_		)
														_UpperCAmelCase				:						Tuple       						=			model(**A_		).loss
														loss.backward()


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


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

										_UpperCAmelCase				:						List[Any]       						=			_config_zero_init(A_		)
										for model_class in self.all_model_classes:
														_UpperCAmelCase				:						Any       						=			model_class(config=A_		)
														for name, param in model.named_parameters():
																		# we skip lambda parameters as these require special initial values
																		# determined by config.layer_scale_init_value
																		if "lambda" in name:
																						continue
																		if param.requires_grad:
																						self.assertIn(
																						    ((param.data.mean() * 1e9).round() / 1e9).item()  ,    [0.0, 1.0]  ,    msg=f'Parameter {name} of model {model_class} seems not properly initialized'  ,    )


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_=2e-4  ,    A_="outputs"  ,    A_=None		):







										'''simple docstring'''


										super().check_pt_tf_outputs(A_  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[str]       						=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*A_		)





						@slow
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase				:						Tuple       						=			DataaVecVisionModel.from_pretrained(A_		)
														self.assertIsNotNone(A_		)







def 							__SCREAMING_SNAKE_CASE							( )      ->				List[str]:
				_UpperCAmelCase				:						Dict       						=			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
				return image





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


						@cached_property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return (
										    BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k"		) if is_vision_available() else None
										)





						@slow
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[str]       						=			DataaVecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k"		).to(
										    A_		)

										_UpperCAmelCase				:						Optional[int]       						=			self.default_image_processor
										_UpperCAmelCase				:						Any       						=			prepare_img()
										_UpperCAmelCase				:						List[Any]       						=			image_processor(images=A_  ,    return_tensors="pt"		).to(A_		)

										# forward pass
										with torch.no_grad():
														_UpperCAmelCase				:						Tuple       						=			model(**A_		)
										_UpperCAmelCase				:						Any       						=			outputs.logits

										# verify the logits
										_UpperCAmelCase				:						Optional[int]       						=			torch.Size((1, 1000)		)
										self.assertEqual(logits.shape  ,    A_		)

										_UpperCAmelCase				:						Any       						=			torch.tensor([0.32_77, -0.13_95, 0.09_11]		).to(A_		)

										self.assertTrue(torch.allclose(logits[0, :3]  ,    A_  ,    atol=1e-4		)		)

										_UpperCAmelCase				:						Any       						=			[model.config.labelaid[i] for i in ["remote control, remote", "tabby, tabby cat"]]
										self.assertEqual(logits[0].topk(2		).indices.cpu().tolist()  ,    A_		)





from __future__ import annotations

class  a     :


						def __init__(				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			vertices
										_UpperCAmelCase				:						List[str]       						=			[[0] * vertices for _ in range(A_		)]


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										print("Vertex \t Distance from Source"		)
										for vertex in range(self.vertices		):
														print(A_  ,    "\t\t"  ,    distances_from_source[vertex]		)


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Tuple       						=			1e7
										_UpperCAmelCase				:						Any       						=			0

										# Search not nearest vertex not in the shortest path tree
										for vertex in range(self.vertices		):
														if distances_from_source[vertex] < minimum and visited[vertex] is False:
																		_UpperCAmelCase				:						List[Any]       						=			distances_from_source[vertex]
																		_UpperCAmelCase				:						Tuple       						=			vertex
										return min_index





						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			[int(1e7		)] * self.vertices  # distances from the source
										_UpperCAmelCase				:						Optional[Any]       						=			0
										_UpperCAmelCase				:						List[str]       						=			[False] * self.vertices

										for _ in range(self.vertices		):
														_UpperCAmelCase				:						str       						=			self.minimum_distance(A_  ,    A_		)
														_UpperCAmelCase				:						str       						=			True

														# Update dist value of the adjacent vertices
														# of the picked vertex only if the current
														# distance is greater than new distance and
														# the vertex in not in the shortest path tree
														for v in range(self.vertices		):
																		if (
																		    self.graph[u][v] > 0
																		    and visited[v] is False
																		    and distances[v] > distances[u] + self.graph[u][v]
																		):
																						_UpperCAmelCase				:						str       						=			distances[u] + self.graph[u][v]

										self.print_solution(A_		)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_						=					Graph(9)
					SCREAMING_SNAKE_CASE_						=					[
					    [0, 4, 0, 0, 0, 0, 0, 8, 0],
					    [4, 0, 8, 0, 0, 0, 0, 11, 0],
					    [0, 8, 0, 7, 0, 4, 0, 0, 2],
					    [0, 0, 7, 0, 9, 14, 0, 0, 0],
					    [0, 0, 0, 9, 0, 10, 0, 0, 0],
					    [0, 0, 4, 14, 10, 0, 2, 0, 0],
					    [0, 0, 0, 0, 0, 2, 0, 1, 6],
					    [8, 11, 0, 0, 0, 0, 1, 0, 7],
					    [0, 0, 2, 0, 0, 0, 6, 7, 0],
					]
					graph.dijkstra(0)


