





import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
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 torch import nn

  from transformers import ViTMAEForPreTraining, ViTMAEModel
  from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor






class 					lowercase__ :

   '''simple docstring'''


   def __init__(					self,	__magic_name__,	__magic_name__=13,	__magic_name__=30,	__magic_name__=2,	__magic_name__=3,	__magic_name__=True,	__magic_name__=True,	__magic_name__=32,	__magic_name__=5,	__magic_name__=4,	__magic_name__=37,	__magic_name__="gelu",	__magic_name__=0.1,	__magic_name__=0.1,	__magic_name__=10,	__magic_name__=0.02,	__magic_name__=3,	__magic_name__=0.6,	__magic_name__=None,	)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       str      =   parent
       UpperCamelCase__							:       Tuple      =   batch_size
       UpperCamelCase__							:       Dict      =   image_size
       UpperCamelCase__							:       Optional[Any]      =   patch_size
       UpperCamelCase__							:       Union[str, Any]      =   num_channels
       UpperCamelCase__							:       Optional[Any]      =   is_training
       UpperCamelCase__							:       Optional[int]      =   use_labels
       UpperCamelCase__							:       Union[str, Any]      =   hidden_size
       UpperCamelCase__							:       Dict      =   num_hidden_layers
       UpperCamelCase__							:       Union[str, Any]      =   num_attention_heads
       UpperCamelCase__							:       int      =   intermediate_size
       UpperCamelCase__							:       int      =   hidden_act
       UpperCamelCase__							:       Optional[int]      =   hidden_dropout_prob
       UpperCamelCase__							:       Union[str, Any]      =   attention_probs_dropout_prob
       UpperCamelCase__							:       Optional[int]      =   type_sequence_label_size
       UpperCamelCase__							:       Dict      =   initializer_range
       UpperCamelCase__							:       Optional[int]      =   mask_ratio
       UpperCamelCase__							:       Any      =   scope

       # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
       # (we add 1 for the [CLS] token)
       UpperCamelCase__							:       Tuple      =   (image_size // patch_size) ** 2
       UpperCamelCase__							:       Any      =   int(math.ceil((1 - mask_ratio) * (num_patches + 1)							)							)




   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__							:       Any      =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

       UpperCamelCase__							:       Any      =   None
       if self.use_labels:
           UpperCamelCase__							:       Dict      =   ids_tensor([self.batch_size],	self.type_sequence_label_size							)

       UpperCamelCase__							:       Tuple      =   self.get_config()

       return config, pixel_values, labels




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       return ViTMAEConfig(
           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=__magic_name__,	initializer_range=self.initializer_range,	mask_ratio=self.mask_ratio,	)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   ViTMAEModel(config=__magic_name__							)
       model.to(__magic_name__							)
       model.eval()
       UpperCamelCase__							:       Optional[Any]      =   model(__magic_name__							)
       self.parent.assertEqual(result.last_hidden_state.shape,	(self.batch_size, self.seq_length, self.hidden_size)							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   ViTMAEForPreTraining(__magic_name__							)
       model.to(__magic_name__							)
       model.eval()
       UpperCamelCase__							:       Tuple      =   model(__magic_name__							)
       UpperCamelCase__							:       Any      =   (self.image_size // self.patch_size) ** 2
       UpperCamelCase__							:       str      =   self.patch_size**2 * self.num_channels
       self.parent.assertEqual(result.logits.shape,	(self.batch_size, num_patches, expected_num_channels)							)

       # test greyscale images
       UpperCamelCase__							:       Dict      =   1
       UpperCamelCase__							:       List[str]      =   ViTMAEForPreTraining(__magic_name__							)
       model.to(__magic_name__							)
       model.eval()
       UpperCamelCase__							:       Union[str, Any]      =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
       UpperCamelCase__							:       str      =   model(__magic_name__							)
       UpperCamelCase__							:       str      =   self.patch_size**2
       self.parent.assertEqual(result.logits.shape,	(self.batch_size, num_patches, expected_num_channels)							)




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       Any      =   self.prepare_config_and_inputs()
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Tuple      =   config_and_inputs
       UpperCamelCase__							:       Union[str, Any]      =   {'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_torch
class 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							Union[str, Any]     	=	(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
   a		:							List[str]     	=	{"feature-extraction": ViTMAEModel} if is_torch_available() else {}

   a		:							Union[str, Any]     	=	False
   a		:							int     	=	False
   a		:							Any     	=	False
   a		:							Any     	=	False




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






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   ViTMAEModelTester(self							)
       UpperCamelCase__							:       Optional[Any]      =   ConfigTester(self,	config_class=__magic_name__,	has_text_modality=__magic_name__,	hidden_size=37							)




   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       self.config_tester.run_common_tests()




   @unittest.skip(reason='''ViTMAE does not use inputs_embeds'''							)
   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       pass




   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       Dict      =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase__							:       Any      =   model_class(__magic_name__							)
           self.assertIsInstance(model.get_input_embeddings(),	(nn.Module)							)
           UpperCamelCase__							:       Dict      =   model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(__magic_name__,	nn.Linear							)							)




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






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       Any      =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase__							:       Optional[Any]      =   model_class(__magic_name__							)
           UpperCamelCase__							:       List[Any]      =   inspect.signature(model.forward							)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           UpperCamelCase__							:       Tuple      =   [*signature.parameters.keys()]

           UpperCamelCase__							:       Dict      =   ['''pixel_values''']
           self.assertListEqual(arg_names[:1],	__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       Any      =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   str:






       """simple docstring"""
       # make masks reproducible
       np.random.seed(2							)

       UpperCamelCase__							:       Optional[int]      =   int((pt_model.config.image_size // pt_model.config.patch_size) ** 2							)
       UpperCamelCase__							:       Union[str, Any]      =   np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
       UpperCamelCase__							:       Optional[int]      =   torch.from_numpy(__magic_name__							)

       # Add `noise` argument.
       # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
       UpperCamelCase__							:       Optional[Any]      =   pt_noise

       super().check_pt_tf_models(__magic_name__,	__magic_name__,	__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       Tuple      =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase__							:       Tuple      =   model_class(__magic_name__							)
           model.to(__magic_name__							)
           model.eval()
           # make random mask reproducible
           torch.manual_seed(2							)
           with torch.no_grad():
               UpperCamelCase__							:       str      =   model(**self._prepare_for_class(__magic_name__,	__magic_name__							)							)

           UpperCamelCase__							:       Optional[Any]      =   outputs[0].cpu().numpy()
           UpperCamelCase__							:       Any      =   0

           with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_pretrained(__magic_name__							)
               UpperCamelCase__							:       Optional[Any]      =   model_class.from_pretrained(__magic_name__							)
               model.to(__magic_name__							)
               # make random mask reproducible
               torch.manual_seed(2							)
               with torch.no_grad():
                   UpperCamelCase__							:       int      =   model(**self._prepare_for_class(__magic_name__,	__magic_name__							)							)

               # Make sure we don't have nans
               UpperCamelCase__							:       str      =   after_outputs[0].cpu().numpy()
               UpperCamelCase__							:       List[Any]      =   0
               UpperCamelCase__							:       Tuple      =   np.amax(np.abs(out_a - out_a							)							)
               self.assertLessEqual(__magic_name__,	1E-5							)




   @unittest.skip(
       reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''							)
   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       pass




   @unittest.skip(
       reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''							)
   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       pass




   @unittest.skip(
       reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''							)
   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       pass




   @unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''							)
   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       pass




   @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''							)
   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       pass




   @slow
   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase__							:       List[str]      =   ViTMAEModel.from_pretrained(__magic_name__							)
           self.assertIsNotNone(__magic_name__							)




def 	lowerCAmelCase_					(						)   ->			Tuple:
    UpperCamelCase__							:       int      =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
    return image






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

   '''simple docstring'''


   @cached_property
   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''							) if is_vision_available() else None




   @slow
   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       # make random mask reproducible across the PT and TF model
       np.random.seed(2							)

       UpperCamelCase__							:       List[Any]      =   ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''							).to(__magic_name__							)

       UpperCamelCase__							:       str      =   self.default_image_processor
       UpperCamelCase__							:       int      =   prepare_img()
       UpperCamelCase__							:       Optional[Any]      =   image_processor(images=__magic_name__,	return_tensors='''pt'''							).to(__magic_name__							)

       # prepare a noise vector that will be also used for testing the TF model
       # (this way we can ensure that the PT and TF models operate on the same inputs)
       UpperCamelCase__							:       Optional[Any]      =   ViTMAEConfig()
       UpperCamelCase__							:       Any      =   int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2							)
       UpperCamelCase__							:       Optional[Any]      =   np.random.uniform(size=(1, num_patches)							)

       # forward pass
       with torch.no_grad():
           UpperCamelCase__							:       Dict      =   model(**__magic_name__,	noise=torch.from_numpy(__magic_name__							).to(device=__magic_name__							)							)

       # verify the logits
       UpperCamelCase__							:       List[str]      =   torch.Size((1, 196, 768)							)
       self.assertEqual(outputs.logits.shape,	__magic_name__							)

       UpperCamelCase__							:       Union[str, Any]      =   torch.tensor(
           [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]							)

       self.assertTrue(torch.allclose(outputs.logits[0, :3, :3],	expected_slice.to(__magic_name__							),	atol=1E-4							)							)








def 	lowerCAmelCase_					(						__UpperCAmelCase:    dict   )   ->			set:
    UpperCamelCase__							:       int      =   set()
    # edges = list of graph's edges
    UpperCamelCase__							:       str      =   get_edges(__UpperCAmelCase   )

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        UpperCamelCase__     ,UpperCamelCase__							:       List[Any]      =   edges.pop()
        chosen_vertices.add(__UpperCAmelCase   )
        chosen_vertices.add(__UpperCAmelCase   )
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(__UpperCAmelCase   )
    return chosen_vertices




def 	lowerCAmelCase_					(						__UpperCAmelCase:    dict   )   ->			set:
    UpperCamelCase__							:       Optional[int]      =   set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node)   )
    return edges


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
  # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")

