




"""simple docstring"""

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       :       Tuple    ,							_UpperCAmelCase       :       Any    ,							_UpperCAmelCase       :       List[Any]=13    ,							_UpperCAmelCase       :       Dict=30    ,							_UpperCAmelCase       :       Dict=2    ,							_UpperCAmelCase       :       int=3    ,							_UpperCAmelCase       :       Dict=True    ,							_UpperCAmelCase       :       str=True    ,							_UpperCAmelCase       :       Dict=32    ,							_UpperCAmelCase       :       List[Any]=5    ,							_UpperCAmelCase       :       str=4    ,							_UpperCAmelCase       :       List[Any]=37    ,							_UpperCAmelCase       :       Dict="gelu"    ,							_UpperCAmelCase       :       Optional[Any]=0.1    ,							_UpperCAmelCase       :       Tuple=0.1    ,							_UpperCAmelCase       :       List[Any]=10    ,							_UpperCAmelCase       :       List[Any]=0.02    ,							_UpperCAmelCase       :       Dict=3    ,							_UpperCAmelCase       :       int=0.6    ,							_UpperCAmelCase       :       Union[str, Any]=None    ,							):
       _A					   =   parent
       _A					   =   batch_size
       _A					   =   image_size
       _A					   =   patch_size
       _A					   =   num_channels
       _A					   =   is_training
       _A					   =   use_labels
       _A					   =   hidden_size
       _A					   =   num_hidden_layers
       _A					   =   num_attention_heads
       _A					   =   intermediate_size
       _A					   =   hidden_act
       _A					   =   hidden_dropout_prob
       _A					   =   attention_probs_dropout_prob
       _A					   =   type_sequence_label_size
       _A					   =   initializer_range
       _A					   =   mask_ratio
       _A					   =   scope

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

 def 			lowerCAmelCase_							( self       :       List[Any]		):
       _A					   =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

       _A					   =   None
       if self.use_labels:
             _A					   =   ids_tensor([self.batch_size]    ,							self.type_sequence_label_size		)

       _A					   =   self.get_config()

       return config, pixel_values, labels

 def 			lowerCAmelCase_							( self       :       str		):
       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=_UpperCAmelCase    ,							initializer_range=self.initializer_range    ,							mask_ratio=self.mask_ratio    ,							)

 def 			lowerCAmelCase_							( self       :       Optional[Any]    ,							_UpperCAmelCase       :       Tuple    ,							_UpperCAmelCase       :       Optional[Any]    ,							_UpperCAmelCase       :       int		):
       _A					   =   ViTMAEModel(config=_UpperCAmelCase		)
       model.to(_UpperCAmelCase		)
       model.eval()
       _A					   =   model(_UpperCAmelCase		)
       self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, self.seq_length, self.hidden_size)		)

 def 			lowerCAmelCase_							( self       :       List[str]    ,							_UpperCAmelCase       :       Optional[Any]    ,							_UpperCAmelCase       :       Any    ,							_UpperCAmelCase       :       Union[str, Any]		):
       _A					   =   ViTMAEForPreTraining(_UpperCAmelCase		)
       model.to(_UpperCAmelCase		)
       model.eval()
       _A					   =   model(_UpperCAmelCase		)
       _A					   =   (self.image_size // self.patch_size) ** 2
       _A					   =   self.patch_size**2 * self.num_channels
       self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, num_patches, expected_num_channels)		)

       # test greyscale images
       _A					   =   1
       _A					   =   ViTMAEForPreTraining(_UpperCAmelCase		)
       model.to(_UpperCAmelCase		)
       model.eval()
       _A					   =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
       _A					   =   model(_UpperCAmelCase		)
       _A					   =   self.patch_size**2
       self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, num_patches, expected_num_channels)		)



 def 			lowerCAmelCase_							( self       :       Dict		):
       _A					   =   self.prepare_config_and_inputs()
       _A					   =   config_and_inputs
       _A					   =   {'pixel_values': pixel_values}
       return config, inputs_dict





@require_torch
class 				lowercase_       (     __UpperCamelCase			,	__UpperCamelCase			,	unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase    :     Union[str, Any]        =	(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
 UpperCAmelCase    :     List[Any]        =	{'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

 UpperCAmelCase    :     Any        =	False
 UpperCAmelCase    :     str        =	False
 UpperCAmelCase    :     Any        =	False
 UpperCAmelCase    :     Union[str, Any]        =	False

 def 			lowerCAmelCase_							( self       :       List[str]		):
       _A					   =   ViTMAEModelTester(self		)
       _A					   =   ConfigTester(self    ,							config_class=_UpperCAmelCase    ,							has_text_modality=_UpperCAmelCase    ,							hidden_size=37		)

 def 			lowerCAmelCase_							( self       :       str		):
       self.config_tester.run_common_tests()

 @unittest.skip(reason='ViTMAE does not use inputs_embeds'		)
 def 			lowerCAmelCase_							( self       :       Tuple		):
       pass

 def 			lowerCAmelCase_							( self       :       List[str]		):
       _A					   =   self.model_tester.prepare_config_and_inputs_for_common()

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

 def 			lowerCAmelCase_							( self       :       Optional[int]		):
       _A					   =   self.model_tester.prepare_config_and_inputs_for_common()

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

             _A					   =   ['pixel_values']
             self.assertListEqual(arg_names[:1]    ,							_UpperCAmelCase		)

 def 			lowerCAmelCase_							( self       :       List[Any]		):
       _A					   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase		)

 def 			lowerCAmelCase_							( self       :       Any		):
       _A					   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*_UpperCAmelCase		)

 def 			lowerCAmelCase_							( self       :       str    ,							_UpperCAmelCase       :       List[Any]    ,							_UpperCAmelCase       :       Optional[Any]    ,							_UpperCAmelCase       :       int		):
       # make masks reproducible
       np.random.seed(2		)

       _A					   =   int((pt_model.config.image_size // pt_model.config.patch_size) ** 2		)
       _A					   =   np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)
       _A					   =   torch.from_numpy(_UpperCAmelCase		)

       # Add `noise` argument.
       # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
       _A					   =   pt_noise

       super().check_pt_tf_models(_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase		)

 def 			lowerCAmelCase_							( self       :       List[Any]		):
       _A					   =   self.model_tester.prepare_config_and_inputs_for_common()

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

             _A					   =   outputs[0].cpu().numpy()
             _A					   =   0

             with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_pretrained(_UpperCAmelCase		)
                   _A					   =   model_class.from_pretrained(_UpperCAmelCase		)
                   model.to(_UpperCAmelCase		)
                   # make random mask reproducible
                   torch.manual_seed(2		)
                   with torch.no_grad():
                         _A					   =   model(**self._prepare_for_class(_UpperCAmelCase    ,							_UpperCAmelCase		)		)

                   # Make sure we don't have nans
                   _A					   =   after_outputs[0].cpu().numpy()
                   _A					   =   0
                   _A					   =   np.amax(np.abs(out_a - out_a		)		)
                   self.assertLessEqual(_UpperCAmelCase    ,							1E-5		)

 @unittest.skip(
     reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'		)
 def 			lowerCAmelCase_							( self       :       int		):
       pass

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

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

 @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'		)
 def 			lowerCAmelCase_							( self       :       Tuple		):
       pass

 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'		)
 def 			lowerCAmelCase_							( self       :       List[Any]		):
       pass



 @slow
 def 			lowerCAmelCase_							( self       :       int		):
       for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A					   =   ViTMAEModel.from_pretrained(_UpperCAmelCase		)
             self.assertIsNotNone(_UpperCAmelCase		)






def 						_snake_case     (					)    ->					List[str]:






      '''simple docstring'''


      _A					   =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
      return image







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



 '''simple docstring'''

 @cached_property
 def 			lowerCAmelCase_							( self       :       Dict		):
       return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'		) if is_vision_available() else None



 @slow
 def 			lowerCAmelCase_							( self       :       str		):
       # make random mask reproducible across the PT and TF model
       np.random.seed(2		)

       _A					   =   ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'		).to(_UpperCAmelCase		)

       _A					   =   self.default_image_processor
       _A					   =   prepare_img()
       _A					   =   image_processor(images=_UpperCAmelCase    ,							return_tensors='pt'		).to(_UpperCAmelCase		)

       # 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)
       _A					   =   ViTMAEConfig()
       _A					   =   int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2		)
       _A					   =   np.random.uniform(size=(1, num_patches)		)

       # forward pass
       with torch.no_grad():
             _A					   =   model(**_UpperCAmelCase    ,							noise=torch.from_numpy(_UpperCAmelCase		).to(device=_UpperCAmelCase		)		)

       # verify the logits
       _A					   =   torch.Size((1, 196, 768)		)
       self.assertEqual(outputs.logits.shape    ,							_UpperCAmelCase		)

       _A					   =   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(_UpperCAmelCase		)    ,							atol=1E-4		)		)








def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     set:
    lowerCamelCase__    :      Optional[Any]					 =		set()
    # edges = list of graph's edges
    lowerCamelCase__    :      List[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:
        lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     set:
    lowerCamelCase__    :      Union[str, Any]					 =		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)}")





