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 				a							:







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=13		,				lowerCAmelCase_=30		,				lowerCAmelCase_=2		,				lowerCAmelCase_=3		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=32		,				lowerCAmelCase_=5		,				lowerCAmelCase_=4		,				lowerCAmelCase_=37		,				lowerCAmelCase_="gelu"		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=10		,				lowerCAmelCase_=0.02		,				lowerCAmelCase_=3		,				lowerCAmelCase_=0.6		,				lowerCAmelCase_=None		,				)  ->					int:
        _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      UpperCAmelCase							(					self							)  ->					Union[str, 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      UpperCAmelCase							(					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=lowerCAmelCase_		,				initializer_range=self.initializer_range		,				mask_ratio=self.mask_ratio		,				)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Any:
        _A        =  ViTMAEModel(config=lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  model(lowerCAmelCase_							)
        self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					List[Any]:
        _A        =  ViTMAEForPreTraining(lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  model(lowerCAmelCase_							)
        _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(lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
        _A        =  model(lowerCAmelCase_							)
        _A        =  self.patch_size**2
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, num_patches, expected_num_channels)							)



 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  self.prepare_config_and_inputs()
        _A   ,  _A   ,  _A        =  config_and_inputs
        _A        =  {"""pixel_values""": pixel_values}
        return config, inputs_dict



@require_torch
class 				a							(			__lowerCAmelCase       ,			__lowerCAmelCase       ,			unittest.TestCase       ):







 """simple docstring"""






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

 lowerCamelCase	:List[Any]    			=		False
 lowerCamelCase	:Tuple    			=		False
 lowerCamelCase	:int    			=		False
 lowerCamelCase	:Any    			=		False



 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  ViTMAEModelTester(self							)
        _A        =  ConfigTester(self		,				config_class=lowerCAmelCase_		,				has_text_modality=lowerCAmelCase_		,				hidden_size=37							)



 def      UpperCAmelCase							(					self							)  ->					str:
        self.config_tester.run_common_tests()



 @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""							)
 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        pass



 def      UpperCAmelCase							(					self							)  ->					str:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

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



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               _A        =  model_class(lowerCAmelCase_							)
               _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]		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Any:
        # 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(lowerCAmelCase_							)

        # 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(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

                      # 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(lowerCAmelCase_		,				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							)  ->					Tuple:
        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							)  ->					str:
        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							)  ->					str:
        pass



 @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""							)
 def      UpperCAmelCase							(					self							)  ->					Dict:
        pass



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



 @slow
 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _A        =  ViTMAEModel.from_pretrained(lowerCAmelCase_							)
               self.assertIsNotNone(lowerCAmelCase_							)






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







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







 """simple docstring"""






 @cached_property
 def      UpperCAmelCase							(					self							)  ->					int:
        return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""							) if is_vision_available() else None



 @slow
 def      UpperCAmelCase							(					self							)  ->					Any:
        # make random mask reproducible across the PT and TF model
        np.random.seed(2							)

        _A        =  ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""							).to(lowerCAmelCase_							)

        _A        =  self.default_image_processor
        _A        =  prepare_img()
        _A        =  image_processor(images=lowerCAmelCase_		,				return_tensors="""pt"""							).to(lowerCAmelCase_							)

        # 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(**lowerCAmelCase_		,				noise=torch.from_numpy(lowerCAmelCase_							).to(device=lowerCAmelCase_							)							)

        # verify the logits
        _A        =  torch.Size((1, 1_96, 7_68)							)
        self.assertEqual(outputs.logits.shape		,				lowerCAmelCase_							)

        _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(lowerCAmelCase_							)		,				atol=1E-4							)							)





import random
from typing import Any






def  snake_case  (       snake_case__			:list)						->   list[Any]:
       for _ in range(len(snake_case__)):
              _A        =  random.randint(0   ,	len(snake_case__) - 1)
              _A        =  random.randint(0   ,	len(snake_case__) - 1)
              _A   ,  _A        =  data[b], data[a]
       return data


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						[0, 1, 2, 3, 4, 5, 6, 7]
    _SCREAMING_SNAKE_CASE										=						['python', 'says', 'hello', '!']
    print('Fisher-Yates Shuffle:')
    print('List', integers, strings)
    print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))




