





"""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  __UpperCamelCase       :






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

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






       def 					__a     (     self   )			->				Any:
        a   :	Dict			=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

        a   :	Tuple			=       None
        if self.use_labels:
         a   :	str			=       ids_tensor([self.batch_size]   ,      self.type_sequence_label_size   )

        a   :	Any			=       self.get_config()

        return config, pixel_values, labels






       def 					__a     (     self   )			->				Optional[int]:
        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=_a   ,      initializer_range=self.initializer_range   ,      mask_ratio=self.mask_ratio   ,      )






       def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				str:
        a   :	int			=       ViTMAEModel(config=_a   )
        model.to(_a   )
        model.eval()
        a   :	Tuple			=       model(_a   )
        self.parent.assertEqual(result.last_hidden_state.shape   ,      (self.batch_size, self.seq_length, self.hidden_size)   )






       def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				List[str]:
        a   :	Union[str, Any]			=       ViTMAEForPreTraining(_a   )
        model.to(_a   )
        model.eval()
        a   :	Optional[Any]			=       model(_a   )
        a   :	Any			=       (self.image_size // self.patch_size) ** 2
        a   :	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
        a   :	int			=       1
        a   :	Tuple			=       ViTMAEForPreTraining(_a   )
        model.to(_a   )
        model.eval()
        a   :	str			=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
        a   :	str			=       model(_a   )
        a   :	Any			=       self.patch_size**2
        self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, num_patches, expected_num_channels)   )






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


@require_torch
class  __UpperCamelCase       (		__SCREAMING_SNAKE_CASE	, __SCREAMING_SNAKE_CASE	, unittest.TestCase		):
       lowerCamelCase     :      str         =(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
       lowerCamelCase     :      List[Any]         ={"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

       lowerCamelCase     :      Optional[int]         =False
       lowerCamelCase     :      Tuple         =False
       lowerCamelCase     :      Dict         =False
       lowerCamelCase     :      Optional[int]         =False






       def 					__a     (     self   )			->				str:
        a   :	List[Any]			=       ViTMAEModelTester(self   )
        a   :	str			=       ConfigTester(self   ,      config_class=_a   ,      has_text_modality=_a   ,      hidden_size=37   )






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






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






       def 					__a     (     self   )			->				Optional[Any]:
        a   :	Dict			=       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         a   :	List[str]			=       model_class(_a   )
         self.assertIsInstance(model.get_input_embeddings()   ,      (nn.Module)   )
         a   :	Dict			=       model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(_a   ,      nn.Linear   )   )






       def 					__a     (     self   )			->				int:
        a   :	int			=       self.model_tester.prepare_config_and_inputs_for_common()

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

         a   :	Optional[int]			=       ["""pixel_values"""]
         self.assertListEqual(arg_names[:1]   ,      _a   )






       def 					__a     (     self   )			->				List[Any]:
        a   :	Optional[Any]			=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_a   )






       def 					__a     (     self   )			->				List[str]:
        a   :	Union[str, Any]			=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*_a   )






       def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Any:
        np.random.seed(2   )

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

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

        super().check_pt_tf_models(_a   ,      _a   ,      _a   )






       def 					__a     (     self   )			->				Tuple:
        a   :	Any			=       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         a   :	List[str]			=       model_class(_a   )
         model.to(_a   )
         model.eval()
         # make random mask reproducible
         torch.manual_seed(2   )
         with torch.no_grad():
          a   :	Dict			=       model(**self._prepare_for_class(_a   ,      _a   )   )

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

         with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_pretrained(_a   )
          a   :	Union[str, Any]			=       model_class.from_pretrained(_a   )
          model.to(_a   )
          # make random mask reproducible
          torch.manual_seed(2   )
          with torch.no_grad():
           a   :	Union[str, Any]			=       model(**self._prepare_for_class(_a   ,      _a   )   )

          # Make sure we don't have nans
          a   :	Optional[int]			=       after_outputs[0].cpu().numpy()
          a   :	Tuple			=       0
          a   :	int			=       np.amax(np.abs(out_a - out_a   )   )
          self.assertLessEqual(_a   ,      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 					__a     (     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 					__a     (     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 					__a     (     self   )			->				Optional[int]:
        pass






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






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






       @slow
       def 					__a     (     self   )			->				int:




        for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a   :	Optional[int]			=       ViTMAEModel.from_pretrained(_a   )
         self.assertIsNotNone(_a   )






def    _SCREAMING_SNAKE_CASE	(			)				->int:






 '''simple docstring'''






 a   :	Optional[int]			=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
 return image







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






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






       @slow
       def 					__a     (     self   )			->				Any:
        np.random.seed(2   )

        a   :	Optional[Any]			=       ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"   ).to(_a   )

        a   :	Dict			=       self.default_image_processor
        a   :	Union[str, Any]			=       prepare_img()
        a   :	List[Any]			=       image_processor(images=_a   ,      return_tensors="pt"   ).to(_a   )

        # 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   :	Optional[int]			=       ViTMAEConfig()
        a   :	int			=       int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2   )
        a   :	Dict			=       np.random.uniform(size=(1, num_patches)   )

        # forward pass
        with torch.no_grad():
         a   :	Tuple			=       model(**_a   ,      noise=torch.from_numpy(_a   ).to(device=_a   )   )

        # verify the logits
        a   :	Any			=       torch.Size((1, 196, 768)   )
        self.assertEqual(outputs.logits.shape   ,      _a   )

        a   :	int			=       torch.tensor(
            [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]   )

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









"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      torch.FloatTensor
							lowerCamelCase     :      torch.FloatTensor
							lowerCamelCase     :      Optional[torch.FloatTensor]         =None


class  __UpperCamelCase       (		a__	, a__		):
							lowerCamelCase     :      Tuple         =2






							@register_to_config
							def __init__(     self   ,      lowerCAmelCase__ = 0.02   ,      lowerCAmelCase__ = 100   ,      lowerCAmelCase__ = 1.007   ,      lowerCAmelCase__ = 80   ,      lowerCAmelCase__ = 0.05   ,      lowerCAmelCase__ = 50   ,      )			->				Union[str, Any]:
								# standard deviation of the initial noise distribution
								a   :	Tuple			=       sigma_max

								# setable values
								a   :	int			=       None
								a   :	np.IntTensor			=       None
								a   :	torch.FloatTensor			=       None  # sigma(t_i)






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				torch.FloatTensor:
								return sample






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[str]:
								a   :	List[Any]			=       num_inference_steps
								a   :	List[str]			=       np.arange(0   ,      self.num_inference_steps   )[::-1].copy()
								a   :	int			=       torch.from_numpy(lowerCAmelCase__   ).to(lowerCAmelCase__   )
								a   :	List[str]			=       [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								a   :	Any			=       torch.tensor(lowerCAmelCase__   ,      dtype=torch.floataa   ,      device=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				Tuple[torch.FloatTensor, float]:
								if self.config.s_min <= sigma <= self.config.s_max:
									a   :	str			=       min(self.config.s_churn / self.num_inference_steps   ,      2**0.5 - 1   )
								else:
									a   :	Dict			=       0

								# sample eps ~ N(0, S_noise^2 * I)
								a   :	Union[str, Any]			=       self.config.s_noise * randn_tensor(sample.shape   ,      generator=lowerCAmelCase__   ).to(sample.device   )
								a   :	Any			=       sigma + gamma * sigma
								a   :	Tuple			=       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      )			->				Union[KarrasVeOutput, Tuple]:
								a   :	Union[str, Any]			=       sample_hat + sigma_hat * model_output
								a   :	Tuple			=       (sample_hat - pred_original_sample) / sigma_hat
								a   :	List[Any]			=       sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
									return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=lowerCAmelCase__   ,      derivative=lowerCAmelCase__   ,      pred_original_sample=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      )			->				Union[KarrasVeOutput, Tuple]:
								a   :	Optional[int]			=       sample_prev + sigma_prev * model_output
								a   :	str			=       (sample_prev - pred_original_sample) / sigma_prev
								a   :	Dict			=       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
									return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=lowerCAmelCase__   ,      derivative=lowerCAmelCase__   ,      pred_original_sample=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				str:
								raise NotImplementedError()


