


'''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      snake_case    :





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase=13			,  UpperCamelCase=30			,  UpperCamelCase=2			,  UpperCamelCase=3			,  UpperCamelCase=True			,  UpperCamelCase=True			,  UpperCamelCase=32			,  UpperCamelCase=5			,  UpperCamelCase=4			,  UpperCamelCase=37			,  UpperCamelCase="gelu"			,  UpperCamelCase=0.1			,  UpperCamelCase=0.1			,  UpperCamelCase=10			,  UpperCamelCase=0.02			,  UpperCamelCase=3			,  UpperCamelCase=0.6			,  UpperCamelCase=None			,  ):







   """simple docstring"""
   lowerCamelCase_      =  parent
   lowerCamelCase_      =  batch_size
   lowerCamelCase_      =  image_size
   lowerCamelCase_      =  patch_size
   lowerCamelCase_      =  num_channels
   lowerCamelCase_      =  is_training
   lowerCamelCase_      =  use_labels
   lowerCamelCase_      =  hidden_size
   lowerCamelCase_      =  num_hidden_layers
   lowerCamelCase_      =  num_attention_heads
   lowerCamelCase_      =  intermediate_size
   lowerCamelCase_      =  hidden_act
   lowerCamelCase_      =  hidden_dropout_prob
   lowerCamelCase_      =  attention_probs_dropout_prob
   lowerCamelCase_      =  type_sequence_label_size
   lowerCamelCase_      =  initializer_range
   lowerCamelCase_      =  mask_ratio
   lowerCamelCase_      =  scope

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






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

   lowerCamelCase_      =  self.get_config()

   return config, pixel_values, labels






  def   snake_case		(    self     ):







   """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=UpperCamelCase			,  initializer_range=self.initializer_range			,  mask_ratio=self.mask_ratio			,  )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  ViTMAEModel(config=UpperCamelCase     )
   model.to(UpperCamelCase     )
   model.eval()
   lowerCamelCase_      =  model(UpperCamelCase     )
   self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  ViTMAEForPreTraining(UpperCamelCase     )
   model.to(UpperCamelCase     )
   model.eval()
   lowerCamelCase_      =  model(UpperCamelCase     )
   lowerCamelCase_      =  (self.image_size // self.patch_size) ** 2
   lowerCamelCase_      =  self.patch_size**2 * self.num_channels
   self.parent.assertEqual(result.logits.shape			,  (self.batch_size, num_patches, expected_num_channels)     )

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







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.prepare_config_and_inputs()
   lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  config_and_inputs
   lowerCamelCase_      =  {"pixel_values": pixel_values}
   return config, inputs_dict





@require_torch
class      snake_case    (    lowercase			,						lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
  _lowerCamelCase		    =  {"feature-extraction": ViTMAEModel} if is_torch_available() else {}

  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  ViTMAEModelTester(self     )
   lowerCamelCase_      =  ConfigTester(self			,  config_class=UpperCamelCase			,  has_text_modality=UpperCamelCase			,  hidden_size=37     )






  def   snake_case		(    self     ):







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






  @unittest.skip(reason="ViTMAE does not use inputs_embeds"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_common()

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






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_common()

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

    lowerCamelCase_      =  ["pixel_values"]
    self.assertListEqual(arg_names[:1]			,  UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_pretraining(*UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







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

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

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

   super().check_pt_tf_models(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

     # Make sure we don't have nans
     lowerCamelCase_      =  after_outputs[0].cpu().numpy()
     lowerCamelCase_      =  0
     lowerCamelCase_      =  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   snake_case		(    self     ):







   """simple docstring"""
   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   snake_case		(    self     ):







   """simple docstring"""
   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   snake_case		(    self     ):







   """simple docstring"""
   pass






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







   """simple docstring"""
   pass






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







   """simple docstring"""
   pass







  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    lowerCamelCase_      =  ViTMAEModel.from_pretrained(UpperCamelCase     )
    self.assertIsNotNone(UpperCamelCase     )





def __snake_case					(      ):
 lowerCamelCase_      =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
 return image







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





  """simple docstring"""




  @cached_property
  def   snake_case		(    self     ):







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







  @slow
  def   snake_case		(    self     ):







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

   lowerCamelCase_      =  ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"     ).to(UpperCamelCase     )

   lowerCamelCase_      =  self.default_image_processor
   lowerCamelCase_      =  prepare_img()
   lowerCamelCase_      =  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)
   lowerCamelCase_      =  ViTMAEConfig()
   lowerCamelCase_      =  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2     )
   lowerCamelCase_      =  np.random.uniform(size=(1, num_patches)     )

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

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

   lowerCamelCase_      =  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(UpperCamelCase     )			,  atol=1e-4     )     )







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case							:       Union[str, Any]     			=      logging.get_logger(__name__)







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    List[Any]          =			['''pixel_values''']
					def __init__(      self   :Optional[int]			,__snake_case   :bool = True			,__snake_case   :Optional[Dict[str, int]] = None			,__snake_case   :PILImageResampling = PILImageResampling.BICUBIC			,__snake_case   :bool = True			,__snake_case   :bool = True			,__snake_case   :Union[int, float] = 1 / 2_55			,__snake_case   :Dict[str, int] = None			,__snake_case   :bool = True			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,**__snake_case   :Dict			,)					->    None:
												super().__init__(**__snake_case      )
												a__             =     size if size is not None else {'height': 2_24, 'width': 2_24}
												a__             =     get_size_dict(__snake_case      )
												a__             =     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
												a__             =     get_size_dict(__snake_case			,default_to_square=__snake_case			,param_name='crop_size'      )

												a__             =     do_resize
												a__             =     do_rescale
												a__             =     do_normalize
												a__             =     do_center_crop
												a__             =     crop_size
												a__             =     size
												a__             =     resample
												a__             =     rescale_factor
												a__             =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
												a__             =     image_std if image_std is not None else IMAGENET_DEFAULT_STD
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :np.ndarray			,__snake_case   :Dict[str, int]			,__snake_case   :PILImageResampling = PILImageResampling.BILINEAR			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :List[Any]			,)					->    np.ndarray:
												a__             =     get_size_dict(__snake_case      )

												if "shortest_edge" in size:
																			a__             =     get_resize_output_image_size(__snake_case			,size=size['shortest_edge']			,default_to_square=__snake_case      )
																			# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
												elif "height" in size and "width" in size:
																			a__             =     (size['height'], size['width'])
												else:
																			raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'      )
												return resize(__snake_case			,size=__snake_case			,resample=__snake_case			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :np.ndarray			,__snake_case   :Dict[str, int]			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :Any			,)					->    np.ndarray:
												a__             =     get_size_dict(__snake_case      )
												if "height" not in size or "width" not in size:
																			raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'      )
												return center_crop(__snake_case			,size=(size['height'], size['width'])			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :List[Any]			,__snake_case   :np.ndarray			,__snake_case   :float			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :int      )					->    np.ndarray:
												return rescale(__snake_case			,scale=__snake_case			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :Tuple			,__snake_case   :np.ndarray			,__snake_case   :Union[float, List[float]]			,__snake_case   :Union[float, List[float]]			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :Any			,)					->    np.ndarray:
												return normalize(__snake_case			,mean=__snake_case			,std=__snake_case			,data_format=__snake_case			,**__snake_case      )



					def  lowerCamelCase__(      self   :Any			,__snake_case   :ImageInput			,__snake_case   :Optional[bool] = None			,__snake_case   :Dict[str, int] = None			,__snake_case   :PILImageResampling = None			,__snake_case   :bool = None			,__snake_case   :int = None			,__snake_case   :Optional[bool] = None			,__snake_case   :Optional[float] = None			,__snake_case   :Optional[bool] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[str, TensorType]] = None			,__snake_case   :Union[str, ChannelDimension] = ChannelDimension.FIRST			,**__snake_case   :Optional[int]			,)					->    BatchFeature:
												a__             =     do_resize if do_resize is not None else self.do_resize
												a__             =     do_rescale if do_rescale is not None else self.do_rescale
												a__             =     do_normalize if do_normalize is not None else self.do_normalize
												a__             =     do_center_crop if do_center_crop is not None else self.do_center_crop
												a__             =     crop_size if crop_size is not None else self.crop_size
												a__             =     get_size_dict(__snake_case			,param_name='crop_size'			,default_to_square=__snake_case      )
												a__             =     resample if resample is not None else self.resample
												a__             =     rescale_factor if rescale_factor is not None else self.rescale_factor
												a__             =     image_mean if image_mean is not None else self.image_mean
												a__             =     image_std if image_std is not None else self.image_std

												a__             =     size if size is not None else self.size
												a__             =     get_size_dict(__snake_case      )

												if not is_batched(__snake_case      ):
																			a__             =     [images]

												if not valid_images(__snake_case      ):
																			raise ValueError(
																			    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																			    'torch.Tensor, tf.Tensor or jax.ndarray.'      )

												if do_resize and size is None:
																			raise ValueError('Size must be specified if do_resize is True.'      )

												if do_center_crop and crop_size is None:
																			raise ValueError('Crop size must be specified if do_center_crop is True.'      )

												if do_rescale and rescale_factor is None:
																			raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

												# All transformations expect numpy arrays.
												a__             =     [to_numpy_array(__snake_case      ) for image in images]

												if do_resize:
																			a__             =     [self.resize(image=__snake_case			,size=__snake_case			,resample=__snake_case      ) for image in images]

												if do_center_crop:
																			a__             =     [self.center_crop(image=__snake_case			,size=__snake_case      ) for image in images]

												if do_rescale:
																			a__             =     [self.rescale(image=__snake_case			,scale=__snake_case      ) for image in images]

												if do_normalize:
																			a__             =     [self.normalize(image=__snake_case			,mean=__snake_case			,std=__snake_case      ) for image in images]

												a__             =     [to_channel_dimension_format(__snake_case			,__snake_case      ) for image in images]

												a__             =     {'pixel_values': images}
												return BatchFeature(data=__snake_case			,tensor_type=__snake_case      )




