
"""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 A_	:




  '''simple docstring'''







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

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

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

     UpperCAmelCase_					:					int			     =    self.get_config()

     return config, pixel_values, labels







  def        UpperCamelCase__					(		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=a__				,   initializer_range=self.initializer_range				,   mask_ratio=self.mask_ratio				,   )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    ViTMAEModel(config=a__  )
     model.to(a__  )
     model.eval()
     UpperCAmelCase_					:					List[Any]			     =    model(a__  )
     self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, self.seq_length, self.hidden_size)  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    ViTMAEForPreTraining(a__  )
     model.to(a__  )
     model.eval()
     UpperCAmelCase_					:					Tuple			     =    model(a__  )
     UpperCAmelCase_					:					Dict			     =    (self.image_size // self.patch_size) ** 2
     UpperCAmelCase_					:					Tuple			     =    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_					:					str			     =    1
     UpperCAmelCase_					:					Dict			     =    ViTMAEForPreTraining(a__  )
     model.to(a__  )
     model.eval()
     UpperCAmelCase_					:					Any			     =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
     UpperCAmelCase_					:					List[Any]			     =    model(a__  )
     UpperCAmelCase_					:					List[str]			     =    self.patch_size**2
     self.parent.assertEqual(result.logits.shape				,   (self.batch_size, num_patches, expected_num_channels)  )







  def        UpperCamelCase__					(		self  ):

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




@require_torch
class A_	(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,unittest.TestCase      ):




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

  SCREAMING_SNAKE_CASE__			:						Dict       =		False
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		False
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		False
  SCREAMING_SNAKE_CASE__			:						str       =		False







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    ViTMAEModelTester(self  )
     UpperCAmelCase_					:					List[str]			     =    ConfigTester(self				,   config_class=a__				,   has_text_modality=a__				,   hidden_size=37  )







  def        UpperCamelCase__					(		self  ):

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







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

     """simple docstring"""
     pass







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCAmelCase_					:					Optional[Any]			     =    model_class(a__  )
        self.assertIsInstance(model.get_input_embeddings()				,   (nn.Module)  )
        UpperCAmelCase_					:					Any			     =    model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(a__				,   nn.Linear  )  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    self.model_tester.prepare_config_and_inputs_for_common()

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

        UpperCAmelCase_					:					Any			     =    ["""pixel_values"""]
        self.assertListEqual(arg_names[:1]				,   a__  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*a__  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_pretraining(*a__  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_  ):

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

     UpperCAmelCase_					:					Tuple			     =    int((pt_model.config.image_size // pt_model.config.patch_size) ** 2  )
     UpperCAmelCase_					:					int			     =    np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
     UpperCAmelCase_					:					str			     =    torch.from_numpy(a__  )

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

     super().check_pt_tf_models(a__				,   a__				,   a__  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

           # Make sure we don't have nans
           UpperCAmelCase_					:					Dict			     =    after_outputs[0].cpu().numpy()
           UpperCAmelCase_					:					List[Any]			     =    0
           UpperCAmelCase_					:					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        UpperCamelCase__					(		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        UpperCamelCase__					(		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        UpperCamelCase__					(		self  ):

     """simple docstring"""
     pass







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

     """simple docstring"""
     pass







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

     """simple docstring"""
     pass







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCAmelCase_					:					Optional[Any]			     =    ViTMAEModel.from_pretrained(a__  )
        self.assertIsNotNone(a__  )







def 	__a     (		):
   UpperCAmelCase_					:					Tuple			     =    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  ):

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







  @slow
  def        UpperCamelCase__					(		self  ):

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

     UpperCAmelCase_					:					str			     =    ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"  ).to(a__  )

     UpperCAmelCase_					:					Tuple			     =    self.default_image_processor
     UpperCAmelCase_					:					Optional[int]			     =    prepare_img()
     UpperCAmelCase_					:					Tuple			     =    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)
     UpperCAmelCase_					:					str			     =    ViTMAEConfig()
     UpperCAmelCase_					:					Any			     =    int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
     UpperCAmelCase_					:					Union[str, Any]			     =    np.random.uniform(size=(1, num_patches)  )

     # forward pass
     with torch.no_grad():
        UpperCAmelCase_					:					int			     =    model(**a__				,   noise=torch.from_numpy(a__  ).to(device=a__  )  )

     # verify the logits
     UpperCAmelCase_					:					Dict			     =    torch.Size((1, 196, 768)  )
     self.assertEqual(outputs.logits.shape				,   a__  )

     UpperCAmelCase_					:					Optional[Any]			     =    torch.tensor(
         [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]  )

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



"""simple docstring"""




from __future__ import annotations

_a      :		List[str]           =   10





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       list[int]	)       ->      list[int]:
  _lowerCAmelCase						:       Optional[int]  			=     1
  _lowerCAmelCase						:       Union[str, Any]  			=     max(_lowerCamelCase	)
  while placement <= max_digit:
    # declare and initialize empty buckets
    _lowerCAmelCase						:       list[list]  			=     [[] for _ in range(_lowerCamelCase	)]
    # split list_of_ints between the buckets
    for i in list_of_ints:
      _lowerCAmelCase						:       Tuple  			=     int((i / placement) % RADIX	)
      buckets[tmp].append(_lowerCamelCase	)
    # put each buckets' contents into list_of_ints
    _lowerCAmelCase						:       List[str]  			=     0
    for b in range(_lowerCamelCase	):
      for i in buckets[b]:
        _lowerCAmelCase						:       Any  			=     i
        a += 1
        # move to next
    placement *= RADIX
  return list_of_ints


if __name__ == "__main__":
     import doctest

     doctest.testmod()




