






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  :




      """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    ,	):



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

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



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

          lowerCAmelCase__   :Tuple      							=				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=_lowerCAmelCase    ,	initializer_range=self.initializer_range    ,	mask_ratio=self.mask_ratio    ,	)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				ViTMAEModel(config=_lowerCAmelCase		)
          model.to(_lowerCAmelCase		)
          model.eval()
          lowerCAmelCase__   :List[Any]      							=				model(_lowerCAmelCase		)
          self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.seq_length, self.hidden_size)		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				ViTMAEForPreTraining(_lowerCAmelCase		)
          model.to(_lowerCAmelCase		)
          model.eval()
          lowerCAmelCase__   :Optional[Any]      							=				model(_lowerCAmelCase		)
          lowerCAmelCase__   :Optional[Any]      							=				(self.image_size // self.patch_size) ** 2
          lowerCAmelCase__   :List[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
          lowerCAmelCase__   :List[Any]      							=				1
          lowerCAmelCase__   :Optional[Any]      							=				ViTMAEForPreTraining(_lowerCAmelCase		)
          model.to(_lowerCAmelCase		)
          model.eval()
          lowerCAmelCase__   :List[str]      							=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
          lowerCAmelCase__   :int      							=				model(_lowerCAmelCase		)
          lowerCAmelCase__   :Optional[int]      							=				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__   :Optional[Any]      							=				self.prepare_config_and_inputs()
          lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__   :Optional[Any]      							=				config_and_inputs
          lowerCAmelCase__   :List[Any]      							=				{"pixel_values": pixel_values}
          return config, inputs_dict







@require_torch
class 	_UpperCAmelCase  (  _A				,							_A				,							unittest.TestCase		):




      """simple docstring"""



      A          =    (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
      A          =    {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

      A          =    False
      A          =    False
      A          =    False
      A          =    False
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :str      							=				ViTMAEModelTester(self		)
          lowerCAmelCase__   :str      							=				ConfigTester(self    ,	config_class=_lowerCAmelCase    ,	has_text_modality=_lowerCAmelCase    ,	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__   :Optional[Any]      							=				self.model_tester.prepare_config_and_inputs_for_common()

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



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

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

              lowerCAmelCase__   :Optional[Any]      							=				["pixel_values"]
              self.assertListEqual(arg_names[:1]    ,	_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Any      							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[int]      							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*_lowerCAmelCase		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



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

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

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

          super().check_pt_tf_models(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__							,lowerCAmelCase__   :int      							=				self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

                  # Make sure we don't have nans
                  lowerCAmelCase__   :str      							=				after_outputs[0].cpu().numpy()
                  lowerCAmelCase__   :Tuple      							=				0
                  lowerCAmelCase__   :int      							=				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\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__   :Dict      							=				ViTMAEModel.from_pretrained(_lowerCAmelCase		)
              self.assertIsNotNone(_lowerCAmelCase		)





def 				snake_case__							(							):
    lowerCAmelCase__   :Any      							=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
    return image







@require_torch
@require_vision
class 	_UpperCAmelCase  (  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__   :Optional[int]      							=				ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"		).to(_lowerCAmelCase		)

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

          # forward pass
          with torch.no_grad():
              lowerCAmelCase__   :List[Any]      							=				model(**_lowerCAmelCase    ,	noise=torch.from_numpy(_lowerCAmelCase		).to(device=_lowerCAmelCase		)		)

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

          lowerCAmelCase__   :int      							=				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		)		)











from __future__ import annotations

import math
from collections.abc import Callable


def 				snake_case__							(							UpperCAmelCase							:  Callable[[int | float], int | float]    ,			UpperCAmelCase							:  int | float    ,			UpperCAmelCase							:  int | float    ,			UpperCAmelCase							:  int = 1_0_0    ,			):
    lowerCAmelCase__   :int      							=				x_start
    lowerCAmelCase__   :int      							=				fnc(UpperCAmelCase       )
    lowerCAmelCase__   :List[Any]      							=				0.0

    for _ in range(UpperCAmelCase       ):
        # Approximates curve as a sequence of linear lines and sums their length
        lowerCAmelCase__   :Dict      							=				(x_end - x_start) / steps + xa
        lowerCAmelCase__   :Optional[Any]      							=				fnc(UpperCAmelCase       )
        length += math.hypot(xa - xa    ,			fxa - fxa       )

        # Increment step
        lowerCAmelCase__   :Union[str, Any]      							=				xa
        lowerCAmelCase__   :str      							=				fxa

    return length


if __name__ == "__main__":


 def 				snake_case__							(							UpperCAmelCase							:  int       ):
     return math.sin(1_0 * x       )

 print("""f(x) = sin(10 * x)""")
 print("""The length of the curve from x = -10 to x = 10 is:""")
 _a    :   Any        =			10
 while i <= 10_0000:
  print(f"""With {i} steps: {line_length(f, -10, 10, i)}""")
  i *= 10



