





'''simple docstring'''



from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase=13						,   lowerCamelCase=30						,   lowerCamelCase=2						,   lowerCamelCase=3						,   lowerCamelCase=True						,   lowerCamelCase=True						,   lowerCamelCase=32						,   lowerCamelCase=2						,   lowerCamelCase=4						,   lowerCamelCase=37						,   lowerCamelCase="gelu"						,   lowerCamelCase=0.1						,   lowerCamelCase=0.1						,   lowerCamelCase=10						,   lowerCamelCase=0.02						,   lowerCamelCase=3						,   lowerCamelCase=0.6						,   lowerCamelCase=None						,   ):
       _snake_case     		=				parent
       _snake_case     		=				batch_size
       _snake_case     		=				image_size
       _snake_case     		=				patch_size
       _snake_case     		=				num_channels
       _snake_case     		=				is_training
       _snake_case     		=				use_labels
       _snake_case     		=				hidden_size
       _snake_case     		=				num_hidden_layers
       _snake_case     		=				num_attention_heads
       _snake_case     		=				intermediate_size
       _snake_case     		=				hidden_act
       _snake_case     		=				hidden_dropout_prob
       _snake_case     		=				attention_probs_dropout_prob
       _snake_case     		=				type_sequence_label_size
       _snake_case     		=				initializer_range
       _snake_case     		=				mask_ratio
       _snake_case     		=				scope

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

       _snake_case     		=				self.get_config()

       return config, pixel_values, labels

     def 	UpperCamelCase(					self	):
       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						,   decoder_hidden_size=self.hidden_size						,   decoder_num_hidden_layers=self.num_hidden_layers						,   decoder_num_attention_heads=self.num_attention_heads						,   decoder_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	):
       _snake_case     		=				TFViTMAEModel(config=lowerCamelCase	)
       _snake_case     		=				model(lowerCamelCase						,   training=lowerCamelCase	)
       self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, self.seq_length, self.hidden_size)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				TFViTMAEForPreTraining(lowerCamelCase	)
       _snake_case     		=				model(lowerCamelCase						,   training=lowerCamelCase	)
       # expected sequence length = num_patches
       _snake_case     		=				(self.image_size // self.patch_size) ** 2
       _snake_case     		=				self.patch_size**2 * self.num_channels
       self.parent.assertEqual(result.logits.shape						,   (self.batch_size, num_patches, expected_num_channels)	)

       # test greyscale images
       _snake_case     		=				1
       _snake_case     		=				TFViTMAEForPreTraining(lowerCamelCase	)

       _snake_case     		=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
       _snake_case     		=				model(lowerCamelCase						,   training=lowerCamelCase	)
       _snake_case     		=				self.patch_size**2
       self.parent.assertEqual(result.logits.shape						,   (self.batch_size, num_patches, expected_num_channels)	)






     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.prepare_config_and_inputs()
       ((_snake_case)		,		(_snake_case)		,		(_snake_case))     		=				config_and_inputs
       _snake_case     		=				{"pixel_values": pixel_values}
       return config, inputs_dict







@require_tf
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    __UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						int      =   (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
     UpperCAmelCase__       :						Dict      =   {'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

     UpperCAmelCase__       :						Union[str, Any]      =   False
     UpperCAmelCase__       :						int      =   False
     UpperCAmelCase__       :						Tuple      =   False
     UpperCAmelCase__       :						Any      =   False

     def 	UpperCamelCase(					self	):
       _snake_case     		=				TFViTMAEModelTester(self	)
       _snake_case     		=				ConfigTester(self						,   config_class=lowerCamelCase						,   has_text_modality=lowerCamelCase						,   hidden_size=37	)

     def 	UpperCamelCase(					self	):
       self.config_tester.run_common_tests()

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

     def 	UpperCamelCase(					self	):
       _snake_case		,		_snake_case     		=				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _snake_case     		=				model_class(lowerCamelCase	)
         self.assertIsInstance(model.get_input_embeddings()						,   (tf.keras.layers.Layer)	)
         _snake_case     		=				model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(lowerCamelCase						,   tf.keras.layers.Layer	)	)

     def 	UpperCamelCase(					self	):
       _snake_case		,		_snake_case     		=				self.model_tester.prepare_config_and_inputs_for_common()

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

         _snake_case     		=				["pixel_values"]
         self.assertListEqual(arg_names[:1]						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       # make the mask reproducible
       np.random.seed(2	)

       _snake_case		,		_snake_case     		=				self.model_tester.prepare_config_and_inputs_for_common()

       _snake_case     		=				int((config.image_size // config.patch_size) ** 2	)
       _snake_case     		=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

       for model_class in self.all_model_classes:
         _snake_case     		=				model_class(lowerCamelCase	)
         _snake_case     		=				self._prepare_for_class(lowerCamelCase						,   lowerCamelCase	)

         _snake_case     		=				model(lowerCamelCase						,   noise=lowerCamelCase	)

         _snake_case     		=				copy.deepcopy(self._prepare_for_class(lowerCamelCase						,   lowerCamelCase	)	)
         _snake_case     		=				model(**lowerCamelCase						,   noise=lowerCamelCase	)
         _snake_case     		=				outputs_dict[0].numpy()
         _snake_case     		=				outputs_keywords[0].numpy()

         self.assertLess(np.sum(np.abs(output_dict - output_keywords	)	)						,   1e-6	)

     def 	UpperCamelCase(					self	):
       # make the mask reproducible
       np.random.seed(2	)

       _snake_case		,		_snake_case     		=				self.model_tester.prepare_config_and_inputs_for_common()

       _snake_case     		=				int((config.image_size // config.patch_size) ** 2	)
       _snake_case     		=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

       def prepare_numpy_arrays(lowerCamelCase	):
         _snake_case     		=				{}
         for k, v in inputs_dict.items():
           if tf.is_tensor(lowerCamelCase	):
             _snake_case     		=				v.numpy()
           else:
             _snake_case     		=				np.array(lowerCamelCase	)

         return inputs_np_dict

       for model_class in self.all_model_classes:
         _snake_case     		=				model_class(lowerCamelCase	)

         _snake_case     		=				self._prepare_for_class(lowerCamelCase						,   lowerCamelCase	)
         _snake_case     		=				prepare_numpy_arrays(lowerCamelCase	)

         _snake_case     		=				model(lowerCamelCase						,   noise=lowerCamelCase	)
         _snake_case     		=				model(**lowerCamelCase						,   noise=lowerCamelCase	)
         self.assert_outputs_same(lowerCamelCase						,   lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       # make masks reproducible
       np.random.seed(2	)

       _snake_case     		=				int((tf_model.config.image_size // tf_model.config.patch_size) ** 2	)
       _snake_case     		=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)
       _snake_case     		=				tf.constant(lowerCamelCase	)

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

       super().check_pt_tf_models(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       # make mask reproducible
       np.random.seed(2	)

       _snake_case		,		_snake_case     		=				self.model_tester.prepare_config_and_inputs_for_common()

       _snake_case     		=				{
           module_member
           for model_class in self.all_model_classes
           for module in (import_module(model_class.__module__	),)
           for module_member_name in dir(lowerCamelCase	)
           if module_member_name.endswith("MainLayer"	)
           # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
           and module_member_name[: -len("MainLayer"	)] == model_class.__name__[: -len("Model"	)]
           for module_member in (getattr(lowerCamelCase						,   lowerCamelCase	),)
           if isinstance(lowerCamelCase						,   lowerCamelCase	)
           and tf.keras.layers.Layer in module_member.__bases__
           and getattr(lowerCamelCase						,   "_keras_serializable"						,   lowerCamelCase	)
       }

       _snake_case     		=				int((config.image_size // config.patch_size) ** 2	)
       _snake_case     		=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)
       _snake_case     		=				tf.convert_to_tensor(lowerCamelCase	)
       inputs_dict.update({"noise": noise}	)

       for main_layer_class in tf_main_layer_classes:
         _snake_case     		=				main_layer_class(lowerCamelCase	)

         _snake_case     		=				{
             name: tf.keras.Input(tensor.shape[1:]						,   dtype=tensor.dtype	) for name, tensor in inputs_dict.items()
         }

         _snake_case     		=				tf.keras.Model(lowerCamelCase						,   outputs=main_layer(lowerCamelCase	)	)
         _snake_case     		=				model(lowerCamelCase	)

         with tempfile.TemporaryDirectory() as tmpdirname:
           _snake_case     		=				os.path.join(lowerCamelCase						,   "keras_model.h5"	)
           model.save(lowerCamelCase	)
           _snake_case     		=				tf.keras.models.load_model(
               lowerCamelCase						,   custom_objects={main_layer_class.__name__: main_layer_class}	)
           assert isinstance(lowerCamelCase						,   tf.keras.Model	)
           _snake_case     		=				model(lowerCamelCase	)
           self.assert_outputs_same(lowerCamelCase						,   lowerCamelCase	)

     @slow
     def 	UpperCamelCase(					self	):
       # make mask reproducible
       np.random.seed(2	)

       _snake_case		,		_snake_case     		=				self.model_tester.prepare_config_and_inputs_for_common()

       _snake_case     		=				int((config.image_size // config.patch_size) ** 2	)
       _snake_case     		=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

       for model_class in self.all_model_classes:
         _snake_case     		=				model_class(lowerCamelCase	)
         _snake_case     		=				self._prepare_for_class(lowerCamelCase						,   lowerCamelCase	)
         _snake_case     		=				model(lowerCamelCase						,   noise=lowerCamelCase	)

         if model_class.__name__ == "TFViTMAEModel":
           _snake_case     		=				outputs.last_hidden_state.numpy()
           _snake_case     		=				0
         else:
           _snake_case     		=				outputs.logits.numpy()
           _snake_case     		=				0

         with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(lowerCamelCase						,   saved_model=lowerCamelCase	)
           _snake_case     		=				model_class.from_pretrained(lowerCamelCase	)
           _snake_case     		=				model(lowerCamelCase						,   noise=lowerCamelCase	)

           if model_class.__name__ == "TFViTMAEModel":
             _snake_case     		=				after_outputs["last_hidden_state"].numpy()
             _snake_case     		=				0
           else:
             _snake_case     		=				after_outputs["logits"].numpy()
             _snake_case     		=				0

           _snake_case     		=				np.amax(np.abs(out_a - out_a	)	)
           self.assertLessEqual(lowerCamelCase						,   1e-5	)

     def 	UpperCamelCase(					self	):
       # make mask reproducible
       np.random.seed(2	)

       _snake_case		,		_snake_case     		=				self.model_tester.prepare_config_and_inputs_for_common()

       _snake_case     		=				int((config.image_size // config.patch_size) ** 2	)
       _snake_case     		=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

       for model_class in self.all_model_classes:
         _snake_case     		=				model_class(lowerCamelCase	)
         _snake_case     		=				self._prepare_for_class(lowerCamelCase						,   lowerCamelCase	)

         _snake_case     		=				model(lowerCamelCase						,   noise=lowerCamelCase	)
         _snake_case     		=				model.get_config()
         # make sure that returned config is jsonifiable, which is required by keras
         json.dumps(lowerCamelCase	)
         _snake_case     		=				model_class.from_config(model.get_config()	)
         # make sure it also accepts a normal config
         _snake_case     		=				model_class.from_config(model.config	)
         _snake_case     		=				new_model(lowerCamelCase	)  # Build model
         new_model.set_weights(model.get_weights()	)
         _snake_case     		=				new_model(lowerCamelCase						,   noise=lowerCamelCase	)

         self.assert_outputs_same(lowerCamelCase						,   lowerCamelCase	)

     @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	):
       pass

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






     @slow
     def 	UpperCamelCase(					self	):
       _snake_case     		=				TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"	)
       self.assertIsNotNone(lowerCamelCase	)







def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
  return image




@require_tf
@require_vision
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






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






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

       _snake_case     		=				TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"	)

       _snake_case     		=				self.default_image_processor
       _snake_case     		=				prepare_img()
       _snake_case     		=				image_processor(images=lowerCamelCase						,   return_tensors="tf"	)

       # 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)
       _snake_case     		=				ViTMAEConfig()
       _snake_case     		=				int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2	)
       _snake_case     		=				np.random.uniform(size=(1, num_patches)	)

       # forward pass
       _snake_case     		=				model(**lowerCamelCase						,   noise=lowerCamelCase	)

       # verify the logits
       _snake_case     		=				tf.convert_to_tensor([1, 196, 768]	)
       self.assertEqual(outputs.logits.shape						,   lowerCamelCase	)

       _snake_case     		=				tf.convert_to_tensor(
           [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]	)

       tf.debugging.assert_near(outputs.logits[0, :3, :3]						,   lowerCamelCase						,   atol=1e-4	)









'''simple docstring'''



from __future__ import annotations

from PIL import Image

# Define glider example
__magic_name__   :						str    =			[
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
__magic_name__   :						List[Any]    =			[[0, 1, 0], [0, 1, 0], [0, 1, 0]]




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				[]
  for i in range(len(SCREAMING_SNAKE_CASE__       )       ):
    _snake_case     		=				[]
    for j in range(len(cells[i]       )       ):
      # Get the number of live neighbours
      _snake_case     		=				0
      if i > 0 and j > 0:
        neighbour_count += cells[i - 1][j - 1]
      if i > 0:
        neighbour_count += cells[i - 1][j]
      if i > 0 and j < len(cells[i]       ) - 1:
        neighbour_count += cells[i - 1][j + 1]
      if j > 0:
        neighbour_count += cells[i][j - 1]
      if j < len(cells[i]       ) - 1:
        neighbour_count += cells[i][j + 1]
      if i < len(SCREAMING_SNAKE_CASE__       ) - 1 and j > 0:
        neighbour_count += cells[i + 1][j - 1]
      if i < len(SCREAMING_SNAKE_CASE__       ) - 1:
        neighbour_count += cells[i + 1][j]
      if i < len(SCREAMING_SNAKE_CASE__       ) - 1 and j < len(cells[i]       ) - 1:
        neighbour_count += cells[i + 1][j + 1]

      # Rules of the game of life (excerpt from Wikipedia):
      # 1. Any live cell with two or three live neighbours survives.
      # 2. Any dead cell with three live neighbours becomes a live cell.
      # 3. All other live cells die in the next generation.
      #    Similarly, all other dead cells stay dead.
      _snake_case     		=				cells[i][j] == 1
      if (
          (alive and 2 <= neighbour_count <= 3)
          or not alive
          and neighbour_count == 3
      ):
        next_generation_row.append(1       )
      else:
        next_generation_row.append(0       )

    next_generation.append(SCREAMING_SNAKE_CASE__       )
  return next_generation




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				[]
  for _ in range(SCREAMING_SNAKE_CASE__       ):
    # Create output image
    _snake_case     		=				Image.new("RGB"					,	(len(cells[0]       ), len(SCREAMING_SNAKE_CASE__       ))       )
    _snake_case     		=				img.load()

    # Save cells to image
    for x in range(len(SCREAMING_SNAKE_CASE__       )       ):
      for y in range(len(cells[0]       )       ):
        _snake_case     		=				2_55 - cells[y][x] * 2_55
        _snake_case     		=				(colour, colour, colour)

        # Save image
    images.append(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				new_generation(SCREAMING_SNAKE_CASE__       )
  return images


if __name__ == "__main__":
 __magic_name__   :						Optional[Any]    =			generate_images(GLIDER, 16)
 images[0].save("""out.gif""", save_all=True, append_images=images[1:])


