





'''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 				a_       :





     def __init__(			self		: Optional[int]     ,				lowercase		: Optional[int]     ,				lowercase		: List[Any]=13     ,				lowercase		: Union[str, Any]=30     ,				lowercase		: Tuple=2     ,				lowercase		: Any=3     ,				lowercase		: int=True     ,				lowercase		: Union[str, Any]=True     ,				lowercase		: List[str]=32     ,				lowercase		: Optional[int]=2     ,				lowercase		: Union[str, Any]=4     ,				lowercase		: Optional[Any]=37     ,				lowercase		: Union[str, Any]="gelu"     ,				lowercase		: List[Any]=0.1     ,				lowercase		: Any=0.1     ,				lowercase		: str=10     ,				lowercase		: List[str]=0.02     ,				lowercase		: int=3     ,				lowercase		: Optional[int]=0.6     ,				lowercase		: Optional[Any]=None     ,				):





        """simple docstring"""




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

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





     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :Tuple         =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

        lowercase_      :int         =			None
        if self.use_labels:
           lowercase_      :Dict         =			ids_tensor([self.batch_size]     ,				self.type_sequence_label_size						)

        lowercase_      :Dict         =			self.get_config()

        return config, pixel_values, labels





     def 							lowercase__				(			self		: List[Any]						):





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





     def 							lowercase__				(			self		: str     ,				lowercase		: Optional[Any]     ,				lowercase		: Union[str, Any]     ,				lowercase		: str						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			TFViTMAEModel(config=snake_case__						)
        lowercase_      :List[Any]         =			model(snake_case__     ,				training=snake_case__						)
        self.parent.assertEqual(result.last_hidden_state.shape     ,				(self.batch_size, self.seq_length, self.hidden_size)						)





     def 							lowercase__				(			self		: Tuple     ,				lowercase		: Optional[int]     ,				lowercase		: List[Any]     ,				lowercase		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			TFViTMAEForPreTraining(snake_case__						)
        lowercase_      :Union[str, Any]         =			model(snake_case__     ,				training=snake_case__						)
        # expected sequence length = num_patches
        lowercase_      :int         =			(self.image_size // self.patch_size) ** 2
        lowercase_      :Dict         =			self.patch_size**2 * self.num_channels
        self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, num_patches, expected_num_channels)						)

        # test greyscale images
        lowercase_      :Tuple         =			1
        lowercase_      :Any         =			TFViTMAEForPreTraining(snake_case__						)

        lowercase_      :List[Any]         =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
        lowercase_      :Dict         =			model(snake_case__     ,				training=snake_case__						)
        lowercase_      :Optional[Any]         =			self.patch_size**2
        self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, num_patches, expected_num_channels)						)







     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        lowercase_      :Tuple         =			self.prepare_config_and_inputs()
        (lowercase_)      :List[str]         =			config_and_inputs
        lowercase_      :int         =			{'pixel_values': pixel_values}
        return config, inputs_dict







@require_tf
class 				a_       (    SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						unittest.TestCase							):
     __A								=      (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
     __A								=      {"feature-extraction": TFViTMAEModel} if is_tf_available() else {}

     __A								=      False
     __A								=      False
     __A								=      False
     __A								=      False





     def 							lowercase__				(			self		: Union[str, Any]						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			TFViTMAEModelTester(self						)
        lowercase_      :Tuple         =			ConfigTester(self     ,				config_class=snake_case__     ,				has_text_modality=snake_case__     ,				hidden_size=37						)





     def 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




        self.config_tester.run_common_tests()





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





        """simple docstring"""




        pass





     def 							lowercase__				(			self		: Union[str, Any]						):





        """simple docstring"""




        lowercase_      :List[Any]         =			self.model_tester.prepare_config_and_inputs_for_common()

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





     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        lowercase_      :Optional[Any]         =			self.model_tester.prepare_config_and_inputs_for_common()

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

           lowercase_      :Optional[int]         =			['pixel_values']
           self.assertListEqual(arg_names[:1]     ,				snake_case__						)





     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :Optional[Any]         =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case__						)





     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        lowercase_      :str         =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*snake_case__						)





     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        np.random.seed(2						)

        lowercase_      :Any         =			self.model_tester.prepare_config_and_inputs_for_common()

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

        for model_class in self.all_model_classes:
           lowercase_      :Optional[int]         =			model_class(snake_case__						)
           lowercase_      :Union[str, Any]         =			self._prepare_for_class(snake_case__     ,				snake_case__						)

           lowercase_      :Optional[int]         =			model(snake_case__     ,				noise=snake_case__						)

           lowercase_      :int         =			copy.deepcopy(self._prepare_for_class(snake_case__     ,				snake_case__						)						)
           lowercase_      :int         =			model(**snake_case__     ,				noise=snake_case__						)
           lowercase_      :str         =			outputs_dict[0].numpy()
           lowercase_      :int         =			outputs_keywords[0].numpy()

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





     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        np.random.seed(2						)

        lowercase_      :int         =			self.model_tester.prepare_config_and_inputs_for_common()

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

        def prepare_numpy_arrays(lowercase		: Optional[int]						):
           lowercase_      :Union[str, Any]         =			{}
           for k, v in inputs_dict.items():
              if tf.is_tensor(snake_case__						):
                 lowercase_      :Tuple         =			v.numpy()
              else:
                 lowercase_      :int         =			np.array(snake_case__						)

           return inputs_np_dict

        for model_class in self.all_model_classes:
           lowercase_      :List[Any]         =			model_class(snake_case__						)

           lowercase_      :Optional[int]         =			self._prepare_for_class(snake_case__     ,				snake_case__						)
           lowercase_      :int         =			prepare_numpy_arrays(snake_case__						)

           lowercase_      :Tuple         =			model(snake_case__     ,				noise=snake_case__						)
           lowercase_      :Optional[int]         =			model(**snake_case__     ,				noise=snake_case__						)
           self.assert_outputs_same(snake_case__     ,				snake_case__						)





     def 							lowercase__				(			self		: str     ,				lowercase		: Optional[Any]     ,				lowercase		: Any     ,				lowercase		: str						):





        """simple docstring"""




        np.random.seed(2						)

        lowercase_      :int         =			int((tf_model.config.image_size // tf_model.config.patch_size) ** 2						)
        lowercase_      :Optional[Any]         =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)
        lowercase_      :Optional[int]         =			tf.constant(snake_case__						)

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

        super().check_pt_tf_models(snake_case__     ,				snake_case__     ,				snake_case__						)





     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        np.random.seed(2						)

        lowercase_      :int         =			self.model_tester.prepare_config_and_inputs_for_common()

        lowercase_      :int         =			{
            module_member
            for model_class in self.all_model_classes
            for module in (import_module(model_class.__module__						),)
            for module_member_name in dir(snake_case__						)
            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(snake_case__     ,				snake_case__						),)
            if isinstance(snake_case__     ,				snake_case__						)
            and tf.keras.layers.Layer in module_member.__bases__
            and getattr(snake_case__     ,				"_keras_serializable"     ,				snake_case__						)
        }

        lowercase_      :List[Any]         =			int((config.image_size // config.patch_size) ** 2						)
        lowercase_      :Optional[Any]         =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)
        lowercase_      :str         =			tf.convert_to_tensor(snake_case__						)
        inputs_dict.update({"noise": noise}						)

        for main_layer_class in tf_main_layer_classes:
           lowercase_      :List[Any]         =			main_layer_class(snake_case__						)

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

           lowercase_      :List[Any]         =			tf.keras.Model(snake_case__     ,				outputs=main_layer(snake_case__						)						)
           lowercase_      :Tuple         =			model(snake_case__						)

           with tempfile.TemporaryDirectory() as tmpdirname:
              lowercase_      :str         =			os.path.join(snake_case__     ,				"keras_model.h5"						)
              model.save(snake_case__						)
              lowercase_      :Optional[int]         =			tf.keras.models.load_model(
                  snake_case__     ,				custom_objects={main_layer_class.__name__: main_layer_class}						)
              assert isinstance(snake_case__     ,				tf.keras.Model						)
              lowercase_      :List[str]         =			model(snake_case__						)
              self.assert_outputs_same(snake_case__     ,				snake_case__						)





     @slow
     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        np.random.seed(2						)

        lowercase_      :Optional[Any]         =			self.model_tester.prepare_config_and_inputs_for_common()

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

        for model_class in self.all_model_classes:
           lowercase_      :List[str]         =			model_class(snake_case__						)
           lowercase_      :str         =			self._prepare_for_class(snake_case__     ,				snake_case__						)
           lowercase_      :str         =			model(snake_case__     ,				noise=snake_case__						)

           if model_class.__name__ == "TFViTMAEModel":
              lowercase_      :Tuple         =			outputs.last_hidden_state.numpy()
              lowercase_      :int         =			0
           else:
              lowercase_      :Optional[int]         =			outputs.logits.numpy()
              lowercase_      :Union[str, Any]         =			0

           with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_pretrained(snake_case__     ,				saved_model=snake_case__						)
              lowercase_      :List[Any]         =			model_class.from_pretrained(snake_case__						)
              lowercase_      :List[Any]         =			model(snake_case__     ,				noise=snake_case__						)

              if model_class.__name__ == "TFViTMAEModel":
                 lowercase_      :int         =			after_outputs['last_hidden_state'].numpy()
                 lowercase_      :Dict         =			0
              else:
                 lowercase_      :Tuple         =			after_outputs['logits'].numpy()
                 lowercase_      :List[Any]         =			0

              lowercase_      :Optional[Any]         =			np.amax(np.abs(out_a - out_a						)						)
              self.assertLessEqual(snake_case__     ,				1e-5						)





     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        np.random.seed(2						)

        lowercase_      :Any         =			self.model_tester.prepare_config_and_inputs_for_common()

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

        for model_class in self.all_model_classes:
           lowercase_      :List[str]         =			model_class(snake_case__						)
           lowercase_      :Optional[Any]         =			self._prepare_for_class(snake_case__     ,				snake_case__						)

           lowercase_      :Tuple         =			model(snake_case__     ,				noise=snake_case__						)
           lowercase_      :Union[str, Any]         =			model.get_config()
           # make sure that returned config is jsonifiable, which is required by keras
           json.dumps(snake_case__						)
           lowercase_      :int         =			model_class.from_config(model.get_config()						)
           # make sure it also accepts a normal config
           lowercase_      :str         =			model_class.from_config(model.config						)
           lowercase_      :int         =			new_model(snake_case__						)  # Build model
           new_model.set_weights(model.get_weights()						)
           lowercase_      :List[str]         =			new_model(snake_case__     ,				noise=snake_case__						)

           self.assert_outputs_same(snake_case__     ,				snake_case__						)





     @unittest.skip(
         reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."						)
     def 							lowercase__				(			self		: Union[str, Any]						):





        """simple docstring"""




        pass





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





        """simple docstring"""




        pass







     @slow
     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        lowercase_      :Tuple         =			TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"						)
        self.assertIsNotNone(snake_case__						)







def 							UpperCAmelCase_  (						):
   lowercase_      :Tuple         =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
   return image


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





     @cached_property
     def 							lowercase__				(			self		: str						):





        """simple docstring"""




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







     @slow
     def 							lowercase__				(			self		: Union[str, Any]						):





        """simple docstring"""




        np.random.seed(2						)

        lowercase_      :Optional[int]         =			TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"						)

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

        # forward pass
        lowercase_      :Dict         =			model(**snake_case__     ,				noise=snake_case__						)

        # verify the logits
        lowercase_      :Tuple         =			tf.convert_to_tensor([1, 196, 768]						)
        self.assertEqual(outputs.logits.shape     ,				snake_case__						)

        lowercase_      :int         =			tf.convert_to_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]]						)

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









'''simple docstring'''


def 							UpperCAmelCase_  (						__lowerCamelCase : int | float | str			):
   try:
      lowercase_      :Optional[int]         =			float(__lowerCamelCase			)
   except ValueError:
      raise ValueError("Please enter a valid number"			)
   lowercase_      :Dict         =			decimal - int(__lowerCamelCase			)
   if fractional_part == 0:
      return int(__lowerCamelCase			), 1
   else:
      lowercase_      :Tuple         =			len(str(__lowerCamelCase			).split("."			)[1]			)
      lowercase_      :Optional[Any]         =			int(decimal * (10**number_of_frac_digits)			)
      lowercase_      :Dict         =			10**number_of_frac_digits
      lowercase_      ,							lowercase_      :Optional[int]         =			denominator, numerator
      while True:
         lowercase_      :Any         =			dividend % divisor
         if remainder == 0:
            break
         lowercase_      ,							lowercase_      :Optional[int]         =			divisor, remainder
      lowercase_      ,							lowercase_      :Optional[int]         =			numerator / divisor, denominator / divisor
      return int(__lowerCamelCase			), int(__lowerCamelCase			)


if __name__ == "__main__":
 print(F'''{decimal_to_fraction(2) = }''')
 print(F'''{decimal_to_fraction(89.0) = }''')
 print(F'''{decimal_to_fraction('67') = }''')
 print(F'''{decimal_to_fraction('45.0') = }''')
 print(F'''{decimal_to_fraction(1.5) = }''')
 print(F'''{decimal_to_fraction('6.25') = }''')
 print(F'''{decimal_to_fraction('78td') = }''')


