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   snake_case_       :






   def __init__(      self				:     Optional[int]	,						lowercase_				:     Tuple	,						lowercase_				:     Dict=13	,						lowercase_				:     Optional[int]=30	,						lowercase_				:     Tuple=2	,						lowercase_				:     List[str]=3	,						lowercase_				:     str=True	,						lowercase_				:     Union[str, Any]=True	,						lowercase_				:     Dict=32	,						lowercase_				:     Optional[int]=2	,						lowercase_				:     int=4	,						lowercase_				:     Optional[Any]=37	,						lowercase_				:     Union[str, Any]="gelu"	,						lowercase_				:     Tuple=0.1	,						lowercase_				:     Optional[Any]=0.1	,						lowercase_				:     Any=10	,						lowercase_				:     Optional[int]=0.02	,						lowercase_				:     List[str]=3	,						lowercase_				:     Any=0.6	,						lowercase_				:     Tuple=None	,						)					->						List[str]:
       lowercase__ :   Dict    					=				parent
       lowercase__ :   Union[str, Any]    					=				batch_size
       lowercase__ :   Optional[Any]    					=				image_size
       lowercase__ :   Optional[int]    					=				patch_size
       lowercase__ :   List[str]    					=				num_channels
       lowercase__ :   Union[str, Any]    					=				is_training
       lowercase__ :   Optional[int]    					=				use_labels
       lowercase__ :   List[str]    					=				hidden_size
       lowercase__ :   List[Any]    					=				num_hidden_layers
       lowercase__ :   Tuple    					=				num_attention_heads
       lowercase__ :   List[Any]    					=				intermediate_size
       lowercase__ :   Dict    					=				hidden_act
       lowercase__ :   Any    					=				hidden_dropout_prob
       lowercase__ :   int    					=				attention_probs_dropout_prob
       lowercase__ :   Optional[int]    					=				type_sequence_label_size
       lowercase__ :   Dict    					=				initializer_range
       lowercase__ :   Union[str, Any]    					=				mask_ratio
       lowercase__ :   Optional[int]    					=				scope

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






   def       __UpperCamelCase     (      self				:     List[str]  )					->						Any:
       lowercase__ :   Tuple    					=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

       lowercase__ :   int    					=				self.get_config()

       return config, pixel_values, labels






   def       __UpperCamelCase     (      self				:     Optional[Any]  )					->						List[Any]:
       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=lowercase_	,						initializer_range=self.initializer_range	,						mask_ratio=self.mask_ratio	,						)






   def       __UpperCamelCase     (      self				:     Union[str, Any]	,						lowercase_				:     str	,						lowercase_				:     str	,						lowercase_				:     int  )					->						Any:
       lowercase__ :   Optional[int]    					=				TFViTMAEModel(config=lowercase_  )
       lowercase__ :   str    					=				model(lowercase_	,						training=lowercase_  )
       self.parent.assertEqual(result.last_hidden_state.shape	,						(self.batch_size, self.seq_length, self.hidden_size)  )






   def       __UpperCamelCase     (      self				:     Dict	,						lowercase_				:     Optional[int]	,						lowercase_				:     Optional[int]	,						lowercase_				:     Union[str, Any]  )					->						List[Any]:
       lowercase__ :   List[Any]    					=				TFViTMAEForPreTraining(lowercase_  )
       lowercase__ :   Dict    					=				model(lowercase_	,						training=lowercase_  )
       # expected sequence length = num_patches
       lowercase__ :   List[str]    					=				(self.image_size // self.patch_size) ** 2
       lowercase__ :   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
       lowercase__ :   Tuple    					=				1
       lowercase__ :   Union[str, Any]    					=				TFViTMAEForPreTraining(lowercase_  )

       lowercase__ :   int    					=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
       lowercase__ :   str    					=				model(lowercase_	,						training=lowercase_  )
       lowercase__ :   Dict    					=				self.patch_size**2
       self.parent.assertEqual(result.logits.shape	,						(self.batch_size, num_patches, expected_num_channels)  )







   def       __UpperCamelCase     (      self				:     Tuple  )					->						List[str]:
       lowercase__ :   Any    					=				self.prepare_config_and_inputs()
       ((lowercase__)					,   (lowercase__)					,   (lowercase__)) :   List[str]    					=				config_and_inputs
       lowercase__ :   str    					=				{"pixel_values": pixel_values}
       return config, inputs_dict



@require_tf
class   snake_case_       (		__A  ,__A  ,unittest.TestCase       ):
   __A	:		str         =				(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
   __A	:		List[Any]         =				{"feature-extraction": TFViTMAEModel} if is_tf_available() else {}

   __A	:		int         =				False
   __A	:		Optional[int]         =				False
   __A	:		Any         =				False
   __A	:		Union[str, Any]         =				False






   def       __UpperCamelCase     (      self				:     Tuple  )					->						str:
       lowercase__ :   Optional[Any]    					=				TFViTMAEModelTester(self  )
       lowercase__ :   Any    					=				ConfigTester(self	,						config_class=lowercase_	,						has_text_modality=lowercase_	,						hidden_size=37  )






   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						str:
       self.config_tester.run_common_tests()






   @unittest.skip(reason="ViTMAE does not use inputs_embeds"  )
   def       __UpperCamelCase     (      self				:     Any  )					->						Tuple:
       pass






   def       __UpperCamelCase     (      self				:     int  )					->						List[Any]:
       lowercase__					,   lowercase__ :   Tuple    					=				self.model_tester.prepare_config_and_inputs_for_common()

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






   def       __UpperCamelCase     (      self				:     Tuple  )					->						List[Any]:
       lowercase__					,   lowercase__ :   List[str]    					=				self.model_tester.prepare_config_and_inputs_for_common()

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

           lowercase__ :   List[str]    					=				["pixel_values"]
           self.assertListEqual(arg_names[:1]	,						lowercase_  )






   def       __UpperCamelCase     (      self				:     Optional[Any]  )					->						Tuple:
       lowercase__ :   str    					=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowercase_  )






   def       __UpperCamelCase     (      self				:     List[str]  )					->						List[str]:
       lowercase__ :   Tuple    					=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*lowercase_  )






   def       __UpperCamelCase     (      self				:     str  )					->						Dict:
       # make the mask reproducible
       np.random.seed(2  )

       lowercase__					,   lowercase__ :   Tuple    					=				self.model_tester.prepare_config_and_inputs_for_common()

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

       for model_class in self.all_model_classes:
           lowercase__ :   int    					=				model_class(lowercase_  )
           lowercase__ :   str    					=				self._prepare_for_class(lowercase_	,						lowercase_  )

           lowercase__ :   Dict    					=				model(lowercase_	,						noise=lowercase_  )

           lowercase__ :   Dict    					=				copy.deepcopy(self._prepare_for_class(lowercase_	,						lowercase_  )  )
           lowercase__ :   Union[str, Any]    					=				model(**lowercase_	,						noise=lowercase_  )
           lowercase__ :   str    					=				outputs_dict[0].numpy()
           lowercase__ :   Tuple    					=				outputs_keywords[0].numpy()

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






   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Any:
       # make the mask reproducible
       np.random.seed(2  )

       lowercase__					,   lowercase__ :   Dict    					=				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)  )

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

           return inputs_np_dict

       for model_class in self.all_model_classes:
           lowercase__ :   Dict    					=				model_class(lowercase_  )

           lowercase__ :   List[Any]    					=				self._prepare_for_class(lowercase_	,						lowercase_  )
           lowercase__ :   Any    					=				prepare_numpy_arrays(lowercase_  )

           lowercase__ :   List[Any]    					=				model(lowercase_	,						noise=lowercase_  )
           lowercase__ :   Any    					=				model(**lowercase_	,						noise=lowercase_  )
           self.assert_outputs_same(lowercase_	,						lowercase_  )






   def       __UpperCamelCase     (      self				:     Any	,						lowercase_				:     Any	,						lowercase_				:     List[str]	,						lowercase_				:     List[str]  )					->						Tuple:
       # make masks reproducible
       np.random.seed(2  )

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

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

       super().check_pt_tf_models(lowercase_	,						lowercase_	,						lowercase_  )






   def       __UpperCamelCase     (      self				:     str  )					->						Optional[Any]:
       # make mask reproducible
       np.random.seed(2  )

       lowercase__					,   lowercase__ :   int    					=				self.model_tester.prepare_config_and_inputs_for_common()

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

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

       for main_layer_class in tf_main_layer_classes:
           lowercase__ :   str    					=				main_layer_class(lowercase_  )

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

           lowercase__ :   Any    					=				tf.keras.Model(lowercase_	,						outputs=main_layer(lowercase_  )  )
           lowercase__ :   Union[str, Any]    					=				model(lowercase_  )

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






   @slow
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Optional[Any]:
       # make mask reproducible
       np.random.seed(2  )

       lowercase__					,   lowercase__ :   int    					=				self.model_tester.prepare_config_and_inputs_for_common()

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

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

           if model_class.__name__ == "TFViTMAEModel":
               lowercase__ :   str    					=				outputs.last_hidden_state.numpy()
               lowercase__ :   List[Any]    					=				0
           else:
               lowercase__ :   str    					=				outputs.logits.numpy()
               lowercase__ :   Any    					=				0

           with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_pretrained(lowercase_	,						saved_model=lowercase_  )
               lowercase__ :   Dict    					=				model_class.from_pretrained(lowercase_  )
               lowercase__ :   Optional[int]    					=				model(lowercase_	,						noise=lowercase_  )

               if model_class.__name__ == "TFViTMAEModel":
                   lowercase__ :   Optional[Any]    					=				after_outputs["last_hidden_state"].numpy()
                   lowercase__ :   str    					=				0
               else:
                   lowercase__ :   str    					=				after_outputs["logits"].numpy()
                   lowercase__ :   Dict    					=				0

               lowercase__ :   List[str]    					=				np.amax(np.abs(out_a - out_a  )  )
               self.assertLessEqual(lowercase_	,						1E-5  )






   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						Any:
       # make mask reproducible
       np.random.seed(2  )

       lowercase__					,   lowercase__ :   str    					=				self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

           self.assert_outputs_same(lowercase_	,						lowercase_  )






   @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				:     Dict  )					->						Tuple:
       pass






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







   @slow
   def       __UpperCamelCase     (      self				:     Dict  )					->						List[Any]:
       lowercase__ :   Tuple    					=				TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"  )
       self.assertIsNotNone(lowercase_  )



def 			lowercase_				(			):
    lowercase__ :   Union[str, Any]    					=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
    return image



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






   @cached_property
   def       __UpperCamelCase     (      self				:     List[str]  )					->						str:
       return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"  ) if is_vision_available() else None







   @slow
   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						Tuple:
       # make random mask reproducible across the PT and TF model
       np.random.seed(2  )

       lowercase__ :   int    					=				TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"  )

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

       # forward pass
       lowercase__ :   Tuple    					=				model(**lowercase_	,						noise=lowercase_  )

       # verify the logits
       lowercase__ :   Optional[Any]    					=				tf.convert_to_tensor([1, 1_96, 7_68]  )
       self.assertEqual(outputs.logits.shape	,						lowercase_  )

       lowercase__ :   str    					=				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]	,						lowercase_	,						atol=1E-4  )








'''simple docstring'''





def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise ValueError('''multiplicative_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''multiplicative_persistence() does not accept negative values''')

     _a      =  0
     _a      =  str(__A)

     while len(__A) != 1:
          _a      =  [int(__A) for i in num_string]

          _a      =  1
          for i in range(0  ,	len(__A)):
               total *= numbers[i]

          _a      =  str(__A)

          steps += 1
     return steps




def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise ValueError('''additive_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''additive_persistence() does not accept negative values''')

     _a      =  0
     _a      =  str(__A)

     while len(__A) != 1:
          _a      =  [int(__A) for i in num_string]

          _a      =  0
          for i in range(0  ,	len(__A)):
               total += numbers[i]

          _a      =  str(__A)

          steps += 1
     return steps


if __name__ == "__main__":
  import doctest

  doctest.testmod()



