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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   parent
     SCREAMING_SNAKE_CASE__    							=   batch_size
     SCREAMING_SNAKE_CASE__    							=   image_size
     SCREAMING_SNAKE_CASE__    							=   patch_size
     SCREAMING_SNAKE_CASE__    							=   num_channels
     SCREAMING_SNAKE_CASE__    							=   is_training
     SCREAMING_SNAKE_CASE__    							=   use_labels
     SCREAMING_SNAKE_CASE__    							=   hidden_size
     SCREAMING_SNAKE_CASE__    							=   num_hidden_layers
     SCREAMING_SNAKE_CASE__    							=   num_attention_heads
     SCREAMING_SNAKE_CASE__    							=   intermediate_size
     SCREAMING_SNAKE_CASE__    							=   hidden_act
     SCREAMING_SNAKE_CASE__    							=   hidden_dropout_prob
     SCREAMING_SNAKE_CASE__    							=   attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__    							=   type_sequence_label_size
     SCREAMING_SNAKE_CASE__    							=   initializer_range
     SCREAMING_SNAKE_CASE__    							=   mask_ratio
     SCREAMING_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)
     SCREAMING_SNAKE_CASE__    							=   (image_size // patch_size) ** 2
     SCREAMING_SNAKE_CASE__    							=   int(math.ceil((1 - mask_ratio) * (num_patches + 1)   )   )




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

     SCREAMING_SNAKE_CASE__    							=   self.get_config()

     return config, pixel_values, labels




   def 			__a		(					self    :       Union[str, 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=_lowercase ,			initializer_range=self.initializer_range ,			mask_ratio=self.mask_ratio ,			)




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       List[Any] ,			_lowercase    :       str ,			_lowercase    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   TFViTMAEModel(config=_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			training=_lowercase   )
     self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)   )




   def 			__a		(					self    :       str ,			_lowercase    :       int ,			_lowercase    :       Dict ,			_lowercase    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   TFViTMAEForPreTraining(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			training=_lowercase   )
     # expected sequence length = num_patches
     SCREAMING_SNAKE_CASE__    							=   (self.image_size // self.patch_size) ** 2
     SCREAMING_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
     SCREAMING_SNAKE_CASE__    							=   1
     SCREAMING_SNAKE_CASE__    							=   TFViTMAEForPreTraining(_lowercase   )

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




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






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







@require_tf
class __snake_case       (		lowerCamelCase_     ,	lowerCamelCase_     ,	unittest.TestCase	):
   lowerCAmelCase_														=							(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
   lowerCAmelCase_														=							{"feature-extraction": TFViTMAEModel} if is_tf_available() else {}

   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   TFViTMAEModelTester(self   )
     SCREAMING_SNAKE_CASE__    							=   ConfigTester(self ,			config_class=_lowercase ,			has_text_modality=_lowercase ,			hidden_size=37   )




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     self.config_tester.run_common_tests()




   @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""   )
   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     pass




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs_for_common()

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




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs_for_common()

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

       SCREAMING_SNAKE_CASE__    							=   ["""pixel_values"""]
       self.assertListEqual(arg_names[:1] ,			_lowercase   )




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowercase   )




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_pretraining(*_lowercase   )




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     np.random.seed(2   )

     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs_for_common()

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

     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE__    							=   model_class(_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   self._prepare_for_class(_lowercase ,			_lowercase   )

       SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			noise=_lowercase   )

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

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




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     np.random.seed(2   )

     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs_for_common()

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

     def prepare_numpy_arrays(_lowercase    :       List[str]   ):
       SCREAMING_SNAKE_CASE__    							=   {}
       for k, v in inputs_dict.items():
         if tf.is_tensor(_lowercase   ):
           SCREAMING_SNAKE_CASE__    							=   v.numpy()
         else:
           SCREAMING_SNAKE_CASE__    							=   np.array(_lowercase   )

       return inputs_np_dict

     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE__    							=   model_class(_lowercase   )

       SCREAMING_SNAKE_CASE__    							=   self._prepare_for_class(_lowercase ,			_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   prepare_numpy_arrays(_lowercase   )

       SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			noise=_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   model(**_lowercase ,			noise=_lowercase   )
       self.assert_outputs_same(_lowercase ,			_lowercase   )




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       Optional[Any] ,			_lowercase    :       int ,			_lowercase    :       Any   ):



     """simple docstring"""






     np.random.seed(2   )

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

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

     super().check_pt_tf_models(_lowercase ,			_lowercase ,			_lowercase   )




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     np.random.seed(2   )

     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_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(_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   )
     }

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

     for main_layer_class in tf_main_layer_classes:
       SCREAMING_SNAKE_CASE__    							=   main_layer_class(_lowercase   )

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

       SCREAMING_SNAKE_CASE__    							=   tf.keras.Model(_lowercase ,			outputs=main_layer(_lowercase   )   )
       SCREAMING_SNAKE_CASE__    							=   model(_lowercase   )

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




   @slow
   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     np.random.seed(2   )

     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs_for_common()

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

     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE__    							=   model_class(_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   self._prepare_for_class(_lowercase ,			_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			noise=_lowercase   )

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

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

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

         SCREAMING_SNAKE_CASE__    							=   np.amax(np.abs(out_a - out_a   )   )
         self.assertLessEqual(_lowercase ,			1E-5   )




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



     """simple docstring"""






     np.random.seed(2   )

     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs_for_common()

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

     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE__    							=   model_class(_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   self._prepare_for_class(_lowercase ,			_lowercase   )

       SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			noise=_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   model.get_config()
       # make sure that returned config is jsonifiable, which is required by keras
       json.dumps(_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   model_class.from_config(model.get_config()   )
       # make sure it also accepts a normal config
       SCREAMING_SNAKE_CASE__    							=   model_class.from_config(model.config   )
       SCREAMING_SNAKE_CASE__    							=   new_model(_lowercase   )  # Build model
       new_model.set_weights(model.get_weights()   )
       SCREAMING_SNAKE_CASE__    							=   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
    to get deterministic results."""   )
   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     pass




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



     """simple docstring"""






     pass




   @slow
   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""   )
     self.assertIsNotNone(_lowercase   )





def 			__SCREAMING_SNAKE_CASE						(      )							->      Tuple:







  """simple docstring"""





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







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




   @cached_property
   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






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




   @slow
   def 			__a		(					self    :       str   ):



     """simple docstring"""






     np.random.seed(2   )

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

     SCREAMING_SNAKE_CASE__    							=   self.default_image_processor
     SCREAMING_SNAKE_CASE__    							=   prepare_img()
     SCREAMING_SNAKE_CASE__    							=   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)
     SCREAMING_SNAKE_CASE__    							=   ViTMAEConfig()
     SCREAMING_SNAKE_CASE__    							=   int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2   )
     SCREAMING_SNAKE_CASE__    							=   np.random.uniform(size=(1, num_patches)   )

     # forward pass
     SCREAMING_SNAKE_CASE__    							=   model(**_lowercase ,			noise=_lowercase   )

     # verify the logits
     SCREAMING_SNAKE_CASE__    							=   tf.convert_to_tensor([1, 1_96, 7_68]   )
     self.assertEqual(outputs.logits.shape ,			_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   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   )

from math import isqrt



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int			)							->      bool:







  """simple docstring"""





  return all(number % divisor != 0 for divisor in range(2				,  isqrt(__UpperCamelCase			) + 1			)			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int = 10**6			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   0
  SCREAMING_SNAKE_CASE__    							=   1
  SCREAMING_SNAKE_CASE__    							=   7
  while prime_candidate < max_prime:
    primes_count += is_prime(__UpperCamelCase			)

    cube_index += 1
    prime_candidate += 6 * cube_index

  return primes_count


if __name__ == "__main__":
   print(F"""{solution() = }""")
