"""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							,	a__							,	a__=13							,	a__=30							,	a__=2							,	a__=3							,	a__=True							,	a__=True							,	a__=32							,	a__=2							,	a__=4							,	a__=37							,	a__="gelu"							,	a__=0.1							,	a__=0.1							,	a__=10							,	a__=0.0_2							,	a__=3							,	a__=0.6							,	a__=None							,	):
       _lowerCAmelCase		:			List[str]         =	parent
       _lowerCAmelCase		:			List[str]         =	batch_size
       _lowerCAmelCase		:			Any         =	image_size
       _lowerCAmelCase		:			str         =	patch_size
       _lowerCAmelCase		:			Optional[Any]         =	num_channels
       _lowerCAmelCase		:			Tuple         =	is_training
       _lowerCAmelCase		:			List[Any]         =	use_labels
       _lowerCAmelCase		:			Union[str, Any]         =	hidden_size
       _lowerCAmelCase		:			Any         =	num_hidden_layers
       _lowerCAmelCase		:			List[str]         =	num_attention_heads
       _lowerCAmelCase		:			Union[str, Any]         =	intermediate_size
       _lowerCAmelCase		:			Dict         =	hidden_act
       _lowerCAmelCase		:			int         =	hidden_dropout_prob
       _lowerCAmelCase		:			str         =	attention_probs_dropout_prob
       _lowerCAmelCase		:			Optional[Any]         =	type_sequence_label_size
       _lowerCAmelCase		:			Optional[Any]         =	initializer_range
       _lowerCAmelCase		:			Union[str, Any]         =	mask_ratio
       _lowerCAmelCase		:			Tuple         =	scope

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






     def        __A    (						self    ):
       _lowerCAmelCase		:			Tuple         =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

       _lowerCAmelCase		:			Any         =	self.get_config()

       return config, pixel_values, labels






     def        __A    (						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=a__							,	initializer_range=self.initializer_range							,	mask_ratio=self.mask_ratio							,	)






     def        __A    (						self							,	a__							,	a__							,	a__    ):
       _lowerCAmelCase		:			Optional[Any]         =	TFViTMAEModel(config=a__    )
       _lowerCAmelCase		:			List[Any]         =	model(a__							,	training=a__    )
       self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, self.seq_length, self.hidden_size)    )






     def        __A    (						self							,	a__							,	a__							,	a__    ):
       _lowerCAmelCase		:			Optional[int]         =	TFViTMAEForPreTraining(a__    )
       _lowerCAmelCase		:			List[Any]         =	model(a__							,	training=a__    )
       # expected sequence length = num_patches
       _lowerCAmelCase		:			List[str]         =	(self.image_size // self.patch_size) ** 2
       _lowerCAmelCase		:			int         =	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		:			int         =	1
       _lowerCAmelCase		:			Optional[Any]         =	TFViTMAEForPreTraining(a__    )

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






     def        __A    (						self    ):
       _lowerCAmelCase		:			int         =	self.prepare_config_and_inputs()
       (_lowerCAmelCase)		:			Union[str, Any]         =	config_and_inputs
       _lowerCAmelCase		:			Tuple         =	{'''pixel_values''': pixel_values}
       return config, inputs_dict







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

     _UpperCamelCase				:     List[str]						   =						False
     _UpperCamelCase				:     Tuple						   =						False
     _UpperCamelCase				:     Optional[int]						   =						False
     _UpperCamelCase				:     Tuple						   =						False






     def        __A    (						self    ):
       _lowerCAmelCase		:			List[Any]         =	TFViTMAEModelTester(self    )
       _lowerCAmelCase		:			Tuple         =	ConfigTester(self							,	config_class=a__							,	has_text_modality=a__							,	hidden_size=37    )






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






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






     def        __A    (						self    ):
       _lowerCAmelCase		:			Optional[Any]         =	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _lowerCAmelCase		:			Dict         =	model_class(a__    )
         self.assertIsInstance(model.get_input_embeddings()							,	(tf.keras.layers.Layer)    )
         _lowerCAmelCase		:			Optional[Any]         =	model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(a__							,	tf.keras.layers.Layer    )    )






     def        __A    (						self    ):
       _lowerCAmelCase		:			Dict         =	self.model_tester.prepare_config_and_inputs_for_common()

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

         _lowerCAmelCase		:			List[Any]         =	['''pixel_values''']
         self.assertListEqual(arg_names[:1]							,	a__    )






     def        __A    (						self    ):
       _lowerCAmelCase		:			Dict         =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a__    )






     def        __A    (						self    ):
       _lowerCAmelCase		:			Tuple         =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*a__    )






     def        __A    (						self    ):
       np.random.seed(2    )

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

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

       for model_class in self.all_model_classes:
         _lowerCAmelCase		:			List[Any]         =	model_class(a__    )
         _lowerCAmelCase		:			Optional[Any]         =	self._prepare_for_class(a__							,	a__    )

         _lowerCAmelCase		:			Dict         =	model(a__							,	noise=a__    )

         _lowerCAmelCase		:			Optional[int]         =	copy.deepcopy(self._prepare_for_class(a__							,	a__    )    )
         _lowerCAmelCase		:			List[Any]         =	model(**a__							,	noise=a__    )
         _lowerCAmelCase		:			List[Any]         =	outputs_dict[0].numpy()
         _lowerCAmelCase		:			Dict         =	outputs_keywords[0].numpy()

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






     def        __A    (						self    ):
       np.random.seed(2    )

       _lowerCAmelCase		:			int         =	self.model_tester.prepare_config_and_inputs_for_common()

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

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

         return inputs_np_dict

       for model_class in self.all_model_classes:
         _lowerCAmelCase		:			List[str]         =	model_class(a__    )

         _lowerCAmelCase		:			Any         =	self._prepare_for_class(a__							,	a__    )
         _lowerCAmelCase		:			List[Any]         =	prepare_numpy_arrays(a__    )

         _lowerCAmelCase		:			str         =	model(a__							,	noise=a__    )
         _lowerCAmelCase		:			List[str]         =	model(**a__							,	noise=a__    )
         self.assert_outputs_same(a__							,	a__    )






     def        __A    (						self							,	a__							,	a__							,	a__    ):
       np.random.seed(2    )

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

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

       super().check_pt_tf_models(a__							,	a__							,	a__    )






     def        __A    (						self    ):
       np.random.seed(2    )

       _lowerCAmelCase		:			Any         =	self.model_tester.prepare_config_and_inputs_for_common()

       _lowerCAmelCase		:			Optional[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(a__    )
           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(a__							,	a__    ),)
           if isinstance(a__							,	a__    )
           and tf.keras.layers.Layer in module_member.__bases__
           and getattr(a__							,	"""_keras_serializable"""							,	a__    )
       }

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

       for main_layer_class in tf_main_layer_classes:
         _lowerCAmelCase		:			List[str]         =	main_layer_class(a__    )

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

         _lowerCAmelCase		:			str         =	tf.keras.Model(a__							,	outputs=main_layer(a__    )    )
         _lowerCAmelCase		:			Any         =	model(a__    )

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






     @slow
     def        __A    (						self    ):
       np.random.seed(2    )

       _lowerCAmelCase		:			Any         =	self.model_tester.prepare_config_and_inputs_for_common()

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

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

         if model_class.__name__ == "TFViTMAEModel":
           _lowerCAmelCase		:			Dict         =	outputs.last_hidden_state.numpy()
           _lowerCAmelCase		:			Optional[int]         =	0
         else:
           _lowerCAmelCase		:			Dict         =	outputs.logits.numpy()
           _lowerCAmelCase		:			Optional[Any]         =	0

         with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(a__							,	saved_model=a__    )
           _lowerCAmelCase		:			Tuple         =	model_class.from_pretrained(a__    )
           _lowerCAmelCase		:			Dict         =	model(a__							,	noise=a__    )

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

           _lowerCAmelCase		:			Any         =	np.amax(np.abs(out_a - out_a    )    )
           self.assertLessEqual(a__							,	1e-5    )






     def        __A    (						self    ):
       np.random.seed(2    )

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

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

       for model_class in self.all_model_classes:
         _lowerCAmelCase		:			Dict         =	model_class(a__    )
         _lowerCAmelCase		:			Optional[Any]         =	self._prepare_for_class(a__							,	a__    )

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

         self.assert_outputs_same(a__							,	a__    )






     @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    ):
       pass






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






     @slow
     def        __A    (						self    ):
       _lowerCAmelCase		:			int         =	TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""    )
       self.assertIsNotNone(a__    )







def 				SCREAMING_SNAKE_CASE	(     )			-> Any:
  _lowerCAmelCase		:			Any         =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
  return image






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






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






     @slow
     def        __A    (						self    ):
       np.random.seed(2    )

       _lowerCAmelCase		:			List[Any]         =	TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""    )

       _lowerCAmelCase		:			Tuple         =	self.default_image_processor
       _lowerCAmelCase		:			int         =	prepare_img()
       _lowerCAmelCase		:			Optional[Any]         =	image_processor(images=a__							,	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)
       _lowerCAmelCase		:			str         =	ViTMAEConfig()
       _lowerCAmelCase		:			Union[str, Any]         =	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
       _lowerCAmelCase		:			Optional[int]         =	model(**a__							,	noise=a__    )

       # verify the logits
       _lowerCAmelCase		:			int         =	tf.convert_to_tensor([1, 196, 768]    )
       self.assertEqual(outputs.logits.shape							,	a__    )

       _lowerCAmelCase		:			Optional[Any]         =	tf.convert_to_tensor(
           [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]    )

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











from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase      							=   logging.get_logger(__name__)

_lowerCamelCase      							=   {
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   """vit_msn"""


      def __init__(		self   ,				a__=768   ,				a__=12   ,				a__=12   ,				a__=3072   ,				a__="gelu"   ,				a__=0.0   ,				a__=0.0   ,				a__=0.02   ,				a__=1e-06   ,				a__=224   ,				a__=16   ,				a__=3   ,				a__=True   ,				**a__   ,				):



             """simple docstring"""





             super().__init__(**a__)

             _lowerCamelCase				:  Tuple						=		hidden_size
             _lowerCamelCase				:  str						=		num_hidden_layers
             _lowerCamelCase				:  int						=		num_attention_heads
             _lowerCamelCase				:  Optional[int]						=		intermediate_size
             _lowerCamelCase				:  Union[str, Any]						=		hidden_act
             _lowerCamelCase				:  str						=		hidden_dropout_prob
             _lowerCamelCase				:  List[Any]						=		attention_probs_dropout_prob
             _lowerCamelCase				:  Optional[int]						=		initializer_range
             _lowerCamelCase				:  Optional[Any]						=		layer_norm_eps
             _lowerCamelCase				:  Dict						=		image_size
             _lowerCamelCase				:  Tuple						=		patch_size
             _lowerCamelCase				:  List[str]						=		num_channels
             _lowerCamelCase				:  Optional[int]						=		qkv_bias

