


"""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 			__snake_case   :




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

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




							def   __a   (     self:    int   ):
										__lowerCamelCase          =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

										__lowerCamelCase          =      self.get_config()

										return config, pixel_values, labels




							def   __a   (     self:    Dict   ):
										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:    Tuple     ,							A_:    Optional[Any]     ,							A_:    List[Any]     ,							A_:    Any   ):
										__lowerCamelCase          =      TFViTMAEModel(config=A_   )
										__lowerCamelCase          =      model(A_     ,							training=A_   )
										self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)   )




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

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




							def   __a   (     self:    Dict   ):
										__lowerCamelCase          =      self.prepare_config_and_inputs()
										((__lowerCamelCase)     ,(__lowerCamelCase)     ,(__lowerCamelCase))          =      config_and_inputs
										__lowerCamelCase          =      {"""pixel_values""": pixel_values}
										return config, inputs_dict




@require_tf
class 			__snake_case   (lowerCamelCase			,		lowerCamelCase			,		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   __a   (     self:    Union[str, Any]   ):
										__lowerCamelCase          =      TFViTMAEModelTester(self   )
										__lowerCamelCase          =      ConfigTester(self     ,							config_class=A_     ,							has_text_modality=A_     ,							hidden_size=37   )




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




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




							def   __a   (     self:    Optional[int]   ):
										__lowerCamelCase     ,__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs_for_common()

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




							def   __a   (     self:    Tuple   ):
										__lowerCamelCase     ,__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs_for_common()

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

													__lowerCamelCase          =      ["""pixel_values"""]
													self.assertListEqual(arg_names[:1]     ,							A_   )




							def   __a   (     self:    Any   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_   )




							def   __a   (     self:    Dict   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_pretraining(*A_   )




							def   __a   (     self:    List[Any]   ):
										# make the mask reproducible
										np.random.seed(2   )

										__lowerCamelCase     ,__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs_for_common()

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

										for model_class in self.all_model_classes:
													__lowerCamelCase          =      model_class(A_   )
													__lowerCamelCase          =      self._prepare_for_class(A_     ,							A_   )

													__lowerCamelCase          =      model(A_     ,							noise=A_   )

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

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




							def   __a   (     self:    List[str]   ):
										# make the mask reproducible
										np.random.seed(2   )

										__lowerCamelCase     ,__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs_for_common()

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

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

													return inputs_np_dict

										for model_class in self.all_model_classes:
													__lowerCamelCase          =      model_class(A_   )

													__lowerCamelCase          =      self._prepare_for_class(A_     ,							A_   )
													__lowerCamelCase          =      prepare_numpy_arrays(A_   )

													__lowerCamelCase          =      model(A_     ,							noise=A_   )
													__lowerCamelCase          =      model(**A_     ,							noise=A_   )
													self.assert_outputs_same(A_     ,							A_   )




							def   __a   (     self:    Tuple     ,							A_:    Tuple     ,							A_:    List[str]     ,							A_:    Optional[int]   ):
										# make masks reproducible
										np.random.seed(2   )

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

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

										super().check_pt_tf_models(A_     ,							A_     ,							A_   )




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

										__lowerCamelCase     ,__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs_for_common()

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

										for main_layer_class in tf_main_layer_classes:
													__lowerCamelCase          =      main_layer_class(A_   )

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

													__lowerCamelCase          =      tf.keras.Model(A_     ,							outputs=main_layer(A_   )   )
													__lowerCamelCase          =      model(A_   )

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




							@slow
							def   __a   (     self:    List[str]   ):
										# make mask reproducible
										np.random.seed(2   )

										__lowerCamelCase     ,__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs_for_common()

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

										for model_class in self.all_model_classes:
													__lowerCamelCase          =      model_class(A_   )
													__lowerCamelCase          =      self._prepare_for_class(A_     ,							A_   )
													__lowerCamelCase          =      model(A_     ,							noise=A_   )

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

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

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

																__lowerCamelCase          =      np.amax(np.abs(out_a - out_a   )   )
																self.assertLessEqual(A_     ,							1E-5   )




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

										__lowerCamelCase     ,__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs_for_common()

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

										for model_class in self.all_model_classes:
													__lowerCamelCase          =      model_class(A_   )
													__lowerCamelCase          =      self._prepare_for_class(A_     ,							A_   )

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




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




							@slow
							def   __a   (     self:    List[str]   ):
										__lowerCamelCase          =      TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""   )
										self.assertIsNotNone(A_   )





def       a_ (	):
			__lowerCamelCase          =      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:    str   ):
										return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""   ) if is_vision_available() else None




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

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

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

										# forward pass
										__lowerCamelCase          =      model(**A_     ,							noise=A_   )

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

										__lowerCamelCase          =      tf.convert_to_tensor(
										    [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]   )

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








"""simple docstring"""




def       a_ (	lowercase__ :str     ):
			__lowerCamelCase          =      [int(lowercase__     ) for i in ip_va_address.split("""."""     ) if i.isdigit()]
			return len(lowercase__     ) == 4 and all(0 <= int(lowercase__     ) <= 254 for octet in octets     )


if __name__ == "__main__":
					__magic_name__						: Tuple    =				input().strip()
					__magic_name__						: Optional[int]    =				'valid' if is_ip_va_address_valid(ip) else 'invalid'
					print(f"""{ip} is a {valid_or_invalid} IP v4 address.""")




