




import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import ViTMAEForPreTraining, ViTMAEModel
			from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor







class a :
						"""simple docstring"""

						def __init__( self :     str				,							lowerCamelCase__ :     Optional[int]				,							lowerCamelCase__ :     List[str]=13				,							lowerCamelCase__ :     Union[str, Any]=30				,							lowerCamelCase__ :     int=2				,							lowerCamelCase__ :     str=3				,							lowerCamelCase__ :     Any=True				,							lowerCamelCase__ :     str=True				,							lowerCamelCase__ :     Any=32				,							lowerCamelCase__ :     str=5				,							lowerCamelCase__ :     List[str]=4				,							lowerCamelCase__ :     List[str]=37				,							lowerCamelCase__ :     Optional[Any]="gelu"				,							lowerCamelCase__ :     int=0.1				,							lowerCamelCase__ :     Union[str, Any]=0.1				,							lowerCamelCase__ :     List[str]=10				,							lowerCamelCase__ :     int=0.0_2				,							lowerCamelCase__ :     Tuple=3				,							lowerCamelCase__ :     Dict=0.6				,							lowerCamelCase__ :     str=None				,							)    ->						str:
													"""simple docstring"""


													__lowercase      =    parent
													__lowercase      =    batch_size
													__lowercase      =    image_size
													__lowercase      =    patch_size
													__lowercase      =    num_channels
													__lowercase      =    is_training
													__lowercase      =    use_labels
													__lowercase      =    hidden_size
													__lowercase      =    num_hidden_layers
													__lowercase      =    num_attention_heads
													__lowercase      =    intermediate_size
													__lowercase      =    hidden_act
													__lowercase      =    hidden_dropout_prob
													__lowercase      =    attention_probs_dropout_prob
													__lowercase      =    type_sequence_label_size
													__lowercase      =    initializer_range
													__lowercase      =    mask_ratio
													__lowercase      =    scope

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






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Optional[int]:
													"""simple docstring"""


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

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

													__lowercase      =    self.get_config()

													return config, pixel_values, labels






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						str:
													"""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				,							hidden_act=self.hidden_act				,							hidden_dropout_prob=self.hidden_dropout_prob				,							attention_probs_dropout_prob=self.attention_probs_dropout_prob				,							is_decoder=lowerCamelCase__				,							initializer_range=self.initializer_range				,							mask_ratio=self.mask_ratio				,							)






						def     UpperCAmelCase_      ( self :     str				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     Tuple				,							lowerCamelCase__ :     Tuple				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    ViTMAEModel(config=lowerCamelCase__				)
													model.to(lowerCamelCase__				)
													model.eval()
													__lowercase      =    model(lowerCamelCase__				)
													self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size)				)






						def     UpperCAmelCase_      ( self :     Dict				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     List[Any]				,							lowerCamelCase__ :     int				)    ->						str:
													"""simple docstring"""


													__lowercase      =    ViTMAEForPreTraining(lowerCamelCase__				)
													model.to(lowerCamelCase__				)
													model.eval()
													__lowercase      =    model(lowerCamelCase__				)
													__lowercase      =    (self.image_size // self.patch_size) ** 2
													__lowercase      =    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      =    1
													__lowercase      =    ViTMAEForPreTraining(lowerCamelCase__				)
													model.to(lowerCamelCase__				)
													model.eval()
													__lowercase      =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
													__lowercase      =    model(lowerCamelCase__				)
													__lowercase      =    self.patch_size**2
													self.parent.assertEqual(result.logits.shape				,							(self.batch_size, num_patches, expected_num_channels)				)






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    self.prepare_config_and_inputs()
													__lowercase     ,     __lowercase     ,     __lowercase      =    config_and_inputs
													__lowercase      =    {'''pixel_values''': pixel_values}
													return config, inputs_dict







@require_torch
class a (     __SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       int       	=		(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
						UpperCamelCase_	:       Optional[int]       	=		{'feature-extraction': ViTMAEModel} if is_torch_available() else {}

						UpperCamelCase_	:       Union[str, Any]       	=		False
						UpperCamelCase_	:       Union[str, Any]       	=		False
						UpperCamelCase_	:       Union[str, Any]       	=		False
						UpperCamelCase_	:       Any       	=		False






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						int:
													"""simple docstring"""


													__lowercase      =    ViTMAEModelTester(self				)
													__lowercase      =    ConfigTester(self				,							config_class=lowerCamelCase__				,							has_text_modality=lowerCamelCase__				,							hidden_size=37				)






						def     UpperCAmelCase_      ( self :     Any				)    ->						Union[str, Any]:
													"""simple docstring"""


													self.config_tester.run_common_tests()






						@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''				)
						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													pass






						def     UpperCAmelCase_      ( self :     str				)    ->						Tuple:
													"""simple docstring"""


													__lowercase     ,     __lowercase      =    self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__lowercase      =    model_class(lowerCamelCase__				)
																				self.assertIsInstance(model.get_input_embeddings()				,							(nn.Module)				)
																				__lowercase      =    model.get_output_embeddings()
																				self.assertTrue(x is None or isinstance(lowerCamelCase__				,							nn.Linear				)				)






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Any:
													"""simple docstring"""


													__lowercase     ,     __lowercase      =    self.model_tester.prepare_config_and_inputs_for_common()

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

																				__lowercase      =    ['''pixel_values''']
																				self.assertListEqual(arg_names[:1]				,							lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_pretraining(*lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     Any				)    ->						str:
													"""simple docstring"""


													np.random.seed(2				)

													__lowercase      =    int((pt_model.config.image_size // pt_model.config.patch_size) ** 2				)
													__lowercase      =    np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)
													__lowercase      =    torch.from_numpy(lowerCamelCase__				)

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

													super().check_pt_tf_models(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     int				)    ->						Any:
													"""simple docstring"""


													__lowercase     ,     __lowercase      =    self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__lowercase      =    model_class(lowerCamelCase__				)
																				model.to(lowerCamelCase__				)
																				model.eval()
																				# make random mask reproducible
																				torch.manual_seed(2				)
																				with torch.no_grad():
																											__lowercase      =    model(**self._prepare_for_class(lowerCamelCase__				,							lowerCamelCase__				)				)

																				__lowercase      =    outputs[0].cpu().numpy()
																				__lowercase      =    0

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											model.save_pretrained(lowerCamelCase__				)
																											__lowercase      =    model_class.from_pretrained(lowerCamelCase__				)
																											model.to(lowerCamelCase__				)
																											# make random mask reproducible
																											torch.manual_seed(2				)
																											with torch.no_grad():
																																		__lowercase      =    model(**self._prepare_for_class(lowerCamelCase__				,							lowerCamelCase__				)				)

																											# Make sure we don't have nans
																											__lowercase      =    after_outputs[0].cpu().numpy()
																											__lowercase      =    0
																											__lowercase      =    np.amax(np.abs(out_a - out_a				)				)
																											self.assertLessEqual(lowerCamelCase__				,							1e-5				)






						@unittest.skip(
						    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''				)
						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Optional[Any]:
													"""simple docstring"""


													pass






						@unittest.skip(
						    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''				)
						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						str:
													"""simple docstring"""


													pass






						@unittest.skip(
						    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''				)
						def     UpperCAmelCase_      ( self :     Any				)    ->						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     UpperCAmelCase_      ( self :     int				)    ->						Optional[int]:
													"""simple docstring"""


													pass






						@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Any:
													"""simple docstring"""


													pass






						@slow
						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Union[str, Any]:
													"""simple docstring"""


													for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__lowercase      =    ViTMAEModel.from_pretrained(lowerCamelCase__				)
																				self.assertIsNotNone(lowerCamelCase__				)







def 		_A(       )    -> List[str]:
							'''simple docstring'''


							__lowercase      =    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
							return image







@require_torch
@require_vision
class a (     unittest.TestCase  ):
						"""simple docstring"""

						@cached_property
						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Optional[Any]:
													"""simple docstring"""


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






						@slow
						def     UpperCAmelCase_      ( self :     Tuple				)    ->						List[str]:
													"""simple docstring"""


													np.random.seed(2				)

													__lowercase      =    ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''				).to(lowerCamelCase__				)

													__lowercase      =    self.default_image_processor
													__lowercase      =    prepare_img()
													__lowercase      =    image_processor(images=lowerCamelCase__				,							return_tensors='''pt'''				).to(lowerCamelCase__				)

													# 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      =    ViTMAEConfig()
													__lowercase      =    int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2				)
													__lowercase      =    np.random.uniform(size=(1, num_patches)				)

													# forward pass
													with torch.no_grad():
																				__lowercase      =    model(**lowerCamelCase__				,							noise=torch.from_numpy(lowerCamelCase__				).to(device=lowerCamelCase__				)				)

													# verify the logits
													__lowercase      =    torch.Size((1, 196, 768)				)
													self.assertEqual(outputs.logits.shape				,							lowerCamelCase__				)

													__lowercase      =    torch.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]]				)

													self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]				,							expected_slice.to(lowerCamelCase__				)				,							atol=1e-4				)				)












import itertools
import string
from collections.abc import Generator, Iterable







def 		_A(       UpperCamelCase__							:			Iterable[str]				,       UpperCamelCase__							:			int				)    -> Generator[tuple[str, ...], None, None]:
							'''simple docstring'''


							__lowercase      =    iter(UpperCamelCase__				)
							while True:
														__lowercase      =    tuple(itertools.islice(UpperCamelCase__				,       UpperCamelCase__				)				)
														if not chunk:
																					return
														yield chunk







def 		_A(       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


							__lowercase      =    ''''''.join([c.upper() for c in dirty if c in string.ascii_letters]				)
							__lowercase      =    ''''''

							if len(UpperCamelCase__				) < 2:
														return dirty

							for i in range(len(UpperCamelCase__				) - 1				):
														clean += dirty[i]

														if dirty[i] == dirty[i + 1]:
																					clean += "X"

							clean += dirty[-1]

							if len(UpperCamelCase__				) & 1:
														clean += "X"

							return clean







def 		_A(       UpperCamelCase__							:			str				)    -> list[str]:
							'''simple docstring'''


							__lowercase      =    '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
							# we're using a list instead of a '2d' array because it makes the math
							# for setting up the table and doing the actual encoding/decoding simpler
							__lowercase      =    []

							# copy key chars into the table if they are in `alphabet` ignoring duplicates
							for char in key.upper():
														if char not in table and char in alphabet:
																					table.append(UpperCamelCase__				)

    # fill the rest of the table in with the remaining alphabet chars
							for char in alphabet:
														if char not in table:
																					table.append(UpperCamelCase__				)

							return table







def 		_A(       UpperCamelCase__							:			str				,       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


							__lowercase      =    generate_table(UpperCamelCase__				)
							__lowercase      =    prepare_input(UpperCamelCase__				)
							__lowercase      =    ''''''

							# https://en.wikipedia.org/wiki/Playfair_cipher#Description
							for chara, chara in chunker(UpperCamelCase__				,       2				):
														__lowercase     ,     __lowercase      =    divmod(table.index(UpperCamelCase__				)				,       5				)
														__lowercase     ,     __lowercase      =    divmod(table.index(UpperCamelCase__				)				,       5				)

														if rowa == rowa:
																					ciphertext += table[rowa * 5 + (cola + 1) % 5]
																					ciphertext += table[rowa * 5 + (cola + 1) % 5]
														elif cola == cola:
																					ciphertext += table[((rowa + 1) % 5) * 5 + cola]
																					ciphertext += table[((rowa + 1) % 5) * 5 + cola]
														else:  # rectangle
																					ciphertext += table[rowa * 5 + cola]
																					ciphertext += table[rowa * 5 + cola]

							return ciphertext







def 		_A(       UpperCamelCase__							:			str				,       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


							__lowercase      =    generate_table(UpperCamelCase__				)
							__lowercase      =    ''''''

							# https://en.wikipedia.org/wiki/Playfair_cipher#Description
							for chara, chara in chunker(UpperCamelCase__				,       2				):
														__lowercase     ,     __lowercase      =    divmod(table.index(UpperCamelCase__				)				,       5				)
														__lowercase     ,     __lowercase      =    divmod(table.index(UpperCamelCase__				)				,       5				)

														if rowa == rowa:
																					plaintext += table[rowa * 5 + (cola - 1) % 5]
																					plaintext += table[rowa * 5 + (cola - 1) % 5]
														elif cola == cola:
																					plaintext += table[((rowa - 1) % 5) * 5 + cola]
																					plaintext += table[((rowa - 1) % 5) * 5 + cola]
														else:  # rectangle
																					plaintext += table[rowa * 5 + cola]
																					plaintext += table[rowa * 5 + cola]

							return plaintext






