






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      lowercase__     :

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase=13		,    __UpperCAmelCase=30		,    __UpperCAmelCase=2		,    __UpperCAmelCase=3		,    __UpperCAmelCase=True		,    __UpperCAmelCase=True		,    __UpperCAmelCase=32		,    __UpperCAmelCase=5		,    __UpperCAmelCase=4		,    __UpperCAmelCase=37		,    __UpperCAmelCase="gelu"		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=10		,    __UpperCAmelCase=0.02		,    __UpperCAmelCase=3		,    __UpperCAmelCase=0.6		,    __UpperCAmelCase=None		,    )->					str:







							'''simple docstring'''





							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 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							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 		UpperCAmelCase  (							self       )->					Tuple:







							'''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=__UpperCAmelCase		,    initializer_range=self.initializer_range		,    mask_ratio=self.mask_ratio		,    )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					Tuple:







							'''simple docstring'''





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

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					int:







							'''simple docstring'''





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


	def 		UpperCAmelCase  (							self       )->					Union[str, Any]:







							'''simple docstring'''





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





@require_torch
class      lowercase__     (			_UpperCAmelCase,					_UpperCAmelCase,					unittest.TestCase	):
	a_         =(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
	a_         ={"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

	a_         =False
	a_         =False
	a_         =False
	a_         =False

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						ViTMAEModelTester(self       )
							lowerCAmelCase__       =						ConfigTester(self		,    config_class=__UpperCAmelCase		,    has_text_modality=__UpperCAmelCase		,    hidden_size=37       )

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							self.config_tester.run_common_tests()

	@unittest.skip(reason="ViTMAE does not use inputs_embeds"       )
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							pass

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()

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

	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()

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

													lowerCAmelCase__       =						["pixel_values"]
													self.assertListEqual(arg_names[:1]		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					Dict:







							'''simple docstring'''





							np.random.seed(2       )

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

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

							super().check_pt_tf_models(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

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

	@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       )->					Tuple:







							'''simple docstring'''





							pass

	@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       )->					Union[str, Any]:







							'''simple docstring'''





							pass

	@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       )->					int:







							'''simple docstring'''





							pass

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







							'''simple docstring'''





							pass

	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							pass


	@slow
	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





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





def        _a  (     ) ->   str:
						"""simple docstring"""







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





@require_torch
@require_vision
class      lowercase__     (			unittest.TestCase	):

	@cached_property
	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





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


	@slow
	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							np.random.seed(2       )

							lowerCAmelCase__       =						ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"       ).to(__UpperCAmelCase       )

							lowerCAmelCase__       =						self.default_image_processor
							lowerCAmelCase__       =						prepare_img()
							lowerCAmelCase__       =						image_processor(images=__UpperCAmelCase		,    return_tensors="pt"       ).to(__UpperCAmelCase       )

							# 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
							with torch.no_grad():
													lowerCAmelCase__       =						model(**__UpperCAmelCase		,    noise=torch.from_numpy(__UpperCAmelCase       ).to(device=__UpperCAmelCase       )       )

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

							lowerCAmelCase__       =						torch.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]]       )

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














# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


a_							=    subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
a_							=    subprocess.check_output(F"git diff --name-only {fork_point_sha}".split()).decode('''utf-8''').split()

a_							=    '''|'''.join(sys.argv[1:])
a_							=    re.compile(rF"^({joined_dirs}).*?\.py$")

a_							=    [x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')






