


'''simple docstring'''






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       :
	def __init__(	self ,			__magic_name__ ,			__magic_name__=13 ,			__magic_name__=30 ,			__magic_name__=2 ,			__magic_name__=3 ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=32 ,			__magic_name__=5 ,			__magic_name__=4 ,			__magic_name__=37 ,			__magic_name__="gelu" ,			__magic_name__=0.1 ,			__magic_name__=0.1 ,			__magic_name__=10 ,			__magic_name__=0.0_2 ,			__magic_name__=3 ,			__magic_name__=0.6 ,			__magic_name__=None ,			)   ->							int:
						_a      =							parent
						_a      =							batch_size
						_a      =							image_size
						_a      =							patch_size
						_a      =							num_channels
						_a      =							is_training
						_a      =							use_labels
						_a      =							hidden_size
						_a      =							num_hidden_layers
						_a      =							num_attention_heads
						_a      =							intermediate_size
						_a      =							hidden_act
						_a      =							hidden_dropout_prob
						_a      =							attention_probs_dropout_prob
						_a      =							type_sequence_label_size
						_a      =							initializer_range
						_a      =							mask_ratio
						_a      =							scope

						# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
						# (we add 1 for the [CLS] token)
						_a      =							(image_size // patch_size) ** 2
						_a      =							int(math.ceil((1 - mask_ratio) * (num_patches + 1)  )  )
	def 							__UpperCAmelCase						(	self  )   ->							str:
						_a      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

						_a      =							self.get_config()

						return config, pixel_values, labels
	def 							__UpperCAmelCase						(	self  )   ->							Any:
						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=__magic_name__ ,			initializer_range=self.initializer_range ,			mask_ratio=self.mask_ratio ,			)
	def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Optional[Any]:
						_a      =							ViTMAEModel(config=__magic_name__  )
						model.to(__magic_name__  )
						model.eval()
						_a      =							model(__magic_name__  )
						self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)  )
	def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Any:
						_a      =							ViTMAEForPreTraining(__magic_name__  )
						model.to(__magic_name__  )
						model.eval()
						_a      =							model(__magic_name__  )
						_a      =							(self.image_size // self.patch_size) ** 2
						_a      =							self.patch_size**2 * self.num_channels
						self.parent.assertEqual(result.logits.shape ,			(self.batch_size, num_patches, expected_num_channels)  )

						# test greyscale images
						_a      =							1
						_a      =							ViTMAEForPreTraining(__magic_name__  )
						model.to(__magic_name__  )
						model.eval()
						_a      =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
						_a      =							model(__magic_name__  )
						_a      =							self.patch_size**2
						self.parent.assertEqual(result.logits.shape ,			(self.batch_size, num_patches, expected_num_channels)  )




	def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
						_a      =							self.prepare_config_and_inputs()
						_a ,				_a ,				_a      =							config_and_inputs
						_a      =							{'pixel_values': pixel_values}
						return config, inputs_dict





@require_torch
class 	a       (       _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			unittest.TestCase    ):
	_lowerCAmelCase	   =		(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
	_lowerCAmelCase	   =		{"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

	_lowerCAmelCase	   =		False
	_lowerCAmelCase	   =		False
	_lowerCAmelCase	   =		False
	_lowerCAmelCase	   =		False
	def 							__UpperCAmelCase						(	self  )   ->							int:
						_a      =							ViTMAEModelTester(self  )
						_a      =							ConfigTester(self ,			config_class=__magic_name__ ,			has_text_modality=__magic_name__ ,			hidden_size=37  )
	def 							__UpperCAmelCase						(	self  )   ->							Dict:
						self.config_tester.run_common_tests()
	@unittest.skip(reason='ViTMAE does not use inputs_embeds'  )
	def 							__UpperCAmelCase						(	self  )   ->							List[str]:
						pass
	def 							__UpperCAmelCase						(	self  )   ->							Any:
						_a ,				_a      =							self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											_a      =							model_class(__magic_name__  )
											self.assertIsInstance(model.get_input_embeddings() ,			(nn.Module)  )
											_a      =							model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(__magic_name__ ,			nn.Linear  )  )
	def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
						_a ,				_a      =							self.model_tester.prepare_config_and_inputs_for_common()

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

											_a      =							['pixel_values']
											self.assertListEqual(arg_names[:1] ,			__magic_name__  )
	def 							__UpperCAmelCase						(	self  )   ->							List[str]:
						_a      =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__magic_name__  )
	def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
						_a      =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*__magic_name__  )
	def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Any:
						# make masks reproducible
						np.random.seed(2  )

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

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

						super().check_pt_tf_models(__magic_name__ ,			__magic_name__ ,			__magic_name__  )
	def 							__UpperCAmelCase						(	self  )   ->							Dict:
						_a ,				_a      =							self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

																# Make sure we don't have nans
																_a      =							after_outputs[0].cpu().numpy()
																_a      =							0
																_a      =							np.amax(np.abs(out_a - out_a  )  )
																self.assertLessEqual(__magic_name__ ,			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  )   ->							Any:
						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  )   ->							Optional[int]:
						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  )   ->							List[Any]:
						pass
	@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'  )
	def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
						pass
	@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'  )
	def 							__UpperCAmelCase						(	self  )   ->							str:
						pass




	@slow
	def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
						for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_a      =							ViTMAEModel.from_pretrained(__magic_name__  )
											self.assertIsNotNone(__magic_name__  )






def 		_A			()				->					int:




					'''simple docstring'''



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






@require_torch
@require_vision
class 	a       (       unittest.TestCase    ):
	@cached_property
	def 							__UpperCAmelCase						(	self  )   ->							Any:
						return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'  ) if is_vision_available() else None




	@slow
	def 							__UpperCAmelCase						(	self  )   ->							List[str]:
						# make random mask reproducible across the PT and TF model
						np.random.seed(2  )

						_a      =							ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'  ).to(__magic_name__  )

						_a      =							self.default_image_processor
						_a      =							prepare_img()
						_a      =							image_processor(images=__magic_name__ ,			return_tensors='pt'  ).to(__magic_name__  )

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

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

						# verify the logits
						_a      =							torch.Size((1, 1_96, 7_68)  )
						self.assertEqual(outputs.logits.shape ,			__magic_name__  )

						_a      =							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(__magic_name__  ) ,			atol=1e-4  )  )





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_   :      str				    =      {
    "configuration_nllb_moe": [
        "NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "NllbMoeConfig",
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Tuple				    =      [
     "NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST",
     "NllbMoeForConditionalGeneration",
     "NllbMoeModel",
     "NllbMoePreTrainedModel",
     "NllbMoeTop2Router",
     "NllbMoeSparseMLP",
 ]


if TYPE_CHECKING:
 from .configuration_nllb_moe import (
     NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
     NllbMoeConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nllb_moe import (
      NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
      NllbMoeForConditionalGeneration,
      NllbMoeModel,
      NllbMoePreTrainedModel,
      NllbMoeSparseMLP,
      NllbMoeTopaRouter,
  )


else:
 import sys

 a_   :      str				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

