





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 	UpperCamelCase_							:


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



									"""simple docstring"""



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

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


							def   _snake_case	(				self     :List[Any]		) -> Tuple:



									"""simple docstring"""



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

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

									SCREAMING_SNAKE_CASE__								=   self.get_config()

									return config, pixel_values, labels


							def   _snake_case	(				self     :Tuple		) -> Any:



									"""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=__A				,					initializer_range=self.initializer_range				,					mask_ratio=self.mask_ratio				,					)


							def   _snake_case	(				self     :Optional[int]				,					__A     :Any				,					__A     :Tuple				,					__A     :Tuple		) -> Any:



									"""simple docstring"""



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


							def   _snake_case	(				self     :Tuple				,					__A     :int				,					__A     :Dict				,					__A     :Optional[Any]		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   ViTMAEForPreTraining(__A		)
									model.to(__A		)
									model.eval()
									SCREAMING_SNAKE_CASE__								=   model(__A		)
									SCREAMING_SNAKE_CASE__								=   (self.image_size // self.patch_size) ** 2
									SCREAMING_SNAKE_CASE__								=   self.patch_size**2 * self.num_channels
									self.parent.assertEqual(result.logits.shape				,					(self.batch_size, num_patches, expected_num_channels)		)

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


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



									"""simple docstring"""



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




@require_torch
class 	UpperCamelCase_							(  UpperCamelCase__					,	UpperCamelCase__					,	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   _snake_case	(				self     :Optional[Any]		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   ViTMAEModelTester(self		)
									SCREAMING_SNAKE_CASE__								=   ConfigTester(self				,					config_class=__A				,					has_text_modality=__A				,					hidden_size=37		)


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



									"""simple docstring"""



									self.config_tester.run_common_tests()


							@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""		)
							def   _snake_case	(				self     :Dict		) -> List[str]:



									"""simple docstring"""



									pass


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs_for_common()

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


							def   _snake_case	(				self     :List[Any]		) -> List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs_for_common()

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

											SCREAMING_SNAKE_CASE__								=   ["""pixel_values"""]
											self.assertListEqual(arg_names[:1]				,					__A		)


							def   _snake_case	(				self     :Dict		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__A		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*__A		)


							def   _snake_case	(				self     :List[Any]				,					__A     :List[str]				,					__A     :Union[str, Any]				,					__A     :List[str]		) -> Tuple:



									"""simple docstring"""



									np.random.seed(2		)

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

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

									super().check_pt_tf_models(__A				,					__A				,					__A		)


							def   _snake_case	(				self     :Optional[Any]		) -> Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

													# Make sure we don't have nans
													SCREAMING_SNAKE_CASE__								=   after_outputs[0].cpu().numpy()
													SCREAMING_SNAKE_CASE__								=   0
													SCREAMING_SNAKE_CASE__								=   np.amax(np.abs(out_a - out_a		)		)
													self.assertLessEqual(__A				,					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   _snake_case	(				self     :List[Any]		) -> List[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   _snake_case	(				self     :Any		) -> Tuple:



									"""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   _snake_case	(				self     :Tuple		) -> 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   _snake_case	(				self     :Dict		) -> Tuple:



									"""simple docstring"""



									pass


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



									"""simple docstring"""



									pass


							@slow
							def   _snake_case	(				self     :Optional[int]		) -> int:



									"""simple docstring"""



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






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




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


							@cached_property
							def   _snake_case	(				self     :Union[str, Any]		) -> Any:



									"""simple docstring"""



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


							@slow
							def   _snake_case	(				self     :str		) -> str:



									"""simple docstring"""



									np.random.seed(2		)

									SCREAMING_SNAKE_CASE__								=   ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""		).to(__A		)

									SCREAMING_SNAKE_CASE__								=   self.default_image_processor
									SCREAMING_SNAKE_CASE__								=   prepare_img()
									SCREAMING_SNAKE_CASE__								=   image_processor(images=__A				,					return_tensors="""pt"""		).to(__A		)

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

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

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

									SCREAMING_SNAKE_CASE__								=   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(__A		)				,					atol=1E-4		)		)






'''simple docstring'''




from __future__ import annotations

_SCREAMING_SNAKE_CASE							     =			1.6021E-19  # units = C





def 	__lowerCamelCase			(      __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    )		->  tuple[str, float]:
   if (conductivity, electron_conc, mobility).count(0		) != 1:
      raise ValueError("""You cannot supply more or less than 2 values"""		)
   elif conductivity < 0:
      raise ValueError("""Conductivity cannot be negative"""		)
   elif electron_conc < 0:
      raise ValueError("""Electron concentration cannot be negative"""		)
   elif mobility < 0:
      raise ValueError("""mobility cannot be negative"""		)
   elif conductivity == 0:
      return (
          "conductivity",
          mobility * electron_conc * ELECTRON_CHARGE,
      )
   elif electron_conc == 0:
      return (
          "electron_conc",
          conductivity / (mobility * ELECTRON_CHARGE),
      )
   else:
      return (
          "mobility",
          conductivity / (electron_conc * ELECTRON_CHARGE),
      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

