"""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				,     a__				,     a__=13				,     a__=30				,     a__=2				,     a__=3				,     a__=True				,     a__=True				,     a__=32				,     a__=5				,     a__=4				,     a__=37				,     a__="gelu"				,     a__=0.1				,     a__=0.1				,     a__=10				,     a__=0.0_2				,     a__=3				,     a__=0.6				,     a__=None				,     ):
       _lowerCAmelCase						:       str  			=     parent
       _lowerCAmelCase						:       int  			=     batch_size
       _lowerCAmelCase						:       Optional[int]  			=     image_size
       _lowerCAmelCase						:       Tuple  			=     patch_size
       _lowerCAmelCase						:       Any  			=     num_channels
       _lowerCAmelCase						:       Dict  			=     is_training
       _lowerCAmelCase						:       Any  			=     use_labels
       _lowerCAmelCase						:       str  			=     hidden_size
       _lowerCAmelCase						:       Any  			=     num_hidden_layers
       _lowerCAmelCase						:       int  			=     num_attention_heads
       _lowerCAmelCase						:       Any  			=     intermediate_size
       _lowerCAmelCase						:       Any  			=     hidden_act
       _lowerCAmelCase						:       List[str]  			=     hidden_dropout_prob
       _lowerCAmelCase						:       Tuple  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       int  			=     type_sequence_label_size
       _lowerCAmelCase						:       Tuple  			=     initializer_range
       _lowerCAmelCase						:       Union[str, Any]  			=     mask_ratio
       _lowerCAmelCase						:       Tuple  			=     scope

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






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

       _lowerCAmelCase						:       List[str]  			=     None
       if self.use_labels:
         _lowerCAmelCase						:       Tuple  			=     ids_tensor([self.batch_size]				,     self.type_sequence_label_size  )

       _lowerCAmelCase						:       int  			=     self.get_config()

       return config, pixel_values, labels






     def     __A  (		self  ):
       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     __A  (		self				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       int  			=     ViTMAEModel(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       List[Any]  			=     model(a__  )
       self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)  )






     def     __A  (		self				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       int  			=     ViTMAEForPreTraining(a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Tuple  			=     model(a__  )
       _lowerCAmelCase						:       Dict  			=     (self.image_size // self.patch_size) ** 2
       _lowerCAmelCase						:       Tuple  			=     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						:       str  			=     1
       _lowerCAmelCase						:       Dict  			=     ViTMAEForPreTraining(a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Any  			=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
       _lowerCAmelCase						:       List[Any]  			=     model(a__  )
       _lowerCAmelCase						:       List[str]  			=     self.patch_size**2
       self.parent.assertEqual(result.logits.shape				,     (self.batch_size, num_patches, expected_num_channels)  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     self.prepare_config_and_inputs()
       _lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase						:       Dict  			=     config_and_inputs
       _lowerCAmelCase						:       Dict  			=     {"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_torch
class 						__A							( SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			List[str]							=		(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
     _UpperCamelCase							:			str							=		{"feature-extraction": ViTMAEModel} if is_torch_available() else {}

     _UpperCamelCase							:			Dict							=		False
     _UpperCamelCase							:			Optional[Any]							=		False
     _UpperCamelCase							:			Optional[int]							=		False
     _UpperCamelCase							:			str							=		False






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     ViTMAEModelTester(self  )
       _lowerCAmelCase						:       List[str]  			=     ConfigTester(self				,     config_class=a__				,     has_text_modality=a__				,     hidden_size=37  )






     def     __A  (		self  ):
       self.config_tester.run_common_tests()






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






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _lowerCAmelCase						:       Optional[Any]  			=     model_class(a__  )
         self.assertIsInstance(model.get_input_embeddings()				,     (nn.Module)  )
         _lowerCAmelCase						:       Any  			=     model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(a__				,     nn.Linear  )  )






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       int  			=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _lowerCAmelCase						:       Optional[Any]  			=     model_class(a__  )
         _lowerCAmelCase						:       Optional[Any]  			=     inspect.signature(model.forward  )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _lowerCAmelCase						:       Union[str, Any]  			=     [*signature.parameters.keys()]

         _lowerCAmelCase						:       Any  			=     ["""pixel_values"""]
         self.assertListEqual(arg_names[:1]				,     a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*a__  )






     def     __A  (		self				,     a__				,     a__				,     a__  ):
       # make masks reproducible
       np.random.seed(2  )

       _lowerCAmelCase						:       Tuple  			=     int((pt_model.config.image_size // pt_model.config.patch_size) ** 2  )
       _lowerCAmelCase						:       int  			=     np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
       _lowerCAmelCase						:       str  			=     torch.from_numpy(a__  )

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

       super().check_pt_tf_models(a__				,     a__				,     a__  )






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       Union[str, Any]  			=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _lowerCAmelCase						:       Union[str, Any]  			=     model_class(a__  )
         model.to(a__  )
         model.eval()
         # make random mask reproducible
         torch.manual_seed(2  )
         with torch.no_grad():
           _lowerCAmelCase						:       List[str]  			=     model(**self._prepare_for_class(a__				,     a__  )  )

         _lowerCAmelCase						:       Any  			=     outputs[0].cpu().numpy()
         _lowerCAmelCase						:       Optional[Any]  			=     0

         with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(a__  )
           _lowerCAmelCase						:       Union[str, Any]  			=     model_class.from_pretrained(a__  )
           model.to(a__  )
           # make random mask reproducible
           torch.manual_seed(2  )
           with torch.no_grad():
             _lowerCAmelCase						:       List[Any]  			=     model(**self._prepare_for_class(a__				,     a__  )  )

           # Make sure we don't have nans
           _lowerCAmelCase						:       Dict  			=     after_outputs[0].cpu().numpy()
           _lowerCAmelCase						:       List[Any]  			=     0
           _lowerCAmelCase						:       int  			=     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     __A  (		self  ):
       pass






     @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  ):
       pass






     @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  ):
       pass






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






     @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
     def     __A  (		self  ):
       pass






     @slow
     def     __A  (		self  ):
       for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						:       Optional[Any]  			=     ViTMAEModel.from_pretrained(a__  )
         self.assertIsNotNone(a__  )







def 							SCREAMING_SNAKE_CASE     (			)       ->      Tuple:
  _lowerCAmelCase						:       Tuple  			=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
  return image






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






     @cached_property
     def     __A  (		self  ):
       return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""  ) if is_vision_available() else None






     @slow
     def     __A  (		self  ):
       # make random mask reproducible across the PT and TF model
       np.random.seed(2  )

       _lowerCAmelCase						:       str  			=     ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""  ).to(a__  )

       _lowerCAmelCase						:       Tuple  			=     self.default_image_processor
       _lowerCAmelCase						:       Optional[int]  			=     prepare_img()
       _lowerCAmelCase						:       Tuple  			=     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)
       _lowerCAmelCase						:       str  			=     ViTMAEConfig()
       _lowerCAmelCase						:       Any  			=     int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
       _lowerCAmelCase						:       Union[str, Any]  			=     np.random.uniform(size=(1, num_patches)  )

       # forward pass
       with torch.no_grad():
         _lowerCAmelCase						:       int  			=     model(**a__				,     noise=torch.from_numpy(a__  ).to(device=a__  )  )

       # verify the logits
       _lowerCAmelCase						:       Dict  			=     torch.Size((1, 196, 768)  )
       self.assertEqual(outputs.logits.shape				,     a__  )

       _lowerCAmelCase						:       Optional[Any]  			=     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  )  )







from __future__ import annotations




def 			UpperCAmelCase__				(			_A      : float	,      _A      : float	,      _A      : float	,      ):





  '''simple docstring'''







  if (electron_conc, hole_conc, intrinsic_conc).count(0							) != 1:
    raise ValueError('''You cannot supply more or less than 2 values'''							)
  elif electron_conc < 0:
    raise ValueError('''Electron concentration cannot be negative in a semiconductor'''							)
  elif hole_conc < 0:
    raise ValueError('''Hole concentration cannot be negative in a semiconductor'''							)
  elif intrinsic_conc < 0:
    raise ValueError(
        '''Intrinsic concentration cannot be negative in a semiconductor'''							)
  elif electron_conc == 0:
    return (
        "electron_conc",
        intrinsic_conc**2 / hole_conc,
    )
  elif hole_conc == 0:
    return (
        "hole_conc",
        intrinsic_conc**2 / electron_conc,
    )
  elif intrinsic_conc == 0:
    return (
        "intrinsic_conc",
        (electron_conc * hole_conc) ** 0.5,
    )
  else:
    return (-1, -1)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
