




'''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 		lowercase_   :


   """simple docstring"""

   def __init__(   self :     Optional[Any]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Union[str, Any]=1_3  ,				__lowerCamelCase :     List[str]=3_0  ,				__lowerCamelCase :     Any=2  ,				__lowerCamelCase :     Dict=3  ,				__lowerCamelCase :     List[str]=True  ,				__lowerCamelCase :     Optional[int]=True  ,				__lowerCamelCase :     Dict=3_2  ,				__lowerCamelCase :     Optional[int]=5  ,				__lowerCamelCase :     List[Any]=4  ,				__lowerCamelCase :     str=3_7  ,				__lowerCamelCase :     Dict="gelu"  ,				__lowerCamelCase :     str=0.1  ,				__lowerCamelCase :     Optional[int]=0.1  ,				__lowerCamelCase :     int=1_0  ,				__lowerCamelCase :     List[Any]=0.0_2  ,				__lowerCamelCase :     Any=3  ,				__lowerCamelCase :     int=0.6  ,				__lowerCamelCase :     List[str]=None  ,				):

      """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 							lowerCAmelCase_      (   self :     int   ):

      """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 							lowerCAmelCase_      (   self :     int   ):

      """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 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				ViTMAEModel(config=__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase   )
      self.parent.assertEqual(result.last_hidden_state.shape  ,				(self.batch_size, self.seq_length, self.hidden_size)   )



   def 							lowerCAmelCase_      (   self :     str  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				ViTMAEForPreTraining(__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase   )
      _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(__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				self.patch_size**2
      self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, num_patches, expected_num_channels)   )



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """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 		lowercase_   (			A					,  A					,  unittest.TestCase       ):


   """simple docstring"""

   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 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				ViTMAEModelTester(self   )
      _SCREAMING_SNAKE_CASE	=				ConfigTester(self  ,				config_class=__lowerCamelCase  ,				has_text_modality=__lowerCamelCase  ,				hidden_size=3_7   )



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      self.config_tester.run_common_tests()



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

      """simple docstring"""
      pass



   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """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(__lowerCamelCase   )
         self.assertIsInstance(model.get_input_embeddings()  ,				(nn.Module)   )
         _SCREAMING_SNAKE_CASE	=				model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(__lowerCamelCase  ,				nn.Linear   )   )



   def 							lowerCAmelCase_      (   self :     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(__lowerCamelCase   )
         _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]  ,				__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_pretraining(*__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Any  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      # make masks reproducible
      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(__lowerCamelCase   )

      # 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(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     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(__lowerCamelCase   )
         model.to(__lowerCamelCase   )
         model.eval()
         # make random mask reproducible
         torch.manual_seed(2   )
         with torch.no_grad():
            _SCREAMING_SNAKE_CASE	=				model(**self._prepare_for_class(__lowerCamelCase  ,				__lowerCamelCase   )   )

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

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

            # 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(__lowerCamelCase  ,				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 							lowerCAmelCase_      (   self :     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 							lowerCAmelCase_      (   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 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      pass



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

      """simple docstring"""
      pass



   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."   )
   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      pass



   @slow
   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _SCREAMING_SNAKE_CASE	=				ViTMAEModel.from_pretrained(__lowerCamelCase   )
         self.assertIsNotNone(__lowerCamelCase   )


def 			SCREAMING_SNAKE_CASE_			(							)			->			Union[str, Any]:
   _SCREAMING_SNAKE_CASE	=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
   return image







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


   """simple docstring"""

   @cached_property
   def 							lowerCAmelCase_      (   self :     Any   ):

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



   @slow
   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      # make random mask reproducible across the PT and TF model
      np.random.seed(2   )

      _SCREAMING_SNAKE_CASE	=				ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"   ).to(__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				self.default_image_processor
      _SCREAMING_SNAKE_CASE	=				prepare_img()
      _SCREAMING_SNAKE_CASE	=				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)
      _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(**__lowerCamelCase  ,				noise=torch.from_numpy(__lowerCamelCase   ).to(device=__lowerCamelCase   )   )

      # verify the logits
      _SCREAMING_SNAKE_CASE	=				torch.Size((1, 1_9_6, 7_6_8)   )
      self.assertEqual(outputs.logits.shape  ,				__lowerCamelCase   )

      _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(__lowerCamelCase   )  ,				atol=1e-4   )   )







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_			=						{
    'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
    'tokenization_ctrl': ['CTRLTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'CTRLForSequenceClassification',
     'CTRLLMHeadModel',
     'CTRLModel',
     'CTRLPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFCTRLForSequenceClassification',
     'TFCTRLLMHeadModel',
     'TFCTRLModel',
     'TFCTRLPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
 from .tokenization_ctrl import CTRLTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ctrl import (
      CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
      CTRLForSequenceClassification,
      CTRLLMHeadModel,
      CTRLModel,
      CTRLPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_ctrl import (
      TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCTRLForSequenceClassification,
      TFCTRLLMHeadModel,
      TFCTRLModel,
      TFCTRLPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase_			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

