





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       :

  """simple docstring"""


  def __init__(							self					:						List[Any]   ,    lowerCAmelCase_					:						Tuple   ,    lowerCAmelCase_					:						Optional[int]=1_3   ,    lowerCAmelCase_					:						Any=3_0   ,    lowerCAmelCase_					:						Dict=2   ,    lowerCAmelCase_					:						List[Any]=3   ,    lowerCAmelCase_					:						Optional[Any]=True   ,    lowerCAmelCase_					:						Union[str, Any]=True   ,    lowerCAmelCase_					:						Tuple=3_2   ,    lowerCAmelCase_					:						Union[str, Any]=5   ,    lowerCAmelCase_					:						str=4   ,    lowerCAmelCase_					:						Tuple=3_7   ,    lowerCAmelCase_					:						str="gelu"   ,    lowerCAmelCase_					:						Optional[int]=0.1   ,    lowerCAmelCase_					:						Dict=0.1   ,    lowerCAmelCase_					:						Any=1_0   ,    lowerCAmelCase_					:						List[str]=0.02   ,    lowerCAmelCase_					:						Tuple=3   ,    lowerCAmelCase_					:						int=0.6   ,    lowerCAmelCase_					:						Tuple=None   ,    )				->		List[Any]:
     __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 							lowercase						(							self					:						Any	)				->		Optional[Any]:
     __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 							lowercase						(							self					:						Any	)				->		int:
     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 							lowercase						(							self					:						List[str]   ,    lowerCAmelCase_					:						List[str]   ,    lowerCAmelCase_					:						int   ,    lowerCAmelCase_					:						Optional[int]	)				->		Union[str, Any]:
     __lowerCAmelCase				      =      ViTMAEModel(config=lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.eval()
     __lowerCAmelCase				      =      model(lowerCAmelCase_	)
     self.parent.assertEqual(result.last_hidden_state.shape   ,    (self.batch_size, self.seq_length, self.hidden_size)	)






  def 							lowercase						(							self					:						Dict   ,    lowerCAmelCase_					:						List[str]   ,    lowerCAmelCase_					:						str   ,    lowerCAmelCase_					:						int	)				->		List[str]:
     __lowerCAmelCase				      =      ViTMAEForPreTraining(lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.eval()
     __lowerCAmelCase				      =      model(lowerCAmelCase_	)
     __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(lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.eval()
     __lowerCAmelCase				      =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
     __lowerCAmelCase				      =      model(lowerCAmelCase_	)
     __lowerCAmelCase				      =      self.patch_size**2
     self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, num_patches, expected_num_channels)	)






  def 							lowercase						(							self					:						Tuple	)				->		List[Any]:
     __lowerCAmelCase				      =      self.prepare_config_and_inputs()
     __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase				      =      config_and_inputs
     __lowerCAmelCase				      =      {'pixel_values': pixel_values}
     return config, inputs_dict





@require_torch
class 							_UpperCAmelCase       (       _UpperCamelCase						,       _UpperCamelCase						,       unittest.TestCase       ):

  """simple docstring"""


  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 							lowercase						(							self					:						str	)				->		Optional[Any]:
     __lowerCAmelCase				      =      ViTMAEModelTester(self	)
     __lowerCAmelCase				      =      ConfigTester(self   ,    config_class=lowerCAmelCase_   ,    has_text_modality=lowerCAmelCase_   ,    hidden_size=3_7	)






  def 							lowercase						(							self					:						List[Any]	)				->		List[str]:
     self.config_tester.run_common_tests()






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






  def 							lowercase						(							self					:						List[str]	)				->		List[str]:
     __lowerCAmelCase      ,      __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs_for_common()

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






  def 							lowercase						(							self					:						int	)				->		int:
     __lowerCAmelCase      ,      __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase				      =      model_class(lowerCAmelCase_	)
        __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]   ,    lowerCAmelCase_	)






  def 							lowercase						(							self					:						Union[str, Any]	)				->		Any:
     __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase_	)






  def 							lowercase						(							self					:						List[Any]	)				->		int:
     __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase_	)






  def 							lowercase						(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						int   ,    lowerCAmelCase_					:						str   ,    lowerCAmelCase_					:						List[Any]	)				->		List[Any]:
     # make masks reproducible
     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(lowerCAmelCase_	)

     # 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(lowerCAmelCase_   ,    lowerCAmelCase_   ,    lowerCAmelCase_	)






  def 							lowercase						(							self					:						Tuple	)				->		Union[str, Any]:
     __lowerCAmelCase      ,      __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

           # 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(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 							lowercase						(							self					:						Dict	)				->		Optional[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 							lowercase						(							self					:						str	)				->		List[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 							lowercase						(							self					:						int	)				->		List[str]:
     pass






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






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






  @slow
  def 							lowercase						(							self					:						Dict	)				->		List[Any]:
     for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowerCAmelCase				      =      ViTMAEModel.from_pretrained(lowerCAmelCase_	)
        self.assertIsNotNone(lowerCAmelCase_	)





def     a_       (					):
   __lowerCAmelCase				      =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
   return image




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

  """simple docstring"""


  @cached_property
  def 							lowercase						(							self					:						Tuple	)				->		List[Any]:
     return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'	) if is_vision_available() else None






  @slow
  def 							lowercase						(							self					:						Optional[int]	)				->		Union[str, Any]:
     # make random mask reproducible across the PT and TF model
     np.random.seed(2	)

     __lowerCAmelCase				      =      ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'	).to(lowerCAmelCase_	)

     __lowerCAmelCase				      =      self.default_image_processor
     __lowerCAmelCase				      =      prepare_img()
     __lowerCAmelCase				      =      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)
     __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(**lowerCAmelCase_   ,    noise=torch.from_numpy(lowerCAmelCase_	).to(device=lowerCAmelCase_	)	)

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

     __lowerCAmelCase				      =      torch.tensor(
         [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]	)

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





from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
    import datasets



class 	__lowerCamelCase			(_a						):







       @slow
       @require_torch
       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny','prajjwal1/bert-tiny'					)
            __UpperCamelCase =			BertTokenizer.from_pretrained('bert-base-uncased'					)

            __UpperCamelCase =			bertabert.config.encoder.vocab_size
            __UpperCamelCase =			tokenizer.sep_token_id
            __UpperCamelCase =			tokenizer.cls_token_id
            __UpperCamelCase =			128

            __UpperCamelCase =			datasets.load_dataset('cnn_dailymail','3.0.0',split='train[:1%]'					)
            __UpperCamelCase =			datasets.load_dataset('cnn_dailymail','3.0.0',split='validation[:1%]'					)

            __UpperCamelCase =			train_dataset.select(range(32					)					)
            __UpperCamelCase =			val_dataset.select(range(16					)					)

            __UpperCamelCase =			4

            def _map_to_encoder_decoder_inputs(A_: Dict					):
                 # Tokenizer will automatically set [BOS] <text> [EOS]
                 __UpperCamelCase =			tokenizer(batch['article'],padding='max_length',truncation=A_,max_length=512					)
                 __UpperCamelCase =			tokenizer(batch['highlights'],padding='max_length',truncation=A_,max_length=128					)
                 __UpperCamelCase =			inputs.input_ids
                 __UpperCamelCase =			inputs.attention_mask

                 __UpperCamelCase =			outputs.input_ids
                 __UpperCamelCase =			outputs.input_ids.copy()
                 __UpperCamelCase =			[
                     [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                 ]
                 __UpperCamelCase =			outputs.attention_mask

                 assert all(len(A_					) == 512 for x in inputs.input_ids					)
                 assert all(len(A_					) == 128 for x in outputs.input_ids					)

                 return batch

            def _compute_metrics(A_: str					):
                 __UpperCamelCase =			pred.label_ids
                 __UpperCamelCase =			pred.predictions

                 # all unnecessary tokens are removed
                 __UpperCamelCase =			tokenizer.batch_decode(A_,skip_special_tokens=A_					)
                 __UpperCamelCase =			tokenizer.batch_decode(A_,skip_special_tokens=A_					)

                 __UpperCamelCase =			sum([int(pred_str[i] == label_str[i]					) for i in range(len(A_					)					)]					) / len(A_					)

                 return {"accuracy": accuracy}

            # map train dataset
            __UpperCamelCase =			train_dataset.map(
                _map_to_encoder_decoder_inputs,batched=A_,batch_size=A_,remove_columns=['article', 'highlights'],)
            train_dataset.set_format(
                type='torch',columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'],)

            # same for validation dataset
            __UpperCamelCase =			val_dataset.map(
                _map_to_encoder_decoder_inputs,batched=A_,batch_size=A_,remove_columns=['article', 'highlights'],)
            val_dataset.set_format(
                type='torch',columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'],)

            __UpperCamelCase =			self.get_auto_remove_tmp_dir()

            __UpperCamelCase =			SeqaSeqTrainingArguments(
                output_dir=A_,per_device_train_batch_size=A_,per_device_eval_batch_size=A_,predict_with_generate=A_,evaluation_strategy='steps',do_train=A_,do_eval=A_,warmup_steps=0,eval_steps=2,logging_steps=2,)

            # instantiate trainer
            __UpperCamelCase =			SeqaSeqTrainer(
                model=A_,args=A_,compute_metrics=_compute_metrics,train_dataset=A_,eval_dataset=A_,tokenizer=A_,)

            # start training
            trainer.train()


