


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 	__lowerCamelCase			:







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






            '''simple docstring'''




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

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







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




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

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

            __UpperCamelCase =			self.get_config()

            return config, pixel_values, labels







       def 		snake_case_	(			self: Dict					):






            '''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[Any],A_: List[str],A_: List[Any],A_: Union[str, Any]					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: List[Any],A_: Optional[int],A_: Dict,A_: Dict					):






            '''simple docstring'''




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

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







       def 		snake_case_	(			self: List[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			self.prepare_config_and_inputs()
            __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			config_and_inputs
            __UpperCamelCase =			{'pixel_values': pixel_values}
            return config, inputs_dict



@require_torch
class 	__lowerCamelCase			(_a				,      _a				,      unittest.TestCase						):
       _lowercase											=     (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
       _lowercase											=     {"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

       _lowercase											=     False
       _lowercase											=     False
       _lowercase											=     False
       _lowercase											=     False







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			ViTMAEModelTester(self					)
            __UpperCamelCase =			ConfigTester(self,config_class=A_,has_text_modality=A_,hidden_size=37					)







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            self.config_tester.run_common_tests()







       @unittest.skip(reason='ViTMAE does not use inputs_embeds'					)
       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            pass







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase,    __UpperCamelCase =			self.model_tester.prepare_config_and_inputs_for_common()

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







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase,    __UpperCamelCase =			self.model_tester.prepare_config_and_inputs_for_common()

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

                 __UpperCamelCase =			['pixel_values']
                 self.assertListEqual(arg_names[:1],A_					)







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*A_					)







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






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_pretraining(*A_					)







       def 		snake_case_	(			self: str,A_: str,A_: List[str],A_: Union[str, Any]					):






            '''simple docstring'''




            np.random.seed(2					)

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

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

            super().check_pt_tf_models(A_,A_,A_					)







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase,    __UpperCamelCase =			self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

                      # Make sure we don't have nans
                      __UpperCamelCase =			after_outputs[0].cpu().numpy()
                      __UpperCamelCase =			0
                      __UpperCamelCase =			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\n    to get deterministic results.'					)
       def 		snake_case_	(			self: List[str]					):






            '''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 		snake_case_	(			self: Dict					):






            '''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 		snake_case_	(			self: int					):






            '''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					):






            '''simple docstring'''




            pass







       @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'					)
       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            pass







       @slow
       def 		snake_case_	(			self: str					):






            '''simple docstring'''




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






def 		_A		(		)					-> Any:


     """simple docstring"""


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



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







       @cached_property
       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




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







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






            '''simple docstring'''




            np.random.seed(2					)

            __UpperCamelCase =			ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'					).to(A_					)

            __UpperCamelCase =			self.default_image_processor
            __UpperCamelCase =			prepare_img()
            __UpperCamelCase =			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)
            __UpperCamelCase =			ViTMAEConfig()
            __UpperCamelCase =			int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2					)
            __UpperCamelCase =			np.random.uniform(size=(1, num_patches)					)

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

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

            __UpperCamelCase =			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					)					)






import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__snake_case   							=						1_6
__snake_case   							=						3_2






def 		_A		(		_lowercase					,  _lowercase = 16					,  _lowercase = "bert-base-cased" )					-> Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase =			AutoTokenizer.from_pretrained(_lowercase )
     __UpperCamelCase =			load_dataset('glue'					,  'mrpc' )

     def tokenize_function(_lowercase ):
          # max_length=None => use the model max length (it's actually the default)
          __UpperCamelCase =			tokenizer(examples['sentence1']					,  examples['sentence2']					,  truncation=_lowercase					,  max_length=_lowercase )
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     __UpperCamelCase =			datasets.map(
         _lowercase					,  batched=_lowercase					,  remove_columns=['idx', 'sentence1', 'sentence2']					,  load_from_cache_file=_lowercase )

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     __UpperCamelCase =			tokenized_datasets.rename_column('label'					,  'labels' )

     def collate_fn(_lowercase ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(_lowercase					,  padding='max_length'					,  max_length=1_28					,  return_tensors='pt' )
          return tokenizer.pad(_lowercase					,  padding='longest'					,  return_tensors='pt' )

     # Instantiate dataloaders.
     __UpperCamelCase =			DataLoader(
         tokenized_datasets['train']					,  shuffle=_lowercase					,  collate_fn=_lowercase					,  batch_size=_lowercase )
     __UpperCamelCase =			DataLoader(
         tokenized_datasets['validation']					,  shuffle=_lowercase					,  collate_fn=_lowercase					,  batch_size=_lowercase )

     return train_dataloader, eval_dataloader






def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     __UpperCamelCase =			Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __UpperCamelCase =			config['lr']
     __UpperCamelCase =			int(config['num_epochs'] )
     __UpperCamelCase =			int(config['seed'] )
     __UpperCamelCase =			int(config['batch_size'] )
     __UpperCamelCase =			args.model_name_or_path

     set_seed(_lowercase )
     __UpperCamelCase,    __UpperCamelCase =			get_dataloaders(_lowercase					,  _lowercase					,  _lowercase )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __UpperCamelCase =			AutoModelForSequenceClassification.from_pretrained(_lowercase					,  return_dict=_lowercase )

     # Instantiate optimizer
     __UpperCamelCase =			(
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     __UpperCamelCase =			optimizer_cls(params=model.parameters()					,  lr=_lowercase )

     if accelerator.state.deepspeed_plugin is not None:
          __UpperCamelCase =			accelerator.state.deepspeed_plugin.deepspeed_config[
              'gradient_accumulation_steps'
          ]
     else:
          __UpperCamelCase =			1
     __UpperCamelCase =			(len(_lowercase ) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          __UpperCamelCase =			get_linear_schedule_with_warmup(
              optimizer=_lowercase					,  num_warmup_steps=0					,  num_training_steps=_lowercase					,  )
     else:
          __UpperCamelCase =			DummyScheduler(_lowercase					,  total_num_steps=_lowercase					,  warmup_num_steps=0 )

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			accelerator.prepare(
         _lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )

     # We need to keep track of how many total steps we have iterated over
     __UpperCamelCase =			0
     # We also need to keep track of the stating epoch so files are named properly
     __UpperCamelCase =			0

     # Now we train the model
     __UpperCamelCase =			evaluate.load('glue'					,  'mrpc' )
     __UpperCamelCase =			0
     __UpperCamelCase =			{}
     for epoch in range(_lowercase					,  _lowercase ):
          model.train()
          for step, batch in enumerate(_lowercase ):
               __UpperCamelCase =			model(**_lowercase )
               __UpperCamelCase =			outputs.loss
               __UpperCamelCase =			loss / gradient_accumulation_steps
               accelerator.backward(_lowercase )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

          model.eval()
          __UpperCamelCase =			0
          for step, batch in enumerate(_lowercase ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device )
               with torch.no_grad():
                    __UpperCamelCase =			model(**_lowercase )
               __UpperCamelCase =			outputs.logits.argmax(dim=-1 )
               # It is slightly faster to call this once, than multiple times
               __UpperCamelCase,    __UpperCamelCase =			accelerator.gather(
                   (predictions, batch['labels']) )  # If we are in a multiprocess environment, the last batch has duplicates
               if accelerator.use_distributed:
                    if step == len(_lowercase ) - 1:
                         __UpperCamelCase =			predictions[: len(eval_dataloader.dataset ) - samples_seen]
                         __UpperCamelCase =			references[: len(eval_dataloader.dataset ) - samples_seen]
                    else:
                         samples_seen += references.shape[0]
               metric.add_batch(
                   predictions=_lowercase					,  references=_lowercase					,  )

          __UpperCamelCase =			metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}:'''					,  _lowercase )
          __UpperCamelCase =			eval_metric['accuracy']

          if best_performance < eval_metric["accuracy"]:
               __UpperCamelCase =			eval_metric['accuracy']

     if args.performance_lower_bound is not None:
          assert (
              args.performance_lower_bound <= best_performance
          ), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
          with open(os.path.join(args.output_dir					,  'all_results.json' )					,  'w' ) as f:
               json.dump(_lowercase					,  _lowercase )






def 		_A		(		)					-> List[str]:


     """simple docstring"""


     __UpperCamelCase =			argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
     parser.add_argument(
         '--model_name_or_path'					,  type=_lowercase					,  default='bert-base-cased'					,  help='Path to pretrained model or model identifier from huggingface.co/models.'					,  required=_lowercase					,  )
     parser.add_argument(
         '--output_dir'					,  type=_lowercase					,  default='.'					,  help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'					,  )
     parser.add_argument(
         '--performance_lower_bound'					,  type=_lowercase					,  default=_lowercase					,  help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'					,  )
     parser.add_argument(
         '--num_epochs'					,  type=_lowercase					,  default=3					,  help='Number of train epochs.'					,  )
     __UpperCamelCase =			parser.parse_args()
     __UpperCamelCase =			{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
     training_function(_lowercase					,  _lowercase )


if __name__ == "__main__":
    main()


