


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,      lowerCamelCase,      lowerCamelCase=13,      lowerCamelCase=30,      lowerCamelCase=2,      lowerCamelCase=3,      lowerCamelCase=True,      lowerCamelCase=True,      lowerCamelCase=32,      lowerCamelCase=5,      lowerCamelCase=4,      lowerCamelCase=37,      lowerCamelCase="gelu",      lowerCamelCase=0.1,      lowerCamelCase=0.1,      lowerCamelCase=10,      lowerCamelCase=0.0_2,      lowerCamelCase=3,      lowerCamelCase=0.6,      lowerCamelCase=None,      )       ->   int:




           """simple docstring"""

           _lowercase       :							str      		=		parent
           _lowercase       :							Union[str, Any]      		=		batch_size
           _lowercase       :							Dict      		=		image_size
           _lowercase       :							Optional[Any]      		=		patch_size
           _lowercase       :							List[Any]      		=		num_channels
           _lowercase       :							Union[str, Any]      		=		is_training
           _lowercase       :							Dict      		=		use_labels
           _lowercase       :							List[str]      		=		hidden_size
           _lowercase       :							Union[str, Any]      		=		num_hidden_layers
           _lowercase       :							List[str]      		=		num_attention_heads
           _lowercase       :							Tuple      		=		intermediate_size
           _lowercase       :							List[str]      		=		hidden_act
           _lowercase       :							Dict      		=		hidden_dropout_prob
           _lowercase       :							str      		=		attention_probs_dropout_prob
           _lowercase       :							List[str]      		=		type_sequence_label_size
           _lowercase       :							Union[str, Any]      		=		initializer_range
           _lowercase       :							int      		=		mask_ratio
           _lowercase       :							List[str]      		=		scope

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




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           _lowercase       :							int      		=		None
           if self.use_labels:
               _lowercase       :							List[Any]      		=		ids_tensor([self.batch_size],      self.type_sequence_label_size)

           _lowercase       :							Optional[int]      		=		self.get_config()

           return config, pixel_values, labels
       def 			UpperCamelCase     (					self)       ->   Optional[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=lowerCamelCase,      initializer_range=self.initializer_range,      mask_ratio=self.mask_ratio,      )
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Dict:




           """simple docstring"""

           _lowercase       :							Tuple      		=		ViTMAEModel(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Tuple      		=		model(lowerCamelCase)
           self.parent.assertEqual(result.last_hidden_state.shape,      (self.batch_size, self.seq_length, self.hidden_size))
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		ViTMAEForPreTraining(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Optional[int]      		=		model(lowerCamelCase)
           _lowercase       :							int      		=		(self.image_size // self.patch_size) ** 2
           _lowercase       :							List[str]      		=		self.patch_size**2 * self.num_channels
           self.parent.assertEqual(result.logits.shape,      (self.batch_size, num_patches, expected_num_channels))

           # test greyscale images
           _lowercase       :							Tuple      		=		1
           _lowercase       :							Any      		=		ViTMAEForPreTraining(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Optional[Any]      		=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           _lowercase       :							Dict      		=		model(lowerCamelCase)
           _lowercase       :							List[str]      		=		self.patch_size**2
           self.parent.assertEqual(result.logits.shape,      (self.batch_size, num_patches, expected_num_channels))




       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Dict      		=		self.prepare_config_and_inputs()
           _lowercase		,				_lowercase		,				_lowercase       :							Tuple      		=		config_and_inputs
           _lowercase       :							Any      		=		{'pixel_values': pixel_values}
           return config, inputs_dict






@require_torch
class _lowerCamelCase(						_a, _a, unittest.TestCase   ):
       lowercase_ :	Optional[Any]     =			(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
       lowercase_ :	str     =			{"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

       lowercase_ :	Optional[int]     =			False
       lowercase_ :	List[str]     =			False
       lowercase_ :	Dict     =			False
       lowercase_ :	List[str]     =			False
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		ViTMAEModelTester(self)
           _lowercase       :							Union[str, Any]      		=		ConfigTester(self,      config_class=lowerCamelCase,      has_text_modality=lowerCamelCase,      hidden_size=37)
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           self.config_tester.run_common_tests()
       @unittest.skip(reason='ViTMAE does not use inputs_embeds')
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           pass
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase		,				_lowercase       :							Any      		=		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               _lowercase       :							Union[str, Any]      		=		model_class(lowerCamelCase)
               self.assertIsInstance(model.get_input_embeddings(),      (nn.Module))
               _lowercase       :							str      		=		model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCamelCase,      nn.Linear))
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           _lowercase		,				_lowercase       :							Dict      		=		self.model_tester.prepare_config_and_inputs_for_common()

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

               _lowercase       :							Optional[Any]      		=		['pixel_values']
               self.assertListEqual(arg_names[:1],      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							str      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							int      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_pretraining(*lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Dict:




           """simple docstring"""

           np.random.seed(2)

           _lowercase       :							Optional[Any]      		=		int((pt_model.config.image_size // pt_model.config.patch_size) ** 2)
           _lowercase       :							Optional[int]      		=		np.random.uniform(size=(self.model_tester.batch_size, num_patches))
           _lowercase       :							List[str]      		=		torch.from_numpy(lowerCamelCase)

           # Add `noise` argument.
           # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
           _lowercase       :							List[str]      		=		pt_noise

           super().check_pt_tf_models(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase		,				_lowercase       :							Tuple      		=		self.model_tester.prepare_config_and_inputs_for_common()

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

               _lowercase       :							str      		=		outputs[0].cpu().numpy()
               _lowercase       :							List[str]      		=		0

               with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_pretrained(lowerCamelCase)
                   _lowercase       :							Tuple      		=		model_class.from_pretrained(lowerCamelCase)
                   model.to(lowerCamelCase)
                   # make random mask reproducible
                   torch.manual_seed(2)
                   with torch.no_grad():
                       _lowercase       :							Optional[int]      		=		model(**self._prepare_for_class(lowerCamelCase,      lowerCamelCase))

                   # Make sure we don't have nans
                   _lowercase       :							Tuple      		=		after_outputs[0].cpu().numpy()
                   _lowercase       :							List[Any]      		=		0
                   _lowercase       :							Any      		=		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 			UpperCamelCase     (					self)       ->   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 			UpperCamelCase     (					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 			UpperCamelCase     (					self)       ->   Dict:




           """simple docstring"""

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




           """simple docstring"""

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




           """simple docstring"""

           pass




       @slow
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase       :							Tuple      		=		ViTMAEModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)



def 				UpperCamelCase_(      )    ->						Tuple:
    _lowercase       :							int      		=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
    return image






@require_torch
@require_vision
class _lowerCamelCase(						unittest.TestCase   ):
       @cached_property
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

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




       @slow
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           np.random.seed(2)

           _lowercase       :							Dict      		=		ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base').to(lowerCamelCase)

           _lowercase       :							int      		=		self.default_image_processor
           _lowercase       :							List[Any]      		=		prepare_img()
           _lowercase       :							List[Any]      		=		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)
           _lowercase       :							Optional[Any]      		=		ViTMAEConfig()
           _lowercase       :							Dict      		=		int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
           _lowercase       :							Dict      		=		np.random.uniform(size=(1, num_patches))

           # forward pass
           with torch.no_grad():
               _lowercase       :							List[Any]      		=		model(**lowerCamelCase,      noise=torch.from_numpy(lowerCamelCase).to(device=lowerCamelCase))

           # verify the logits
           _lowercase       :							Optional[Any]      		=		torch.Size((1, 1_96, 7_68))
           self.assertEqual(outputs.logits.shape,      lowerCamelCase)

           _lowercase       :							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(lowerCamelCase),      atol=1E-4))







'''simple docstring'''





import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
 from sagemaker import Session, TrainingJobAnalytics
 from sagemaker.huggingface import HuggingFace






@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''						, '''False'''     )     ) is not True						, reason='''Skipping test because should only be run when releasing minor transformers version'''						, )
@pytest.mark.usefixtures('''sm_env'''     )
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_ddp.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf_dist.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]     )
class        lowerCAmelCase_(				unittest.TestCase     ):
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      if self.framework == "pytorch":
          subprocess.run(
              F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()  ,encoding="""utf-8"""  ,check=__UpperCAmelCase  ,)
      assert hasattr(self  ,"""env"""      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Optional[Any]:
      lowerCAmelCase__ :  Optional[int]							      =					F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
      # distributed data settings
      lowerCAmelCase__ :  Any							      =					{"""smdistributed""": {"""dataparallel""": {"""enabled""": True}}} if self.script != """run_ddp.py""" else None

      # creates estimator
      return HuggingFace(
          entry_point=self.script  ,source_dir=self.env.test_path  ,role=self.env.role  ,image_uri=self.env.image_uri  ,base_job_name=__UpperCAmelCase  ,instance_count=__UpperCAmelCase  ,instance_type=self.instance_type  ,debugger_hook_config=__UpperCAmelCase  ,hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}  ,metric_definitions=self.env.metric_definitions  ,distribution=__UpperCAmelCase  ,py_version="""py36"""  ,)




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Optional[Any]:
      TrainingJobAnalytics(__UpperCAmelCase      ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""      )






  @parameterized.expand([(2,)]      )
  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Any:
      # create estimator
      lowerCAmelCase__ :  List[Any]							      =					self.create_estimator(__UpperCAmelCase      )

      # run training
      estimator.fit()

      # result dataframe
      lowerCAmelCase__ :  Any							      =					TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

      # extract kpis
      lowerCAmelCase__ :  int							      =					list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]      )
      lowerCAmelCase__ :  List[Any]							      =					list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]      )
      # get train time from SageMaker job, this includes starting, preprocessing, stopping
      lowerCAmelCase__ :  List[str]							      =					(
          Session().describe_training_job(estimator.latest_training_job.name      ).get("""TrainingTimeInSeconds"""  ,99_9999      )
      )

      # assert kpis
      assert train_runtime <= self.results["train_runtime"]
      assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy      )
      assert all(t <= self.results["""eval_loss"""] for t in eval_loss      )

      # dump tests result into json file to share in PR
      with open(F"""{estimator.latest_training_job.name}.json"""  ,"""w"""      ) as outfile:
          json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss}  ,__UpperCAmelCase      )


