
'''simple docstring'''





import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
 import jax

 from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
 from PIL import Image

 from transformers import BeitImageProcessor


class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      def __init__(      self		:       Any				,       _snake_case		:       int				,       _snake_case		:       str=100				,       _snake_case		:       str=13				,       _snake_case		:       Tuple=30				,       _snake_case		:       List[str]=2				,       _snake_case		:       Optional[int]=3				,       _snake_case		:       Any=True				,       _snake_case		:       Union[str, Any]=True				,       _snake_case		:       Optional[int]=32				,       _snake_case		:       List[Any]=5				,       _snake_case		:       Dict=4				,       _snake_case		:       Union[str, Any]=37				,       _snake_case		:       List[Any]="gelu"				,       _snake_case		:       Optional[int]=0.1				,       _snake_case		:       Union[str, Any]=0.1				,       _snake_case		:       List[Any]=10				,       _snake_case		:       str=0.02				,       _snake_case		:       List[str]=3				,       ):
          __lowercase		:			Tuple	    =       parent
          __lowercase		:			Dict	    =       vocab_size
          __lowercase		:			List[str]	    =       batch_size
          __lowercase		:			Optional[Any]	    =       image_size
          __lowercase		:			int	    =       patch_size
          __lowercase		:			Optional[int]	    =       num_channels
          __lowercase		:			str	    =       is_training
          __lowercase		:			Tuple	    =       use_labels
          __lowercase		:			Optional[int]	    =       hidden_size
          __lowercase		:			Dict	    =       num_hidden_layers
          __lowercase		:			int	    =       num_attention_heads
          __lowercase		:			List[str]	    =       intermediate_size
          __lowercase		:			Any	    =       hidden_act
          __lowercase		:			List[Any]	    =       hidden_dropout_prob
          __lowercase		:			Union[str, Any]	    =       attention_probs_dropout_prob
          __lowercase		:			Optional[int]	    =       type_sequence_label_size
          __lowercase		:			Any	    =       initializer_range

          # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          __lowercase		:			Dict	    =       (image_size // patch_size) ** 2
          __lowercase		:			str	    =       num_patches + 1




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Dict	    =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

          __lowercase		:			Optional[int]	    =       None
          if self.use_labels:
              __lowercase		:			str	    =       ids_tensor([self.batch_size]				,       self.type_sequence_label_size				)

          __lowercase		:			int	    =       BeitConfig(
              vocab_size=self.vocab_size				,       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=snake_case_				,       initializer_range=self.initializer_range				,       )

          return config, pixel_values, labels




      def 		snake_case_			(      self		:       Union[str, Any]				,       _snake_case		:       Optional[Any]				,       _snake_case		:       Dict				,       _snake_case		:       Tuple				):
          __lowercase		:			Any	    =       FlaxBeitModel(config=snake_case_				)
          __lowercase		:			List[Any]	    =       model(snake_case_				)
          self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)				)




      def 		snake_case_			(      self		:       List[Any]				,       _snake_case		:       Dict				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       Optional[Any]				):
          __lowercase		:			List[str]	    =       FlaxBeitForMaskedImageModeling(config=snake_case_				)
          __lowercase		:			Union[str, Any]	    =       model(snake_case_				)
          self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length - 1, self.vocab_size)				)




      def 		snake_case_			(      self		:       int				,       _snake_case		:       Optional[Any]				,       _snake_case		:       Optional[int]				,       _snake_case		:       str				):
          __lowercase		:			Optional[int]	    =       self.type_sequence_label_size
          __lowercase		:			Any	    =       FlaxBeitForImageClassification(config=snake_case_				)
          __lowercase		:			Union[str, Any]	    =       model(snake_case_				)
          self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.type_sequence_label_size)				)

          # test greyscale images
          __lowercase		:			int	    =       1
          __lowercase		:			Tuple	    =       FlaxBeitForImageClassification(snake_case_				)

          __lowercase		:			List[str]	    =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
          __lowercase		:			int	    =       model(snake_case_				)




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Optional[Any]	    =       self.prepare_config_and_inputs()
          (
              (
              __lowercase						
          )    ,      (
              __lowercase						
          )    ,      (
              __lowercase						
          )    ,      						
          )		:			Optional[Any]	    =       config_and_inputs
          __lowercase		:			str	    =       {'''pixel_values''': pixel_values}
          return config, inputs_dict






@require_flax
class 						__lowerCAmelCase	(	_a , unittest.TestCase      ):



      """simple docstring"""







      A__	:			int        =    (
          (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
      )




      def 		snake_case_			(      self		:       int				):
          __lowercase		:			List[str]	    =       FlaxBeitModelTester(self				)
          __lowercase		:			Optional[Any]	    =       ConfigTester(self				,       config_class=snake_case_				,       has_text_modality=snake_case_				,       hidden_size=37				)




      def 		snake_case_			(      self		:       Any				):
          self.config_tester.run_common_tests()




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase    ,      __lowercase		:			List[str]	    =       self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __lowercase		:			List[str]	    =       model_class(snake_case_				)
              __lowercase		:			str	    =       inspect.signature(model.__call__				)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              __lowercase		:			List[str]	    =       [*signature.parameters.keys()]

              __lowercase		:			Tuple	    =       ['''pixel_values''']
              self.assertListEqual(arg_names[:1]				,       snake_case_				)




      def 		snake_case_			(      self		:       List[str]				):
          __lowercase    ,      __lowercase		:			Optional[int]	    =       self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__				):
                  __lowercase		:			List[Any]	    =       self._prepare_for_class(snake_case_				,       snake_case_				)
                  __lowercase		:			List[Any]	    =       model_class(snake_case_				)

                  @jax.jit
                  def model_jitted(_snake_case		:       Union[str, Any]				,       **_snake_case		:       int				):
                      return model(pixel_values=snake_case_				,       **snake_case_				)

                  with self.subTest('''JIT Enabled'''				):
                      __lowercase		:			Any	    =       model_jitted(**snake_case_				).to_tuple()

                  with self.subTest('''JIT Disabled'''				):
                      with jax.disable_jit():
                          __lowercase		:			Optional[Any]	    =       model_jitted(**snake_case_				).to_tuple()

                  self.assertEqual(len(snake_case_				)				,       len(snake_case_				)				)
                  for jitted_output, output in zip(snake_case_				,       snake_case_				):
                      self.assertEqual(jitted_output.shape				,       output.shape				)




      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			List[Any]	    =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case_				)




      def 		snake_case_			(      self		:       int				):
          __lowercase		:			int	    =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*snake_case_				)




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			Dict	    =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case_				)




      @slow
      def 		snake_case_			(      self		:       int				):
          for model_class_name in self.all_model_classes:
              __lowercase		:			Dict	    =       model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''				)
              __lowercase		:			Optional[Any]	    =       model(np.ones((1, 3, 224, 224)				)				)
              self.assertIsNotNone(snake_case_				)






def     UpperCAmelCase_  (						) ->						Optional[int]:
    __lowercase		:			Union[str, Any]	    =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
    return image





@require_vision
@require_flax
class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      @cached_property
      def 		snake_case_			(      self		:       Optional[int]				):
          return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''				) if is_vision_available() else None




      @slow
      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			List[str]	    =       FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''				)

          __lowercase		:			str	    =       self.default_image_processor
          __lowercase		:			List[Any]	    =       prepare_img()
          __lowercase		:			Tuple	    =       image_processor(images=snake_case_				,       return_tensors='''np'''				).pixel_values

          # prepare bool_masked_pos
          __lowercase		:			Tuple	    =       np.ones((1, 196)				,       dtype=snake_case_				)

          # forward pass
          __lowercase		:			Any	    =       model(pixel_values=snake_case_				,       bool_masked_pos=snake_case_				)
          __lowercase		:			Optional[int]	    =       outputs.logits

          # verify the logits
          __lowercase		:			Any	    =       (1, 196, 8192)
          self.assertEqual(logits.shape				,       snake_case_				)

          __lowercase		:			Optional[Any]	    =       np.array(
              [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]				)

          self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]				,       snake_case_				,       atol=1E-2				)				)




      @slow
      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			Tuple	    =       FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''				)

          __lowercase		:			Any	    =       self.default_image_processor
          __lowercase		:			Dict	    =       prepare_img()
          __lowercase		:			Optional[int]	    =       image_processor(images=snake_case_				,       return_tensors='''np'''				)

          # forward pass
          __lowercase		:			Optional[Any]	    =       model(**snake_case_				)
          __lowercase		:			List[Any]	    =       outputs.logits

          # verify the logits
          __lowercase		:			Any	    =       (1, 1000)
          self.assertEqual(logits.shape				,       snake_case_				)

          __lowercase		:			Optional[Any]	    =       np.array([-1.23_85, -1.09_87, -1.01_08]				)

          self.assertTrue(np.allclose(logits[0, :3]				,       snake_case_				,       atol=1E-4				)				)

          __lowercase		:			List[str]	    =       281
          self.assertEqual(logits.argmax(-1				).item()				,       snake_case_				)




      @slow
      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			Tuple	    =       FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''				)

          __lowercase		:			Optional[Any]	    =       self.default_image_processor
          __lowercase		:			Tuple	    =       prepare_img()
          __lowercase		:			List[str]	    =       image_processor(images=snake_case_				,       return_tensors='''np'''				)

          # forward pass
          __lowercase		:			Dict	    =       model(**snake_case_				)
          __lowercase		:			Optional[int]	    =       outputs.logits

          # verify the logits
          __lowercase		:			Any	    =       (1, 2_1841)
          self.assertEqual(logits.shape				,       snake_case_				)

          __lowercase		:			str	    =       np.array([1.68_81, -0.27_87, 0.59_01]				)

          self.assertTrue(np.allclose(logits[0, :3]				,       snake_case_				,       atol=1E-4				)				)

          __lowercase		:			Optional[int]	    =       2396
          self.assertEqual(logits.argmax(-1				).item()				,       snake_case_				)




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			Optional[Any]        =    (DDPMScheduler,)




      def 		snake_case_			(      self		:       Dict				,       **_snake_case		:       List[str]				):
          __lowercase		:			Tuple	    =       {
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.00_01,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''variance_type''': '''fixed_small''',
              '''clip_sample''': True,
          }

          config.update(**_snake_case				)
          return config




      def 		snake_case_			(      self		:       Union[str, Any]				):
          for timesteps in [1, 5, 100, 1000]:
              self.check_over_configs(num_train_timesteps=_snake_case				)




      def 		snake_case_			(      self		:       Dict				):
          for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]				,       [0.0_02, 0.02, 0.2, 2]				):
              self.check_over_configs(beta_start=_snake_case				,       beta_end=_snake_case				)




      def 		snake_case_			(      self		:       Optional[Any]				):
          for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=_snake_case				)




      def 		snake_case_			(      self		:       Optional[int]				):
          for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=_snake_case				)




      def 		snake_case_			(      self		:       Tuple				):
          for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=_snake_case				)




      def 		snake_case_			(      self		:       Any				):
          self.check_over_configs(thresholding=_snake_case				)
          for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                  self.check_over_configs(
                      thresholding=_snake_case				,       prediction_type=_snake_case				,       sample_max_value=_snake_case				,       )




      def 		snake_case_			(      self		:       List[str]				):
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(prediction_type=_snake_case				)




      def 		snake_case_			(      self		:       Dict				):
          for t in [0, 500, 999]:
              self.check_over_forward(time_step=_snake_case				)




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			int	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config()
          __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

          assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(487				) - 0.0_09_79				)				) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.02				)				) < 1E-5




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			str	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config()
          __lowercase		:			int	    =       scheduler_class(**_snake_case				)

          __lowercase		:			List[Any]	    =       len(_snake_case				)

          __lowercase		:			Dict	    =       self.dummy_model()
          __lowercase		:			Tuple	    =       self.dummy_sample_deter
          __lowercase		:			List[str]	    =       torch.manual_seed(0				)

          for t in reversed(range(_snake_case				)				):
              # 1. predict noise residual
              __lowercase		:			Optional[int]	    =       model(_snake_case				,       _snake_case				)

              # 2. predict previous mean of sample x_t-1
              __lowercase		:			int	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       generator=_snake_case				).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              __lowercase		:			Any	    =       pred_prev_sample

          __lowercase		:			str	    =       torch.sum(torch.abs(_snake_case				)				)
          __lowercase		:			Any	    =       torch.mean(torch.abs(_snake_case				)				)

          assert abs(result_sum.item() - 2_58.96_06				) < 1E-2
          assert abs(result_mean.item() - 0.33_72				) < 1E-3




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			str	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config(prediction_type='''v_prediction'''				)
          __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			List[Any]	    =       len(_snake_case				)

          __lowercase		:			List[Any]	    =       self.dummy_model()
          __lowercase		:			str	    =       self.dummy_sample_deter
          __lowercase		:			Dict	    =       torch.manual_seed(0				)

          for t in reversed(range(_snake_case				)				):
              # 1. predict noise residual
              __lowercase		:			Tuple	    =       model(_snake_case				,       _snake_case				)

              # 2. predict previous mean of sample x_t-1
              __lowercase		:			Optional[int]	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       generator=_snake_case				).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              __lowercase		:			Any	    =       pred_prev_sample

          __lowercase		:			int	    =       torch.sum(torch.abs(_snake_case				)				)
          __lowercase		:			List[Any]	    =       torch.mean(torch.abs(_snake_case				)				)

          assert abs(result_sum.item() - 2_02.02_96				) < 1E-2
          assert abs(result_mean.item() - 0.26_31				) < 1E-3




      def 		snake_case_			(      self		:       str				):
          __lowercase		:			int	    =       self.scheduler_classes[0]
          __lowercase		:			List[str]	    =       self.get_scheduler_config()
          __lowercase		:			List[str]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Optional[Any]	    =       [100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=_snake_case				)

          __lowercase		:			int	    =       scheduler.timesteps

          for i, timestep in enumerate(_snake_case				):
              if i == len(_snake_case				) - 1:
                  __lowercase		:			Optional[Any]	    =       -1
              else:
                  __lowercase		:			List[str]	    =       timesteps[i + 1]

              __lowercase		:			Optional[Any]	    =       scheduler.previous_timestep(_snake_case				)
              __lowercase		:			str	    =       prev_t.item()

              self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Tuple	    =       self.scheduler_classes[0]
          __lowercase		:			Optional[Any]	    =       self.get_scheduler_config()
          __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Any	    =       [100, 87, 50, 51, 0]

          with self.assertRaises(_snake_case				,       msg='''`custom_timesteps` must be in descending order.'''				):
              scheduler.set_timesteps(timesteps=_snake_case				)




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Optional[int]	    =       self.scheduler_classes[0]
          __lowercase		:			Union[str, Any]	    =       self.get_scheduler_config()
          __lowercase		:			int	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Dict	    =       [100, 87, 50, 1, 0]
          __lowercase		:			str	    =       len(_snake_case				)

          with self.assertRaises(_snake_case				,       msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''				):
              scheduler.set_timesteps(num_inference_steps=_snake_case				,       timesteps=_snake_case				)




      def 		snake_case_			(      self		:       str				):
          __lowercase		:			List[str]	    =       self.scheduler_classes[0]
          __lowercase		:			Optional[int]	    =       self.get_scheduler_config()
          __lowercase		:			int	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Optional[int]	    =       [scheduler.config.num_train_timesteps]

          with self.assertRaises(
              _snake_case				,       msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''				,       ):
              scheduler.set_timesteps(timesteps=_snake_case				)


