





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class __a (   _snake_case			):
   __UpperCamelCase   : List[Any]	      =	(CMStochasticIterativeScheduler,)
   __UpperCamelCase   : Tuple	      =	10





   def      UpperCAmelCase__     (					self				:			Any				,**lowerCamelCase				:			int    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       {
          """num_train_timesteps""": 201,
          """sigma_min""": 0.002,
          """sigma_max""": 80.0,
      }

      config.update(**lowercase_    )
      return config





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       10

      __SCREAMING_SNAKE_CASE      							=       self.get_scheduler_config()
      __SCREAMING_SNAKE_CASE      							=       self.scheduler_classes[0](**lowercase_    )

      scheduler.set_timesteps(lowercase_    )

      __SCREAMING_SNAKE_CASE      							=       scheduler.timesteps[0]
      __SCREAMING_SNAKE_CASE      							=       scheduler.timesteps[1]

      __SCREAMING_SNAKE_CASE      							=       self.dummy_sample
      __SCREAMING_SNAKE_CASE      							=       0.1 * sample

      __SCREAMING_SNAKE_CASE      							=       scheduler.step(lowercase_				,lowercase_				,lowercase_    ).prev_sample
      __SCREAMING_SNAKE_CASE      							=       scheduler.step(lowercase_				,lowercase_				,lowercase_    ).prev_sample

      self.assertEqual(output_a.shape				,sample.shape    )
      self.assertEqual(output_a.shape				,output_a.shape    )





   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      for timesteps in [10, 50, 100, 1000]:
         self.check_over_configs(num_train_timesteps=lowercase_    )





   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      for clip_denoised in [True, False]:
         self.check_over_configs(clip_denoised=lowercase_    )





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.scheduler_classes[0]
      __SCREAMING_SNAKE_CASE      							=       self.get_scheduler_config()
      __SCREAMING_SNAKE_CASE      							=       scheduler_class(**lowercase_    )

      __SCREAMING_SNAKE_CASE      							=       1
      scheduler.set_timesteps(lowercase_    )
      __SCREAMING_SNAKE_CASE      							=       scheduler.timesteps

      __SCREAMING_SNAKE_CASE      							=       torch.manual_seed(0    )

      __SCREAMING_SNAKE_CASE      							=       self.dummy_model()
      __SCREAMING_SNAKE_CASE      							=       self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(lowercase_    ):
         # 1. scale model input
         __SCREAMING_SNAKE_CASE      							=       scheduler.scale_model_input(lowercase_				,lowercase_    )

         # 2. predict noise residual
         __SCREAMING_SNAKE_CASE      							=       model(lowercase_				,lowercase_    )

         # 3. predict previous sample x_t-1
         __SCREAMING_SNAKE_CASE      							=       scheduler.step(lowercase_				,lowercase_				,lowercase_				,generator=lowercase_    ).prev_sample

         __SCREAMING_SNAKE_CASE      							=       pred_prev_sample

      __SCREAMING_SNAKE_CASE      							=       torch.sum(torch.abs(lowercase_    )    )
      __SCREAMING_SNAKE_CASE      							=       torch.mean(torch.abs(lowercase_    )    )

      assert abs(result_sum.item() - 192.7614    ) < 1E-2
      assert abs(result_mean.item() - 0.2_510    ) < 1E-3





   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.scheduler_classes[0]
      __SCREAMING_SNAKE_CASE      							=       self.get_scheduler_config()
      __SCREAMING_SNAKE_CASE      							=       scheduler_class(**lowercase_    )

      __SCREAMING_SNAKE_CASE      							=       [106, 0]
      scheduler.set_timesteps(timesteps=lowercase_    )
      __SCREAMING_SNAKE_CASE      							=       scheduler.timesteps

      __SCREAMING_SNAKE_CASE      							=       torch.manual_seed(0    )

      __SCREAMING_SNAKE_CASE      							=       self.dummy_model()
      __SCREAMING_SNAKE_CASE      							=       self.dummy_sample_deter * scheduler.init_noise_sigma

      for t in timesteps:
         # 1. scale model input
         __SCREAMING_SNAKE_CASE      							=       scheduler.scale_model_input(lowercase_				,lowercase_    )

         # 2. predict noise residual
         __SCREAMING_SNAKE_CASE      							=       model(lowercase_				,lowercase_    )

         # 3. predict previous sample x_t-1
         __SCREAMING_SNAKE_CASE      							=       scheduler.step(lowercase_				,lowercase_				,lowercase_				,generator=lowercase_    ).prev_sample

         __SCREAMING_SNAKE_CASE      							=       pred_prev_sample

      __SCREAMING_SNAKE_CASE      							=       torch.sum(torch.abs(lowercase_    )    )
      __SCREAMING_SNAKE_CASE      							=       torch.mean(torch.abs(lowercase_    )    )

      assert abs(result_sum.item() - 347.6357    ) < 1E-2
      assert abs(result_mean.item() - 0.4_527    ) < 1E-3





   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.scheduler_classes[0]
      __SCREAMING_SNAKE_CASE      							=       self.get_scheduler_config()
      __SCREAMING_SNAKE_CASE      							=       scheduler_class(**lowercase_    )

      __SCREAMING_SNAKE_CASE      							=       [39, 30, 12, 15, 0]

      with self.assertRaises(lowercase_				,msg="""`timesteps` must be in descending order."""    ):
         scheduler.set_timesteps(timesteps=lowercase_    )





   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.scheduler_classes[0]
      __SCREAMING_SNAKE_CASE      							=       self.get_scheduler_config()
      __SCREAMING_SNAKE_CASE      							=       scheduler_class(**lowercase_    )

      __SCREAMING_SNAKE_CASE      							=       [39, 30, 12, 1, 0]
      __SCREAMING_SNAKE_CASE      							=       len(lowercase_    )

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





   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.scheduler_classes[0]
      __SCREAMING_SNAKE_CASE      							=       self.get_scheduler_config()
      __SCREAMING_SNAKE_CASE      							=       scheduler_class(**lowercase_    )

      __SCREAMING_SNAKE_CASE      							=       [scheduler.config.num_train_timesteps]

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












'''simple docstring'''

import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

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


if is_flax_available():
    import jax

    from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel







class __a (   unittest.TestCase			):





   def __init__(					self				:			Optional[int]				,lowerCamelCase				:			str				,lowerCamelCase				:			List[str]=13				,lowerCamelCase				:			Optional[Any]=30				,lowerCamelCase				:			Dict=2				,lowerCamelCase				:			List[Any]=3				,lowerCamelCase				:			List[str]=True				,lowerCamelCase				:			str=True				,lowerCamelCase				:			Optional[int]=32				,lowerCamelCase				:			Dict=5				,lowerCamelCase				:			Optional[int]=4				,lowerCamelCase				:			List[Any]=37				,lowerCamelCase				:			Union[str, Any]="gelu"				,lowerCamelCase				:			List[Any]=0.1				,lowerCamelCase				:			Any=0.1				,lowerCamelCase				:			str=10				,lowerCamelCase				:			Dict=0.02				,):

      '''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

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





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE      							=       ViTConfig(
          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				,)

      return config, pixel_values





   def      UpperCAmelCase__     (					self				:			Tuple				,lowerCamelCase				:			int				,lowerCamelCase				:			Optional[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       FlaxViTModel(config=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       model(lowerCamelCase    )
      # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
      __SCREAMING_SNAKE_CASE      							=       (self.image_size, self.image_size)
      __SCREAMING_SNAKE_CASE      							=       (self.patch_size, self.patch_size)
      __SCREAMING_SNAKE_CASE      							=       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
      self.parent.assertEqual(result.last_hidden_state.shape				,(self.batch_size, num_patches + 1, self.hidden_size)    )





   def      UpperCAmelCase__     (					self				:			Union[str, Any]				,lowerCamelCase				:			Optional[int]				,lowerCamelCase				:			Dict    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.type_sequence_label_size
      __SCREAMING_SNAKE_CASE      							=       FlaxViTForImageClassification(config=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       model(lowerCamelCase    )
      self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.type_sequence_label_size)    )

      # test greyscale images
      __SCREAMING_SNAKE_CASE      							=       1
      __SCREAMING_SNAKE_CASE      							=       FlaxViTForImageClassification(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
      __SCREAMING_SNAKE_CASE      							=       model(lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.prepare_config_and_inputs()
      (
          (
          __SCREAMING_SNAKE_CASE
      )							,     (
          __SCREAMING_SNAKE_CASE
      )							,     
      )      							=       config_and_inputs
      __SCREAMING_SNAKE_CASE      							=       {"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_flax
class __a (   _snake_case,   unittest.TestCase			):
   __UpperCamelCase   : Any	      =	(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       FlaxViTModelTester(self    )
      __SCREAMING_SNAKE_CASE      							=       ConfigTester(self				,config_class=lowerCamelCase				,has_text_modality=lowerCamelCase				,hidden_size=37    )





   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      self.config_tester.run_common_tests()





   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			List[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.__call__    )
         # 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      UpperCAmelCase__     (					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:
         with self.subTest(model_class.__name__    ):
            __SCREAMING_SNAKE_CASE      							=       self._prepare_for_class(lowerCamelCase				,lowerCamelCase    )
            __SCREAMING_SNAKE_CASE      							=       model_class(lowerCamelCase    )

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

            with self.subTest("""JIT Enabled"""    ):
               __SCREAMING_SNAKE_CASE      							=       model_jitted(**lowerCamelCase    ).to_tuple()

            with self.subTest("""JIT Disabled"""    ):
               with jax.disable_jit():
                  __SCREAMING_SNAKE_CASE      							=       model_jitted(**lowerCamelCase    ).to_tuple()

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





   @slow
   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      for model_class_name in self.all_model_classes:
         __SCREAMING_SNAKE_CASE      							=       model_class_name.from_pretrained("""google/vit-base-patch16-224"""    )
         __SCREAMING_SNAKE_CASE      							=       model(np.ones((1, 3, 224, 224)    )    )
         self.assertIsNotNone(lowerCamelCase    )





