





"""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 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase    , __UpperCAmelCase=1_3    , __UpperCAmelCase=3_0    , __UpperCAmelCase=2    , __UpperCAmelCase=3    , __UpperCAmelCase=True    , __UpperCAmelCase=True    , __UpperCAmelCase=3_2    , __UpperCAmelCase=5    , __UpperCAmelCase=4    , __UpperCAmelCase=3_7    , __UpperCAmelCase="gelu"    , __UpperCAmelCase=0.1    , __UpperCAmelCase=0.1    , __UpperCAmelCase=1_0    , __UpperCAmelCase=0.02    , ):



         '''simple docstring'''

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

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






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

         lowerCAmelCase__			:List[Any]											=							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=_SCREAMING_SNAKE_CASE    , initializer_range=self.initializer_range    , )

         return config, pixel_values






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							FlaxViTModel(config=_SCREAMING_SNAKE_CASE      )
         lowerCAmelCase__			:List[Any]											=							model(_SCREAMING_SNAKE_CASE      )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         lowerCAmelCase__			:List[Any]											=							(self.image_size, self.image_size)
         lowerCAmelCase__			:int											=							(self.patch_size, self.patch_size)
         lowerCAmelCase__			:Union[str, Any]											=							(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      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[int]											=							self.type_sequence_label_size
         lowerCAmelCase__			:List[Any]											=							FlaxViTForImageClassification(config=_SCREAMING_SNAKE_CASE      )
         lowerCAmelCase__			:List[Any]											=							model(_SCREAMING_SNAKE_CASE      )
         self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.type_sequence_label_size)      )

         # test greyscale images
         lowerCAmelCase__			:Tuple											=							1
         lowerCAmelCase__			:Optional[Any]											=							FlaxViTForImageClassification(_SCREAMING_SNAKE_CASE      )

         lowerCAmelCase__			:Any											=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
         lowerCAmelCase__			:Any											=							model(_SCREAMING_SNAKE_CASE      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[int]											=							self.prepare_config_and_inputs()
         (
             lowerCAmelCase__       
         )			:List[str]											=							config_and_inputs
         lowerCAmelCase__			:Optional[int]											=							{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_flax
class 					_lowerCAmelCase   (     a  ,   unittest.TestCase       ):
       """simple docstring"""




       __magic_name__     :List[Any]		  =					(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							FlaxViTModelTester(self      )
         lowerCAmelCase__			:Dict											=							ConfigTester(self    , config_class=_SCREAMING_SNAKE_CASE    , has_text_modality=_SCREAMING_SNAKE_CASE    , hidden_size=3_7      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         self.config_tester.run_common_tests()






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							self.model_tester.prepare_config_and_inputs_for_common()

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

           lowerCAmelCase__			:Dict											=							['''pixel_values''']
           self.assertListEqual(arg_names[:1]    , _SCREAMING_SNAKE_CASE      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__      ):
             lowerCAmelCase__			:Union[str, Any]											=							self._prepare_for_class(_SCREAMING_SNAKE_CASE    , _SCREAMING_SNAKE_CASE      )
             lowerCAmelCase__			:Union[str, Any]											=							model_class(_SCREAMING_SNAKE_CASE      )

             @jax.jit
             def model_jitted(__UpperCAmelCase    , **__UpperCAmelCase      ):
               return model(pixel_values=_SCREAMING_SNAKE_CASE    , **_SCREAMING_SNAKE_CASE      )

             with self.subTest('JIT Enabled'      ):
               lowerCAmelCase__			:Tuple											=							model_jitted(**_SCREAMING_SNAKE_CASE      ).to_tuple()

             with self.subTest('JIT Disabled'      ):
               with jax.disable_jit():
                 lowerCAmelCase__			:Any											=							model_jitted(**_SCREAMING_SNAKE_CASE      ).to_tuple()

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






       @slow
       def      snake_case						(    self      ):



         '''simple docstring'''

         for model_class_name in self.all_model_classes:
           lowerCAmelCase__			:Any											=							model_class_name.from_pretrained('google/vit-base-patch16-224'      )
           lowerCAmelCase__			:Tuple											=							model(np.ones((1, 3, 2_2_4, 2_2_4)      )      )
           self.assertIsNotNone(_SCREAMING_SNAKE_CASE      )




import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class   _lowerCamelCase (					UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    DDIMPipeline
       snake_case      =    UNCONDITIONAL_IMAGE_GENERATION_PARAMS
       snake_case      =    PipelineTesterMixin.required_optional_params - {
           "num_images_per_prompt",
           "latents",
           "callback",
           "callback_steps",
       }
       snake_case      =    UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
       snake_case      =    False



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




         '''simple docstring'''





         torch.manual_seed(0		)
         A_      :				List[str]		     =					UNetaDModel(
             block_out_channels=(32, 64)				,       layers_per_block=2				,       sample_size=32				,       in_channels=3				,       out_channels=3				,       down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')				,       up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')				,       )
         A_      :				Optional[Any]		     =					DDIMScheduler()
         A_      :				str		     =					{'''unet''': unet, '''scheduler''': scheduler}
         return components



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=0		)->Optional[Any]:




         '''simple docstring'''





         if str(_SCREAMING_SNAKE_CASE		).startswith('''mps'''		):
           A_      :				Any		     =					torch.manual_seed(_SCREAMING_SNAKE_CASE		)
         else:
           A_      :				Optional[int]		     =					torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					{
             '''batch_size''': 1,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs



       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					'''cpu'''

         A_      :				Dict		     =					self.get_dummy_components()
         A_      :				str		     =					self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
         pipe.to(_SCREAMING_SNAKE_CASE		)
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				str		     =					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					pipe(**_SCREAMING_SNAKE_CASE		).images
         A_      :				int		     =					image[0, -3:, -3:, -1]

         self.assertEqual(image.shape				,       (1, 32, 32, 3)		)
         A_      :				List[Any]		     =					np.array(
             [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]		)
         A_      :				str		     =					np.abs(image_slice.flatten() - expected_slice		).max()
         self.assertLessEqual(_SCREAMING_SNAKE_CASE				,       1e-3		)



       def        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3		)



       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





         super().test_save_load_local(expected_max_difference=3e-3		)



       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





         super().test_save_load_optional_components(expected_max_difference=3e-3		)







       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         super().test_inference_batch_single_identical(expected_max_diff=3e-3		)






@slow
@require_torch_gpu
class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



       def        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





         A_      :				int		     =					'''google/ddpm-cifar10-32'''

         A_      :				Tuple		     =					UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
         A_      :				str		     =					DDIMScheduler()

         A_      :				str		     =					DDIMPipeline(unet=_SCREAMING_SNAKE_CASE				,       scheduler=_SCREAMING_SNAKE_CASE		)
         ddim.to(_SCREAMING_SNAKE_CASE		)
         ddim.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					torch.manual_seed(0		)
         A_      :				Any		     =					ddim(generator=_SCREAMING_SNAKE_CASE				,       eta=0.0				,       output_type='''numpy'''		).images

         A_      :				Any		     =					image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A_      :				Any		     =					np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2







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




         '''simple docstring'''





         A_      :				Tuple		     =					'''google/ddpm-ema-bedroom-256'''

         A_      :				int		     =					UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[Any]		     =					DDIMPipeline(unet=_SCREAMING_SNAKE_CASE				,       scheduler=_SCREAMING_SNAKE_CASE		)
         ddpm.to(_SCREAMING_SNAKE_CASE		)
         ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Dict		     =					torch.manual_seed(0		)
         A_      :				List[str]		     =					ddpm(generator=_SCREAMING_SNAKE_CASE				,       output_type='''numpy'''		).images

         A_      :				Any		     =					image[0, -3:, -3:, -1]

         assert image.shape == (1, 256, 256, 3)
         A_      :				Tuple		     =					np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2




