'''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 	__A				(			unittest.TestCase     ):



    def __init__(self   :   Tuple       ,      __a   :   Optional[int]       ,      __a   :   Any=100       ,      __a   :   Optional[Any]=13       ,      __a   :   Optional[int]=30       ,      __a   :   Dict=2       ,      __a   :   Tuple=3       ,      __a   :   Any=True       ,      __a   :   Any=True       ,      __a   :   Any=32       ,      __a   :   str=5       ,      __a   :   Optional[int]=4       ,      __a   :   Dict=37       ,      __a   :   Union[str, Any]="gelu"       ,      __a   :   str=0.1       ,      __a   :   Dict=0.1       ,      __a   :   Any=10       ,      __a   :   Tuple=0.02       ,      __a   :   Optional[int]=3       ,      ):
         UpperCAmelCase_	   =       parent
         UpperCAmelCase_	   =       vocab_size
         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

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



    def  _lowercase						(self   :   int			):
         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_	   =       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=__a       ,      initializer_range=self.initializer_range       ,      )

         return config, pixel_values, labels



    def  _lowercase						(self   :   Tuple       ,      __a   :   List[Any]       ,      __a   :   List[Any]       ,      __a   :   int			):
         UpperCAmelCase_	   =       FlaxBeitModel(config=__a			)
         UpperCAmelCase_	   =       model(__a			)
         self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.seq_length, self.hidden_size)			)



    def  _lowercase						(self   :   Optional[int]       ,      __a   :   List[Any]       ,      __a   :   List[str]       ,      __a   :   Tuple			):
         UpperCAmelCase_	   =       FlaxBeitForMaskedImageModeling(config=__a			)
         UpperCAmelCase_	   =       model(__a			)
         self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length - 1, self.vocab_size)			)



    def  _lowercase						(self   :   int       ,      __a   :   Any       ,      __a   :   Tuple       ,      __a   :   int			):
         UpperCAmelCase_	   =       self.type_sequence_label_size
         UpperCAmelCase_	   =       FlaxBeitForImageClassification(config=__a			)
         UpperCAmelCase_	   =       model(__a			)
         self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.type_sequence_label_size)			)

         # test greyscale images
         UpperCAmelCase_	   =       1
         UpperCAmelCase_	   =       FlaxBeitForImageClassification(__a			)

         UpperCAmelCase_	   =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
         UpperCAmelCase_	   =       model(__a			)






    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			
         )	   =       config_and_inputs
         UpperCAmelCase_	   =       {"pixel_values": pixel_values}
         return config, inputs_dict




@require_flax
class 	__A				(			UpperCamelCase__		,     unittest.TestCase     ):
    a__	:						Dict	       =  (
        (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
    )



    def  _lowercase						(self   :   Union[str, Any]			):
         UpperCAmelCase_	   =       FlaxBeitModelTester(self			)
         UpperCAmelCase_	   =       ConfigTester(self       ,      config_class=__a       ,      has_text_modality=__a       ,      hidden_size=37			)



    def  _lowercase						(self   :   List[Any]			):
         self.config_tester.run_common_tests()



    def  _lowercase						(self   :   Union[str, Any]			):
         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.__call__			)
              # 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  _lowercase						(self   :   Optional[int]			):
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__			):
                   UpperCAmelCase_	   =       self._prepare_for_class(__a       ,      __a			)
                   UpperCAmelCase_	   =       model_class(__a			)

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

                   with self.subTest("JIT Enabled"			):
                        UpperCAmelCase_	   =       model_jitted(**__a			).to_tuple()

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

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



    def  _lowercase						(self   :   Any			):
         UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__a			)



    def  _lowercase						(self   :   Optional[int]			):
         UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*__a			)



    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__a			)






    @slow
    def  _lowercase						(self   :   Dict			):
         for model_class_name in self.all_model_classes:
              UpperCAmelCase_	   =       model_class_name.from_pretrained("microsoft/beit-base-patch16-224"			)
              UpperCAmelCase_	   =       model(np.ones((1, 3, 224, 224)			)			)
              self.assertIsNotNone(__a			)





def      lowerCAmelCase_    ( )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
     return image






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



    @cached_property
    def  _lowercase						(self   :   List[Any]			):
         return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"			) if is_vision_available() else None



    @slow
    def  _lowercase						(self   :   Optional[int]			):
         UpperCAmelCase_	   =       FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"			)

         UpperCAmelCase_	   =       self.default_image_processor
         UpperCAmelCase_	   =       prepare_img()
         UpperCAmelCase_	   =       image_processor(images=__a       ,      return_tensors="np"			).pixel_values

         # prepare bool_masked_pos
         UpperCAmelCase_	   =       np.ones((1, 196)       ,      dtype=__a			)

         # forward pass
         UpperCAmelCase_	   =       model(pixel_values=__a       ,      bool_masked_pos=__a			)
         UpperCAmelCase_	   =       outputs.logits

         # verify the logits
         UpperCAmelCase_	   =       (1, 196, 8192)
         self.assertEqual(logits.shape       ,      __a			)

         UpperCAmelCase_	   =       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]       ,      __a       ,      atol=1E-2			)			)



    @slow
    def  _lowercase						(self   :   List[str]			):
         UpperCAmelCase_	   =       FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"			)

         UpperCAmelCase_	   =       self.default_image_processor
         UpperCAmelCase_	   =       prepare_img()
         UpperCAmelCase_	   =       image_processor(images=__a       ,      return_tensors="np"			)

         # forward pass
         UpperCAmelCase_	   =       model(**__a			)
         UpperCAmelCase_	   =       outputs.logits

         # verify the logits
         UpperCAmelCase_	   =       (1, 1000)
         self.assertEqual(logits.shape       ,      __a			)

         UpperCAmelCase_	   =       np.array([-1.23_85, -1.09_87, -1.01_08]			)

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

         UpperCAmelCase_	   =       281
         self.assertEqual(logits.argmax(-1			).item()       ,      __a			)






    @slow
    def  _lowercase						(self   :   Any			):
         UpperCAmelCase_	   =       FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"			)

         UpperCAmelCase_	   =       self.default_image_processor
         UpperCAmelCase_	   =       prepare_img()
         UpperCAmelCase_	   =       image_processor(images=__a       ,      return_tensors="np"			)

         # forward pass
         UpperCAmelCase_	   =       model(**__a			)
         UpperCAmelCase_	   =       outputs.logits

         # verify the logits
         UpperCAmelCase_	   =       (1, 21841)
         self.assertEqual(logits.shape       ,      __a			)

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

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

         UpperCAmelCase_	   =       2396
         self.assertEqual(logits.argmax(-1			).item()       ,      __a			)





'''simple docstring'''





import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   Union[str, Any]       ,      __a   :   VQModel       ,      __a   :   UNetaDModel       ,      __a   :   DDIMScheduler			):
         super().__init__()
         self.register_modules(vqvae=__a       ,      unet=__a       ,      scheduler=__a			)






    @torch.no_grad()
    def __call__(self   :   str       ,      __a   :   int = 1       ,      __a   :   Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,      __a   :   float = 0.0       ,      __a   :   int = 50       ,      __a   :   Optional[str] = "pil"       ,      __a   :   bool = True       ,      **__a   :   Optional[int]       ,      ):
         UpperCAmelCase_	   =       randn_tensor(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,      generator=__a       ,      )
         UpperCAmelCase_	   =       latents.to(self.device			)

         # scale the initial noise by the standard deviation required by the scheduler
         UpperCAmelCase_	   =       latents * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(__a			)

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         UpperCAmelCase_	   =       "eta" in set(inspect.signature(self.scheduler.step			).parameters.keys()			)

         UpperCAmelCase_	   =       {}
         if accepts_eta:
              UpperCAmelCase_	   =       eta

         for t in self.progress_bar(self.scheduler.timesteps			):
              UpperCAmelCase_	   =       self.scheduler.scale_model_input(__a       ,      __a			)
              # predict the noise residual
              UpperCAmelCase_	   =       self.unet(__a       ,      __a			).sample
              # compute the previous noisy sample x_t -> x_t-1
              UpperCAmelCase_	   =       self.scheduler.step(__a       ,      __a       ,      __a       ,      **__a			).prev_sample

         # decode the image latents with the VAE
         UpperCAmelCase_	   =       self.vqvae.decode(__a			).sample

         UpperCAmelCase_	   =       (image / 2 + 0.5).clamp(0       ,      1			)
         UpperCAmelCase_	   =       image.cpu().permute(0       ,      2       ,      3       ,      1			).numpy()
         if output_type == "pil":
              UpperCAmelCase_	   =       self.numpy_to_pil(__a			)

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=__a			)




