




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 			_UpperCamelCase      (				unittest.TestCase		):
      def __init__(			self   :str ,      lowerCamelCase   :Union[str, Any] ,      lowerCamelCase   :Optional[Any]=100 ,      lowerCamelCase   :Optional[int]=13 ,      lowerCamelCase   :Union[str, Any]=30 ,      lowerCamelCase   :Optional[Any]=2 ,      lowerCamelCase   :Any=3 ,      lowerCamelCase   :Tuple=True ,      lowerCamelCase   :List[Any]=True ,      lowerCamelCase   :List[Any]=32 ,      lowerCamelCase   :List[Any]=5 ,      lowerCamelCase   :List[str]=4 ,      lowerCamelCase   :Optional[int]=37 ,      lowerCamelCase   :List[str]="gelu" ,      lowerCamelCase   :Union[str, Any]=0.1 ,      lowerCamelCase   :List[str]=0.1 ,      lowerCamelCase   :Optional[Any]=10 ,      lowerCamelCase   :Any=0.02 ,      lowerCamelCase   :Any=3 ,      )     ->			Any:
       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 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			Dict:
       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=lowerCamelCase ,      initializer_range=self.initializer_range ,      )

       return config, pixel_values, labels
      def 	UpperCAmelCase_						(			self   :int ,      lowerCamelCase   :Tuple ,      lowerCamelCase   :Tuple ,      lowerCamelCase   :int      )     ->			int:
       UpperCAmelCase__							 =						FlaxBeitModel(config=lowerCamelCase      )
       UpperCAmelCase__							 =						model(lowerCamelCase      )
       self.parent.assertEqual(result.last_hidden_state.shape ,      (self.batch_size, self.seq_length, self.hidden_size)      )
      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :Union[str, Any] ,      lowerCamelCase   :Tuple ,      lowerCamelCase   :int      )     ->			Dict:
       UpperCAmelCase__							 =						FlaxBeitForMaskedImageModeling(config=lowerCamelCase      )
       UpperCAmelCase__							 =						model(lowerCamelCase      )
       self.parent.assertEqual(result.logits.shape ,      (self.batch_size, self.seq_length - 1, self.vocab_size)      )
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :Union[str, Any] ,      lowerCamelCase   :Tuple ,      lowerCamelCase   :int      )     ->			Dict:
       UpperCAmelCase__							 =						self.type_sequence_label_size
       UpperCAmelCase__							 =						FlaxBeitForImageClassification(config=lowerCamelCase      )
       UpperCAmelCase__							 =						model(lowerCamelCase      )
       self.parent.assertEqual(result.logits.shape ,      (self.batch_size, self.type_sequence_label_size)      )

       # test greyscale images
       UpperCAmelCase__							 =						1
       UpperCAmelCase__							 =						FlaxBeitForImageClassification(lowerCamelCase      )

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







      def 	UpperCAmelCase_						(			self   :Any      )     ->			List[Any]:
       UpperCAmelCase__							 =						self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase__
       ) , (
           UpperCAmelCase__
       ) , (
           UpperCAmelCase__
       ) , 
       )							 =						config_and_inputs
       UpperCAmelCase__							 =						{"pixel_values": pixel_values}
       return config, inputs_dict



@require_flax
class 			_UpperCamelCase      (				lowerCAmelCase	,       unittest.TestCase		):
      UpperCAmelCase_        =		(
          (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
      )
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			None:
       UpperCAmelCase__							 =						FlaxBeitModelTester(self      )
       UpperCAmelCase__							 =						ConfigTester(self ,      config_class=lowerCamelCase ,      has_text_modality=lowerCamelCase ,      hidden_size=37      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Tuple:
       self.config_tester.run_common_tests()
      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			List[str]:
       UpperCAmelCase__ , UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        UpperCAmelCase__							 =						model_class(lowerCamelCase      )
        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] ,      lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :str      )     ->			Optional[Any]:
       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(lowerCamelCase ,      lowerCamelCase      )
         UpperCAmelCase__							 =						model_class(lowerCamelCase      )

         @jax.jit
         def model_jitted(lowerCamelCase   :Optional[Any] ,      **lowerCamelCase   :Tuple      ):
          return model(pixel_values=lowerCamelCase ,      **lowerCamelCase      )

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

         with self.subTest("JIT Disabled"      ):
          with jax.disable_jit():
           UpperCAmelCase__							 =						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      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Any:
       UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :str      )     ->			int:
       UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Any      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCamelCase      )







      @slow
      def 	UpperCAmelCase_						(			self   :str      )     ->			int:
       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(lowerCamelCase      )



def 						lowerCAmelCase		(		):




 """simple docstring"""







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


@require_vision
@require_flax
class 			_UpperCamelCase      (				unittest.TestCase		):
      @cached_property
      def 	UpperCAmelCase_						(			self   :int      )     ->			Union[str, Any]:
       return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"      ) if is_vision_available() else None
      @slow
      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"      )

       UpperCAmelCase__							 =						self.default_image_processor
       UpperCAmelCase__							 =						prepare_img()
       UpperCAmelCase__							 =						image_processor(images=lowerCamelCase ,      return_tensors="np"      ).pixel_values

       # prepare bool_masked_pos
       UpperCAmelCase__							 =						np.ones((1, 196) ,      dtype=lowerCamelCase      )

       # forward pass
       UpperCAmelCase__							 =						model(pixel_values=lowerCamelCase ,      bool_masked_pos=lowerCamelCase      )
       UpperCAmelCase__							 =						outputs.logits

       # verify the logits
       UpperCAmelCase__							 =						(1, 196, 8192)
       self.assertEqual(logits.shape ,      lowerCamelCase      )

       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] ,      lowerCamelCase ,      atol=1e-2      )      )
      @slow
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"      )

       UpperCAmelCase__							 =						self.default_image_processor
       UpperCAmelCase__							 =						prepare_img()
       UpperCAmelCase__							 =						image_processor(images=lowerCamelCase ,      return_tensors="np"      )

       # forward pass
       UpperCAmelCase__							 =						model(**lowerCamelCase      )
       UpperCAmelCase__							 =						outputs.logits

       # verify the logits
       UpperCAmelCase__							 =						(1, 1000)
       self.assertEqual(logits.shape ,      lowerCamelCase      )

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

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

       UpperCAmelCase__							 =						281
       self.assertEqual(logits.argmax(-1      ).item() ,      lowerCamelCase      )







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

       UpperCAmelCase__							 =						self.default_image_processor
       UpperCAmelCase__							 =						prepare_img()
       UpperCAmelCase__							 =						image_processor(images=lowerCamelCase ,      return_tensors="np"      )

       # forward pass
       UpperCAmelCase__							 =						model(**lowerCamelCase      )
       UpperCAmelCase__							 =						outputs.logits

       # verify the logits
       UpperCAmelCase__							 =						(1, 2_1841)
       self.assertEqual(logits.shape ,      lowerCamelCase      )

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

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

       UpperCAmelCase__							 =						2396
       self.assertEqual(logits.argmax(-1      ).item() ,      lowerCamelCase      )











import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		(IPNDMScheduler,)
      UpperCAmelCase_        =		(("""num_inference_steps""", 50),)
      def 	UpperCAmelCase_						(			self   :List[str] ,      **lowerCamelCase   :List[Any]      )     ->			List[str]:
       UpperCAmelCase__							 =						{"num_train_timesteps": 1000}
       config.update(**lowerCamelCase      )
       return config
      def 	UpperCAmelCase_						(			self   :str ,      lowerCamelCase   :Union[str, Any]=0 ,      **lowerCamelCase   :str      )     ->			str:
       UpperCAmelCase__							 =						dict(self.forward_default_kwargs      )
       UpperCAmelCase__							 =						kwargs.pop("num_inference_steps" ,      lowerCamelCase      )
       UpperCAmelCase__							 =						self.dummy_sample
       UpperCAmelCase__							 =						0.1 * sample
       UpperCAmelCase__							 =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
        UpperCAmelCase__							 =						self.get_scheduler_config(**lowerCamelCase      )
        UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )
        scheduler.set_timesteps(lowerCamelCase      )
        # copy over dummy past residuals
        UpperCAmelCase__							 =						dummy_past_residuals[:]

        if time_step is None:
         UpperCAmelCase__							 =						scheduler.timesteps[len(scheduler.timesteps      ) // 2]

        with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(lowerCamelCase      )
         UpperCAmelCase__							 =						scheduler_class.from_pretrained(lowerCamelCase      )
         new_scheduler.set_timesteps(lowerCamelCase      )
         # copy over dummy past residuals
         UpperCAmelCase__							 =						dummy_past_residuals[:]

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						new_scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						new_scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Tuple:
       pass
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :List[str]=0 ,      **lowerCamelCase   :List[str]      )     ->			Optional[int]:
       UpperCAmelCase__							 =						dict(self.forward_default_kwargs      )
       UpperCAmelCase__							 =						kwargs.pop("num_inference_steps" ,      lowerCamelCase      )
       UpperCAmelCase__							 =						self.dummy_sample
       UpperCAmelCase__							 =						0.1 * sample
       UpperCAmelCase__							 =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
        UpperCAmelCase__							 =						self.get_scheduler_config()
        UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )
        scheduler.set_timesteps(lowerCamelCase      )

        # copy over dummy past residuals (must be after setting timesteps)
        UpperCAmelCase__							 =						dummy_past_residuals[:]

        if time_step is None:
         UpperCAmelCase__							 =						scheduler.timesteps[len(scheduler.timesteps      ) // 2]

        with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(lowerCamelCase      )
         UpperCAmelCase__							 =						scheduler_class.from_pretrained(lowerCamelCase      )
         # copy over dummy past residuals
         new_scheduler.set_timesteps(lowerCamelCase      )

         # copy over dummy past residual (must be after setting timesteps)
         UpperCAmelCase__							 =						dummy_past_residuals[:]

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						new_scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						new_scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"
      def 	UpperCAmelCase_						(			self   :int ,      **lowerCamelCase   :Any      )     ->			int:
       UpperCAmelCase__							 =						self.scheduler_classes[0]
       UpperCAmelCase__							 =						self.get_scheduler_config(**lowerCamelCase      )
       UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )

       UpperCAmelCase__							 =						10
       UpperCAmelCase__							 =						self.dummy_model()
       UpperCAmelCase__							 =						self.dummy_sample_deter
       scheduler.set_timesteps(lowerCamelCase      )

       for i, t in enumerate(scheduler.timesteps      ):
        UpperCAmelCase__							 =						model(lowerCamelCase ,      lowerCamelCase      )
        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      ).prev_sample

       for i, t in enumerate(scheduler.timesteps      ):
        UpperCAmelCase__							 =						model(lowerCamelCase ,      lowerCamelCase      )
        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      ).prev_sample

       return sample
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						dict(self.forward_default_kwargs      )

       UpperCAmelCase__							 =						kwargs.pop("num_inference_steps" ,      lowerCamelCase      )

       for scheduler_class in self.scheduler_classes:
        UpperCAmelCase__							 =						self.get_scheduler_config()
        UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )

        UpperCAmelCase__							 =						self.dummy_sample
        UpperCAmelCase__							 =						0.1 * sample

        if num_inference_steps is not None and hasattr(lowerCamelCase ,      "set_timesteps"      ):
         scheduler.set_timesteps(lowerCamelCase      )
        elif num_inference_steps is not None and not hasattr(lowerCamelCase ,      "set_timesteps"      ):
         UpperCAmelCase__							 =						num_inference_steps

        # copy over dummy past residuals (must be done after set_timesteps)
        UpperCAmelCase__							 =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
        UpperCAmelCase__							 =						dummy_past_residuals[:]

        UpperCAmelCase__							 =						scheduler.timesteps[5]
        UpperCAmelCase__							 =						scheduler.timesteps[6]

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

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

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        self.assertEqual(output_a.shape ,      sample.shape      )
        self.assertEqual(output_a.shape ,      output_a.shape      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Tuple:
       for timesteps in [100, 1000]:
        self.check_over_configs(num_train_timesteps=lowerCamelCase ,      time_step=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Optional[int]:
       for t, num_inference_steps in zip([1, 5, 10] ,      [10, 50, 100]      ):
        self.check_over_forward(num_inference_steps=lowerCamelCase ,      time_step=lowerCamelCase      )







      def 	UpperCAmelCase_						(			self   :Any      )     ->			Dict:
       UpperCAmelCase__							 =						self.full_loop()
       UpperCAmelCase__							 =						torch.mean(torch.abs(lowerCamelCase      )      )

       assert abs(result_mean.item() - 254_0529      ) < 10





