"""simple docstring"""




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 		__A							(				SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    str	      =	(PNDMScheduler,)
     _UpperCamelCase							:    Optional[Any]	      =	(("num_inference_steps", 50),)






     def 					__A						(	self       ,  **a__						):
       _lowerCAmelCase		: Optional[int]						    =  {
           """num_train_timesteps""": 1000,
           """beta_start""": 0.0_0_0_1,
           """beta_end""": 0.0_2,
           """beta_schedule""": """linear""",
       }

       config.update(**a__						)
       return config






     def 					__A						(	self       ,  a__=0       ,  **a__						):
       _lowerCAmelCase		: str						    =  dict(self.forward_default_kwargs						)
       _lowerCAmelCase		: str						    =  kwargs.pop("""num_inference_steps"""       ,  a__						)
       _lowerCAmelCase		: Tuple						    =  self.dummy_sample
       _lowerCAmelCase		: Dict						    =  0.1 * sample
       _lowerCAmelCase		: Optional[Any]						    =  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

       for scheduler_class in self.scheduler_classes:
         _lowerCAmelCase		: Any						    =  self.get_scheduler_config(**a__						)
         _lowerCAmelCase		: Any						    =  scheduler_class(**a__						)
         scheduler.set_timesteps(a__						)
         # copy over dummy past residuals
         _lowerCAmelCase		: Optional[Any]						    =  dummy_past_residuals[:]

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

         _lowerCAmelCase		: Optional[int]						    =  scheduler.step_prk(a__       ,  a__       ,  a__       ,  **a__						).prev_sample
         _lowerCAmelCase		: Any						    =  new_scheduler.step_prk(a__       ,  a__       ,  a__       ,  **a__						).prev_sample

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

         _lowerCAmelCase		: Tuple						    =  scheduler.step_plms(a__       ,  a__       ,  a__       ,  **a__						).prev_sample
         _lowerCAmelCase		: Optional[int]						    =  new_scheduler.step_plms(a__       ,  a__       ,  a__       ,  **a__						).prev_sample

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






     def 					__A						(	self						):
       pass






     def 					__A						(	self       ,  a__=0       ,  **a__						):
       _lowerCAmelCase		: List[str]						    =  dict(self.forward_default_kwargs						)
       _lowerCAmelCase		: Any						    =  kwargs.pop("""num_inference_steps"""       ,  a__						)
       _lowerCAmelCase		: Any						    =  self.dummy_sample
       _lowerCAmelCase		: str						    =  0.1 * sample
       _lowerCAmelCase		: List[Any]						    =  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

       for scheduler_class in self.scheduler_classes:
         _lowerCAmelCase		: Optional[int]						    =  self.get_scheduler_config()
         _lowerCAmelCase		: str						    =  scheduler_class(**a__						)
         scheduler.set_timesteps(a__						)

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

         with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(a__						)
           _lowerCAmelCase		: List[Any]						    =  scheduler_class.from_pretrained(a__						)
           # copy over dummy past residuals
           new_scheduler.set_timesteps(a__						)

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

         _lowerCAmelCase		: int						    =  scheduler.step_prk(a__       ,  a__       ,  a__       ,  **a__						).prev_sample
         _lowerCAmelCase		: int						    =  new_scheduler.step_prk(a__       ,  a__       ,  a__       ,  **a__						).prev_sample

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

         _lowerCAmelCase		: List[str]						    =  scheduler.step_plms(a__       ,  a__       ,  a__       ,  **a__						).prev_sample
         _lowerCAmelCase		: Optional[Any]						    =  new_scheduler.step_plms(a__       ,  a__       ,  a__       ,  **a__						).prev_sample

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






     def 					__A						(	self       ,  **a__						):
       _lowerCAmelCase		: List[Any]						    =  self.scheduler_classes[0]
       _lowerCAmelCase		: Dict						    =  self.get_scheduler_config(**a__						)
       _lowerCAmelCase		: Tuple						    =  scheduler_class(**a__						)

       _lowerCAmelCase		: List[Any]						    =  10
       _lowerCAmelCase		: Optional[int]						    =  self.dummy_model()
       _lowerCAmelCase		: int						    =  self.dummy_sample_deter
       scheduler.set_timesteps(a__						)

       for i, t in enumerate(scheduler.prk_timesteps						):
         _lowerCAmelCase		: int						    =  model(a__       ,  a__						)
         _lowerCAmelCase		: List[Any]						    =  scheduler.step_prk(a__       ,  a__       ,  a__						).prev_sample

       for i, t in enumerate(scheduler.plms_timesteps						):
         _lowerCAmelCase		: Dict						    =  model(a__       ,  a__						)
         _lowerCAmelCase		: Tuple						    =  scheduler.step_plms(a__       ,  a__       ,  a__						).prev_sample

       return sample






     def 					__A						(	self						):
       _lowerCAmelCase		: Dict						    =  dict(self.forward_default_kwargs						)

       _lowerCAmelCase		: Optional[Any]						    =  kwargs.pop("""num_inference_steps"""       ,  a__						)

       for scheduler_class in self.scheduler_classes:
         _lowerCAmelCase		: Any						    =  self.get_scheduler_config()
         _lowerCAmelCase		: Dict						    =  scheduler_class(**a__						)

         _lowerCAmelCase		: Optional[int]						    =  self.dummy_sample
         _lowerCAmelCase		: Optional[int]						    =  0.1 * sample

         if num_inference_steps is not None and hasattr(a__       ,  """set_timesteps"""						):
           scheduler.set_timesteps(a__						)
         elif num_inference_steps is not None and not hasattr(a__       ,  """set_timesteps"""						):
           _lowerCAmelCase		: Optional[int]						    =  num_inference_steps

         # copy over dummy past residuals (must be done after set_timesteps)
         _lowerCAmelCase		: List[Any]						    =  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
         _lowerCAmelCase		: Dict						    =  dummy_past_residuals[:]

         _lowerCAmelCase		: Union[str, Any]						    =  scheduler.step_prk(a__       ,  0       ,  a__       ,  **a__						).prev_sample
         _lowerCAmelCase		: Any						    =  scheduler.step_prk(a__       ,  1       ,  a__       ,  **a__						).prev_sample

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

         _lowerCAmelCase		: Tuple						    =  scheduler.step_plms(a__       ,  0       ,  a__       ,  **a__						).prev_sample
         _lowerCAmelCase		: List[Any]						    =  scheduler.step_plms(a__       ,  1       ,  a__       ,  **a__						).prev_sample

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






     def 					__A						(	self						):
       for timesteps in [100, 1000]:
         self.check_over_configs(num_train_timesteps=a__						)






     def 					__A						(	self						):
       for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=a__						)

       _lowerCAmelCase		: int						    =  self.scheduler_classes[0]
       _lowerCAmelCase		: Optional[int]						    =  self.get_scheduler_config(steps_offset=1						)
       _lowerCAmelCase		: Optional[Any]						    =  scheduler_class(**a__						)
       scheduler.set_timesteps(10						)
       assert torch.equal(
           scheduler.timesteps       ,  torch.LongTensor(
               [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]						)       ,  )






     def 					__A						(	self						):
       for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]       ,  [0.0_0_2, 0.0_2]						):
         self.check_over_configs(beta_start=a__       ,  beta_end=a__						)






     def 					__A						(	self						):
       for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=a__						)






     def 					__A						(	self						):
       for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=a__						)






     def 					__A						(	self						):
       for t in [1, 5, 10]:
         self.check_over_forward(time_step=a__						)






     def 					__A						(	self						):
       for t, num_inference_steps in zip([1, 5, 10]       ,  [10, 50, 100]						):
         self.check_over_forward(num_inference_steps=a__						)






     def 					__A						(	self						):
       # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
       _lowerCAmelCase		: Dict						    =  27

       for scheduler_class in self.scheduler_classes:
         _lowerCAmelCase		: Optional[int]						    =  self.dummy_sample
         _lowerCAmelCase		: Union[str, Any]						    =  0.1 * sample

         _lowerCAmelCase		: Union[str, Any]						    =  self.get_scheduler_config()
         _lowerCAmelCase		: int						    =  scheduler_class(**a__						)

         scheduler.set_timesteps(a__						)

         # before power of 3 fix, would error on first step, so we only need to do two
         for i, t in enumerate(scheduler.prk_timesteps[:2]						):
           _lowerCAmelCase		: Optional[Any]						    =  scheduler.step_prk(a__       ,  a__       ,  a__						).prev_sample






     def 					__A						(	self						):
       with self.assertRaises(a__						):
         _lowerCAmelCase		: List[Any]						    =  self.scheduler_classes[0]
         _lowerCAmelCase		: List[Any]						    =  self.get_scheduler_config()
         _lowerCAmelCase		: Optional[Any]						    =  scheduler_class(**a__						)

         scheduler.step_plms(self.dummy_sample       ,  1       ,  self.dummy_sample						).prev_sample






     def 					__A						(	self						):
       _lowerCAmelCase		: Dict						    =  self.full_loop()
       _lowerCAmelCase		: Union[str, Any]						    =  torch.sum(torch.abs(a__						)						)
       _lowerCAmelCase		: int						    =  torch.mean(torch.abs(a__						)						)

       assert abs(result_sum.item() - 198.1318						) < 1e-2
       assert abs(result_mean.item() - 0.2_5_8_0						) < 1e-3






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  self.full_loop(prediction_type="""v_prediction"""						)
       _lowerCAmelCase		: Optional[int]						    =  torch.sum(torch.abs(a__						)						)
       _lowerCAmelCase		: int						    =  torch.mean(torch.abs(a__						)						)

       assert abs(result_sum.item() - 67.3986						) < 1e-2
       assert abs(result_mean.item() - 0.0_8_7_8						) < 1e-3






     def 					__A						(	self						):
       # We specify different beta, so that the first alpha is 0.99
       _lowerCAmelCase		: str						    =  self.full_loop(set_alpha_to_one=a__       ,  beta_start=0.0_1						)
       _lowerCAmelCase		: Dict						    =  torch.sum(torch.abs(a__						)						)
       _lowerCAmelCase		: Tuple						    =  torch.mean(torch.abs(a__						)						)

       assert abs(result_sum.item() - 230.0399						) < 1e-2
       assert abs(result_mean.item() - 0.2_9_9_5						) < 1e-3






     def 					__A						(	self						):
       # We specify different beta, so that the first alpha is 0.99
       _lowerCAmelCase		: Dict						    =  self.full_loop(set_alpha_to_one=a__       ,  beta_start=0.0_1						)
       _lowerCAmelCase		: Optional[Any]						    =  torch.sum(torch.abs(a__						)						)
       _lowerCAmelCase		: Union[str, Any]						    =  torch.mean(torch.abs(a__						)						)

       assert abs(result_sum.item() - 186.9482						) < 1e-2
       assert abs(result_mean.item() - 0.2_4_3_4						) < 1e-3





"""simple docstring"""




import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Optional[int]	      =	DiTPipeline
     _UpperCamelCase							:    Union[str, Any]	      =	CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
     _UpperCamelCase							:    Union[str, Any]	      =	PipelineTesterMixin.required_optional_params - {
         "latents",
         "num_images_per_prompt",
         "callback",
         "callback_steps",
     }
     _UpperCamelCase							:    Dict	      =	CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
     _UpperCamelCase							:    Union[str, Any]	      =	False






     def 					__A						(	self						):
       torch.manual_seed(0						)
       _lowerCAmelCase		: Tuple						    =  TransformeraDModel(
           sample_size=16       ,  num_layers=2       ,  patch_size=4       ,  attention_head_dim=8       ,  num_attention_heads=2       ,  in_channels=4       ,  out_channels=8       ,  attention_bias=a__       ,  activation_fn="""gelu-approximate"""       ,  num_embeds_ada_norm=1000       ,  norm_type="""ada_norm_zero"""       ,  norm_elementwise_affine=a__       ,  )
       _lowerCAmelCase		: Optional[int]						    =  AutoencoderKL()
       _lowerCAmelCase		: Union[str, Any]						    =  DDIMScheduler()
       _lowerCAmelCase		: Optional[Any]						    =  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
       return components






     def 					__A						(	self       ,  a__       ,  a__=0						):
       if str(a__						).startswith("""mps"""						):
         _lowerCAmelCase		: Any						    =  torch.manual_seed(a__						)
       else:
         _lowerCAmelCase		: Tuple						    =  torch.Generator(device=a__						).manual_seed(a__						)
       _lowerCAmelCase		: Any						    =  {
           """class_labels""": [1],
           """generator""": generator,
           """num_inference_steps""": 2,
           """output_type""": """numpy""",
       }
       return inputs






     def 					__A						(	self						):
       _lowerCAmelCase		: List[Any]						    =  """cpu"""

       _lowerCAmelCase		: Tuple						    =  self.get_dummy_components()
       _lowerCAmelCase		: Optional[int]						    =  self.pipeline_class(**a__						)
       pipe.to(a__						)
       pipe.set_progress_bar_config(disable=a__						)

       _lowerCAmelCase		: Tuple						    =  self.get_dummy_inputs(a__						)
       _lowerCAmelCase		: List[str]						    =  pipe(**a__						).images
       _lowerCAmelCase		: Optional[int]						    =  image[0, -3:, -3:, -1]

       self.assertEqual(image.shape       ,  (1, 16, 16, 3)						)
       _lowerCAmelCase		: List[Any]						    =  np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]						)
       _lowerCAmelCase		: Optional[int]						    =  np.abs(image_slice.flatten() - expected_slice						).max()
       self.assertLessEqual(a__       ,  1e-3						)






     def 					__A						(	self						):
       self._test_inference_batch_single_identical(relax_max_difference=a__       ,  expected_max_diff=1e-3						)






     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()       ,  reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,  )
     def 					__A						(	self						):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)







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






     def 					__A						(	self						):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  torch.manual_seed(0						)

       _lowerCAmelCase		: int						    =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""						)
       pipe.to("""cuda"""						)

       _lowerCAmelCase		: Dict						    =  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
       _lowerCAmelCase		: Union[str, Any]						    =  pipe.get_label_ids(a__						)

       _lowerCAmelCase		: Any						    =  pipe(a__       ,  generator=a__       ,  num_inference_steps=40       ,  output_type="""np"""						).images

       for word, image in zip(a__       ,  a__						):
         _lowerCAmelCase		: str						    =  load_numpy(
             F"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"						)
         assert np.abs((expected_image - image).max()						) < 1e-2






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""						)
       _lowerCAmelCase		: Dict						    =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
       pipe.to("""cuda"""						)

       _lowerCAmelCase		: List[str]						    =  ["""vase""", """umbrella"""]
       _lowerCAmelCase		: Optional[int]						    =  pipe.get_label_ids(a__						)

       _lowerCAmelCase		: str						    =  torch.manual_seed(0						)
       _lowerCAmelCase		: List[str]						    =  pipe(a__       ,  generator=a__       ,  num_inference_steps=25       ,  output_type="""np"""						).images

       for word, image in zip(a__       ,  a__						):
         _lowerCAmelCase		: str						    =  load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             F"/dit/{word}_512.npy"						)

         assert np.abs((expected_image - image).max()						) < 1e-1




