

"""simple docstring"""
import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		(PNDMScheduler,)
      a__				=		(("""num_inference_steps""", 50),)



      def   lowerCamelCase_       (						self   ,      **lowercase)     ->      str:





        '''simple docstring'''





        a__:					List[Any]      							=     {
            'num_train_timesteps': 10_00,
            'beta_start': 0.0001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
        }

        config.update(**lowercase)
        return config



      def   lowerCamelCase_       (						self   ,      lowercase=0   ,      **lowercase)     ->      Any:





        '''simple docstring'''





        a__:					Optional[int]      							=     dict(self.forward_default_kwargs)
        a__:					Union[str, Any]      							=     kwargs.pop('num_inference_steps'   ,      lowercase)
        a__:					List[Any]      							=     self.dummy_sample
        a__:					List[str]      							=     0.1 * sample
        a__:					int      							=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
          a__:					Optional[int]      							=     self.get_scheduler_config(**lowercase)
          a__:					Union[str, Any]      							=     scheduler_class(**lowercase)
          scheduler.set_timesteps(lowercase)
          # copy over dummy past residuals
          a__:					List[str]      							=     dummy_past_residuals[:]

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

          a__:					Any      							=     scheduler.step_prk(lowercase   ,      lowercase   ,      lowercase   ,      **lowercase).prev_sample
          a__:					Tuple      							=     new_scheduler.step_prk(lowercase   ,      lowercase   ,      lowercase   ,      **lowercase).prev_sample

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

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

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



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        pass



      def   lowerCamelCase_       (						self   ,      lowercase=0   ,      **lowercase)     ->      Tuple:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     dict(self.forward_default_kwargs)
        a__:					Dict      							=     kwargs.pop('num_inference_steps'   ,      lowercase)
        a__:					str      							=     self.dummy_sample
        a__:					int      							=     0.1 * sample
        a__:					Any      							=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

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

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

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

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

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



      def   lowerCamelCase_       (						self   ,      **lowercase)     ->      Any:





        '''simple docstring'''





        a__:					str      							=     self.scheduler_classes[0]
        a__:					List[Any]      							=     self.get_scheduler_config(**lowercase)
        a__:					int      							=     scheduler_class(**lowercase)

        a__:					List[str]      							=     10
        a__:					Any      							=     self.dummy_model()
        a__:					List[Any]      							=     self.dummy_sample_deter
        scheduler.set_timesteps(lowercase)

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

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

        return sample



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					Optional[Any]      							=     dict(self.forward_default_kwargs)

        a__:					Union[str, Any]      							=     kwargs.pop('num_inference_steps'   ,      lowercase)

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

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

          if num_inference_steps is not None and hasattr(lowercase   ,      'set_timesteps'):
            scheduler.set_timesteps(lowercase)
          elif num_inference_steps is not None and not hasattr(lowercase   ,      'set_timesteps'):
            a__:					int      							=     num_inference_steps

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

          a__:					int      							=     scheduler.step_prk(lowercase   ,      0   ,      lowercase   ,      **lowercase).prev_sample
          a__:					List[str]      							=     scheduler.step_prk(lowercase   ,      1   ,      lowercase   ,      **lowercase).prev_sample

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

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

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



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





        '''simple docstring'''





        for timesteps in [1_00, 10_00]:
          self.check_over_configs(num_train_timesteps=lowercase)



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





        '''simple docstring'''





        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=lowercase)

        a__:					str      							=     self.scheduler_classes[0]
        a__:					Any      							=     self.get_scheduler_config(steps_offset=1)
        a__:					int      							=     scheduler_class(**lowercase)
        scheduler.set_timesteps(10)
        assert torch.equal(
            scheduler.timesteps   ,      torch.LongTensor(
                [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1])   ,      )



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        for beta_start, beta_end in zip([0.0001, 0.001]   ,      [0.002, 0.02]):
          self.check_over_configs(beta_start=lowercase   ,      beta_end=lowercase)



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





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        for t, num_inference_steps in zip([1, 5, 10]   ,      [10, 50, 1_00]):
          self.check_over_forward(num_inference_steps=lowercase)



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        a__:					Optional[Any]      							=     27

        for scheduler_class in self.scheduler_classes:
          a__:					str      							=     self.dummy_sample
          a__:					str      							=     0.1 * sample

          a__:					str      							=     self.get_scheduler_config()
          a__:					Optional[Any]      							=     scheduler_class(**lowercase)

          scheduler.set_timesteps(lowercase)

          # 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]):
            a__:					int      							=     scheduler.step_prk(lowercase   ,      lowercase   ,      lowercase).prev_sample



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





        '''simple docstring'''





        with self.assertRaises(lowercase):
          a__:					Tuple      							=     self.scheduler_classes[0]
          a__:					Optional[Any]      							=     self.get_scheduler_config()
          a__:					List[Any]      							=     scheduler_class(**lowercase)

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



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     self.full_loop()
        a__:					Any      							=     torch.sum(torch.abs(lowercase))
        a__:					List[str]      							=     torch.mean(torch.abs(lowercase))

        assert abs(result_sum.item() - 198.1318) < 1e-2
        assert abs(result_mean.item() - 0.2580) < 1e-3



      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        a__:					List[str]      							=     self.full_loop(prediction_type='v_prediction')
        a__:					int      							=     torch.sum(torch.abs(lowercase))
        a__:					Optional[Any]      							=     torch.mean(torch.abs(lowercase))

        assert abs(result_sum.item() - 67.3986) < 1e-2
        assert abs(result_mean.item() - 0.0878) < 1e-3



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					int      							=     self.full_loop(set_alpha_to_one=lowercase   ,      beta_start=0.01)
        a__:					Tuple      							=     torch.sum(torch.abs(lowercase))
        a__:					Dict      							=     torch.mean(torch.abs(lowercase))

        assert abs(result_sum.item() - 230.0399) < 1e-2
        assert abs(result_mean.item() - 0.2995) < 1e-3



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        a__:					Tuple      							=     self.full_loop(set_alpha_to_one=lowercase   ,      beta_start=0.01)
        a__:					Optional[Any]      							=     torch.sum(torch.abs(lowercase))
        a__:					Tuple      							=     torch.mean(torch.abs(lowercase))

        assert abs(result_sum.item() - 186.9482) < 1e-2
        assert abs(result_mean.item() - 0.2434) < 1e-3



"""simple docstring"""
import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()




class        __snake_case	(					__lowerCAmelCase	,				unittest.TestCase    ):
      a__				=		PriorTransformer
      a__				=		"""hidden_states"""



      @property
      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     4
        a__:					Any      							=     8
        a__:					Optional[Any]      							=     7

        a__:					Tuple      							=     floats_tensor((batch_size, embedding_dim)).to(lowercase)

        a__:					Optional[int]      							=     floats_tensor((batch_size, embedding_dim)).to(lowercase)
        a__:					List[str]      							=     floats_tensor((batch_size, num_embeddings, embedding_dim)).to(lowercase)

        return {
            "hidden_states": hidden_states,
            "timestep": 2,
            "proj_embedding": proj_embedding,
            "encoder_hidden_states": encoder_hidden_states,
        }



      def   lowerCamelCase_       (						self   ,      lowercase=0)     ->      str:





        '''simple docstring'''





        torch.manual_seed(lowercase)
        a__:					Optional[Any]      							=     4
        a__:					Optional[Any]      							=     8
        a__:					Union[str, Any]      							=     7

        a__:					Optional[Any]      							=     torch.randn((batch_size, embedding_dim)).to(lowercase)

        a__:					List[str]      							=     torch.randn((batch_size, embedding_dim)).to(lowercase)
        a__:					Tuple      							=     torch.randn((batch_size, num_embeddings, embedding_dim)).to(lowercase)

        return {
            "hidden_states": hidden_states,
            "timestep": 2,
            "proj_embedding": proj_embedding,
            "encoder_hidden_states": encoder_hidden_states,
        }



      @property
      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        return (4, 8)



      @property
      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        return (4, 8)



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					int      							=     {
            'num_attention_heads': 2,
            'attention_head_dim': 4,
            'num_layers': 2,
            'embedding_dim': 8,
            'num_embeddings': 7,
            'additional_embeddings': 4,
        }
        a__:					Union[str, Any]      							=     self.dummy_input
        return init_dict, inputs_dict



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





        '''simple docstring'''





        a__							,   a__:					Union[str, Any]      							=     PriorTransformer.from_pretrained(
            'hf-internal-testing/prior-dummy'   ,      output_loading_info=lowercase)
        self.assertIsNotNone(lowercase)
        self.assertEqual(len(loading_info['missing_keys'])   ,      0)

        model.to(lowercase)
        a__:					Any      							=     model(**self.dummy_input)[0]

        assert hidden_states is not None, "Make sure output is not None"



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





        '''simple docstring'''





        a__							,   a__:					Tuple      							=     self.prepare_init_args_and_inputs_for_common()

        a__:					Any      							=     self.model_class(**lowercase)
        a__:					str      							=     inspect.signature(model.forward)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        a__:					Tuple      							=     [*signature.parameters.keys()]

        a__:					List[Any]      							=     ['hidden_states', 'timestep']
        self.assertListEqual(arg_names[:2]   ,      lowercase)



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





        '''simple docstring'''





        a__:					str      							=     PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy')
        a__:					str      							=     model.to(lowercase)

        if hasattr(lowercase   ,      'set_default_attn_processor'):
          model.set_default_attn_processor()

        a__:					Dict      							=     self.get_dummy_seed_input()

        with torch.no_grad():
          a__:					str      							=     model(**lowercase)[0]

        a__:					str      							=     output[0, :5].flatten().cpu()
        print(lowercase)

        # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
        # the expected output slices are not the same for CPU and GPU.
        a__:					Any      							=     torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239])
        self.assertTrue(torch_all_close(lowercase   ,      lowercase   ,      rtol=1e-2))







@slow
class        __snake_case	(					unittest.TestCase    ):



      def   lowerCamelCase_       (						self   ,      lowercase=1   ,      lowercase=7_68   ,      lowercase=77   ,      lowercase=0)     ->      int:





        '''simple docstring'''





        torch.manual_seed(lowercase)
        a__:					Union[str, Any]      							=     batch_size
        a__:					List[str]      							=     embedding_dim
        a__:					str      							=     num_embeddings

        a__:					Tuple      							=     torch.randn((batch_size, embedding_dim)).to(lowercase)

        a__:					List[str]      							=     torch.randn((batch_size, embedding_dim)).to(lowercase)
        a__:					str      							=     torch.randn((batch_size, num_embeddings, embedding_dim)).to(lowercase)

        return {
            "hidden_states": hidden_states,
            "timestep": 2,
            "proj_embedding": proj_embedding,
            "encoder_hidden_states": encoder_hidden_states,
        }



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





        '''simple docstring'''





        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



      @parameterized.expand(
          [
              # fmt: off
              [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
              [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
              # fmt: on
          ])
      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase)     ->      str:





        '''simple docstring'''





        a__:					Tuple      							=     PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'   ,      subfolder='prior')
        model.to(lowercase)
        a__:					Optional[Any]      							=     self.get_dummy_seed_input(seed=lowercase)

        with torch.no_grad():
          a__:					Optional[int]      							=     model(**lowercase)[0]

        assert list(sample.shape) == [1, 7_68]

        a__:					List[str]      							=     sample[0, :8].flatten().cpu()
        print(lowercase)
        a__:					Union[str, Any]      							=     torch.tensor(lowercase)

        assert torch_all_close(lowercase   ,      lowercase   ,      atol=1e-3)


