
'''simple docstring'''

import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   import torch

   from transformers import GPTaLMHeadModel







@require_torch
@require_sentencepiece
@require_tokenizers
class 	__lowerCAmelCase    (  unittest.TestCase				):



     @slow
     @unittest.skip("""Model is not available."""	)
     def     snake_case_ (self	):
          _UpperCAmelCase					:  Any         =    """nvidia/megatron-gpt2-345m/"""
          if "MYDIR" in os.environ:
               _UpperCAmelCase					:  int         =    os.path.join(os.environ["""MYDIR"""]						,	lowerCAmelCase__	)
          _UpperCAmelCase					:  List[Any]         =    GPTaLMHeadModel.from_pretrained(lowerCAmelCase__	)
          model.to(lowerCAmelCase__	)
          model.half()

          _UpperCAmelCase					:  Optional[int]         =    torch.tensor(
              [[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]]						,	device=lowerCAmelCase__						,	dtype=torch.long						,	)

          with torch.no_grad():
               _UpperCAmelCase					:  int         =    model(lowerCAmelCase__	).logits

          _UpperCAmelCase					:  Optional[int]         =    torch.Size((1, 9, 5_0_2_5_7)	)
          self.assertEqual(output.shape						,	lowerCAmelCase__	)

          _UpperCAmelCase					:  Dict         =    torch.tensor(
              [
                  4.9_4_1_4,
                  -0.2_9_2_0,
                  -1.2_1_4_8,
                  -4.0_2_7_3,
                  -0.5_1_6_1,
                  -5.2_1_0_9,
                  -1.2_4_1_2,
                  -1.8_3_0_1,
                  -1.7_7_3_4,
                  -4.7_1_4_8,
                  -0.2_3_1_7,
                  -1.0_8_1_1,
                  -2.1_7_7_7,
                  0.4_1_4_1,
                  -3.7_9_6_9,
                  -4.0_5_8_6,
                  -2.5_3_3_2,
                  -3.3_8_0_9,
                  4.3_8_6_7,
              ]						,	device=lowerCAmelCase__						,	dtype=torch.half						,	)

          for i in range(1_9	):
               _UpperCAmelCase  ,     _UpperCAmelCase					:  str         =    8 * i // 1_7, 2_7_9_2 * i  # along the diagonal
               _UpperCAmelCase  ,     _UpperCAmelCase					:  int         =    output[0, r, c], expected_diag[i]
               _UpperCAmelCase					:  Tuple         =    F"row={r} col={c} computed={computed} expected={expected}"
               self.assertAlmostEqual(lowerCAmelCase__						,	lowerCAmelCase__						,	delta=1e-4						,	msg=lowerCAmelCase__	)




'''simple docstring'''

import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 	__lowerCAmelCase    (  __a				):
     snake_case   :    List[str]							      =      (HeunDiscreteScheduler,)
     snake_case   :    Optional[Any]							      =      1_0



     def     snake_case_ (self						,	**lowerCAmelCase__	):
          _UpperCAmelCase					:  Union[str, Any]         =    {
              """num_train_timesteps""": 1_1_0_0,
              """beta_start""": 0.0_0_0_1,
              """beta_end""": 0.0_2,
              """beta_schedule""": """linear""",
          }

          config.update(**lowerCAmelCase__	)
          return config



     def     snake_case_ (self	):
          for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
               self.check_over_configs(num_train_timesteps=lowerCAmelCase__	)



     def     snake_case_ (self	):
          for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]						,	[0.0_0_0_2, 0.0_0_2, 0.0_2]	):
               self.check_over_configs(beta_start=lowerCAmelCase__						,	beta_end=lowerCAmelCase__	)



     def     snake_case_ (self	):
          for schedule in ["linear", "scaled_linear", "exp"]:
               self.check_over_configs(beta_schedule=lowerCAmelCase__	)



     def     snake_case_ (self	):
          for clip_sample_range in [1.0, 2.0, 3.0]:
               self.check_over_configs(clip_sample_range=lowerCAmelCase__						,	clip_sample=lowerCAmelCase__	)



     def     snake_case_ (self	):
          for prediction_type in ["epsilon", "v_prediction", "sample"]:
               self.check_over_configs(prediction_type=lowerCAmelCase__	)



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Tuple         =    self.scheduler_classes[0]
          _UpperCAmelCase					:  Optional[int]         =    self.get_scheduler_config()
          _UpperCAmelCase					:  List[str]         =    scheduler_class(**lowerCAmelCase__	)

          scheduler.set_timesteps(self.num_inference_steps	)

          _UpperCAmelCase					:  str         =    self.dummy_model()
          _UpperCAmelCase					:  List[Any]         =    self.dummy_sample_deter * scheduler.init_noise_sigma
          _UpperCAmelCase					:  Tuple         =    sample.to(lowerCAmelCase__	)

          for i, t in enumerate(scheduler.timesteps	):
               _UpperCAmelCase					:  Union[str, Any]         =    scheduler.scale_model_input(lowerCAmelCase__						,	lowerCAmelCase__	)

               _UpperCAmelCase					:  Any         =    model(lowerCAmelCase__						,	lowerCAmelCase__	)

               _UpperCAmelCase					:  Tuple         =    scheduler.step(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	)
               _UpperCAmelCase					:  Optional[Any]         =    output.prev_sample

          _UpperCAmelCase					:  Optional[Any]         =    torch.sum(torch.abs(lowerCAmelCase__	)	)
          _UpperCAmelCase					:  int         =    torch.mean(torch.abs(lowerCAmelCase__	)	)

          if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 0.1_2_3_3	) < 1e-2
               assert abs(result_mean.item() - 0.0_0_0_2	) < 1e-3
          else:
               # CUDA
               assert abs(result_sum.item() - 0.1_2_3_3	) < 1e-2
               assert abs(result_mean.item() - 0.0_0_0_2	) < 1e-3



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Optional[int]         =    self.scheduler_classes[0]
          _UpperCAmelCase					:  Any         =    self.get_scheduler_config(prediction_type="""v_prediction"""	)
          _UpperCAmelCase					:  Any         =    scheduler_class(**lowerCAmelCase__	)

          scheduler.set_timesteps(self.num_inference_steps	)

          _UpperCAmelCase					:  Optional[Any]         =    self.dummy_model()
          _UpperCAmelCase					:  Any         =    self.dummy_sample_deter * scheduler.init_noise_sigma
          _UpperCAmelCase					:  Any         =    sample.to(lowerCAmelCase__	)

          for i, t in enumerate(scheduler.timesteps	):
               _UpperCAmelCase					:  List[str]         =    scheduler.scale_model_input(lowerCAmelCase__						,	lowerCAmelCase__	)

               _UpperCAmelCase					:  List[Any]         =    model(lowerCAmelCase__						,	lowerCAmelCase__	)

               _UpperCAmelCase					:  Optional[Any]         =    scheduler.step(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	)
               _UpperCAmelCase					:  Union[str, Any]         =    output.prev_sample

          _UpperCAmelCase					:  Union[str, Any]         =    torch.sum(torch.abs(lowerCAmelCase__	)	)
          _UpperCAmelCase					:  str         =    torch.mean(torch.abs(lowerCAmelCase__	)	)

          if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6_934e-07	) < 1e-2
               assert abs(result_mean.item() - 6.1_112e-10	) < 1e-3
          else:
               # CUDA
               assert abs(result_sum.item() - 4.693_428_650_170_972e-07	) < 1e-2
               assert abs(result_mean.item() - 0.0_0_0_2	) < 1e-3



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Tuple         =    self.scheduler_classes[0]
          _UpperCAmelCase					:  Any         =    self.get_scheduler_config()
          _UpperCAmelCase					:  Optional[int]         =    scheduler_class(**lowerCAmelCase__	)

          scheduler.set_timesteps(self.num_inference_steps						,	device=lowerCAmelCase__	)

          _UpperCAmelCase					:  List[Any]         =    self.dummy_model()
          _UpperCAmelCase					:  Dict         =    self.dummy_sample_deter.to(lowerCAmelCase__	) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
               _UpperCAmelCase					:  Union[str, Any]         =    scheduler.scale_model_input(lowerCAmelCase__						,	lowerCAmelCase__	)

               _UpperCAmelCase					:  Optional[Any]         =    model(lowerCAmelCase__						,	lowerCAmelCase__	)

               _UpperCAmelCase					:  Optional[int]         =    scheduler.step(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	)
               _UpperCAmelCase					:  int         =    output.prev_sample

          _UpperCAmelCase					:  Any         =    torch.sum(torch.abs(lowerCAmelCase__	)	)
          _UpperCAmelCase					:  Optional[Any]         =    torch.mean(torch.abs(lowerCAmelCase__	)	)

          if str(lowerCAmelCase__	).startswith("""cpu"""	):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 0.1_2_3_3	) < 1e-2
               assert abs(result_mean.item() - 0.0_0_0_2	) < 1e-3
          elif str(lowerCAmelCase__	).startswith("""mps"""	):
               # Larger tolerance on mps
               assert abs(result_mean.item() - 0.0_0_0_2	) < 1e-2
          else:
               # CUDA
               assert abs(result_sum.item() - 0.1_2_3_3	) < 1e-2
               assert abs(result_mean.item() - 0.0_0_0_2	) < 1e-3



     def     snake_case_ (self	):
          _UpperCAmelCase					:  int         =    self.scheduler_classes[0]
          _UpperCAmelCase					:  str         =    self.get_scheduler_config()
          _UpperCAmelCase					:  Union[str, Any]         =    scheduler_class(**lowerCAmelCase__						,	use_karras_sigmas=lowerCAmelCase__	)

          scheduler.set_timesteps(self.num_inference_steps						,	device=lowerCAmelCase__	)

          _UpperCAmelCase					:  Optional[int]         =    self.dummy_model()
          _UpperCAmelCase					:  Optional[Any]         =    self.dummy_sample_deter.to(lowerCAmelCase__	) * scheduler.init_noise_sigma
          _UpperCAmelCase					:  Union[str, Any]         =    sample.to(lowerCAmelCase__	)

          for t in scheduler.timesteps:
               _UpperCAmelCase					:  Dict         =    scheduler.scale_model_input(lowerCAmelCase__						,	lowerCAmelCase__	)

               _UpperCAmelCase					:  Optional[Any]         =    model(lowerCAmelCase__						,	lowerCAmelCase__	)

               _UpperCAmelCase					:  Optional[int]         =    scheduler.step(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	)
               _UpperCAmelCase					:  Optional[Any]         =    output.prev_sample

          _UpperCAmelCase					:  Union[str, Any]         =    torch.sum(torch.abs(lowerCAmelCase__	)	)
          _UpperCAmelCase					:  List[Any]         =    torch.mean(torch.abs(lowerCAmelCase__	)	)

          assert abs(result_sum.item() - 0.0_0_0_1_5	) < 1e-2
          assert abs(result_mean.item() - 1.9_869_554_535_034_695e-07	) < 1e-2


