




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 	UpperCamelCase__			(lowerCAmelCase__  ):

    '''simple docstring'''



    lowerCamelCase_   :			int			  =   (EulerAncestralDiscreteScheduler,)
    lowerCamelCase_   :			Union[str, Any]			  =   1_0







    def 					_lowercase  (					self  ,						**UpperCamelCase__  )  ->   Tuple:
           lowerCamelCase     :   List[str]    				=      {
               "num_train_timesteps": 1100,
               "beta_start": 0.0001,
               "beta_end": 0.02,
               "beta_schedule": "linear",
           }

           config.update(**UpperCamelCase__  )
           return config







    def 					_lowercase  (					self  )  ->   Union[str, Any]:
           for timesteps in [10, 50, 100, 1000]:
                  self.check_over_configs(num_train_timesteps=UpperCamelCase__  )







    def 					_lowercase  (					self  )  ->   List[Any]:
           for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]  ,						[0.0002, 0.002, 0.02]  ):
                  self.check_over_configs(beta_start=UpperCamelCase__  ,						beta_end=UpperCamelCase__  )







    def 					_lowercase  (					self  )  ->   Optional[int]:
           for schedule in ["linear", "scaled_linear"]:
                  self.check_over_configs(beta_schedule=UpperCamelCase__  )







    def 					_lowercase  (					self  )  ->   Optional[Any]:
           for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(prediction_type=UpperCamelCase__  )







    def 					_lowercase  (					self  )  ->   Optional[Any]:
           lowerCamelCase     :   Dict    				=      self.scheduler_classes[0]
           lowerCamelCase     :   List[str]    				=      self.get_scheduler_config()
           lowerCamelCase     :   List[Any]    				=      scheduler_class(**UpperCamelCase__  )

           scheduler.set_timesteps(self.num_inference_steps  )

           lowerCamelCase     :   Union[str, Any]    				=      torch.manual_seed(0  )

           lowerCamelCase     :   Optional[Any]    				=      self.dummy_model()
           lowerCamelCase     :   Optional[int]    				=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           lowerCamelCase     :   Optional[Any]    				=      sample.to(UpperCamelCase__  )

           for i, t in enumerate(scheduler.timesteps  ):
                  lowerCamelCase     :   Optional[Any]    				=      scheduler.scale_model_input(UpperCamelCase__  ,						UpperCamelCase__  )

                  lowerCamelCase     :   Optional[int]    				=      model(UpperCamelCase__  ,						UpperCamelCase__  )

                  lowerCamelCase     :   Tuple    				=      scheduler.step(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						generator=UpperCamelCase__  )
                  lowerCamelCase     :   Union[str, Any]    				=      output.prev_sample

           lowerCamelCase     :   List[Any]    				=      torch.sum(torch.abs(UpperCamelCase__  )  )
           lowerCamelCase     :   str    				=      torch.mean(torch.abs(UpperCamelCase__  )  )

           assert abs(result_sum.item() - 152.3192  ) < 1e-2
           assert abs(result_mean.item() - 0.1983  ) < 1e-3







    def 					_lowercase  (					self  )  ->   int:
           lowerCamelCase     :   Union[str, Any]    				=      self.scheduler_classes[0]
           lowerCamelCase     :   str    				=      self.get_scheduler_config(prediction_type="v_prediction"  )
           lowerCamelCase     :   Dict    				=      scheduler_class(**UpperCamelCase__  )

           scheduler.set_timesteps(self.num_inference_steps  )

           lowerCamelCase     :   Union[str, Any]    				=      torch.manual_seed(0  )

           lowerCamelCase     :   Optional[Any]    				=      self.dummy_model()
           lowerCamelCase     :   Optional[Any]    				=      self.dummy_sample_deter * scheduler.init_noise_sigma
           lowerCamelCase     :   Tuple    				=      sample.to(UpperCamelCase__  )

           for i, t in enumerate(scheduler.timesteps  ):
                  lowerCamelCase     :   Dict    				=      scheduler.scale_model_input(UpperCamelCase__  ,						UpperCamelCase__  )

                  lowerCamelCase     :   Optional[Any]    				=      model(UpperCamelCase__  ,						UpperCamelCase__  )

                  lowerCamelCase     :   int    				=      scheduler.step(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						generator=UpperCamelCase__  )
                  lowerCamelCase     :   str    				=      output.prev_sample

           lowerCamelCase     :   Union[str, Any]    				=      torch.sum(torch.abs(UpperCamelCase__  )  )
           lowerCamelCase     :   Dict    				=      torch.mean(torch.abs(UpperCamelCase__  )  )

           assert abs(result_sum.item() - 108.4439  ) < 1e-2
           assert abs(result_mean.item() - 0.1412  ) < 1e-3







    def 					_lowercase  (					self  )  ->   Tuple:
           lowerCamelCase     :   List[str]    				=      self.scheduler_classes[0]
           lowerCamelCase     :   int    				=      self.get_scheduler_config()
           lowerCamelCase     :   Dict    				=      scheduler_class(**UpperCamelCase__  )

           scheduler.set_timesteps(self.num_inference_steps  ,						device=UpperCamelCase__  )
           lowerCamelCase     :   str    				=      torch.manual_seed(0  )

           lowerCamelCase     :   Optional[int]    				=      self.dummy_model()
           lowerCamelCase     :   List[Any]    				=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           lowerCamelCase     :   Optional[int]    				=      sample.to(UpperCamelCase__  )

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

                  lowerCamelCase     :   List[str]    				=      model(UpperCamelCase__  ,						UpperCamelCase__  )

                  lowerCamelCase     :   List[str]    				=      scheduler.step(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						generator=UpperCamelCase__  )
                  lowerCamelCase     :   List[Any]    				=      output.prev_sample

           lowerCamelCase     :   List[Any]    				=      torch.sum(torch.abs(UpperCamelCase__  )  )
           lowerCamelCase     :   List[Any]    				=      torch.mean(torch.abs(UpperCamelCase__  )  )

           assert abs(result_sum.item() - 152.3192  ) < 1e-2
           assert abs(result_mean.item() - 0.1983  ) < 1e-3












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 	UpperCamelCase__			(unittest.TestCase  ):

    '''simple docstring'''



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

           lowerCamelCase     :   Dict    				=      torch.tensor(
               [[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]  ,						device=UpperCamelCase__  ,						dtype=torch.long  ,						)

           with torch.no_grad():
                  lowerCamelCase     :   Any    				=      model(UpperCamelCase__  ).logits

           lowerCamelCase     :   Union[str, Any]    				=      torch.Size((1, 9, 5_0257)  )
           self.assertEqual(output.shape  ,						UpperCamelCase__  )

           lowerCamelCase     :   List[str]    				=      torch.tensor(
               [
                   4.9414,
                   -0.2920,
                   -1.2148,
                   -4.0273,
                   -0.5161,
                   -5.2109,
                   -1.2412,
                   -1.8301,
                   -1.7734,
                   -4.7148,
                   -0.2317,
                   -1.0811,
                   -2.1777,
                   0.4141,
                   -3.7969,
                   -4.0586,
                   -2.5332,
                   -3.3809,
                   4.3867,
               ]  ,						device=UpperCamelCase__  ,						dtype=torch.half  ,						)

           for i in range(19  ):
                  lowerCamelCase				,   lowerCamelCase     :   Optional[int]    				=      8 * i // 17, 2792 * i  # along the diagonal
                  lowerCamelCase				,   lowerCamelCase     :   Tuple    				=      output[0, r, c], expected_diag[i]
                  lowerCamelCase     :   List[str]    				=      F'''row={r} col={c} computed={computed} expected={expected}'''
                  self.assertAlmostEqual(UpperCamelCase__  ,						UpperCamelCase__  ,						delta=1e-4  ,						msg=UpperCamelCase__  )






