






"""simple docstring"""



import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class 	_UpperCamelCase ( __UpperCamelCase							):



    '''simple docstring'''


    __lowercase    :   int											=						(DPMSolverMultistepScheduler,)
    __lowercase    :   Dict											=						(('num_inference_steps', 25),)


    def 		A__      (      self   ,			**__lowercase			):
     UpperCAmelCase__ 						=				{
         """num_train_timesteps""": 1000,
         """beta_start""": 0.0_001,
         """beta_end""": 0.02,
         """beta_schedule""": """linear""",
         """solver_order""": 2,
         """prediction_type""": """epsilon""",
         """thresholding""": False,
         """sample_max_value""": 1.0,
         """algorithm_type""": """dpmsolver++""",
         """solver_type""": """midpoint""",
         """lower_order_final""": False,
         """lambda_min_clipped""": -float("""inf"""			),
         """variance_type""": None,
     }

     config.update(**__lowercase			)
     return config


    def 		A__      (      self   ,			__lowercase=0   ,			**__lowercase			):
     UpperCAmelCase__ 						=				dict(self.forward_default_kwargs			)
     UpperCAmelCase__ 						=				kwargs.pop("""num_inference_steps"""   ,			__lowercase			)
     UpperCAmelCase__ 						=				self.dummy_sample
     UpperCAmelCase__ 						=				0.1 * sample
     UpperCAmelCase__ 						=				[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

      UpperCAmelCase__						,	UpperCAmelCase__ 						=				sample, sample
      for t in range(__lowercase   ,			time_step + scheduler.config.solver_order + 1			):
       UpperCAmelCase__ 						=				scheduler.step(__lowercase   ,			__lowercase   ,			__lowercase   ,			**__lowercase			).prev_sample
       UpperCAmelCase__ 						=				new_scheduler.step(__lowercase   ,			__lowercase   ,			__lowercase   ,			**__lowercase			).prev_sample

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


    def 		A__      (      self			):
     pass


    def 		A__      (      self   ,			__lowercase=0   ,			**__lowercase			):
     UpperCAmelCase__ 						=				dict(self.forward_default_kwargs			)
     UpperCAmelCase__ 						=				kwargs.pop("""num_inference_steps"""   ,			__lowercase			)
     UpperCAmelCase__ 						=				self.dummy_sample
     UpperCAmelCase__ 						=				0.1 * sample
     UpperCAmelCase__ 						=				[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

      with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(__lowercase			)
       UpperCAmelCase__ 						=				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)
       UpperCAmelCase__ 						=				dummy_past_residuals[: new_scheduler.config.solver_order]

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

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


    def 		A__      (      self   ,			__lowercase=None   ,			**__lowercase			):
     if scheduler is None:
      UpperCAmelCase__ 						=				self.scheduler_classes[0]
      UpperCAmelCase__ 						=				self.get_scheduler_config(**__lowercase			)
      UpperCAmelCase__ 						=				scheduler_class(**__lowercase			)

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

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

     return sample


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				dict(self.forward_default_kwargs			)

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

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

      UpperCAmelCase__ 						=				self.dummy_sample
      UpperCAmelCase__ 						=				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"""			):
       UpperCAmelCase__ 						=				num_inference_steps

      # copy over dummy past residuals (must be done after set_timesteps)
      UpperCAmelCase__ 						=				[residual + 0.2, residual + 0.15, residual + 0.10]
      UpperCAmelCase__ 						=				dummy_past_residuals[: scheduler.config.solver_order]

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

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

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


    def 		A__      (      self			):
     for timesteps in [25, 50, 100, 999, 1000]:
      self.check_over_configs(num_train_timesteps=__lowercase			)


    def 		A__      (      self			):
     self.check_over_configs(thresholding=__lowercase			)
     for order in [1, 2, 3]:
      for solver_type in ["midpoint", "heun"]:
       for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "sample"]:
         self.check_over_configs(
             thresholding=__lowercase   ,			prediction_type=__lowercase   ,			sample_max_value=__lowercase   ,			algorithm_type="""dpmsolver++"""   ,			solver_order=__lowercase   ,			solver_type=__lowercase   ,			)


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


    def 		A__      (      self			):
     for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
      for solver_type in ["midpoint", "heun"]:
       for order in [1, 2, 3]:
        for prediction_type in ["epsilon", "sample"]:
         if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
          if order == 3:
           continue
         else:
          self.check_over_configs(
              solver_order=__lowercase   ,			solver_type=__lowercase   ,			prediction_type=__lowercase   ,			algorithm_type=__lowercase   ,			)
         UpperCAmelCase__ 						=				self.full_loop(
             solver_order=__lowercase   ,			solver_type=__lowercase   ,			prediction_type=__lowercase   ,			algorithm_type=__lowercase   ,			)
         assert not torch.isnan(__lowercase			).any(), "Samples have nan numbers"


    def 		A__      (      self			):
     self.check_over_configs(lower_order_final=__lowercase			)
     self.check_over_configs(lower_order_final=__lowercase			)


    def 		A__      (      self			):
     self.check_over_configs(lambda_min_clipped=-float("""inf"""			)			)
     self.check_over_configs(lambda_min_clipped=-5.1			)


    def 		A__      (      self			):
     self.check_over_configs(variance_type=__lowercase			)
     self.check_over_configs(variance_type="""learned_range"""			)


    def 		A__      (      self			):
     for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
      self.check_over_forward(num_inference_steps=__lowercase   ,			time_step=0			)


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				self.full_loop()
     UpperCAmelCase__ 						=				torch.mean(torch.abs(__lowercase			)			)

     assert abs(result_mean.item() - 0.3_301			) < 1e-3


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				self.full_loop(thresholding=__lowercase   ,			dynamic_thresholding_ratio=0.87   ,			sample_max_value=0.5			)
     UpperCAmelCase__ 						=				torch.mean(torch.abs(__lowercase			)			)

     assert abs(result_mean.item() - 1.1_364			) < 1e-3


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				self.full_loop(prediction_type="""v_prediction"""			)
     UpperCAmelCase__ 						=				torch.mean(torch.abs(__lowercase			)			)

     assert abs(result_mean.item() - 0.2_251			) < 1e-3


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				self.full_loop(prediction_type="""v_prediction"""   ,			use_karras_sigmas=__lowercase			)
     UpperCAmelCase__ 						=				torch.mean(torch.abs(__lowercase			)			)

     assert abs(result_mean.item() - 0.2_096			) < 1e-3


    def 		A__      (      self			):
     # make sure that iterating over schedulers with same config names gives same results
     # for defaults
     UpperCAmelCase__ 						=				DPMSolverMultistepScheduler(**self.get_scheduler_config()			)
     UpperCAmelCase__ 						=				self.full_loop(scheduler=__lowercase			)
     UpperCAmelCase__ 						=				torch.mean(torch.abs(__lowercase			)			)

     assert abs(result_mean.item() - 0.3_301			) < 1e-3

     UpperCAmelCase__ 						=				DPMSolverSinglestepScheduler.from_config(scheduler.config			)
     UpperCAmelCase__ 						=				UniPCMultistepScheduler.from_config(scheduler.config			)
     UpperCAmelCase__ 						=				DEISMultistepScheduler.from_config(scheduler.config			)
     UpperCAmelCase__ 						=				DPMSolverMultistepScheduler.from_config(scheduler.config			)

     UpperCAmelCase__ 						=				self.full_loop(scheduler=__lowercase			)
     UpperCAmelCase__ 						=				torch.mean(torch.abs(__lowercase			)			)

     assert abs(result_mean.item() - 0.3_301			) < 1e-3


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				self.scheduler_classes[0]
     UpperCAmelCase__ 						=				self.get_scheduler_config(thresholding=__lowercase   ,			dynamic_thresholding_ratio=0			)
     UpperCAmelCase__ 						=				scheduler_class(**__lowercase			)

     UpperCAmelCase__ 						=				10
     UpperCAmelCase__ 						=				self.dummy_model()
     UpperCAmelCase__ 						=				self.dummy_sample_deter.half()
     scheduler.set_timesteps(__lowercase			)

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

     assert sample.dtype == torch.floataa






    def 		A__      (      self   ,			**__lowercase			):
     for scheduler_class in self.scheduler_classes:
      UpperCAmelCase__ 						=				self.get_scheduler_config(**__lowercase			)
      UpperCAmelCase__ 						=				scheduler_class(**__lowercase			)

      scheduler.set_timesteps(scheduler.config.num_train_timesteps			)
      assert len(scheduler.timesteps.unique()			) == scheduler.num_inference_steps











"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


a  :						Optional[Any]					       =							logging.get_logger(__name__)

a  :						int					       =							{
    '''facebook/opt-125m''': '''https://huggingface.co/facebook/opt-125m/blob/main/config.json''',
    '''facebook/opt-350m''': '''https://huggingface.co/facebook/opt-350m/blob/main/config.json''',
    '''facebook/opt-1.3b''': '''https://huggingface.co/facebook/opt-1.3b/blob/main/config.json''',
    '''facebook/opt-2.7b''': '''https://huggingface.co/facebook/opt-2.7b/blob/main/config.json''',
    '''facebook/opt-6.7b''': '''https://huggingface.co/facebook/opt-6.7b/blob/main/config.json''',
    '''facebook/opt-13b''': '''https://huggingface.co/facebook/opt-13b/blob/main/config.json''',
}


class 	_UpperCamelCase ( __UpperCamelCase							):



    '''simple docstring'''


    __lowercase    :   Optional[Any]											=						'opt'
    __lowercase    :   str											=						['past_key_values']


    def __init__(      self   ,			__lowercase=50272   ,			__lowercase=768   ,			__lowercase=12   ,			__lowercase=3072   ,			__lowercase=2048   ,			__lowercase=True   ,			__lowercase=False   ,			__lowercase=None   ,			__lowercase=0.1   ,			__lowercase=0.0   ,			__lowercase=12   ,			__lowercase="relu"   ,			__lowercase=0.0   ,			__lowercase=0.02   ,			__lowercase=True   ,			__lowercase=1   ,			__lowercase=2   ,			__lowercase=2   ,			__lowercase=True   ,			__lowercase=True   ,			**__lowercase   ,			):
     super().__init__(
         pad_token_id=__lowercase   ,			bos_token_id=__lowercase   ,			eos_token_id=__lowercase   ,			**__lowercase   ,			)
     UpperCAmelCase__ 						=				vocab_size
     UpperCAmelCase__ 						=				max_position_embeddings
     UpperCAmelCase__ 						=				num_attention_heads
     UpperCAmelCase__ 						=				word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
     UpperCAmelCase__ 						=				ffn_dim
     UpperCAmelCase__ 						=				hidden_size
     UpperCAmelCase__ 						=				num_hidden_layers
     UpperCAmelCase__ 						=				dropout
     UpperCAmelCase__ 						=				attention_dropout
     UpperCAmelCase__ 						=				activation_function
     UpperCAmelCase__ 						=				init_std
     UpperCAmelCase__ 						=				layerdrop
     UpperCAmelCase__ 						=				use_cache
     UpperCAmelCase__ 						=				do_layer_norm_before
     # We keep these variables at `True` for backward compatibility.
     UpperCAmelCase__ 						=				enable_bias
     UpperCAmelCase__ 						=				layer_norm_elementwise_affine

     # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
     # with checkpoints that have been fine-tuned before transformers v4.20.1
     # see https://github.com/facebookresearch/metaseq/pull/164
     UpperCAmelCase__ 						=				_remove_final_layer_norm



