






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class 							__snake_case(       _snake_case   ):
 lowercase			:							Union[str, Any]		   =		(DPMSolverMultistepScheduler,)
 lowercase			:							List[str]		   =		(('''num_inference_steps''', 25),)



 def     UpperCAmelCase     (						self : List[str]			,		**UpperCAmelCase__ : Any						):

    """simple docstring"""






    lowercase__    :						List[str]       =			{
        """num_train_timesteps""": 1_000,
        """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     UpperCAmelCase     (						self : str			,		UpperCAmelCase__ : Union[str, Any]=0			,		**UpperCAmelCase__ : Any						):

    """simple docstring"""






    lowercase__    :						Optional[int]       =			dict(self.forward_default_kwargs						)
    lowercase__    :						Optional[Any]       =			kwargs.pop("""num_inference_steps"""			,		lowercase__						)
    lowercase__    :						Union[str, Any]       =			self.dummy_sample
    lowercase__    :						str       =			0.1 * sample
    lowercase__    :						Union[str, Any]       =			[residual + 0.2, residual + 0.15, residual + 0.10]

    for scheduler_class in self.scheduler_classes:
       lowercase__    :						List[str]       =			self.get_scheduler_config(**lowercase__						)
       lowercase__    :						Optional[int]       =			scheduler_class(**lowercase__						)
       scheduler.set_timesteps(lowercase__						)
       # copy over dummy past residuals
       lowercase__    :						List[str]       =			dummy_past_residuals[: scheduler.config.solver_order]

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

       lowercase__  ,       lowercase__    :						Union[str, Any]       =			sample, sample
       for t in range(lowercase__			,		time_step + scheduler.config.solver_order + 1						):
          lowercase__    :						Optional[Any]       =			scheduler.step(lowercase__			,		lowercase__			,		lowercase__			,		**lowercase__						).prev_sample
          lowercase__    :						List[str]       =			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     UpperCAmelCase     (						self : Dict						):

    """simple docstring"""






    pass



 def     UpperCAmelCase     (						self : List[str]			,		UpperCAmelCase__ : Dict=0			,		**UpperCAmelCase__ : int						):

    """simple docstring"""






    lowercase__    :						Union[str, Any]       =			dict(self.forward_default_kwargs						)
    lowercase__    :						List[Any]       =			kwargs.pop("""num_inference_steps"""			,		lowercase__						)
    lowercase__    :						str       =			self.dummy_sample
    lowercase__    :						Any       =			0.1 * sample
    lowercase__    :						int       =			[residual + 0.2, residual + 0.15, residual + 0.10]

    for scheduler_class in self.scheduler_classes:
       lowercase__    :						Optional[Any]       =			self.get_scheduler_config()
       lowercase__    :						Optional[int]       =			scheduler_class(**lowercase__						)
       scheduler.set_timesteps(lowercase__						)

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

       with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(lowercase__						)
          lowercase__    :						str       =			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)
          lowercase__    :						Optional[int]       =			dummy_past_residuals[: new_scheduler.config.solver_order]

       lowercase__    :						Any       =			scheduler.step(lowercase__			,		lowercase__			,		lowercase__			,		**lowercase__						).prev_sample
       lowercase__    :						str       =			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     UpperCAmelCase     (						self : Optional[Any]			,		UpperCAmelCase__ : Any=None			,		**UpperCAmelCase__ : str						):

    """simple docstring"""






    if scheduler is None:
       lowercase__    :						Tuple       =			self.scheduler_classes[0]
       lowercase__    :						Optional[int]       =			self.get_scheduler_config(**lowercase__						)
       lowercase__    :						Any       =			scheduler_class(**lowercase__						)

    lowercase__    :						Dict       =			10
    lowercase__    :						Any       =			self.dummy_model()
    lowercase__    :						Optional[Any]       =			self.dummy_sample_deter
    scheduler.set_timesteps(lowercase__						)

    for i, t in enumerate(scheduler.timesteps						):
       lowercase__    :						Tuple       =			model(lowercase__			,		lowercase__						)
       lowercase__    :						Dict       =			scheduler.step(lowercase__			,		lowercase__			,		lowercase__						).prev_sample

    return sample



 def     UpperCAmelCase     (						self : int						):

    """simple docstring"""






    lowercase__    :						Optional[int]       =			dict(self.forward_default_kwargs						)

    lowercase__    :						int       =			kwargs.pop("""num_inference_steps"""			,		lowercase__						)

    for scheduler_class in self.scheduler_classes:
       lowercase__    :						Dict       =			self.get_scheduler_config()
       lowercase__    :						List[Any]       =			scheduler_class(**lowercase__						)

       lowercase__    :						Dict       =			self.dummy_sample
       lowercase__    :						Dict       =			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"""						):
          lowercase__    :						List[str]       =			num_inference_steps

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

       lowercase__    :						Any       =			scheduler.timesteps[5]
       lowercase__    :						Union[str, Any]       =			scheduler.timesteps[6]

       lowercase__    :						int       =			scheduler.step(lowercase__			,		lowercase__			,		lowercase__			,		**lowercase__						).prev_sample
       lowercase__    :						Optional[Any]       =			scheduler.step(lowercase__			,		lowercase__			,		lowercase__			,		**lowercase__						).prev_sample

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



 def     UpperCAmelCase     (						self : str						):

    """simple docstring"""






    for timesteps in [25, 50, 100, 999, 1_000]:
       self.check_over_configs(num_train_timesteps=lowercase__						)



 def     UpperCAmelCase     (						self : List[Any]						):

    """simple docstring"""






    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     UpperCAmelCase     (						self : Dict						):

    """simple docstring"""






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



 def     UpperCAmelCase     (						self : Tuple						):

    """simple docstring"""






    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__			,		)
                lowercase__    :						List[str]       =			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     UpperCAmelCase     (						self : Optional[int]						):

    """simple docstring"""






    self.check_over_configs(lower_order_final=lowercase__						)
    self.check_over_configs(lower_order_final=lowercase__						)



 def     UpperCAmelCase     (						self : Dict						):

    """simple docstring"""






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



 def     UpperCAmelCase     (						self : int						):

    """simple docstring"""






    self.check_over_configs(variance_type=lowercase__						)
    self.check_over_configs(variance_type="""learned_range"""						)



 def     UpperCAmelCase     (						self : str						):

    """simple docstring"""






    for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
       self.check_over_forward(num_inference_steps=lowercase__			,		time_step=0						)



 def     UpperCAmelCase     (						self : Dict						):

    """simple docstring"""






    lowercase__    :						Optional[Any]       =			self.full_loop()
    lowercase__    :						List[str]       =			torch.mean(torch.abs(lowercase__						)						)

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



 def     UpperCAmelCase     (						self : Optional[Any]						):

    """simple docstring"""






    lowercase__    :						Dict       =			self.full_loop(thresholding=lowercase__			,		dynamic_thresholding_ratio=0.87			,		sample_max_value=0.5						)
    lowercase__    :						Optional[Any]       =			torch.mean(torch.abs(lowercase__						)						)

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



 def     UpperCAmelCase     (						self : Any						):

    """simple docstring"""






    lowercase__    :						List[str]       =			self.full_loop(prediction_type="""v_prediction"""						)
    lowercase__    :						Optional[Any]       =			torch.mean(torch.abs(lowercase__						)						)

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



 def     UpperCAmelCase     (						self : Optional[Any]						):

    """simple docstring"""






    lowercase__    :						List[Any]       =			self.full_loop(prediction_type="""v_prediction"""			,		use_karras_sigmas=lowercase__						)
    lowercase__    :						List[Any]       =			torch.mean(torch.abs(lowercase__						)						)

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



 def     UpperCAmelCase     (						self : Dict						):

    """simple docstring"""






    lowercase__    :						List[str]       =			DPMSolverMultistepScheduler(**self.get_scheduler_config()						)
    lowercase__    :						Union[str, Any]       =			self.full_loop(scheduler=lowercase__						)
    lowercase__    :						List[str]       =			torch.mean(torch.abs(lowercase__						)						)

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

    lowercase__    :						List[Any]       =			DPMSolverSinglestepScheduler.from_config(scheduler.config						)
    lowercase__    :						Any       =			UniPCMultistepScheduler.from_config(scheduler.config						)
    lowercase__    :						Union[str, Any]       =			DEISMultistepScheduler.from_config(scheduler.config						)
    lowercase__    :						List[Any]       =			DPMSolverMultistepScheduler.from_config(scheduler.config						)

    lowercase__    :						Optional[int]       =			self.full_loop(scheduler=lowercase__						)
    lowercase__    :						Tuple       =			torch.mean(torch.abs(lowercase__						)						)

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



 def     UpperCAmelCase     (						self : Union[str, Any]						):

    """simple docstring"""






    lowercase__    :						str       =			self.scheduler_classes[0]
    lowercase__    :						Dict       =			self.get_scheduler_config(thresholding=lowercase__			,		dynamic_thresholding_ratio=0						)
    lowercase__    :						Optional[int]       =			scheduler_class(**lowercase__						)

    lowercase__    :						List[str]       =			10
    lowercase__    :						str       =			self.dummy_model()
    lowercase__    :						int       =			self.dummy_sample_deter.half()
    scheduler.set_timesteps(lowercase__						)

    for i, t in enumerate(scheduler.timesteps						):
       lowercase__    :						Optional[Any]       =			model(lowercase__			,		lowercase__						)
       lowercase__    :						Optional[int]       =			scheduler.step(lowercase__			,		lowercase__			,		lowercase__						).prev_sample

    assert sample.dtype == torch.floataa







 def     UpperCAmelCase     (						self : List[Any]			,		**UpperCAmelCase__ : List[Any]						):

    """simple docstring"""






    for scheduler_class in self.scheduler_classes:
       lowercase__    :						str       =			self.get_scheduler_config(**lowercase__						)
       lowercase__    :						Dict       =			scheduler_class(**lowercase__						)

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







import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property


class 	a__       (       unittest.TestCase       ):


					"""simple docstring"""



					@cached_property
					def 				__UpperCAmelCase		(		self     :Optional[Any]    ):
										return BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'    )

					@cached_property
					def 				__UpperCAmelCase		(		self     :Any    ):
										return BlenderbotTokenizerFast.from_pretrained('facebook/blenderbot-3B'    )

					def 				__UpperCAmelCase		(		self     :str    ):
										lowercase						   =       self.tokenizer_ab
										lowercase						   =       ' I am a small frog.'
										lowercase						   =       tok([src_text]      ,  padding=lowercase__      ,  truncation=lowercase__    )['input_ids']
										lowercase						   =       tok.batch_decode(lowercase__      ,  skip_special_tokens=lowercase__      ,  clean_up_tokenization_spaces=lowercase__    )[0]
										assert src_text == decoded

					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										lowercase						   =       self.rust_tokenizer_ab
										lowercase						   =       ' I am a small frog.'
										lowercase						   =       tok([src_text]      ,  padding=lowercase__      ,  truncation=lowercase__    )['input_ids']
										lowercase						   =       tok.batch_decode(lowercase__      ,  skip_special_tokens=lowercase__      ,  clean_up_tokenization_spaces=lowercase__    )[0]
										assert src_text == decoded

					def 				__UpperCAmelCase		(		self     :Any    ):
										assert self.tokenizer_ab.add_prefix_space
										assert self.tokenizer_ab([' Sam', 'Sam']    ).input_ids == [[5502, 2], [5502, 2]]



					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										assert self.rust_tokenizer_ab.add_prefix_space
										assert self.rust_tokenizer_ab([' Sam', 'Sam']    ).input_ids == [[5502, 2], [5502, 2]]






