

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= (UniPCMultistepScheduler,)
  a									= (("num_inference_steps", 25),)






  def 				lowercase_		(							self						:		List[str]     ,       **__lowerCamelCase						:		Any )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   {
           '''num_train_timesteps''': 1000,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''solver_order''': 2,
           '''solver_type''': '''bh2''',
       }

       config.update(**__lowerCamelCase )
       return config






  def 				lowercase_		(							self						:		List[str]     ,       __lowerCamelCase						:		Optional[int]=0     ,       **__lowerCamelCase						:		int )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   dict(self.forward_default_kwargs )
       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''num_inference_steps'''     ,       __lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   self.dummy_sample
       SCREAMING_SNAKE_CASE__   =   0.1 * sample
       SCREAMING_SNAKE_CASE__   =   [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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






  def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		str=0     ,       **__lowerCamelCase						:		Optional[Any] )		->							Tuple:
       SCREAMING_SNAKE_CASE__   =   dict(self.forward_default_kwargs )
       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''num_inference_steps'''     ,       __lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   self.dummy_sample
       SCREAMING_SNAKE_CASE__   =   0.1 * sample
       SCREAMING_SNAKE_CASE__   =   [residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config()
            SCREAMING_SNAKE_CASE__   =   scheduler_class(**__lowerCamelCase )
            scheduler.set_timesteps(__lowerCamelCase )

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

            with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(__lowerCamelCase )
                 SCREAMING_SNAKE_CASE__   =   scheduler_class.from_pretrained(__lowerCamelCase )
                 # copy over dummy past residuals
                 new_scheduler.set_timesteps(__lowerCamelCase )

                 # copy over dummy past residual (must be after setting timesteps)
                 SCREAMING_SNAKE_CASE__   =   dummy_past_residuals[: new_scheduler.config.solver_order]

            SCREAMING_SNAKE_CASE__   =   scheduler.step(__lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase     ,       **__lowerCamelCase ).prev_sample
            SCREAMING_SNAKE_CASE__   =   new_scheduler.step(__lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase     ,       **__lowerCamelCase ).prev_sample

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






  def 				lowercase_		(							self						:		Union[str, Any]     ,       __lowerCamelCase						:		Any=None     ,       **__lowerCamelCase						:		Tuple )		->							Tuple:
       if scheduler is None:
            SCREAMING_SNAKE_CASE__   =   self.scheduler_classes[0]
            SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config(**__lowerCamelCase )
            SCREAMING_SNAKE_CASE__   =   scheduler_class(**__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config(**__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   scheduler_class(**__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   10
       SCREAMING_SNAKE_CASE__   =   self.dummy_model()
       SCREAMING_SNAKE_CASE__   =   self.dummy_sample_deter
       scheduler.set_timesteps(__lowerCamelCase )

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

       return sample






  def 				lowercase_		(							self						:		str )		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__   =   dict(self.forward_default_kwargs )

       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''num_inference_steps'''     ,       __lowerCamelCase )

       for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config()
            SCREAMING_SNAKE_CASE__   =   scheduler_class(**__lowerCamelCase )

            SCREAMING_SNAKE_CASE__   =   self.dummy_sample
            SCREAMING_SNAKE_CASE__   =   0.1 * sample

            if num_inference_steps is not None and hasattr(__lowerCamelCase     ,       '''set_timesteps''' ):
                 scheduler.set_timesteps(__lowerCamelCase )
            elif num_inference_steps is not None and not hasattr(__lowerCamelCase     ,       '''set_timesteps''' ):
                 SCREAMING_SNAKE_CASE__   =   num_inference_steps

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

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

            SCREAMING_SNAKE_CASE__   =   scheduler.step(__lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase     ,       **__lowerCamelCase ).prev_sample
            SCREAMING_SNAKE_CASE__   =   scheduler.step(__lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase     ,       **__lowerCamelCase ).prev_sample

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






  def 				lowercase_		(							self						:		Dict )		->							Optional[Any]:
       # make sure that iterating over schedulers with same config names gives same results
       # for defaults
       SCREAMING_SNAKE_CASE__   =   UniPCMultistepScheduler(**self.get_scheduler_config() )
       SCREAMING_SNAKE_CASE__   =   self.full_loop(scheduler=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   torch.mean(torch.abs(__lowerCamelCase ) )

       assert abs(result_mean.item() - 0.2464 ) < 1e-3

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

       SCREAMING_SNAKE_CASE__   =   self.full_loop(scheduler=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   torch.mean(torch.abs(__lowerCamelCase ) )

       assert abs(result_mean.item() - 0.2464 ) < 1e-3






  def 				lowercase_		(							self						:		Any )		->							Tuple:
       for timesteps in [25, 50, 100, 999, 1000]:
            self.check_over_configs(num_train_timesteps=__lowerCamelCase )






  def 				lowercase_		(							self						:		str )		->							Dict:
       self.check_over_configs(thresholding=__lowerCamelCase )
       for order in [1, 2, 3]:
            for solver_type in ["bh1", "bh2"]:
                 for threshold in [0.5, 1.0, 2.0]:
                      for prediction_type in ["epsilon", "sample"]:
                           self.check_over_configs(
                               thresholding=__lowerCamelCase     ,       prediction_type=__lowerCamelCase     ,       sample_max_value=__lowerCamelCase     ,       solver_order=__lowerCamelCase     ,       solver_type=__lowerCamelCase     ,       )






  def 				lowercase_		(							self						:		int )		->							Dict:
       for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=__lowerCamelCase )






  def 				lowercase_		(							self						:		Tuple )		->							List[str]:
       for solver_type in ["bh1", "bh2"]:
            for order in [1, 2, 3]:
                 for prediction_type in ["epsilon", "sample"]:
                      self.check_over_configs(
                          solver_order=__lowerCamelCase     ,       solver_type=__lowerCamelCase     ,       prediction_type=__lowerCamelCase     ,       )
                      SCREAMING_SNAKE_CASE__   =   self.full_loop(
                          solver_order=__lowerCamelCase     ,       solver_type=__lowerCamelCase     ,       prediction_type=__lowerCamelCase     ,       )
                      assert not torch.isnan(__lowerCamelCase ).any(), "Samples have nan numbers"






  def 				lowercase_		(							self						:		List[Any] )		->							Any:
       self.check_over_configs(lower_order_final=__lowerCamelCase )
       self.check_over_configs(lower_order_final=__lowerCamelCase )






  def 				lowercase_		(							self						:		List[str] )		->							Optional[Any]:
       for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
            self.check_over_forward(num_inference_steps=__lowerCamelCase     ,       time_step=0 )






  def 				lowercase_		(							self						:		Optional[Any] )		->							Any:
       SCREAMING_SNAKE_CASE__   =   self.full_loop()
       SCREAMING_SNAKE_CASE__   =   torch.mean(torch.abs(__lowerCamelCase ) )

       assert abs(result_mean.item() - 0.2464 ) < 1e-3






  def 				lowercase_		(							self						:		Dict )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   self.full_loop(prediction_type='''v_prediction''' )
       SCREAMING_SNAKE_CASE__   =   torch.mean(torch.abs(__lowerCamelCase ) )

       assert abs(result_mean.item() - 0.1014 ) < 1e-3






  def 				lowercase_		(							self						:		Any )		->							str:
       SCREAMING_SNAKE_CASE__   =   self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config(thresholding=__lowerCamelCase     ,       dynamic_thresholding_ratio=0 )
       SCREAMING_SNAKE_CASE__   =   scheduler_class(**__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   10
       SCREAMING_SNAKE_CASE__   =   self.dummy_model()
       SCREAMING_SNAKE_CASE__   =   self.dummy_sample_deter.half()
       scheduler.set_timesteps(__lowerCamelCase )

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

       assert sample.dtype == torch.floataa







  def 				lowercase_		(							self						:		str     ,       **__lowerCamelCase						:		Tuple )		->							List[str]:
       for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config(**__lowerCamelCase )
            SCREAMING_SNAKE_CASE__   =   scheduler_class(**__lowerCamelCase )

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





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


_SCREAMING_SNAKE_CASE					:  List[str]             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Tuple             =      {
    '''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "transfo-xl"
  a									= ["mems"]
  a									= {
      "n_token": "vocab_size",
      "hidden_size": "d_model",
      "num_attention_heads": "n_head",
      "num_hidden_layers": "n_layer",
  }






  def __init__(							self						:		Any     ,       __lowerCamelCase						:		int=26_7735     ,       __lowerCamelCase						:		Any=[2_0000, 4_0000, 20_0000]     ,       __lowerCamelCase						:		Dict=1024     ,       __lowerCamelCase						:		Optional[int]=1024     ,       __lowerCamelCase						:		List[Any]=16     ,       __lowerCamelCase						:		Union[str, Any]=64     ,       __lowerCamelCase						:		Dict=4096     ,       __lowerCamelCase						:		int=4     ,       __lowerCamelCase						:		Dict=False     ,       __lowerCamelCase						:		Tuple=18     ,       __lowerCamelCase						:		Optional[int]=1600     ,       __lowerCamelCase						:		str=1000     ,       __lowerCamelCase						:		Any=True     ,       __lowerCamelCase						:		Dict=True     ,       __lowerCamelCase						:		List[Any]=0     ,       __lowerCamelCase						:		int=-1     ,       __lowerCamelCase						:		Tuple=True     ,       __lowerCamelCase						:		List[Any]=0.1     ,       __lowerCamelCase						:		int=0.0     ,       __lowerCamelCase						:		int=True     ,       __lowerCamelCase						:		str="normal"     ,       __lowerCamelCase						:		List[str]=0.01     ,       __lowerCamelCase						:		Any=0.01     ,       __lowerCamelCase						:		List[str]=0.02     ,       __lowerCamelCase						:		List[str]=1e-5     ,       __lowerCamelCase						:		Union[str, Any]=0     ,       **__lowerCamelCase						:		int     ,       )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   vocab_size
       SCREAMING_SNAKE_CASE__   =   []
       self.cutoffs.extend(__lowerCamelCase )
       if proj_share_all_but_first:
            SCREAMING_SNAKE_CASE__   =   [False] + [True] * len(self.cutoffs )
       else:
            SCREAMING_SNAKE_CASE__   =   [False] + [False] * len(self.cutoffs )
       SCREAMING_SNAKE_CASE__   =   d_model
       SCREAMING_SNAKE_CASE__   =   d_embed
       SCREAMING_SNAKE_CASE__   =   d_head
       SCREAMING_SNAKE_CASE__   =   d_inner
       SCREAMING_SNAKE_CASE__   =   div_val
       SCREAMING_SNAKE_CASE__   =   pre_lnorm
       SCREAMING_SNAKE_CASE__   =   n_layer
       SCREAMING_SNAKE_CASE__   =   n_head
       SCREAMING_SNAKE_CASE__   =   mem_len
       SCREAMING_SNAKE_CASE__   =   same_length
       SCREAMING_SNAKE_CASE__   =   attn_type
       SCREAMING_SNAKE_CASE__   =   clamp_len
       SCREAMING_SNAKE_CASE__   =   sample_softmax
       SCREAMING_SNAKE_CASE__   =   adaptive
       SCREAMING_SNAKE_CASE__   =   dropout
       SCREAMING_SNAKE_CASE__   =   dropatt
       SCREAMING_SNAKE_CASE__   =   untie_r
       SCREAMING_SNAKE_CASE__   =   init
       SCREAMING_SNAKE_CASE__   =   init_range
       SCREAMING_SNAKE_CASE__   =   proj_init_std
       SCREAMING_SNAKE_CASE__   =   init_std
       SCREAMING_SNAKE_CASE__   =   layer_norm_epsilon
       super().__init__(eos_token_id=__lowerCamelCase     ,       **__lowerCamelCase )






  @property
  def 				lowercase_		(							self						:		str )		->							Dict:
       # Message copied from Transformer-XL documentation
       logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
       return -1







  @max_position_embeddings.setter
  def 				lowercase_		(							self						:		List[str]     ,       __lowerCamelCase						:		Any )		->							List[Any]:
       # Message copied from Transformer-XL documentation
       raise NotImplementedError(
           f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )


