


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  (UniPCMultistepScheduler,)
  __UpperCamelCase	     =  (("""num_inference_steps""", 25),)







  def 	UpperCAmelCase__		(	self    ,						**_lowercase						):
       lowerCAmelCase_					:       List[Any]    =		{
           """num_train_timesteps""": 1_000,
           """beta_start""": 0.0001,
           """beta_end""": 0.02,
           """beta_schedule""": """linear""",
           """solver_order""": 2,
           """solver_type""": """bh2""",
       }

       config.update(**_lowercase						)
       return config







  def 	UpperCAmelCase__		(	self    ,						_lowercase=0    ,						**_lowercase						):
       lowerCAmelCase_					:       str    =		dict(self.forward_default_kwargs						)
       lowerCAmelCase_					:       List[Any]    =		kwargs.pop("""num_inference_steps"""    ,						_lowercase						)
       lowerCAmelCase_					:       Any    =		self.dummy_sample
       lowerCAmelCase_					:       Any    =		0.1 * sample
       lowerCAmelCase_					:       Dict    =		[residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
            lowerCAmelCase_					:       Optional[Any]    =		self.get_scheduler_config(**_lowercase						)
            lowerCAmelCase_					:       Dict    =		scheduler_class(**_lowercase						)
            scheduler.set_timesteps(_lowercase						)
            # copy over dummy past residuals
            lowerCAmelCase_					:       Dict    =		dummy_past_residuals[: scheduler.config.solver_order]

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

            lowerCAmelCase_					:       Optional[Any]    =		sample, sample
            for t in range(_lowercase    ,						time_step + scheduler.config.solver_order + 1						):
                 lowerCAmelCase_					:       Optional[int]    =		scheduler.step(_lowercase    ,						_lowercase    ,						_lowercase    ,						**_lowercase						).prev_sample
                 lowerCAmelCase_					:       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    ,						_lowercase=0    ,						**_lowercase						):
       lowerCAmelCase_					:       Optional[int]    =		dict(self.forward_default_kwargs						)
       lowerCAmelCase_					:       str    =		kwargs.pop("""num_inference_steps"""    ,						_lowercase						)
       lowerCAmelCase_					:       Dict    =		self.dummy_sample
       lowerCAmelCase_					:       List[str]    =		0.1 * sample
       lowerCAmelCase_					:       str    =		[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

            lowerCAmelCase_					:       Dict    =		scheduler.step(_lowercase    ,						_lowercase    ,						_lowercase    ,						**_lowercase						).prev_sample
            lowerCAmelCase_					:       Optional[Any]    =		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    ,						_lowercase=None    ,						**_lowercase						):
       if scheduler is None:
            lowerCAmelCase_					:       Tuple    =		self.scheduler_classes[0]
            lowerCAmelCase_					:       int    =		self.get_scheduler_config(**_lowercase						)
            lowerCAmelCase_					:       str    =		scheduler_class(**_lowercase						)

       lowerCAmelCase_					:       Optional[Any]    =		self.scheduler_classes[0]
       lowerCAmelCase_					:       List[str]    =		self.get_scheduler_config(**_lowercase						)
       lowerCAmelCase_					:       Optional[Any]    =		scheduler_class(**_lowercase						)

       lowerCAmelCase_					:       Union[str, Any]    =		10
       lowerCAmelCase_					:       str    =		self.dummy_model()
       lowerCAmelCase_					:       Optional[Any]    =		self.dummy_sample_deter
       scheduler.set_timesteps(_lowercase						)

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

       return sample







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       List[str]    =		dict(self.forward_default_kwargs						)

       lowerCAmelCase_					:       int    =		kwargs.pop("""num_inference_steps"""    ,						_lowercase						)

       for scheduler_class in self.scheduler_classes:
            lowerCAmelCase_					:       Tuple    =		self.get_scheduler_config()
            lowerCAmelCase_					:       int    =		scheduler_class(**_lowercase						)

            lowerCAmelCase_					:       Union[str, Any]    =		self.dummy_sample
            lowerCAmelCase_					:       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"""						):
                 lowerCAmelCase_					:       List[str]    =		num_inference_steps

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

            lowerCAmelCase_					:       List[Any]    =		scheduler.timesteps[5]
            lowerCAmelCase_					:       Dict    =		scheduler.timesteps[6]

            lowerCAmelCase_					:       int    =		scheduler.step(_lowercase    ,						_lowercase    ,						_lowercase    ,						**_lowercase						).prev_sample
            lowerCAmelCase_					:       Union[str, 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						):
       # make sure that iterating over schedulers with same config names gives same results
       # for defaults
       lowerCAmelCase_					:       Optional[int]    =		UniPCMultistepScheduler(**self.get_scheduler_config()						)
       lowerCAmelCase_					:       Optional[int]    =		self.full_loop(scheduler=_lowercase						)
       lowerCAmelCase_					:       List[Any]    =		torch.mean(torch.abs(_lowercase						)						)

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

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

       lowerCAmelCase_					:       Union[str, Any]    =		self.full_loop(scheduler=_lowercase						)
       lowerCAmelCase_					:       Tuple    =		torch.mean(torch.abs(_lowercase						)						)

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







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







  def 	UpperCAmelCase__		(	self						):
       self.check_over_configs(thresholding=_lowercase						)
       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=_lowercase    ,						prediction_type=_lowercase    ,						sample_max_value=_lowercase    ,						solver_order=_lowercase    ,						solver_type=_lowercase    ,						)







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







  def 	UpperCAmelCase__		(	self						):
       for solver_type in ["bh1", "bh2"]:
            for order in [1, 2, 3]:
                 for prediction_type in ["epsilon", "sample"]:
                      self.check_over_configs(
                          solver_order=_lowercase    ,						solver_type=_lowercase    ,						prediction_type=_lowercase    ,						)
                      lowerCAmelCase_					:       int    =		self.full_loop(
                          solver_order=_lowercase    ,						solver_type=_lowercase    ,						prediction_type=_lowercase    ,						)
                      assert not torch.isnan(_lowercase						).any(), "Samples have nan numbers"







  def 	UpperCAmelCase__		(	self						):
       self.check_over_configs(lower_order_final=_lowercase						)
       self.check_over_configs(lower_order_final=_lowercase						)







  def 	UpperCAmelCase__		(	self						):
       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						):
       lowerCAmelCase_					:       List[str]    =		self.full_loop()
       lowerCAmelCase_					:       Optional[int]    =		torch.mean(torch.abs(_lowercase						)						)

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







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       str    =		self.full_loop(prediction_type="""v_prediction"""						)
       lowerCAmelCase_					:       Tuple    =		torch.mean(torch.abs(_lowercase						)						)

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







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       List[Any]    =		self.scheduler_classes[0]
       lowerCAmelCase_					:       int    =		self.get_scheduler_config(thresholding=_lowercase    ,						dynamic_thresholding_ratio=0						)
       lowerCAmelCase_					:       List[Any]    =		scheduler_class(**_lowercase						)

       lowerCAmelCase_					:       Union[str, Any]    =		10
       lowerCAmelCase_					:       int    =		self.dummy_model()
       lowerCAmelCase_					:       int    =		self.dummy_sample_deter.half()
       scheduler.set_timesteps(_lowercase						)

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

       assert sample.dtype == torch.floataa







  def 	UpperCAmelCase__		(	self    ,						**_lowercase						):
       for scheduler_class in self.scheduler_classes:
            lowerCAmelCase_					:       str    =		self.get_scheduler_config(**_lowercase						)
            lowerCAmelCase_					:       Optional[Any]    =		scheduler_class(**_lowercase						)

            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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


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

UpperCAmelCase_					:   int 							=						{
    """google/bit-50""": """https://huggingface.co/google/bit-50/resolve/main/config.json""",
}





class      lowercase__    (							__A     ,					__A  ):
  __UpperCamelCase	     =  """bit"""
  __UpperCamelCase	     =  ["""preactivation""", """bottleneck"""]
  __UpperCamelCase	     =  ["""SAME""", """VALID"""]







  def __init__(	self    ,						_lowercase=3    ,						_lowercase=64    ,						_lowercase=[256, 512, 1_024, 2_048]    ,						_lowercase=[3, 4, 6, 3]    ,						_lowercase="preactivation"    ,						_lowercase="relu"    ,						_lowercase=None    ,						_lowercase=32    ,						_lowercase=0.0    ,						_lowercase=False    ,						_lowercase=32    ,						_lowercase=1    ,						_lowercase=None    ,						_lowercase=None    ,						**_lowercase    ,						):
       super().__init__(**_lowercase						)
       if layer_type not in self.layer_types:
            raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types						)}'						)
       if global_padding is not None:
            if global_padding.upper() in self.supported_padding:
                 lowerCAmelCase_					:       int    =		global_padding.upper()
            else:
                 raise ValueError(F'Padding strategy {global_padding} not supported'						)
       lowerCAmelCase_					:       Optional[Any]    =		num_channels
       lowerCAmelCase_					:       List[str]    =		embedding_size
       lowerCAmelCase_					:       Dict    =		hidden_sizes
       lowerCAmelCase_					:       Optional[int]    =		depths
       lowerCAmelCase_					:       List[Any]    =		layer_type
       lowerCAmelCase_					:       Union[str, Any]    =		hidden_act
       lowerCAmelCase_					:       Optional[int]    =		global_padding
       lowerCAmelCase_					:       Optional[Any]    =		num_groups
       lowerCAmelCase_					:       Optional[Any]    =		drop_path_rate
       lowerCAmelCase_					:       Tuple    =		embedding_dynamic_padding
       lowerCAmelCase_					:       Any    =		output_stride
       lowerCAmelCase_					:       List[Any]    =		width_factor

       lowerCAmelCase_					:       Union[str, Any]    =		["""stem"""] + [F'stage{idx}' for idx in range(1    ,						len(_lowercase						) + 1						)]
       lowerCAmelCase_   ,  lowerCAmelCase_					:       Tuple    =		get_aligned_output_features_output_indices(
           out_features=_lowercase    ,						out_indices=_lowercase    ,						stage_names=self.stage_names						)
