


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class   _a      (      lowercase_ ):
    a_    :				List[Any]             =    (DEISMultistepScheduler,)
    a_    :				str             =    (('''num_inference_steps''', 25),)
    def      _UpperCamelCase       ( self							:					Any							,					**SCREAMING_SNAKE_CASE__							:					str				):
          lowerCamelCase__           =		{
              'num_train_timesteps': 10_00,
              'beta_start': 0.00_01,
              'beta_end': 0.02,
              'beta_schedule': 'linear',
              'solver_order': 2,
          }

          config.update(**UpperCamelCase__				)
          return config
    def      _UpperCamelCase       ( self							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					Tuple=0							,					**SCREAMING_SNAKE_CASE__							:					Optional[Any]				):
          lowerCamelCase__           =		dict(self.forward_default_kwargs				)
          lowerCamelCase__           =		kwargs.pop('num_inference_steps'							,					UpperCamelCase__				)
          lowerCamelCase__           =		self.dummy_sample
          lowerCamelCase__           =		0.1 * sample
          lowerCamelCase__           =		[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

                      assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          pass
    def      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					int=0							,					**SCREAMING_SNAKE_CASE__							:					Optional[int]				):
          lowerCamelCase__           =		dict(self.forward_default_kwargs				)
          lowerCamelCase__           =		kwargs.pop('num_inference_steps'							,					UpperCamelCase__				)
          lowerCamelCase__           =		self.dummy_sample
          lowerCamelCase__           =		0.1 * sample
          lowerCamelCase__           =		[residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
                lowerCamelCase__           =		self.get_scheduler_config()
                lowerCamelCase__           =		scheduler_class(**UpperCamelCase__				)
                scheduler.set_timesteps(UpperCamelCase__				)

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

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

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

                lowerCamelCase__           =		scheduler.step(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					**UpperCamelCase__				).prev_sample
                lowerCamelCase__           =		new_scheduler.step(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					**UpperCamelCase__				).prev_sample

                assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"
    def      _UpperCamelCase       ( self							:					int							,					SCREAMING_SNAKE_CASE__							:					List[str]=None							,					**SCREAMING_SNAKE_CASE__							:					List[Any]				):







          if scheduler is None:
                lowerCamelCase__           =		self.scheduler_classes[0]
                lowerCamelCase__           =		self.get_scheduler_config(**UpperCamelCase__				)
                lowerCamelCase__           =		scheduler_class(**UpperCamelCase__				)

          lowerCamelCase__           =		self.scheduler_classes[0]
          lowerCamelCase__           =		self.get_scheduler_config(**UpperCamelCase__				)
          lowerCamelCase__           =		scheduler_class(**UpperCamelCase__				)

          lowerCamelCase__           =		10
          lowerCamelCase__           =		self.dummy_model()
          lowerCamelCase__           =		self.dummy_sample_deter
          scheduler.set_timesteps(UpperCamelCase__				)

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

          return sample
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		dict(self.forward_default_kwargs				)

          lowerCamelCase__           =		kwargs.pop('num_inference_steps'							,					UpperCamelCase__				)

          for scheduler_class in self.scheduler_classes:
                lowerCamelCase__           =		self.get_scheduler_config()
                lowerCamelCase__           =		scheduler_class(**UpperCamelCase__				)

                lowerCamelCase__           =		self.dummy_sample
                lowerCamelCase__           =		0.1 * sample

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

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

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

                lowerCamelCase__           =		scheduler.step(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					**UpperCamelCase__				).prev_sample
                lowerCamelCase__           =		scheduler.step(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					**UpperCamelCase__				).prev_sample

                self.assertEqual(output_a.shape							,					sample.shape				)
                self.assertEqual(output_a.shape							,					output_a.shape				)
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          lowerCamelCase__           =		DEISMultistepScheduler(**self.get_scheduler_config()				)
          lowerCamelCase__           =		self.full_loop(scheduler=UpperCamelCase__				)
          lowerCamelCase__           =		torch.mean(torch.abs(UpperCamelCase__				)				)

          assert abs(result_mean.item() - 0.2_39_16				) < 1e-3

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

          lowerCamelCase__           =		self.full_loop(scheduler=UpperCamelCase__				)
          lowerCamelCase__           =		torch.mean(torch.abs(UpperCamelCase__				)				)

          assert abs(result_mean.item() - 0.2_39_16				) < 1e-3
    def      _UpperCamelCase       ( self							:					int				):







          for timesteps in [25, 50, 1_00, 9_99, 10_00]:
                self.check_over_configs(num_train_timesteps=UpperCamelCase__				)
    def      _UpperCamelCase       ( self							:					str				):
          self.check_over_configs(thresholding=UpperCamelCase__				)
          for order in [1, 2, 3]:
                for solver_type in ["logrho"]:
                      for threshold in [0.5, 1.0, 2.0]:
                            for prediction_type in ["epsilon", "sample"]:
                                  self.check_over_configs(
                                      thresholding=UpperCamelCase__							,					prediction_type=UpperCamelCase__							,					sample_max_value=UpperCamelCase__							,					algorithm_type='deis'							,					solver_order=UpperCamelCase__							,					solver_type=UpperCamelCase__							,					)
    def      _UpperCamelCase       ( self							:					Dict				):







          for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=UpperCamelCase__				)
    def      _UpperCamelCase       ( self							:					Dict				):







          for algorithm_type in ["deis"]:
                for solver_type in ["logrho"]:
                      for order in [1, 2, 3]:
                            for prediction_type in ["epsilon", "sample"]:
                                  self.check_over_configs(
                                      solver_order=UpperCamelCase__							,					solver_type=UpperCamelCase__							,					prediction_type=UpperCamelCase__							,					algorithm_type=UpperCamelCase__							,					)
                                  lowerCamelCase__           =		self.full_loop(
                                      solver_order=UpperCamelCase__							,					solver_type=UpperCamelCase__							,					prediction_type=UpperCamelCase__							,					algorithm_type=UpperCamelCase__							,					)
                                  assert not torch.isnan(UpperCamelCase__				).any(), "Samples have nan numbers"
    def      _UpperCamelCase       ( self							:					Dict				):
          self.check_over_configs(lower_order_final=UpperCamelCase__				)
          self.check_over_configs(lower_order_final=UpperCamelCase__				)
    def      _UpperCamelCase       ( self							:					Tuple				):







          for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
                self.check_over_forward(num_inference_steps=UpperCamelCase__							,					time_step=0				)
    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          lowerCamelCase__           =		self.full_loop()
          lowerCamelCase__           =		torch.mean(torch.abs(UpperCamelCase__				)				)

          assert abs(result_mean.item() - 0.2_39_16				) < 1e-3
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		self.full_loop(prediction_type='v_prediction'				)
          lowerCamelCase__           =		torch.mean(torch.abs(UpperCamelCase__				)				)

          assert abs(result_mean.item() - 0.0_91				) < 1e-3


    def      _UpperCamelCase       ( self							:					Any				):
          lowerCamelCase__           =		self.scheduler_classes[0]
          lowerCamelCase__           =		self.get_scheduler_config(thresholding=UpperCamelCase__							,					dynamic_thresholding_ratio=0				)
          lowerCamelCase__           =		scheduler_class(**UpperCamelCase__				)

          lowerCamelCase__           =		10
          lowerCamelCase__           =		self.dummy_model()
          lowerCamelCase__           =		self.dummy_sample_deter.half()
          scheduler.set_timesteps(UpperCamelCase__				)

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

          assert sample.dtype == torch.floataa







"""simple docstring"""


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


_snake_case          =							logging.get_logger(__name__)

_snake_case          =							{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}



class   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Union[str, Any]             =    'swinv2'

    a_    :				Optional[int]             =    {
        'num_attention_heads': 'num_heads',
        'num_hidden_layers': 'num_layers',
    }
    def __init__( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					int=2_24							,					SCREAMING_SNAKE_CASE__							:					str=4							,					SCREAMING_SNAKE_CASE__							:					List[Any]=3							,					SCREAMING_SNAKE_CASE__							:					str=96							,					SCREAMING_SNAKE_CASE__							:					Dict=[2, 2, 6, 2]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=[3, 6, 12, 24]							,					SCREAMING_SNAKE_CASE__							:					Tuple=7							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=4.0							,					SCREAMING_SNAKE_CASE__							:					List[str]=True							,					SCREAMING_SNAKE_CASE__							:					List[Any]=0.0							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=0.0							,					SCREAMING_SNAKE_CASE__							:					Dict=0.1							,					SCREAMING_SNAKE_CASE__							:					Tuple="gelu"							,					SCREAMING_SNAKE_CASE__							:					Tuple=False							,					SCREAMING_SNAKE_CASE__							:					Any=0.02							,					SCREAMING_SNAKE_CASE__							:					List[Any]=1e-5							,					SCREAMING_SNAKE_CASE__							:					int=32							,					**SCREAMING_SNAKE_CASE__							:					List[str]							,					):
          super().__init__(**SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		image_size
          lowerCamelCase__           =		patch_size
          lowerCamelCase__           =		num_channels
          lowerCamelCase__           =		embed_dim
          lowerCamelCase__           =		depths
          lowerCamelCase__           =		len(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		num_heads
          lowerCamelCase__           =		window_size
          lowerCamelCase__           =		mlp_ratio
          lowerCamelCase__           =		qkv_bias
          lowerCamelCase__           =		hidden_dropout_prob
          lowerCamelCase__           =		attention_probs_dropout_prob
          lowerCamelCase__           =		drop_path_rate
          lowerCamelCase__           =		hidden_act
          lowerCamelCase__           =		use_absolute_embeddings
          lowerCamelCase__           =		layer_norm_eps
          lowerCamelCase__           =		initializer_range
          lowerCamelCase__           =		encoder_stride
          # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          lowerCamelCase__           =		int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE__				) - 1)				)
          lowerCamelCase__           =		(0, 0, 0, 0)



