


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							(UniPCMultistepScheduler,)
      UpperCamelCase										=							(('''num_inference_steps''', 25),)





      def 							_lowercase					(		self    :       Union[str, Any]       ,							**__lowerCamelCase    :       Optional[int]							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  {
             """num_train_timesteps""": 1_0_0_0,
             """beta_start""": 0.0_001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
             """solver_order""": 2,
             """solver_type""": """bh2""",
         }

         config.update(**__lowerCamelCase							)
         return config





      def 							_lowercase					(		self    :       Any       ,							__lowerCamelCase    :       Dict=0       ,							**__lowerCamelCase    :       int							)     -> Dict:






         """simple docstring"""







         UpperCAmelCase      					=  dict(self.forward_default_kwargs							)
         UpperCAmelCase      					=  kwargs.pop("""num_inference_steps"""       ,							__lowerCamelCase							)
         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(**__lowerCamelCase							)
            UpperCAmelCase      					=  scheduler_class(**__lowerCamelCase							)
            scheduler.set_timesteps(__lowerCamelCase							)
            # copy over dummy past residuals
            UpperCAmelCase      					=  dummy_past_residuals[: scheduler.config.solver_order]

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

            UpperCAmelCase					,	UpperCAmelCase      					=  sample, sample
            for t in range(__lowerCamelCase       ,							time_step + scheduler.config.solver_order + 1							):
               UpperCAmelCase      					=  scheduler.step(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase       ,							**__lowerCamelCase							).prev_sample
               UpperCAmelCase      					=  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    :       Optional[Any]       ,							__lowerCamelCase    :       List[Any]=0       ,							**__lowerCamelCase    :       List[Any]							)     -> str:






         """simple docstring"""







         UpperCAmelCase      					=  dict(self.forward_default_kwargs							)
         UpperCAmelCase      					=  kwargs.pop("""num_inference_steps"""       ,							__lowerCamelCase							)
         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(**__lowerCamelCase							)
            scheduler.set_timesteps(__lowerCamelCase							)

            # 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(__lowerCamelCase							)
               UpperCAmelCase      					=  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)
               UpperCAmelCase      					=  dummy_past_residuals[: new_scheduler.config.solver_order]

            UpperCAmelCase      					=  scheduler.step(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase       ,							**__lowerCamelCase							).prev_sample
            UpperCAmelCase      					=  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    :       int=None       ,							**__lowerCamelCase    :       Tuple							)     -> List[str]:






         """simple docstring"""







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

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

         UpperCAmelCase      					=  1_0
         UpperCAmelCase      					=  self.dummy_model()
         UpperCAmelCase      					=  self.dummy_sample_deter
         scheduler.set_timesteps(__lowerCamelCase							)

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

         return sample





      def 							_lowercase					(		self    :       int							)     -> List[str]:






         """simple docstring"""







         UpperCAmelCase      					=  dict(self.forward_default_kwargs							)

         UpperCAmelCase      					=  kwargs.pop("""num_inference_steps"""       ,							__lowerCamelCase							)

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

            UpperCAmelCase      					=  self.dummy_sample
            UpperCAmelCase      					=  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"""							):
               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(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase       ,							**__lowerCamelCase							).prev_sample
            UpperCAmelCase      					=  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    :       Tuple							)     -> List[str]:






         """simple docstring"""







         UpperCAmelCase      					=  UniPCMultistepScheduler(**self.get_scheduler_config()							)
         UpperCAmelCase      					=  self.full_loop(scheduler=__lowerCamelCase							)
         UpperCAmelCase      					=  torch.mean(torch.abs(__lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.2_464							) < 1e-3

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

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

         assert abs(result_mean.item() - 0.2_464							) < 1e-3





      def 							_lowercase					(		self    :       List[Any]							)     -> Optional[Any]:






         """simple docstring"""







         for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=__lowerCamelCase							)





      def 							_lowercase					(		self    :       int							)     -> Optional[Any]:






         """simple docstring"""







         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    :       Dict							)     -> int:






         """simple docstring"""







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





      def 							_lowercase					(		self    :       Tuple							)     -> int:






         """simple docstring"""







         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       ,							)
                  UpperCAmelCase      					=  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]							)     -> List[Any]:






         """simple docstring"""







         self.check_over_configs(lower_order_final=__lowerCamelCase							)
         self.check_over_configs(lower_order_final=__lowerCamelCase							)





      def 							_lowercase					(		self    :       Dict							)     -> Tuple:






         """simple docstring"""







         for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_forward(num_inference_steps=__lowerCamelCase       ,							time_step=0							)





      def 							_lowercase					(		self    :       List[str]							)     -> Optional[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.full_loop()
         UpperCAmelCase      					=  torch.mean(torch.abs(__lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.2_464							) < 1e-3





      def 							_lowercase					(		self    :       str							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  self.full_loop(prediction_type="""v_prediction"""							)
         UpperCAmelCase      					=  torch.mean(torch.abs(__lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.1_014							) < 1e-3





      def 							_lowercase					(		self    :       Any							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  self.scheduler_classes[0]
         UpperCAmelCase      					=  self.get_scheduler_config(thresholding=__lowerCamelCase       ,							dynamic_thresholding_ratio=0							)
         UpperCAmelCase      					=  scheduler_class(**__lowerCamelCase							)

         UpperCAmelCase      					=  1_0
         UpperCAmelCase      					=  self.dummy_model()
         UpperCAmelCase      					=  self.dummy_sample_deter.half()
         scheduler.set_timesteps(__lowerCamelCase							)

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

         assert sample.dtype == torch.floataa





      def 							_lowercase					(		self    :       Tuple       ,							**__lowerCamelCase    :       Optional[int]							)     -> Optional[Any]:






         """simple docstring"""







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

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







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a	 =      logging.get_logger(__name__)

__a	 =      {
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							'''mobilenet_v1'''





      def __init__(		self    :       Any       ,							__lowerCamelCase    :       Union[str, Any]=3       ,							__lowerCamelCase    :       Dict=2_2_4       ,							__lowerCamelCase    :       Union[str, Any]=1.0       ,							__lowerCamelCase    :       Optional[int]=8       ,							__lowerCamelCase    :       Optional[int]="relu6"       ,							__lowerCamelCase    :       Optional[int]=True       ,							__lowerCamelCase    :       List[str]=0.999       ,							__lowerCamelCase    :       Optional[Any]=0.02       ,							__lowerCamelCase    :       str=0.001       ,							**__lowerCamelCase    :       Optional[Any]       ,							)     -> Optional[int]:






         """simple docstring"""







         super().__init__(**__lowerCamelCase							)

         if depth_multiplier <= 0:
            raise ValueError("""depth_multiplier must be greater than zero."""							)

         UpperCAmelCase      					=  num_channels
         UpperCAmelCase      					=  image_size
         UpperCAmelCase      					=  depth_multiplier
         UpperCAmelCase      					=  min_depth
         UpperCAmelCase      					=  hidden_act
         UpperCAmelCase      					=  tf_padding
         UpperCAmelCase      					=  classifier_dropout_prob
         UpperCAmelCase      					=  initializer_range
         UpperCAmelCase      					=  layer_norm_eps






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							version.parse('''1.11'''	)





      @property
      def 							_lowercase					(		self    :       Optional[int]							)     -> Mapping[str, Mapping[int, str]]:






         """simple docstring"""







         return OrderedDict([("""pixel_values""", {0: """batch"""})]							)





      @property
      def 							_lowercase					(		self    :       Union[str, Any]							)     -> Mapping[str, Mapping[int, str]]:






         """simple docstring"""







         if self.task == "image-classification":
            return OrderedDict([("""logits""", {0: """batch"""})]							)
         else:
            return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]							)





      @property
      def 							_lowercase					(		self    :       List[Any]							)     -> float:






         """simple docstring"""







         return 1e-4



