

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest

class 						__snake_case						(							snake_case__     ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE													=       (DDIMScheduler,)
       __SCREAMING_SNAKE_CASE													=       (("eta", 0.0), ("num_inference_steps", 50))


       def  a		(		self			,   **_UpperCamelCase      )			->       List[str]:






              """simple docstring"""


              __snake_case					  =       {
                  """num_train_timesteps""": 10_00,
                  """beta_start""": 0.0001,
                  """beta_end""": 0.02,
                  """beta_schedule""": """linear""",
                  """clip_sample""": True,
              }

              config.update(**_UpperCamelCase      )
              return config


       def  a		(		self			,   **_UpperCamelCase      )			->       Union[str, Any]:






              """simple docstring"""


              __snake_case					  =       self.scheduler_classes[0]
              __snake_case					  =       self.get_scheduler_config(**_UpperCamelCase      )
              __snake_case					  =       scheduler_class(**_UpperCamelCase      )

              __snake_case  ,						__snake_case					  =       10, 0.0

              __snake_case					  =       self.dummy_model()
              __snake_case					  =       self.dummy_sample_deter

              scheduler.set_timesteps(_UpperCamelCase      )

              for t in scheduler.timesteps:
                     __snake_case					  =       model(_UpperCamelCase			,   _UpperCamelCase      )
                     __snake_case					  =       scheduler.step(_UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase      ).prev_sample

              return sample


       def  a		(		self      )			->       List[Any]:






              """simple docstring"""


              for timesteps in [1_00, 5_00, 10_00]:
                     self.check_over_configs(num_train_timesteps=_UpperCamelCase      )


       def  a		(		self      )			->       List[str]:






              """simple docstring"""


              for steps_offset in [0, 1]:
                     self.check_over_configs(steps_offset=_UpperCamelCase      )

              __snake_case					  =       self.scheduler_classes[0]
              __snake_case					  =       self.get_scheduler_config(steps_offset=1      )
              __snake_case					  =       scheduler_class(**_UpperCamelCase      )
              scheduler.set_timesteps(5      )
              assert torch.equal(scheduler.timesteps			,   torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]      )      )


       def  a		(		self      )			->       int:






              """simple docstring"""


              for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]			,   [0.002, 0.02, 0.2, 2]      ):
                     self.check_over_configs(beta_start=_UpperCamelCase			,   beta_end=_UpperCamelCase      )


       def  a		(		self      )			->       List[str]:






              """simple docstring"""


              for schedule in ["linear", "squaredcos_cap_v2"]:
                     self.check_over_configs(beta_schedule=_UpperCamelCase      )


       def  a		(		self      )			->       Optional[int]:






              """simple docstring"""


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


       def  a		(		self      )			->       str:






              """simple docstring"""


              for clip_sample in [True, False]:
                     self.check_over_configs(clip_sample=_UpperCamelCase      )


       def  a		(		self      )			->       List[str]:






              """simple docstring"""


              for timestep_spacing in ["trailing", "leading"]:
                     self.check_over_configs(timestep_spacing=_UpperCamelCase      )


       def  a		(		self      )			->       str:






              """simple docstring"""


              for rescale_betas_zero_snr in [True, False]:
                     self.check_over_configs(rescale_betas_zero_snr=_UpperCamelCase      )


       def  a		(		self      )			->       List[Any]:






              """simple docstring"""


              self.check_over_configs(thresholding=_UpperCamelCase      )
              for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "v_prediction"]:
                            self.check_over_configs(
                                thresholding=_UpperCamelCase			,   prediction_type=_UpperCamelCase			,   sample_max_value=_UpperCamelCase			,   )


       def  a		(		self      )			->       List[Any]:






              """simple docstring"""


              for t in [1, 10, 49]:
                     self.check_over_forward(time_step=_UpperCamelCase      )


       def  a		(		self      )			->       Any:






              """simple docstring"""


              for t, num_inference_steps in zip([1, 10, 50]			,   [10, 50, 5_00]      ):
                     self.check_over_forward(time_step=_UpperCamelCase			,   num_inference_steps=_UpperCamelCase      )


       def  a		(		self      )			->       Optional[int]:






              """simple docstring"""


              for t, eta in zip([1, 10, 49]			,   [0.0, 0.5, 1.0]      ):
                     self.check_over_forward(time_step=_UpperCamelCase			,   eta=_UpperCamelCase      )


       def  a		(		self      )			->       Any:






              """simple docstring"""


              __snake_case					  =       self.scheduler_classes[0]
              __snake_case					  =       self.get_scheduler_config()
              __snake_case					  =       scheduler_class(**_UpperCamelCase      )

              assert torch.sum(torch.abs(scheduler._get_variance(0			,   0      ) - 0.0      )      ) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(4_20			,   4_00      ) - 0.1_4771      )      ) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(9_80			,   9_60      ) - 0.3_2460      )      ) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(0			,   0      ) - 0.0      )      ) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(4_87			,   4_86      ) - 0.0_0979      )      ) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(9_99			,   9_98      ) - 0.02      )      ) < 1E-5


       def  a		(		self      )			->       int:






              """simple docstring"""


              __snake_case					  =       self.full_loop()

              __snake_case					  =       torch.sum(torch.abs(_UpperCamelCase      )      )
              __snake_case					  =       torch.mean(torch.abs(_UpperCamelCase      )      )

              assert abs(result_sum.item() - 172.0067      ) < 1E-2
              assert abs(result_mean.item() - 0.22_3967      ) < 1E-3


       def  a		(		self      )			->       int:






              """simple docstring"""


              __snake_case					  =       self.full_loop(prediction_type="""v_prediction"""      )

              __snake_case					  =       torch.sum(torch.abs(_UpperCamelCase      )      )
              __snake_case					  =       torch.mean(torch.abs(_UpperCamelCase      )      )

              assert abs(result_sum.item() - 52.5302      ) < 1E-2
              assert abs(result_mean.item() - 0.0684      ) < 1E-3


       def  a		(		self      )			->       Optional[int]:






              """simple docstring"""


              __snake_case					  =       self.full_loop(set_alpha_to_one=_UpperCamelCase			,   beta_start=0.01      )
              __snake_case					  =       torch.sum(torch.abs(_UpperCamelCase      )      )
              __snake_case					  =       torch.mean(torch.abs(_UpperCamelCase      )      )

              assert abs(result_sum.item() - 149.8295      ) < 1E-2
              assert abs(result_mean.item() - 0.1951      ) < 1E-3






       def  a		(		self      )			->       Optional[Any]:






              """simple docstring"""


              __snake_case					  =       self.full_loop(set_alpha_to_one=_UpperCamelCase			,   beta_start=0.01      )
              __snake_case					  =       torch.sum(torch.abs(_UpperCamelCase      )      )
              __snake_case					  =       torch.mean(torch.abs(_UpperCamelCase      )      )

              assert abs(result_sum.item() - 149.0784      ) < 1E-2
              assert abs(result_mean.item() - 0.1941      ) < 1E-3




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


UpperCamelCase__		   =    logging.get_logger(__name__)

UpperCamelCase__		   =    {
    '''apple/mobilevitv2-1.0''': '''https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json''',
}

class 						__snake_case						(							snake_case__     ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE													=       "mobilevitv2"


       def __init__(		self			,   _UpperCamelCase=3			,   _UpperCamelCase=2_56			,   _UpperCamelCase=2			,   _UpperCamelCase=2.0			,   _UpperCamelCase="swish"			,   _UpperCamelCase=3			,   _UpperCamelCase=32			,   _UpperCamelCase=0.1			,   _UpperCamelCase=0.02			,   _UpperCamelCase=1E-5			,   _UpperCamelCase=5_12			,   _UpperCamelCase=[6, 12, 18]			,   _UpperCamelCase=0.1			,   _UpperCamelCase=2_55			,   _UpperCamelCase=[2, 4, 3]			,   _UpperCamelCase=[1_28, 1_92, 2_56]			,   _UpperCamelCase=1.0			,   _UpperCamelCase=2			,   _UpperCamelCase=0.0			,   _UpperCamelCase=0.0			,   **_UpperCamelCase			,   )			->       List[Any]:






              """simple docstring"""


              super().__init__(**_UpperCamelCase      )

              __snake_case					  =       num_channels
              __snake_case					  =       image_size
              __snake_case					  =       patch_size
              __snake_case					  =       expand_ratio
              __snake_case					  =       hidden_act
              __snake_case					  =       conv_kernel_size
              __snake_case					  =       output_stride
              __snake_case					  =       initializer_range
              __snake_case					  =       layer_norm_eps
              __snake_case					  =       n_attn_blocks
              __snake_case					  =       base_attn_unit_dims
              __snake_case					  =       width_multiplier
              __snake_case					  =       ffn_multiplier
              __snake_case					  =       ffn_dropout
              __snake_case					  =       attn_dropout
              __snake_case					  =       classifier_dropout_prob

              # decode head attributes for semantic segmentation
              __snake_case					  =       aspp_out_channels
              __snake_case					  =       atrous_rates
              __snake_case					  =       aspp_dropout_prob
              __snake_case					  =       semantic_loss_ignore_index






class 						__snake_case						(							snake_case__     ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE													=       version.parse("1.11"     )


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






              """simple docstring"""


              return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]      )


       @property
       def  a		(		self      )			->       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  a		(		self      )			->       float:






              """simple docstring"""


              return 1E-4

