

from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


UpperCamelCase__		   =    logging.get_logger(__name__)  # pylint: disable=invalid-name

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







       __SCREAMING_SNAKE_CASE													=       42






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







       __SCREAMING_SNAKE_CASE													=       1


       @register_to_config
       def __init__(		self			,   _UpperCamelCase = 40			,   _UpperCamelCase = 0.002			,   _UpperCamelCase = 80.0			,   _UpperCamelCase = 0.5			,   _UpperCamelCase = 1.0			,   _UpperCamelCase = 7.0			,   _UpperCamelCase = True			,   )			->       Optional[Any]:






              """simple docstring"""


              __snake_case					  =       sigma_max

              __snake_case					  =       np.linspace(0			,   1			,   _UpperCamelCase      )
              __snake_case					  =       self._convert_to_karras(_UpperCamelCase      )
              __snake_case					  =       self.sigma_to_t(_UpperCamelCase      )

              # setable values
              __snake_case					  =       None
              __snake_case					  =       torch.from_numpy(_UpperCamelCase      )
              __snake_case					  =       torch.from_numpy(_UpperCamelCase      )
              __snake_case					  =       False
              __snake_case					  =       False


       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase=None      )			->       Optional[int]:






              """simple docstring"""


              if schedule_timesteps is None:
                     __snake_case					  =       self.timesteps

              __snake_case					  =       (schedule_timesteps == timestep).nonzero()
              return indices.item()


       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase      )			->       torch.FloatTensor:






              """simple docstring"""


              if isinstance(_UpperCamelCase			,   torch.Tensor      ):
                     __snake_case					  =       timestep.to(self.timesteps.device      )
              __snake_case					  =       self.index_for_timestep(_UpperCamelCase      )
              __snake_case					  =       self.sigmas[step_idx]

              __snake_case					  =       sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

              __snake_case					  =       True
              return sample


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






              """simple docstring"""


              if not isinstance(_UpperCamelCase			,   np.ndarray      ):
                     __snake_case					  =       np.array(_UpperCamelCase			,   dtype=np.floataa      )

              __snake_case					  =       10_00 * 0.25 * np.log(sigmas + 1E-44      )

              return timesteps


       def  a		(		self			,   _UpperCamelCase = None			,   _UpperCamelCase = None			,   _UpperCamelCase = None			,   )			->       Any:






              """simple docstring"""


              if num_inference_steps is None and timesteps is None:
                     raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""      )

              if num_inference_steps is not None and timesteps is not None:
                     raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""      )

              # Follow DDPMScheduler custom timesteps logic
              if timesteps is not None:
                     for i in range(1			,   len(_UpperCamelCase      )      ):
                            if timesteps[i] >= timesteps[i - 1]:
                                   raise ValueError("""`timesteps` must be in descending order."""      )

                     if timesteps[0] >= self.config.num_train_timesteps:
                            raise ValueError(
                                F'`timesteps` must start before `self.config.train_timesteps`:'
                                F' {self.config.num_train_timesteps}.'      )

                     __snake_case					  =       np.array(_UpperCamelCase			,   dtype=np.intaa      )
                     __snake_case					  =       True
              else:
                     if num_inference_steps > self.config.num_train_timesteps:
                            raise ValueError(
                                F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                                F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                                F' maximal {self.config.num_train_timesteps} timesteps.'      )

                     __snake_case					  =       num_inference_steps

                     __snake_case					  =       self.config.num_train_timesteps // self.num_inference_steps
                     __snake_case					  =       (np.arange(0			,   _UpperCamelCase      ) * step_ratio).round()[::-1].copy().astype(np.intaa      )
                     __snake_case					  =       False

              # Map timesteps to Karras sigmas directly for multistep sampling
              # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
              __snake_case					  =       self.config.num_train_timesteps
              __snake_case					  =       timesteps[::-1].copy()
              __snake_case					  =       ramp / (num_train_timesteps - 1)
              __snake_case					  =       self._convert_to_karras(_UpperCamelCase      )
              __snake_case					  =       self.sigma_to_t(_UpperCamelCase      )

              __snake_case					  =       np.concatenate([sigmas, [self.sigma_min]]      ).astype(np.floataa      )
              __snake_case					  =       torch.from_numpy(_UpperCamelCase      ).to(device=_UpperCamelCase      )

              if str(_UpperCamelCase      ).startswith("""mps"""      ):
                     # mps does not support float64
                     __snake_case					  =       torch.from_numpy(_UpperCamelCase      ).to(_UpperCamelCase			,   dtype=torch.floataa      )
              else:
                     __snake_case					  =       torch.from_numpy(_UpperCamelCase      ).to(device=_UpperCamelCase      )


       def  a		(		self			,   _UpperCamelCase      )			->       Dict:






              """simple docstring"""


              __snake_case					  =       self.config.sigma_min
              __snake_case					  =       self.config.sigma_max

              __snake_case					  =       self.config.rho
              __snake_case					  =       sigma_min ** (1 / rho)
              __snake_case					  =       sigma_max ** (1 / rho)
              __snake_case					  =       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
              return sigmas


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






              """simple docstring"""


              __snake_case					  =       self.config.sigma_data

              __snake_case					  =       sigma_data**2 / (sigma**2 + sigma_data**2)
              __snake_case					  =       sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
              return c_skip, c_out


       def  a		(		self			,   _UpperCamelCase      )			->       int:






              """simple docstring"""


              __snake_case					  =       self.config.sigma_min
              __snake_case					  =       self.config.sigma_data

              __snake_case					  =       sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
              __snake_case					  =       (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
              return c_skip, c_out


       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase = None			,   _UpperCamelCase = True			,   )			->       Union[CMStochasticIterativeSchedulerOutput, Tuple]:






              """simple docstring"""


              if (
                  isinstance(_UpperCamelCase			,   _UpperCamelCase      )
                  or isinstance(_UpperCamelCase			,   torch.IntTensor      )
                  or isinstance(_UpperCamelCase			,   torch.LongTensor      )
              ):
                     raise ValueError(
                         (
                             """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
                             F' `{self.__class__}.step()` is not supported. Make sure to pass'
                             """ one of the `scheduler.timesteps` as a timestep."""
                         )			,   )

              if not self.is_scale_input_called:
                     logger.warning(
                         """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
                         """See `StableDiffusionPipeline` for a usage example."""      )

              if isinstance(_UpperCamelCase			,   torch.Tensor      ):
                     __snake_case					  =       timestep.to(self.timesteps.device      )

              __snake_case					  =       self.config.sigma_min
              __snake_case					  =       self.config.sigma_max

              __snake_case					  =       self.index_for_timestep(_UpperCamelCase      )

              # sigma_next corresponds to next_t in original implementation
              __snake_case					  =       self.sigmas[step_index]
              if step_index + 1 < self.config.num_train_timesteps:
                     __snake_case					  =       self.sigmas[step_index + 1]
              else:
                     # Set sigma_next to sigma_min
                     __snake_case					  =       self.sigmas[-1]

              # Get scalings for boundary conditions
              __snake_case  ,						__snake_case					  =       self.get_scalings_for_boundary_condition(_UpperCamelCase      )

              # 1. Denoise model output using boundary conditions
              __snake_case					  =       c_out * model_output + c_skip * sample
              if self.config.clip_denoised:
                     __snake_case					  =       denoised.clamp(-1			,   1      )

              # 2. Sample z ~ N(0, s_noise^2 * I)
              # Noise is not used for onestep sampling.
              if len(self.timesteps      ) > 1:
                     __snake_case					  =       randn_tensor(
                         model_output.shape			,   dtype=model_output.dtype			,   device=model_output.device			,   generator=_UpperCamelCase      )
              else:
                     __snake_case					  =       torch.zeros_like(_UpperCamelCase      )
              __snake_case					  =       noise * self.config.s_noise

              __snake_case					  =       sigma_next.clamp(min=_UpperCamelCase			,   max=_UpperCamelCase      )

              # 3. Return noisy sample
              # tau = sigma_hat, eps = sigma_min
              __snake_case					  =       denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

              if not return_dict:
                     return (prev_sample,)

              return CMStochasticIterativeSchedulerOutput(prev_sample=_UpperCamelCase      )


       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   )			->       torch.FloatTensor:






              """simple docstring"""


              __snake_case					  =       self.sigmas.to(device=original_samples.device			,   dtype=original_samples.dtype      )
              if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCamelCase      ):
                     # mps does not support float64
                     __snake_case					  =       self.timesteps.to(original_samples.device			,   dtype=torch.floataa      )
                     __snake_case					  =       timesteps.to(original_samples.device			,   dtype=torch.floataa      )
              else:
                     __snake_case					  =       self.timesteps.to(original_samples.device      )
                     __snake_case					  =       timesteps.to(original_samples.device      )

              __snake_case					  =       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

              __snake_case					  =       sigmas[step_indices].flatten()
              while len(sigma.shape      ) < len(original_samples.shape      ):
                     __snake_case					  =       sigma.unsqueeze(-1      )

              __snake_case					  =       original_samples + noise * sigma
              return noisy_samples






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






              """simple docstring"""


              return self.config.num_train_timesteps




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

