



"""simple docstring"""




import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]		,    SCREAMING_SNAKE_CASE__      :		Tuple=0.999		,    SCREAMING_SNAKE_CASE__      :		List[str]="cosine"		,    )    ->  Dict:





     '''simple docstring'''

     if alpha_transform_type == "cosine":

          def alpha_bar_fn(SCREAMING_SNAKE_CASE__      :		Tuple							):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(SCREAMING_SNAKE_CASE__      :		Tuple							):
               return math.exp(t * -12.0							)

     else:
          raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'							)

     _UpperCAmelCase     :      Any														=       []
     for i in range(__snake_case							):
          _UpperCAmelCase     :      List[Any]														=       i / num_diffusion_timesteps
          _UpperCAmelCase     :      List[Any]														=       (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(__snake_case							) / alpha_bar_fn(__snake_case							)		,    __snake_case							)							)
     return torch.tensor(__snake_case		,    dtype=torch.floataa							)






class       UpperCAmelCase_    (							__lowercase			,     __lowercase		):
      __SCREAMING_SNAKE_CASE		:  Any						=					[e.name for e in KarrasDiffusionSchedulers]
      __SCREAMING_SNAKE_CASE		:  Any						=					2



      @register_to_config
      def __init__(	self	:      Optional[Any]	,      A	:      int = 1_0_0_0	,      A	:      float = 0.00_085	,      A	:      float = 0.012	,      A	:      str = "linear"	,      A	:      Optional[Union[np.ndarray, List[float]]] = None	,      A	:      str = "epsilon"	,      A	:      str = "linspace"	,      A	:      int = 0	,      ):
           if trained_betas is not None:
                _UpperCAmelCase     :      int														=       torch.tensor(_A	,      dtype=torch.floataa		)
           elif beta_schedule == "linear":
                _UpperCAmelCase     :      Optional[Any]														=       torch.linspace(_A	,      _A	,      _A	,      dtype=torch.floataa		)
           elif beta_schedule == "scaled_linear":
                # this schedule is very specific to the latent diffusion model.
                _UpperCAmelCase     :      List[str]														=       (
                    torch.linspace(beta_start**0.5	,      beta_end**0.5	,      _A	,      dtype=torch.floataa		) ** 2
                )
           elif beta_schedule == "squaredcos_cap_v2":
                # Glide cosine schedule
                _UpperCAmelCase     :      str														=       betas_for_alpha_bar(_A		)
           else:
                raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'		)

           _UpperCAmelCase     :      str														=       1.0 - self.betas
           _UpperCAmelCase     :      str														=       torch.cumprod(self.alphas	,      dim=0		)

           #  set all values
           self.set_timesteps(_A	,      _A	,      _A		)



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      Dict	,      A	:      List[str]=None		):
           if schedule_timesteps is None:
                _UpperCAmelCase     :      str														=       self.timesteps

           _UpperCAmelCase     :      Tuple														=       (schedule_timesteps == timestep).nonzero()

           # The sigma index that is taken for the **very** first `step`
           # is always the second index (or the last index if there is only 1)
           # This way we can ensure we don't accidentally skip a sigma in
           # case we start in the middle of the denoising schedule (e.g. for image-to-image)
           if len(self._index_counter		) == 0:
                _UpperCAmelCase     :      Optional[int]														=       1 if len(_A		) > 1 else 0
           else:
                _UpperCAmelCase     :      Dict														=       timestep.cpu().item() if torch.is_tensor(_A		) else timestep
                _UpperCAmelCase     :      List[Any]														=       self._index_counter[timestep_int]

           return indices[pos].item()



      @property
      def 		snake_case_   (	self	:      str		):
           # standard deviation of the initial noise distribution
           if self.config.timestep_spacing in ["linspace", "trailing"]:
                return self.sigmas.max()

           return (self.sigmas.max() ** 2 + 1) ** 0.5



      def 		snake_case_   (	self	:      str	,      A	:      torch.FloatTensor	,      A	:      Union[float, torch.FloatTensor]	,      ):
           _UpperCAmelCase     :      Optional[int]														=       self.index_for_timestep(_A		)

           if self.state_in_first_order:
                _UpperCAmelCase     :      Tuple														=       self.sigmas[step_index]
           else:
                _UpperCAmelCase     :      Dict														=       self.sigmas_interpol[step_index]

           _UpperCAmelCase     :      List[str]														=       sample / ((sigma**2 + 1) ** 0.5)
           return sample



      def 		snake_case_   (	self	:      Optional[Any]	,      A	:      int	,      A	:      Union[str, torch.device] = None	,      A	:      Optional[int] = None	,      ):
           _UpperCAmelCase     :      int														=       num_inference_steps

           _UpperCAmelCase     :      Dict														=       num_train_timesteps or self.config.num_train_timesteps

           # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
           if self.config.timestep_spacing == "linspace":
                _UpperCAmelCase     :      int														=       np.linspace(0	,      num_train_timesteps - 1	,      _A	,      dtype=_A		)[::-1].copy()
           elif self.config.timestep_spacing == "leading":
                _UpperCAmelCase     :      Optional[Any]														=       num_train_timesteps // self.num_inference_steps
                # creates integer timesteps by multiplying by ratio
                # casting to int to avoid issues when num_inference_step is power of 3
                _UpperCAmelCase     :      List[Any]														=       (np.arange(0	,      _A		) * step_ratio).round()[::-1].copy().astype(_A		)
                timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
                _UpperCAmelCase     :      Tuple														=       num_train_timesteps / self.num_inference_steps
                # creates integer timesteps by multiplying by ratio
                # casting to int to avoid issues when num_inference_step is power of 3
                _UpperCAmelCase     :      List[Any]														=       (np.arange(_A	,      0	,      -step_ratio		)).round().copy().astype(_A		)
                timesteps -= 1
           else:
                raise ValueError(
                    f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'		)

           _UpperCAmelCase     :      Tuple														=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
           _UpperCAmelCase     :      Union[str, Any]														=       torch.from_numpy(np.log(_A		)		).to(_A		)

           _UpperCAmelCase     :      Any														=       np.interp(_A	,      np.arange(0	,      len(_A		)		)	,      _A		)
           _UpperCAmelCase     :      Dict														=       np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
           _UpperCAmelCase     :      List[Any]														=       torch.from_numpy(_A		).to(device=_A		)

           # interpolate sigmas
           _UpperCAmelCase     :      Optional[Any]														=       sigmas.log().lerp(sigmas.roll(1		).log()	,      0.5		).exp()

           _UpperCAmelCase     :      int														=       torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2		), sigmas[-1:]]		)
           _UpperCAmelCase     :      str														=       torch.cat(
               [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2		), sigmas_interpol[-1:]]		)

           if str(_A		).startswith("mps"		):
                # mps does not support float64
                _UpperCAmelCase     :      Union[str, Any]														=       torch.from_numpy(_A		).to(_A	,      dtype=torch.floataa		)
           else:
                _UpperCAmelCase     :      Optional[Any]														=       torch.from_numpy(_A		).to(_A		)

           # interpolate timesteps
           _UpperCAmelCase     :      Union[str, Any]														=       self.sigma_to_t(_A		).to(_A	,      dtype=timesteps.dtype		)
           _UpperCAmelCase     :      List[str]														=       torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])	,      dim=-1		).flatten()

           _UpperCAmelCase     :      Union[str, Any]														=       torch.cat([timesteps[:1], interleaved_timesteps]		)

           _UpperCAmelCase     :      List[str]														=       None

           # for exp beta schedules, such as the one for `pipeline_shap_e.py`
           # we need an index counter
           _UpperCAmelCase     :      List[Any]														=       defaultdict(_A		)



      def 		snake_case_   (	self	:      Any	,      A	:      List[Any]		):
           # get log sigma
           _UpperCAmelCase     :      Optional[Any]														=       sigma.log()

           # get distribution
           _UpperCAmelCase     :      List[Any]														=       log_sigma - self.log_sigmas[:, None]

           # get sigmas range
           _UpperCAmelCase     :      Union[str, Any]														=       dists.ge(0		).cumsum(dim=0		).argmax(dim=0		).clamp(max=self.log_sigmas.shape[0] - 2		)
           _UpperCAmelCase     :      Dict														=       low_idx + 1

           _UpperCAmelCase     :      Dict														=       self.log_sigmas[low_idx]
           _UpperCAmelCase     :      Optional[Any]														=       self.log_sigmas[high_idx]

           # interpolate sigmas
           _UpperCAmelCase     :      Optional[Any]														=       (low - log_sigma) / (low - high)
           _UpperCAmelCase     :      Optional[int]														=       w.clamp(0	,      1		)

           # transform interpolation to time range
           _UpperCAmelCase     :      Dict														=       (1 - w) * low_idx + w * high_idx
           _UpperCAmelCase     :      List[Any]														=       t.view(sigma.shape		)
           return t



      @property
      def 		snake_case_   (	self	:      Optional[Any]		):
           return self.sample is None



      def 		snake_case_   (	self	:      Optional[int]	,      A	:      Union[torch.FloatTensor, np.ndarray]	,      A	:      Union[float, torch.FloatTensor]	,      A	:      Union[torch.FloatTensor, np.ndarray]	,      A	:      bool = True	,      ):
           _UpperCAmelCase     :      List[Any]														=       self.index_for_timestep(_A		)

           # advance index counter by 1
           _UpperCAmelCase     :      Optional[Any]														=       timestep.cpu().item() if torch.is_tensor(_A		) else timestep
           self._index_counter[timestep_int] += 1

           if self.state_in_first_order:
                _UpperCAmelCase     :      Optional[Any]														=       self.sigmas[step_index]
                _UpperCAmelCase     :      List[str]														=       self.sigmas_interpol[step_index + 1]
                _UpperCAmelCase     :      Optional[int]														=       self.sigmas[step_index + 1]
           else:
                # 2nd order / KDPM2's method
                _UpperCAmelCase     :      Dict														=       self.sigmas[step_index - 1]
                _UpperCAmelCase     :      Union[str, Any]														=       self.sigmas_interpol[step_index]
                _UpperCAmelCase     :      str														=       self.sigmas[step_index]

           # currently only gamma=0 is supported. This usually works best anyways.
           # We can support gamma in the future but then need to scale the timestep before
           # passing it to the model which requires a change in API
           _UpperCAmelCase     :      Any														=       0
           _UpperCAmelCase     :      int														=       sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
                _UpperCAmelCase     :      Union[str, Any]														=       sigma_hat if self.state_in_first_order else sigma_interpol
                _UpperCAmelCase     :      Optional[int]														=       sample - sigma_input * model_output
           elif self.config.prediction_type == "v_prediction":
                _UpperCAmelCase     :      Optional[Any]														=       sigma_hat if self.state_in_first_order else sigma_interpol
                _UpperCAmelCase     :      Optional[int]														=       model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                    sample / (sigma_input**2 + 1)
                )
           elif self.config.prediction_type == "sample":
                raise NotImplementedError("prediction_type not implemented yet: sample"		)
           else:
                raise ValueError(
                    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'		)

           if self.state_in_first_order:
                # 2. Convert to an ODE derivative for 1st order
                _UpperCAmelCase     :      List[str]														=       (sample - pred_original_sample) / sigma_hat
                # 3. delta timestep
                _UpperCAmelCase     :      Union[str, Any]														=       sigma_interpol - sigma_hat

                # store for 2nd order step
                _UpperCAmelCase     :      Tuple														=       sample
           else:
                # DPM-Solver-2
                # 2. Convert to an ODE derivative for 2nd order
                _UpperCAmelCase     :      int														=       (sample - pred_original_sample) / sigma_interpol

                # 3. delta timestep
                _UpperCAmelCase     :      Optional[Any]														=       sigma_next - sigma_hat

                _UpperCAmelCase     :      Optional[int]														=       self.sample
                _UpperCAmelCase     :      Optional[Any]														=       None

           _UpperCAmelCase     :      str														=       sample + derivative * dt

           if not return_dict:
                return (prev_sample,)

           return SchedulerOutput(prev_sample=_A		)



      def 		snake_case_   (	self	:      List[Any]	,      A	:      torch.FloatTensor	,      A	:      torch.FloatTensor	,      A	:      torch.FloatTensor	,      ):
           # Make sure sigmas and timesteps have the same device and dtype as original_samples
           _UpperCAmelCase     :      Optional[int]														=       self.sigmas.to(device=original_samples.device	,      dtype=original_samples.dtype		)
           if original_samples.device.type == "mps" and torch.is_floating_point(_A		):
                # mps does not support float64
                _UpperCAmelCase     :      str														=       self.timesteps.to(original_samples.device	,      dtype=torch.floataa		)
                _UpperCAmelCase     :      Optional[Any]														=       timesteps.to(original_samples.device	,      dtype=torch.floataa		)
           else:
                _UpperCAmelCase     :      List[str]														=       self.timesteps.to(original_samples.device		)
                _UpperCAmelCase     :      List[Any]														=       timesteps.to(original_samples.device		)

           _UpperCAmelCase     :      Union[str, Any]														=       [self.index_for_timestep(_A	,      _A		) for t in timesteps]

           _UpperCAmelCase     :      Optional[Any]														=       sigmas[step_indices].flatten()
           while len(sigma.shape		) < len(original_samples.shape		):
                _UpperCAmelCase     :      str														=       sigma.unsqueeze(-1		)

           _UpperCAmelCase     :      str														=       original_samples + noise * sigma
           return noisy_samples



      def __len__(	self	:      List[str]		):
           return self.config.num_train_timesteps

import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


_lowerCAmelCase       =		HfApi()

_lowerCAmelCase       =		{}
# fmt: off
_lowerCAmelCase       =		torch.tensor([
    -0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
    1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
    -1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
    0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
_lowerCAmelCase       =		torch.tensor([
    -2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
    1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
    -2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
    2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
_lowerCAmelCase       =		torch.tensor([
    -0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
    -0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
    -0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
    0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
_lowerCAmelCase       =		torch.tensor([
    0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
    -0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
    0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
    -0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
_lowerCAmelCase       =		torch.tensor([
    0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
    -0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
    0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
    -0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
_lowerCAmelCase       =		torch.tensor([
    0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
    -0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
    0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
    -0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
_lowerCAmelCase       =		torch.tensor([
    0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
    -0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
    0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
    -0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
_lowerCAmelCase       =		torch.tensor([
    0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
    -0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
    0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
    -0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
_lowerCAmelCase       =		torch.tensor([
    -1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
    1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
    -2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
    1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
_lowerCAmelCase       =		torch.tensor([
    -1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
    0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
    -2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
    1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
_lowerCAmelCase       =		torch.tensor([
    -1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
    0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
    -2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
    1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
_lowerCAmelCase       =		torch.tensor([
    -2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
    1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
    -3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
    3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
_lowerCAmelCase       =		torch.tensor([
    -2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
    1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
    -2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
    2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
_lowerCAmelCase       =		torch.tensor([
    -2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
    1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
    -3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
    3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
_lowerCAmelCase       =		torch.tensor([
    -1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
    1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
    -2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
    1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on

_lowerCAmelCase       =		api.list_models(filter="diffusers")
for mod in models:
					if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
										_lowerCAmelCase       =		"/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]

										print(f'Started running {mod.modelId}!!!')

										if mod.modelId.startswith("CompVis"):
															_lowerCAmelCase       =		UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
										else:
															_lowerCAmelCase       =		UNetaDModel.from_pretrained(local_checkpoint)

										torch.manual_seed(0)
										random.seed(0)

										_lowerCAmelCase       =		torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
										_lowerCAmelCase       =		torch.tensor([10] * noise.shape[0])
										with torch.no_grad():
															_lowerCAmelCase       =		model(noise, time_step).sample

										assert torch.allclose(
										    logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1E-3
										)
										print(f'{mod.modelId} has passed successfully!!!')
