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__       (				_UpperCamelCase							):
   _SCREAMING_SNAKE_CASE	: torch.FloatTensor



class 					snake_case__       (				_UpperCamelCase					,						_UpperCamelCase							):
   _SCREAMING_SNAKE_CASE	: str						    =      1



   @register_to_config
   def __init__(				self   :   Any			,	A__   :   int = 40			,	A__   :   float = 0.002			,	A__   :   float = 80.0			,	A__   :   float = 0.5			,	A__   :   float = 1.0			,	A__   :   float = 7.0			,	A__   :   bool = True			,	)							->     Any:




         '''simple docstring'''




         snake_case_     :			List[str]       =       sigma_max

         snake_case_     :			int       =       np.linspace(0			,	1			,	A__   )
         snake_case_     :			List[Any]       =       self._convert_to_karras(A__   )
         snake_case_     :			Optional[Any]       =       self.sigma_to_t(A__   )

         # setable values
         snake_case_     :			Optional[Any]       =       None
         snake_case_     :			Any       =       torch.from_numpy(A__   )
         snake_case_     :			str       =       torch.from_numpy(A__   )
         snake_case_     :			int       =       False
         snake_case_     :			str       =       False



   def 					UpperCAmelCase__       (				self   :   Tuple			,	A__   :   Tuple			,	A__   :   Union[str, Any]=None   )							->     List[str]:




         '''simple docstring'''




         if schedule_timesteps is None:
               snake_case_     :			Union[str, Any]       =       self.timesteps

         snake_case_     :			Dict       =       (schedule_timesteps == timestep).nonzero()
         return indices.item()



   def 					UpperCAmelCase__       (				self   :   List[str]			,	A__   :   torch.FloatTensor			,	A__   :   Union[float, torch.FloatTensor]   )							->     torch.FloatTensor:




         '''simple docstring'''




         if isinstance(A__			,	torch.Tensor   ):
               snake_case_     :			int       =       timestep.to(self.timesteps.device   )
         snake_case_     :			Any       =       self.index_for_timestep(A__   )
         snake_case_     :			Tuple       =       self.sigmas[step_idx]

         snake_case_     :			List[Any]       =       sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

         snake_case_     :			Optional[Any]       =       True
         return sample



   def 					UpperCAmelCase__       (				self   :   List[str]			,	A__   :   Union[float, np.ndarray]   )							->     int:




         '''simple docstring'''




         if not isinstance(A__			,	np.ndarray   ):
               snake_case_     :			Any       =       np.array(A__			,	dtype=np.floataa   )

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

         return timesteps



   def 					UpperCAmelCase__       (				self   :   Union[str, Any]			,	A__   :   Optional[int] = None			,	A__   :   Union[str, torch.device] = None			,	A__   :   Optional[List[int]] = None			,	)							->     Union[str, 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(A__   )   ):
                     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_     :			str       =       np.array(A__			,	dtype=np.intaa   )
               snake_case_     :			Union[str, Any]       =       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_     :			Dict       =       num_inference_steps

               snake_case_     :			List[str]       =       self.config.num_train_timesteps // self.num_inference_steps
               snake_case_     :			Any       =       (np.arange(0			,	A__   ) * step_ratio).round()[::-1].copy().astype(np.intaa   )
               snake_case_     :			Any       =       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_     :			Tuple       =       self.config.num_train_timesteps
         snake_case_     :			List[str]       =       timesteps[::-1].copy()
         snake_case_     :			Union[str, Any]       =       ramp / (num_train_timesteps - 1)
         snake_case_     :			Optional[Any]       =       self._convert_to_karras(A__   )
         snake_case_     :			Union[str, Any]       =       self.sigma_to_t(A__   )

         snake_case_     :			str       =       np.concatenate([sigmas, [self.sigma_min]]   ).astype(np.floataa   )
         snake_case_     :			Any       =       torch.from_numpy(A__   ).to(device=A__   )

         if str(A__   ).startswith("mps"   ):
               # mps does not support float64
               snake_case_     :			int       =       torch.from_numpy(A__   ).to(A__			,	dtype=torch.floataa   )
         else:
               snake_case_     :			Optional[Any]       =       torch.from_numpy(A__   ).to(device=A__   )



   def 					UpperCAmelCase__       (				self   :   List[str]			,	A__   :   str   )							->     int:




         '''simple docstring'''




         snake_case_     :			float       =       self.config.sigma_min
         snake_case_     :			float       =       self.config.sigma_max

         snake_case_     :			Union[str, Any]       =       self.config.rho
         snake_case_     :			str       =       sigma_min ** (1 / rho)
         snake_case_     :			int       =       sigma_max ** (1 / rho)
         snake_case_     :			Any       =       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas



   def 					UpperCAmelCase__       (				self   :   List[str]			,	A__   :   Dict   )							->     List[str]:




         '''simple docstring'''




         snake_case_     :			str       =       self.config.sigma_data

         snake_case_     :			Optional[int]       =       sigma_data**2 / (sigma**2 + sigma_data**2)
         snake_case_     :			Optional[int]       =       sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out



   def 					UpperCAmelCase__       (				self   :   List[str]			,	A__   :   List[str]   )							->     Tuple:




         '''simple docstring'''




         snake_case_     :			List[str]       =       self.config.sigma_min
         snake_case_     :			Any       =       self.config.sigma_data

         snake_case_     :			Tuple       =       sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
         snake_case_     :			Optional[Any]       =       (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out



   def 					UpperCAmelCase__       (				self   :   Union[str, Any]			,	A__   :   torch.FloatTensor			,	A__   :   Union[float, torch.FloatTensor]			,	A__   :   torch.FloatTensor			,	A__   :   Optional[torch.Generator] = None			,	A__   :   bool = True			,	)							->     Union[CMStochasticIterativeSchedulerOutput, Tuple]:




         '''simple docstring'''




         if (
             isinstance(A__			,	A__   )
             or isinstance(A__			,	torch.IntTensor   )
             or isinstance(A__			,	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(A__			,	torch.Tensor   ):
               snake_case_     :			Optional[int]       =       timestep.to(self.timesteps.device   )

         snake_case_     :			Optional[int]       =       self.config.sigma_min
         snake_case_     :			str       =       self.config.sigma_max

         snake_case_     :			List[Any]       =       self.index_for_timestep(A__   )

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

         # Get scalings for boundary conditions
         snake_case_  ,snake_case_     :			Any       =       self.get_scalings_for_boundary_condition(A__   )

         # 1. Denoise model output using boundary conditions
         snake_case_     :			int       =       c_out * model_output + c_skip * sample
         if self.config.clip_denoised:
               snake_case_     :			str       =       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_     :			Optional[Any]       =       randn_tensor(
                   model_output.shape			,	dtype=model_output.dtype			,	device=model_output.device			,	generator=A__   )
         else:
               snake_case_     :			Dict       =       torch.zeros_like(A__   )
         snake_case_     :			Dict       =       noise * self.config.s_noise

         snake_case_     :			Dict       =       sigma_next.clamp(min=A__			,	max=A__   )

         # 3. Return noisy sample
         # tau = sigma_hat, eps = sigma_min
         snake_case_     :			Optional[int]       =       denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

         if not return_dict:
               return (prev_sample,)

         return CMStochasticIterativeSchedulerOutput(prev_sample=A__   )



   def 					UpperCAmelCase__       (				self   :   Optional[int]			,	A__   :   torch.FloatTensor			,	A__   :   torch.FloatTensor			,	A__   :   torch.FloatTensor			,	)							->     torch.FloatTensor:




         '''simple docstring'''




         snake_case_     :			List[str]       =       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
               snake_case_     :			Optional[int]       =       self.timesteps.to(original_samples.device			,	dtype=torch.floataa   )
               snake_case_     :			Union[str, Any]       =       timesteps.to(original_samples.device			,	dtype=torch.floataa   )
         else:
               snake_case_     :			int       =       self.timesteps.to(original_samples.device   )
               snake_case_     :			Union[str, Any]       =       timesteps.to(original_samples.device   )

         snake_case_     :			Any       =       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

         snake_case_     :			int       =       sigmas[step_indices].flatten()
         while len(sigma.shape   ) < len(original_samples.shape   ):
               snake_case_     :			List[str]       =       sigma.unsqueeze(-1   )

         snake_case_     :			Dict       =       original_samples + noise * sigma
         return noisy_samples



   def __len__(				self   :   Dict   )							->     List[str]:




         '''simple docstring'''




         return self.config.num_train_timesteps




import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
     import torch

     from transformers import GPTaLMHeadModel



@require_torch
@require_sentencepiece
@require_tokenizers
class 					snake_case__       (				unittest.TestCase							):



   @slow
   @unittest.skip("Model is not available."   )
   def 					UpperCAmelCase__       (				self   :   Dict   )							->     Optional[Any]:




         '''simple docstring'''




         snake_case_     :			str       =       "nvidia/megatron-gpt2-345m/"
         if "MYDIR" in os.environ:
               snake_case_     :			int       =       os.path.join(os.environ["MYDIR"]			,	A__   )
         snake_case_     :			str       =       GPTaLMHeadModel.from_pretrained(A__   )
         model.to(A__   )
         model.half()

         snake_case_     :			Optional[int]       =       torch.tensor(
             [[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]			,	device=A__			,	dtype=torch.long			,	)

         with torch.no_grad():
               snake_case_     :			Union[str, Any]       =       model(A__   ).logits

         snake_case_     :			List[str]       =       torch.Size((1, 9, 5_02_57)   )
         self.assertEqual(output.shape			,	A__   )

         snake_case_     :			Dict       =       torch.tensor(
             [
                 4.9414,
                 -0.2920,
                 -1.2148,
                 -4.0273,
                 -0.5161,
                 -5.2109,
                 -1.2412,
                 -1.8301,
                 -1.7734,
                 -4.7148,
                 -0.2317,
                 -1.0811,
                 -2.1777,
                 0.4141,
                 -3.7969,
                 -4.0586,
                 -2.5332,
                 -3.3809,
                 4.3867,
             ]			,	device=A__			,	dtype=torch.half			,	)

         for i in range(19   ):
               snake_case_  ,snake_case_     :			Dict       =       8 * i // 17, 27_92 * i  # along the diagonal
               snake_case_  ,snake_case_     :			Any       =       output[0, r, c], expected_diag[i]
               snake_case_     :			Dict       =       f"row={r} col={c} computed={computed} expected={expected}"
               self.assertAlmostEqual(A__			,	A__			,	delta=1E-4			,	msg=A__   )



