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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Optional[Any] ,	lowerCAmelCase_: int=0.9_9_9 ,	lowerCAmelCase_: Union[str, Any]="cosine" ,	):
      if alpha_transform_type == "cosine":

            def alpha_bar_fn(lowerCAmelCase_: Union[str, Any]     ):
                  return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2     ) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(lowerCAmelCase_: Optional[int]     ):
                  return math.exp(t * -1_2.0     )

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

      snake_case_     :			Union[str, Any]       =       []
      for i in range(UpperCamelCase__     ):
            snake_case_     :			str       =       i / num_diffusion_timesteps
            snake_case_     :			Optional[Any]       =       (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(UpperCamelCase__     ) / alpha_bar_fn(UpperCamelCase__     ) ,	UpperCamelCase__     )     )
      return torch.tensor(UpperCamelCase__ ,	dtype=torch.floataa     )





class 					snake_case__       (				_UpperCamelCase					,						_UpperCamelCase							):
   _SCREAMING_SNAKE_CASE	: Dict						    =      [e.name for e in KarrasDiffusionSchedulers]
   _SCREAMING_SNAKE_CASE	: int						    =      2



   @register_to_config
   def __init__(				self   :   int			,	A__   :   List[Any] = 10_00			,	A__   :   Any = 0.0_0085			,	A__   :   List[str] = 0.012			,	A__   :   List[str] = "linear"			,	A__   :   Optional[int] = None			,	A__   :   str = "epsilon"			,	A__   :   Tuple = "linspace"			,	A__   :   Dict = 0			,	)							->     int:




         '''simple docstring'''




         if trained_betas is not None:
               snake_case_     :			str       =       torch.tensor(__lowerCamelCase			,	dtype=torch.floataa   )
         elif beta_schedule == "linear":
               snake_case_     :			Optional[Any]       =       torch.linspace(__lowerCamelCase			,	__lowerCamelCase			,	__lowerCamelCase			,	dtype=torch.floataa   )
         elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               snake_case_     :			str       =       (
                   torch.linspace(beta_start**0.5			,	beta_end**0.5			,	__lowerCamelCase			,	dtype=torch.floataa   ) ** 2
               )
         elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               snake_case_     :			int       =       betas_for_alpha_bar(__lowerCamelCase   )
         else:
               raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"   )

         snake_case_     :			str       =       1.0 - self.betas
         snake_case_     :			Union[str, Any]       =       torch.cumprod(self.alphas			,	dim=0   )

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



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




         '''simple docstring'''




         if schedule_timesteps is None:
               snake_case_     :			str       =       self.timesteps

         snake_case_     :			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:
               snake_case_     :			Any       =       1 if len(__lowerCamelCase   ) > 1 else 0
         else:
               snake_case_     :			int       =       timestep.cpu().item() if torch.is_tensor(__lowerCamelCase   ) else timestep
               snake_case_     :			int       =       self._index_counter[timestep_int]

         return indices[pos].item()



   @property
   def 					UpperCAmelCase__       (				self   :   Dict   )							->     int:




         '''simple docstring'''




         if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

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



   def 					UpperCAmelCase__       (				self   :   Tuple			,	A__   :   Dict			,	A__   :   Dict			,	)							->     torch.FloatTensor:




         '''simple docstring'''




         snake_case_     :			List[str]       =       self.index_for_timestep(__lowerCamelCase   )

         if self.state_in_first_order:
               snake_case_     :			Union[str, Any]       =       self.sigmas[step_index]
         else:
               snake_case_     :			Union[str, Any]       =       self.sigmas_interpol[step_index - 1]

         snake_case_     :			Dict       =       sample / ((sigma**2 + 1) ** 0.5)
         return sample



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




         '''simple docstring'''




         snake_case_     :			Dict       =       num_inference_steps

         snake_case_     :			List[str]       =       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":
               snake_case_     :			Any       =       np.linspace(0			,	num_train_timesteps - 1			,	__lowerCamelCase			,	dtype=__lowerCamelCase   )[::-1].copy()
         elif self.config.timestep_spacing == "leading":
               snake_case_     :			List[str]       =       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
               snake_case_     :			Optional[int]       =       (np.arange(0			,	__lowerCamelCase   ) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase   )
               timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
               snake_case_     :			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
               snake_case_     :			Optional[int]       =       (np.arange(__lowerCamelCase			,	0			,	-step_ratio   )).round().copy().astype(__lowerCamelCase   )
               timesteps -= 1
         else:
               raise ValueError(
                   f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."   )

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

         snake_case_     :			List[Any]       =       np.interp(__lowerCamelCase			,	np.arange(0			,	len(__lowerCamelCase   )   )			,	__lowerCamelCase   )
         snake_case_     :			Union[str, Any]       =       np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
         snake_case_     :			List[Any]       =       torch.from_numpy(__lowerCamelCase   ).to(device=__lowerCamelCase   )

         # compute up and down sigmas
         snake_case_     :			Union[str, Any]       =       sigmas.roll(-1   )
         snake_case_     :			List[str]       =       0.0
         snake_case_     :			List[str]       =       (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
         snake_case_     :			Optional[int]       =       (sigmas_next**2 - sigmas_up**2) ** 0.5
         snake_case_     :			Tuple       =       0.0

         # compute interpolated sigmas
         snake_case_     :			str       =       sigmas.log().lerp(sigmas_down.log()			,	0.5   ).exp()
         snake_case_     :			List[Any]       =       0.0

         # set sigmas
         snake_case_     :			str       =       torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2   ), sigmas[-1:]]   )
         snake_case_     :			List[Any]       =       torch.cat(
             [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2   ), sigmas_interpol[-1:]]   )
         snake_case_     :			Tuple       =       torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2   ), sigmas_up[-1:]]   )
         snake_case_     :			Any       =       torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2   ), sigmas_down[-1:]]   )

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

         snake_case_     :			Union[str, Any]       =       self.sigma_to_t(__lowerCamelCase   ).to(__lowerCamelCase			,	dtype=timesteps.dtype   )
         snake_case_     :			Optional[int]       =       torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])			,	dim=-1   ).flatten()

         snake_case_     :			Optional[Any]       =       torch.cat([timesteps[:1], interleaved_timesteps]   )

         snake_case_     :			Optional[Any]       =       None

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



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




         '''simple docstring'''




         snake_case_     :			Optional[int]       =       sigma.log()

         # get distribution
         snake_case_     :			Optional[Any]       =       log_sigma - self.log_sigmas[:, None]

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

         snake_case_     :			int       =       self.log_sigmas[low_idx]
         snake_case_     :			Any       =       self.log_sigmas[high_idx]

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

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



   @property
   def 					UpperCAmelCase__       (				self   :   Optional[int]   )							->     str:




         '''simple docstring'''




         return self.sample is None



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




         '''simple docstring'''




         snake_case_     :			List[str]       =       self.index_for_timestep(__lowerCamelCase   )

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

         if self.state_in_first_order:
               snake_case_     :			int       =       self.sigmas[step_index]
               snake_case_     :			Optional[int]       =       self.sigmas_interpol[step_index]
               snake_case_     :			List[str]       =       self.sigmas_up[step_index]
               snake_case_     :			str       =       self.sigmas_down[step_index - 1]
         else:
               # 2nd order / KPDM2's method
               snake_case_     :			Union[str, Any]       =       self.sigmas[step_index - 1]
               snake_case_     :			Dict       =       self.sigmas_interpol[step_index - 1]
               snake_case_     :			str       =       self.sigmas_up[step_index - 1]
               snake_case_     :			Optional[Any]       =       self.sigmas_down[step_index - 1]

         # 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
         snake_case_     :			Dict       =       0
         snake_case_     :			int       =       sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

         snake_case_     :			List[str]       =       model_output.device
         snake_case_     :			int       =       randn_tensor(model_output.shape			,	dtype=model_output.dtype			,	device=__lowerCamelCase			,	generator=__lowerCamelCase   )

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
               snake_case_     :			List[str]       =       sigma_hat if self.state_in_first_order else sigma_interpol
               snake_case_     :			Optional[Any]       =       sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
               snake_case_     :			Any       =       sigma_hat if self.state_in_first_order else sigma_interpol
               snake_case_     :			List[Any]       =       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
               snake_case_     :			Union[str, Any]       =       (sample - pred_original_sample) / sigma_hat
               # 3. delta timestep
               snake_case_     :			List[Any]       =       sigma_interpol - sigma_hat

               # store for 2nd order step
               snake_case_     :			str       =       sample
               snake_case_     :			Optional[int]       =       dt
               snake_case_     :			Optional[int]       =       sample + derivative * dt
         else:
               # DPM-Solver-2
               # 2. Convert to an ODE derivative for 2nd order
               snake_case_     :			Union[str, Any]       =       (sample - pred_original_sample) / sigma_interpol
               # 3. delta timestep
               snake_case_     :			str       =       sigma_down - sigma_hat

               snake_case_     :			Optional[Any]       =       self.sample
               snake_case_     :			Tuple       =       None

               snake_case_     :			Optional[Any]       =       sample + derivative * dt
               snake_case_     :			Optional[int]       =       prev_sample + noise * sigma_up

         if not return_dict:
               return (prev_sample,)

         return SchedulerOutput(prev_sample=__lowerCamelCase   )



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




         '''simple docstring'''




         snake_case_     :			Tuple       =       self.sigmas.to(device=original_samples.device			,	dtype=original_samples.dtype   )
         if original_samples.device.type == "mps" and torch.is_floating_point(__lowerCamelCase   ):
               # mps does not support float64
               snake_case_     :			Optional[Any]       =       self.timesteps.to(original_samples.device			,	dtype=torch.floataa   )
               snake_case_     :			Tuple       =       timesteps.to(original_samples.device			,	dtype=torch.floataa   )
         else:
               snake_case_     :			Optional[Any]       =       self.timesteps.to(original_samples.device   )
               snake_case_     :			List[Any]       =       timesteps.to(original_samples.device   )

         snake_case_     :			List[Any]       =       [self.index_for_timestep(__lowerCamelCase			,	__lowerCamelCase   ) for t in timesteps]

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

         snake_case_     :			List[str]       =       original_samples + noise * sigma
         return noisy_samples



   def __len__(				self   :   Any   )							->     Union[str, Any]:




         '''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__   )



