

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





def 			_snake_case  (     UpperCAmelCase_	:       Dict       , UpperCAmelCase_	:       List[Any]=0.9_99       , UpperCAmelCase_	:       Union[str, Any]="cosine"       , ):
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(UpperCAmelCase_	:       Union[str, Any]      ):
               return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2      ) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(UpperCAmelCase_	:       Dict      ):
               return math.exp(t * -12.0      )

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

     A__				      =	[]
     for i in range(UpperCAmelCase_      ):
          A__				      =	i / num_diffusion_timesteps
          A__				      =	(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        a    (							_lowerCamelCase,						_lowerCamelCase					):



    """simple docstring"""



    UpperCAmelCase    			=       [e.name for e in KarrasDiffusionSchedulers]
    UpperCAmelCase    			=       2

    @register_to_config
    def __init__(  self:			Optional[int]     ,    UpperCamelCase:			int = 10_00     ,    UpperCamelCase:			float = 0.00_085     ,    UpperCamelCase:			float = 0.012     ,    UpperCamelCase:			str = "linear"     ,    UpperCamelCase:			Optional[Union[np.ndarray, List[float]]] = None     ,    UpperCamelCase:			str = "epsilon"     ,    UpperCamelCase:			str = "linspace"     ,    UpperCamelCase:			int = 0     ,    ):






         """simple docstring"""


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

         A__				      =	1.0 - self.betas
         A__				      =	torch.cumprod(self.alphas     ,    dim=0							)

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

    def 					UpperCamelCase						(  self:			Optional[int]     ,    UpperCamelCase:			Dict     ,    UpperCamelCase:			Optional[Any]=None							):






         """simple docstring"""


         if schedule_timesteps is None:
              A__				      =	self.timesteps

         A__				      =	(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:
              A__				      =	1 if len(UpperCamelCase							) > 1 else 0
         else:
              A__				      =	timestep.cpu().item() if torch.is_tensor(UpperCamelCase							) else timestep
              A__				      =	self._index_counter[timestep_int]

         return indices[pos].item()

    @property
    def 					UpperCamelCase						(  self:			Optional[Any]							):






         """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:			int     ,    UpperCamelCase:			torch.FloatTensor     ,    UpperCamelCase:			Union[float, torch.FloatTensor]     ,    ):






         """simple docstring"""


         A__				      =	self.index_for_timestep(UpperCamelCase							)

         if self.state_in_first_order:
              A__				      =	self.sigmas[step_index]
         else:
              A__				      =	self.sigmas_interpol[step_index - 1]

         A__				      =	sample / ((sigma**2 + 1) ** 0.5)
         return sample

    def 					UpperCamelCase						(  self:			Dict     ,    UpperCamelCase:			int     ,    UpperCamelCase:			Union[str, torch.device] = None     ,    UpperCamelCase:			Optional[int] = None     ,    ):






         """simple docstring"""


         A__				      =	num_inference_steps

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

         A__				      =	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
         A__				      =	torch.from_numpy(np.log(UpperCamelCase							)							).to(UpperCamelCase							)

         A__				      =	np.interp(UpperCamelCase     ,    np.arange(0     ,    len(UpperCamelCase							)							)     ,    UpperCamelCase							)
         A__				      =	np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
         A__				      =	torch.from_numpy(UpperCamelCase							).to(device=UpperCamelCase							)

         # compute up and down sigmas
         A__				      =	sigmas.roll(-1							)
         A__				      =	0.0
         A__				      =	(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
         A__				      =	(sigmas_next**2 - sigmas_up**2) ** 0.5
         A__				      =	0.0

         # compute interpolated sigmas
         A__				      =	sigmas.log().lerp(sigmas_down.log()     ,    0.5							).exp()
         A__				      =	0.0

         # set sigmas
         A__				      =	torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2							), sigmas[-1:]]							)
         A__				      =	torch.cat(
             [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2							), sigmas_interpol[-1:]]							)
         A__				      =	torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2							), sigmas_up[-1:]]							)
         A__				      =	torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2							), sigmas_down[-1:]]							)

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

         A__				      =	self.sigma_to_t(UpperCamelCase							).to(UpperCamelCase     ,    dtype=timesteps.dtype							)
         A__				      =	torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])     ,    dim=-1							).flatten()

         A__				      =	torch.cat([timesteps[:1], interleaved_timesteps]							)

         A__				      =	None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         A__				      =	defaultdict(UpperCamelCase							)

    def 					UpperCamelCase						(  self:			Tuple     ,    UpperCamelCase:			Optional[Any]							):






         """simple docstring"""


         A__				      =	sigma.log()

         # get distribution
         A__				      =	log_sigma - self.log_sigmas[:, None]

         # get sigmas range
         A__				      =	dists.ge(0							).cumsum(dim=0							).argmax(dim=0							).clamp(max=self.log_sigmas.shape[0] - 2							)
         A__				      =	low_idx + 1

         A__				      =	self.log_sigmas[low_idx]
         A__				      =	self.log_sigmas[high_idx]

         # interpolate sigmas
         A__				      =	(low - log_sigma) / (low - high)
         A__				      =	w.clamp(0     ,    1							)

         # transform interpolation to time range
         A__				      =	(1 - w) * low_idx + w * high_idx
         A__				      =	t.view(sigma.shape							)
         return t

    @property
    def 					UpperCamelCase						(  self:			Dict							):






         """simple docstring"""


         return self.sample is None

    def 					UpperCamelCase						(  self:			List[Any]     ,    UpperCamelCase:			Union[torch.FloatTensor, np.ndarray]     ,    UpperCamelCase:			Union[float, torch.FloatTensor]     ,    UpperCamelCase:			Union[torch.FloatTensor, np.ndarray]     ,    UpperCamelCase:			Optional[torch.Generator] = None     ,    UpperCamelCase:			bool = True     ,    ):






         """simple docstring"""


         A__				      =	self.index_for_timestep(UpperCamelCase							)

         # advance index counter by 1
         A__				      =	timestep.cpu().item() if torch.is_tensor(UpperCamelCase							) else timestep
         self._index_counter[timestep_int] += 1

         if self.state_in_first_order:
              A__				      =	self.sigmas[step_index]
              A__				      =	self.sigmas_interpol[step_index]
              A__				      =	self.sigmas_up[step_index]
              A__				      =	self.sigmas_down[step_index - 1]
         else:
              # 2nd order / KPDM2's method
              A__				      =	self.sigmas[step_index - 1]
              A__				      =	self.sigmas_interpol[step_index - 1]
              A__				      =	self.sigmas_up[step_index - 1]
              A__				      =	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
         A__				      =	0
         A__				      =	sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

         A__				      =	model_output.device
         A__				      =	randn_tensor(model_output.shape     ,    dtype=model_output.dtype     ,    device=UpperCamelCase     ,    generator=UpperCamelCase							)

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
              A__				      =	sigma_hat if self.state_in_first_order else sigma_interpol
              A__				      =	sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
              A__				      =	sigma_hat if self.state_in_first_order else sigma_interpol
              A__				      =	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
              A__				      =	(sample - pred_original_sample) / sigma_hat
              # 3. delta timestep
              A__				      =	sigma_interpol - sigma_hat

              # store for 2nd order step
              A__				      =	sample
              A__				      =	dt
              A__				      =	sample + derivative * dt
         else:
              # DPM-Solver-2
              # 2. Convert to an ODE derivative for 2nd order
              A__				      =	(sample - pred_original_sample) / sigma_interpol
              # 3. delta timestep
              A__				      =	sigma_down - sigma_hat

              A__				      =	self.sample
              A__				      =	None

              A__				      =	sample + derivative * dt
              A__				      =	prev_sample + noise * sigma_up

         if not return_dict:
              return (prev_sample,)

         return SchedulerOutput(prev_sample=UpperCamelCase							)

    def 					UpperCamelCase						(  self:			str     ,    UpperCamelCase:			torch.FloatTensor     ,    UpperCamelCase:			torch.FloatTensor     ,    UpperCamelCase:			torch.FloatTensor     ,    ):






         """simple docstring"""


         A__				      =	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
              A__				      =	self.timesteps.to(original_samples.device     ,    dtype=torch.floataa							)
              A__				      =	timesteps.to(original_samples.device     ,    dtype=torch.floataa							)
         else:
              A__				      =	self.timesteps.to(original_samples.device							)
              A__				      =	timesteps.to(original_samples.device							)

         A__				      =	[self.index_for_timestep(UpperCamelCase     ,    UpperCamelCase							) for t in timesteps]

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

         A__				      =	original_samples + noise * sigma
         return noisy_samples





    def __len__(  self:			List[Any]							):






         """simple docstring"""


         return self.config.num_train_timesteps









"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_       :						Union[str, Any]					=    {'configuration_gptj': ['GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTJConfig', 'GPTJOnnxConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						Optional[int]					=    [
     'GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST',
     'GPTJForCausalLM',
     'GPTJForQuestionAnswering',
     'GPTJForSequenceClassification',
     'GPTJModel',
     'GPTJPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						List[str]					=    [
     'TFGPTJForCausalLM',
     'TFGPTJForQuestionAnswering',
     'TFGPTJForSequenceClassification',
     'TFGPTJModel',
     'TFGPTJPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						Tuple					=    [
     'FlaxGPTJForCausalLM',
     'FlaxGPTJModel',
     'FlaxGPTJPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gptj import (
      GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTJForCausalLM,
      GPTJForQuestionAnswering,
      GPTJForSequenceClassification,
      GPTJModel,
      GPTJPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_gptj import (
      TFGPTJForCausalLM,
      TFGPTJForQuestionAnswering,
      TFGPTJForSequenceClassification,
      TFGPTJModel,
      TFGPTJPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
 import sys

 SCREAMING_SNAKE_CASE_       :						List[str]					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






