






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


A_	:	List[str]				=				logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       a       :	torch.FloatTensor







class        _lowerCAmelCase( UpperCAmelCase_							,			UpperCAmelCase_      ):





       """simple docstring"""







       a       :	Tuple								=1





       @register_to_config
       def __init__(      self						,    _lowerCamelCase = 4_0						,    _lowerCamelCase = 0.0_0_2						,    _lowerCamelCase = 8_0.0						,    _lowerCamelCase = 0.5						,    _lowerCamelCase = 1.0						,    _lowerCamelCase = 7.0						,    _lowerCamelCase = True						,    ):
         # standard deviation of the initial noise distribution
         UpperCamelCase_:      int					   =       sigma_max

         UpperCamelCase_:      Dict					   =       np.linspace(0						,    1						,    _lowerCamelCase							)
         UpperCamelCase_:      Any					   =       self._convert_to_karras(_lowerCamelCase							)
         UpperCamelCase_:      Optional[Any]					   =       self.sigma_to_t(_lowerCamelCase							)

         # setable values
         UpperCamelCase_:      Dict					   =       None
         UpperCamelCase_:      List[Any]					   =       torch.from_numpy(_lowerCamelCase							)
         UpperCamelCase_:      List[Any]					   =       torch.from_numpy(_lowerCamelCase							)
         UpperCamelCase_:      Optional[Any]					   =       False
         UpperCamelCase_:      Optional[Any]					   =       False





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase=None							):
         if schedule_timesteps is None:
           UpperCamelCase_:      Union[str, Any]					   =       self.timesteps

         UpperCamelCase_:      Optional[int]					   =       (schedule_timesteps == timestep).nonzero()
         return indices.item()





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase							):
         # Get sigma corresponding to timestep
         if isinstance(_lowerCamelCase						,    torch.Tensor							):
           UpperCamelCase_:      List[Any]					   =       timestep.to(self.timesteps.device							)
         UpperCamelCase_:      str					   =       self.index_for_timestep(_lowerCamelCase							)
         UpperCamelCase_:      Any					   =       self.sigmas[step_idx]

         UpperCamelCase_:      Optional[int]					   =       sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

         UpperCamelCase_:      Tuple					   =       True
         return sample





       def     _a					(      self						,    _lowerCamelCase							):
         if not isinstance(_lowerCamelCase						,    np.ndarray							):
           UpperCamelCase_:      List[str]					   =       np.array(_lowerCamelCase						,    dtype=np.floataa							)

         UpperCamelCase_:      Optional[int]					   =       1_0_0_0 * 0.2_5 * np.log(sigmas + 1e-44							)

         return timesteps





       def     _a					(      self						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    ):
         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(_lowerCamelCase							)							):
             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}.'''							)

           UpperCamelCase_:      List[Any]					   =       np.array(_lowerCamelCase						,    dtype=np.intaa							)
           UpperCamelCase_:      Optional[int]					   =       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.'''							)

           UpperCamelCase_:      str					   =       num_inference_steps

           UpperCamelCase_:      Dict					   =       self.config.num_train_timesteps // self.num_inference_steps
           UpperCamelCase_:      Optional[Any]					   =       (np.arange(0						,    _lowerCamelCase							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
           UpperCamelCase_:      int					   =       False

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

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

         if str(_lowerCamelCase							).startswith('mps'							):
           # mps does not support float64
           UpperCamelCase_:      List[str]					   =       torch.from_numpy(_lowerCamelCase							).to(_lowerCamelCase						,    dtype=torch.floataa							)
         else:
           UpperCamelCase_:      Tuple					   =       torch.from_numpy(_lowerCamelCase							).to(device=_lowerCamelCase							)





       def     _a					(      self						,    _lowerCamelCase							):
         UpperCamelCase_:      float					   =       self.config.sigma_min
         UpperCamelCase_:      float					   =       self.config.sigma_max

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





       def     _a					(      self						,    _lowerCamelCase							):
         UpperCamelCase_:      Optional[Any]					   =       self.config.sigma_data

         UpperCamelCase_:      List[str]					   =       sigma_data**2 / (sigma**2 + sigma_data**2)
         UpperCamelCase_:      Tuple					   =       sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out





       def     _a					(      self						,    _lowerCamelCase							):
         UpperCamelCase_:      Dict					   =       self.config.sigma_min
         UpperCamelCase_:      Optional[Any]					   =       self.config.sigma_data

         UpperCamelCase_:      str					   =       sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
         UpperCamelCase_:      int					   =       (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase = None						,    _lowerCamelCase = True						,    ):

         if (
             isinstance(_lowerCamelCase						,    _lowerCamelCase							)
             or isinstance(_lowerCamelCase						,    torch.IntTensor							)
             or isinstance(_lowerCamelCase						,    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(_lowerCamelCase						,    torch.Tensor							):
           UpperCamelCase_:      int					   =       timestep.to(self.timesteps.device							)

         UpperCamelCase_:      str					   =       self.config.sigma_min
         UpperCamelCase_:      List[Any]					   =       self.config.sigma_max

         UpperCamelCase_:      Dict					   =       self.index_for_timestep(_lowerCamelCase							)

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

         # Get scalings for boundary conditions
         UpperCamelCase_   ,UpperCamelCase_:      List[str]					   =       self.get_scalings_for_boundary_condition(_lowerCamelCase							)

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

         UpperCamelCase_:      Tuple					   =       sigma_next.clamp(min=_lowerCamelCase						,    max=_lowerCamelCase							)

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

         if not return_dict:
           return (prev_sample,)

         return CMStochasticIterativeSchedulerOutput(prev_sample=_lowerCamelCase							)





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    ):
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         UpperCamelCase_:      Dict					   =       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
           UpperCamelCase_:      Tuple					   =       self.timesteps.to(original_samples.device						,    dtype=torch.floataa							)
           UpperCamelCase_:      Tuple					   =       timesteps.to(original_samples.device						,    dtype=torch.floataa							)
         else:
           UpperCamelCase_:      Dict					   =       self.timesteps.to(original_samples.device							)
           UpperCamelCase_:      Dict					   =       timesteps.to(original_samples.device							)

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

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

         UpperCamelCase_:      List[Any]					   =       original_samples + noise * sigma
         return noisy_samples





       def __len__(      self							):
         return self.config.num_train_timesteps







from typing import TYPE_CHECKING

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


A_	:	List[Any]				=				{
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_	:	List[str]				=				['OpenAIGPTTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_	:	Any				=				[
       'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'OpenAIGPTDoubleHeadsModel',
       'OpenAIGPTForSequenceClassification',
       'OpenAIGPTLMHeadModel',
       'OpenAIGPTModel',
       'OpenAIGPTPreTrainedModel',
       'load_tf_weights_in_openai_gpt',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_	:	str				=				[
       'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFOpenAIGPTDoubleHeadsModel',
       'TFOpenAIGPTForSequenceClassification',
       'TFOpenAIGPTLMHeadModel',
       'TFOpenAIGPTMainLayer',
       'TFOpenAIGPTModel',
       'TFOpenAIGPTPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
   from .tokenization_openai import OpenAIGPTTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_openai_fast import OpenAIGPTTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_openai import (
          OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OpenAIGPTDoubleHeadsModel,
          OpenAIGPTForSequenceClassification,
          OpenAIGPTLMHeadModel,
          OpenAIGPTModel,
          OpenAIGPTPreTrainedModel,
          load_tf_weights_in_openai_gpt,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_openai import (
          TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFOpenAIGPTDoubleHeadsModel,
          TFOpenAIGPTForSequenceClassification,
          TFOpenAIGPTLMHeadModel,
          TFOpenAIGPTMainLayer,
          TFOpenAIGPTModel,
          TFOpenAIGPTPreTrainedModel,
      )

else:
   import sys

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