




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 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase=0.999						,lowercase="cosine"						,)  ->       str:
   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

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

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

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




class 						__lowercase		(UpperCamelCase__					,		UpperCamelCase__							):




      """simple docstring"""





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

      @register_to_config
      def __init__(				self    ,				A = 1_0_0_0    ,				A = 0.0_00_85    ,				A = 0.0_12    ,				A = "linear"    ,				A = None    ,				A = "epsilon"    ,				A = False    ,				A = False    ,				A = 1.0    ,				A = "linspace"    ,				A = 0    ,				)		->    List[Any]:
         if trained_betas is not None:
            snake_case				:    Any           =	torch.tensor(A    ,				dtype=torch.floataa     )
         elif beta_schedule == "linear":
            snake_case				:    List[str]           =	torch.linspace(A    ,				A    ,				A    ,				dtype=torch.floataa     )
         elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            snake_case				:    Any           =	(
                torch.linspace(beta_start**0.5    ,				beta_end**0.5    ,				A    ,				dtype=torch.floataa     ) ** 2
            )
         elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            snake_case				:    Union[str, Any]           =	betas_for_alpha_bar(A    ,				alpha_transform_type="""cosine"""     )
         elif beta_schedule == "exp":
            snake_case				:    Dict           =	betas_for_alpha_bar(A    ,				alpha_transform_type="""exp"""     )
         else:
            raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""     )

         snake_case				:    Dict           =	1.0 - self.betas
         snake_case				:    Dict           =	torch.cumprod(self.alphas    ,				dim=0     )

         #  set all values
         self.set_timesteps(A    ,				A    ,				A     )
         snake_case				:    List[Any]           =	use_karras_sigmas

      def      UpperCAmelCase				(				self    ,				A    ,				A=None     )		->    Optional[Any]:
         if schedule_timesteps is None:
            snake_case				:    Optional[Any]           =	self.timesteps

         snake_case				:    Optional[int]           =	(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				:    List[Any]           =	1 if len(A     ) > 1 else 0
         else:
            snake_case				:    str           =	timestep.cpu().item() if torch.is_tensor(A     ) else timestep
            snake_case				:    Optional[int]           =	self._index_counter[timestep_int]

         return indices[pos].item()

      @property
      def      UpperCAmelCase				(				self     )		->    List[Any]:
         # 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      UpperCAmelCase				(				self    ,				A    ,				A    ,				)		->    torch.FloatTensor:
         snake_case				:    Union[str, Any]           =	self.index_for_timestep(A     )

         snake_case				:    int           =	self.sigmas[step_index]
         snake_case				:    Dict           =	sample / ((sigma**2 + 1) ** 0.5)
         return sample

      def      UpperCAmelCase				(				self    ,				A    ,				A = None    ,				A = None    ,				)		->    str:
         snake_case				:    str           =	num_inference_steps

         snake_case				:    Tuple           =	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				:    Tuple           =	np.linspace(0    ,				num_train_timesteps - 1    ,				A    ,				dtype=A     )[::-1].copy()
         elif self.config.timestep_spacing == "leading":
            snake_case				:    Dict           =	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				:    str           =	(np.arange(0    ,				A     ) * step_ratio).round()[::-1].copy().astype(A     )
            timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
            snake_case				:    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
            snake_case				:    int           =	(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'."""     )

         snake_case				:    Optional[int]           =	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
         snake_case				:    Tuple           =	np.log(A     )
         snake_case				:    Union[str, Any]           =	np.interp(A    ,				np.arange(0    ,				len(A     )     )    ,				A     )

         if self.config.use_karras_sigmas:
            snake_case				:    List[str]           =	self._convert_to_karras(in_sigmas=A    ,				num_inference_steps=self.num_inference_steps     )
            snake_case				:    int           =	np.array([self._sigma_to_t(A    ,				A     ) for sigma in sigmas]     )

         snake_case				:    Tuple           =	np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )
         snake_case				:    Union[str, Any]           =	torch.from_numpy(A     ).to(device=A     )
         snake_case				:    Dict           =	torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2     ), sigmas[-1:]]     )

         snake_case				:    List[str]           =	torch.from_numpy(A     )
         snake_case				:    str           =	torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2     )]     )

         if str(A     ).startswith("""mps"""     ):
            # mps does not support float64
            snake_case				:    int           =	timesteps.to(A    ,				dtype=torch.floataa     )
         else:
            snake_case				:    Optional[int]           =	timesteps.to(device=A     )

         # empty dt and derivative
         snake_case				:    Union[str, Any]           =	None
         snake_case				:    Optional[int]           =	None

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

      def      UpperCAmelCase				(				self    ,				A    ,				A     )		->    List[str]:
         # get log sigma
         snake_case				:    str           =	np.log(A     )

         # get distribution
         snake_case				:    Tuple           =	log_sigma - log_sigmas[:, np.newaxis]

         # get sigmas range
         snake_case				:    Union[str, Any]           =	np.cumsum((dists >= 0)    ,				axis=0     ).argmax(axis=0     ).clip(max=log_sigmas.shape[0] - 2     )
         snake_case				:    List[Any]           =	low_idx + 1

         snake_case				:    List[str]           =	log_sigmas[low_idx]
         snake_case				:    Optional[Any]           =	log_sigmas[high_idx]

         # interpolate sigmas
         snake_case				:    Optional[Any]           =	(low - log_sigma) / (low - high)
         snake_case				:    Tuple           =	np.clip(A    ,				0    ,				1     )

         # transform interpolation to time range
         snake_case				:    int           =	(1 - w) * low_idx + w * high_idx
         snake_case				:    Union[str, Any]           =	t.reshape(sigma.shape     )
         return t

      def      UpperCAmelCase				(				self    ,				A    ,				A     )		->    torch.FloatTensor:
         snake_case				:    float           =	in_sigmas[-1].item()
         snake_case				:    float           =	in_sigmas[0].item()

         snake_case				:    Dict           =	7.0  # 7.0 is the value used in the paper
         snake_case				:    List[Any]           =	np.linspace(0    ,				1    ,				A     )
         snake_case				:    Optional[int]           =	sigma_min ** (1 / rho)
         snake_case				:    Optional[Any]           =	sigma_max ** (1 / rho)
         snake_case				:    Optional[Any]           =	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas

      @property
      def      UpperCAmelCase				(				self     )		->    List[str]:
         return self.dt is None

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A = True    ,				)		->    Union[SchedulerOutput, Tuple]:
         snake_case				:    Optional[int]           =	self.index_for_timestep(A     )

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

         if self.state_in_first_order:
            snake_case				:    Tuple           =	self.sigmas[step_index]
            snake_case				:    Tuple           =	self.sigmas[step_index + 1]
         else:
            # 2nd order / Heun's method
            snake_case				:    int           =	self.sigmas[step_index - 1]
            snake_case				:    Any           =	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
         snake_case				:    Dict           =	0
         snake_case				:    Dict           =	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":
            snake_case				:    List[str]           =	sigma_hat if self.state_in_first_order else sigma_next
            snake_case				:    Dict           =	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_next
            snake_case				:    int           =	model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                sample / (sigma_input**2 + 1)
            )
         elif self.config.prediction_type == "sample":
            snake_case				:    Any           =	model_output
         else:
            raise ValueError(
                f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""     )

         if self.config.clip_sample:
            snake_case				:    List[Any]           =	pred_original_sample.clamp(
                -self.config.clip_sample_range    ,				self.config.clip_sample_range     )

         if self.state_in_first_order:
            # 2. Convert to an ODE derivative for 1st order
            snake_case				:    Any           =	(sample - pred_original_sample) / sigma_hat
            # 3. delta timestep
            snake_case				:    Tuple           =	sigma_next - sigma_hat

            # store for 2nd order step
            snake_case				:    Optional[int]           =	derivative
            snake_case				:    Optional[int]           =	dt
            snake_case				:    Optional[int]           =	sample
         else:
            # 2. 2nd order / Heun's method
            snake_case				:    Dict           =	(sample - pred_original_sample) / sigma_next
            snake_case				:    List[str]           =	(self.prev_derivative + derivative) / 2

            # 3. take prev timestep & sample
            snake_case				:    List[str]           =	self.dt
            snake_case				:    List[str]           =	self.sample

            # free dt and derivative
            # Note, this puts the scheduler in "first order mode"
            snake_case				:    Tuple           =	None
            snake_case				:    List[str]           =	None
            snake_case				:    int           =	None

         snake_case				:    str           =	sample + derivative * dt

         if not return_dict:
            return (prev_sample,)

         return SchedulerOutput(prev_sample=A     )

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				)		->    torch.FloatTensor:
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         snake_case				:    Any           =	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				:    Dict           =	self.timesteps.to(original_samples.device    ,				dtype=torch.floataa     )
            snake_case				:    Optional[Any]           =	timesteps.to(original_samples.device    ,				dtype=torch.floataa     )
         else:
            snake_case				:    Any           =	self.timesteps.to(original_samples.device     )
            snake_case				:    Optional[Any]           =	timesteps.to(original_samples.device     )

         snake_case				:    Any           =	[self.index_for_timestep(A    ,				A     ) for t in timesteps]

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

         snake_case				:    Union[str, Any]           =	original_samples + noise * sigma
         return noisy_samples

      def __len__(				self     )		->    Union[str, Any]:
         return self.config.num_train_timesteps











from typing import TYPE_CHECKING

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


lowerCamelCase	:	str				=   {
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	List[Any]				=   ['RoFormerTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Dict				=   [
           'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RoFormerForCausalLM',
           'RoFormerForMaskedLM',
           'RoFormerForMultipleChoice',
           'RoFormerForQuestionAnswering',
           'RoFormerForSequenceClassification',
           'RoFormerForTokenClassification',
           'RoFormerLayer',
           'RoFormerModel',
           'RoFormerPreTrainedModel',
           'load_tf_weights_in_roformer',
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Any				=   [
           'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFRoFormerForCausalLM',
           'TFRoFormerForMaskedLM',
           'TFRoFormerForMultipleChoice',
           'TFRoFormerForQuestionAnswering',
           'TFRoFormerForSequenceClassification',
           'TFRoFormerForTokenClassification',
           'TFRoFormerLayer',
           'TFRoFormerModel',
           'TFRoFormerPreTrainedModel',
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Union[str, Any]				=   [
           'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'FlaxRoFormerForMaskedLM',
           'FlaxRoFormerForMultipleChoice',
           'FlaxRoFormerForQuestionAnswering',
           'FlaxRoFormerForSequenceClassification',
           'FlaxRoFormerForTokenClassification',
           'FlaxRoFormerModel',
           'FlaxRoFormerPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
       from .tokenization_roformer import RoFormerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roformer_fast import RoFormerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roformer import (
                  ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RoFormerForCausalLM,
                  RoFormerForMaskedLM,
                  RoFormerForMultipleChoice,
                  RoFormerForQuestionAnswering,
                  RoFormerForSequenceClassification,
                  RoFormerForTokenClassification,
                  RoFormerLayer,
                  RoFormerModel,
                  RoFormerPreTrainedModel,
                  load_tf_weights_in_roformer,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roformer import (
                  TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRoFormerForCausalLM,
                  TFRoFormerForMaskedLM,
                  TFRoFormerForMultipleChoice,
                  TFRoFormerForQuestionAnswering,
                  TFRoFormerForSequenceClassification,
                  TFRoFormerForTokenClassification,
                  TFRoFormerLayer,
                  TFRoFormerModel,
                  TFRoFormerPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roformer import (
                  FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaxRoFormerForMaskedLM,
                  FlaxRoFormerForMultipleChoice,
                  FlaxRoFormerForQuestionAnswering,
                  FlaxRoFormerForSequenceClassification,
                  FlaxRoFormerForTokenClassification,
                  FlaxRoFormerModel,
                  FlaxRoFormerPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase	:	Optional[int]				=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





