from typing import TYPE_CHECKING

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


A   :     Any       =				{'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A   :     Tuple       =				['ReformerTokenizer']

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A   :     Dict       =				['ReformerTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A   :     Tuple       =				[
          'REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'ReformerAttention',
          'ReformerForMaskedLM',
          'ReformerForQuestionAnswering',
          'ReformerForSequenceClassification',
          'ReformerLayer',
          'ReformerModel',
          'ReformerModelWithLMHead',
          'ReformerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer import ReformerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer_fast import ReformerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_reformer import (
                REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                ReformerAttention,
                ReformerForMaskedLM,
                ReformerForQuestionAnswering,
                ReformerForSequenceClassification,
                ReformerLayer,
                ReformerModel,
                ReformerModelWithLMHead,
                ReformerPreTrainedModel,
            )

else:
      import sys

      A   :     Optional[Any]       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









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 	UpperCamelCase__(       UpperCamelCase__			:       List[Any] ,     UpperCamelCase__			:       Tuple=0.999 ,     UpperCamelCase__			:       Any="cosine" ,     )->List[str]:
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(UpperCamelCase__			:       Optional[int]  ):
                     return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(UpperCamelCase__			:       str  ):
                     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     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	[e.name for e in KarrasDiffusionSchedulers]
 __SCREAMING_SNAKE_CASE							       =	2



 @register_to_config
 def __init__(							self,__lowerCamelCase = 1000,__lowerCamelCase = 0.00085,__lowerCamelCase = 0.012,__lowerCamelCase = "linear",__lowerCamelCase = None,__lowerCamelCase = "epsilon",__lowerCamelCase = False,__lowerCamelCase = False,__lowerCamelCase = 1.0,__lowerCamelCase = "linspace",__lowerCamelCase = 0,):
        if trained_betas is not None:
               A__											=						torch.tensor(__lowerCamelCase,dtype=torch.floataa )
        elif beta_schedule == "linear":
               A__											=						torch.linspace(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,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,__lowerCamelCase,dtype=torch.floataa ) ** 2
               )
        elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               A__											=						betas_for_alpha_bar(__lowerCamelCase,alpha_transform_type='''cosine''' )
        elif beta_schedule == "exp":
               A__											=						betas_for_alpha_bar(__lowerCamelCase,alpha_transform_type='''exp''' )
        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(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )
        A__											=						use_karras_sigmas



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase=None ):
        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(__lowerCamelCase ) > 1 else 0
        else:
               A__											=						timestep.cpu().item() if torch.is_tensor(__lowerCamelCase ) else timestep
               A__											=						self._index_counter[timestep_int]

        return indices[pos].item()



 @property
 def 				UpperCamelCase			(							self ):
        # 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,__lowerCamelCase,__lowerCamelCase,):
        A__											=						self.index_for_timestep(__lowerCamelCase )

        A__											=						self.sigmas[step_index]
        A__											=						sample / ((sigma**2 + 1) ** 0.5)
        return sample



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = None,):
        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,__lowerCamelCase,dtype=__lowerCamelCase )[::-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,__lowerCamelCase ) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase )
               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(__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'." )

        A__											=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
        A__											=						np.log(__lowerCamelCase )
        A__											=						np.interp(__lowerCamelCase,np.arange(0,len(__lowerCamelCase ) ),__lowerCamelCase )

        if self.config.use_karras_sigmas:
               A__											=						self._convert_to_karras(in_sigmas=__lowerCamelCase,num_inference_steps=self.num_inference_steps )
               A__											=						np.array([self._sigma_to_t(__lowerCamelCase,__lowerCamelCase ) for sigma in sigmas] )

        A__											=						np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
        A__											=						torch.from_numpy(__lowerCamelCase ).to(device=__lowerCamelCase )
        A__											=						torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )

        A__											=						torch.from_numpy(__lowerCamelCase )
        A__											=						torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )

        if str(__lowerCamelCase ).startswith('''mps''' ):
               # mps does not support float64
               A__											=						timesteps.to(__lowerCamelCase,dtype=torch.floataa )
        else:
               A__											=						timesteps.to(device=__lowerCamelCase )

        # empty dt and derivative
        A__											=						None
        A__											=						None

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



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        # get log sigma
        A__											=						np.log(__lowerCamelCase )

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

        # get sigmas range
        A__											=						np.cumsum((dists >= 0),axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
        A__											=						low_idx + 1

        A__											=						log_sigmas[low_idx]
        A__											=						log_sigmas[high_idx]

        # interpolate sigmas
        A__											=						(low - log_sigma) / (low - high)
        A__											=						np.clip(__lowerCamelCase,0,1 )

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



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						in_sigmas[-1].item()
        A__											=						in_sigmas[0].item()

        A__											=						7.0  # 7.0 is the value used in the paper
        A__											=						np.linspace(0,1,__lowerCamelCase )
        A__											=						sigma_min ** (1 / rho)
        A__											=						sigma_max ** (1 / rho)
        A__											=						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas



 @property
 def 				UpperCamelCase			(							self ):
        return self.dt is None



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = True,):
        A__											=						self.index_for_timestep(__lowerCamelCase )

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

        if self.state_in_first_order:
               A__											=						self.sigmas[step_index]
               A__											=						self.sigmas[step_index + 1]
        else:
               # 2nd order / Heun's method
               A__											=						self.sigmas[step_index - 1]
               A__											=						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
        A__											=						0
        A__											=						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":
               A__											=						sigma_hat if self.state_in_first_order else sigma_next
               A__											=						sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
               A__											=						sigma_hat if self.state_in_first_order else sigma_next
               A__											=						model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                   sample / (sigma_input**2 + 1)
               )
        elif self.config.prediction_type == "sample":
               A__											=						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:
               A__											=						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
               A__											=						(sample - pred_original_sample) / sigma_hat
               # 3. delta timestep
               A__											=						sigma_next - sigma_hat

               # store for 2nd order step
               A__											=						derivative
               A__											=						dt
               A__											=						sample
        else:
               # 2. 2nd order / Heun's method
               A__											=						(sample - pred_original_sample) / sigma_next
               A__											=						(self.prev_derivative + derivative) / 2

               # 3. take prev timestep & sample
               A__											=						self.dt
               A__											=						self.sample

               # free dt and derivative
               # Note, this puts the scheduler in "first order mode"
               A__											=						None
               A__											=						None
               A__											=						None

        A__											=						sample + derivative * dt

        if not return_dict:
               return (prev_sample,)

        return SchedulerOutput(prev_sample=__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,):
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        A__											=						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
               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(__lowerCamelCase,__lowerCamelCase ) 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 ):
        return self.config.num_train_timesteps





