
"""simple docstring"""


import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					torch.FloatTensor
       UpperCamelCase_		:					Optional[torch.FloatTensor]   =			None





def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_=0.999		,      UpperCAmelCase_="cosine"		,      )->							Tuple:







       """simple docstring"""
       if alpha_transform_type == "cosine":

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

       elif alpha_transform_type == "exp":

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

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

       UpperCamelCase					 =		[]
       for i in range(UpperCAmelCase_				):
              UpperCamelCase					 =		i / num_diffusion_timesteps
              UpperCamelCase					 =		(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	):






       @register_to_config
       def __init__(  self :							Union[str, Any]       ,      UpperCAmelCase_ :							int = 1_000       ,      UpperCAmelCase_ :							str = "fixed_small_log"       ,      UpperCAmelCase_ :							bool = True       ,      UpperCAmelCase_ :							Optional[float] = 1.0       ,      UpperCAmelCase_ :							str = "epsilon"       ,      UpperCAmelCase_ :							str = "squaredcos_cap_v2"       ,      )->  Optional[Any]:







              """simple docstring"""
              if beta_schedule != "squaredcos_cap_v2":
                     raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"			)

              UpperCamelCase					 =		betas_for_alpha_bar(UpperCAmelCase_			)

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

              # standard deviation of the initial noise distribution
              UpperCamelCase					 =		1.0

              # setable values
              UpperCamelCase					 =		None
              UpperCamelCase					 =		torch.from_numpy(np.arange(0       ,      UpperCAmelCase_			)[::-1].copy()			)

              UpperCamelCase					 =		variance_type






       def 	_SCREAMING_SNAKE_CASE       (  self :							str       ,      UpperCAmelCase_ :							torch.FloatTensor       ,      UpperCAmelCase_ :							Optional[int] = None			)->  torch.FloatTensor:







              """simple docstring"""
              return sample






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							Union[str, torch.device] = None			)->  List[str]:







              """simple docstring"""
              UpperCamelCase					 =		num_inference_steps
              UpperCamelCase					 =		(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
              UpperCamelCase					 =		(np.arange(0       ,      UpperCAmelCase_			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
              UpperCamelCase					 =		torch.from_numpy(UpperCAmelCase_			).to(UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							Tuple=None       ,      UpperCAmelCase_ :							Optional[int]=None       ,      UpperCAmelCase_ :							Dict=None			)->  List[str]:







              """simple docstring"""
              if prev_timestep is None:
                     UpperCamelCase					 =		t - 1

              UpperCamelCase					 =		self.alphas_cumprod[t]
              UpperCamelCase					 =		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
              UpperCamelCase					 =		1 - alpha_prod_t
              UpperCamelCase					 =		1 - alpha_prod_t_prev

              if prev_timestep == t - 1:
                     UpperCamelCase					 =		self.betas[t]
              else:
                     UpperCamelCase					 =		1 - alpha_prod_t / alpha_prod_t_prev

              # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
              # and sample from it to get previous sample
              # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
              UpperCamelCase					 =		beta_prod_t_prev / beta_prod_t * beta

              if variance_type is None:
                     UpperCamelCase					 =		self.config.variance_type

              # hacks - were probably added for training stability
              if variance_type == "fixed_small_log":
                     UpperCamelCase					 =		torch.log(torch.clamp(UpperCAmelCase_       ,      min=1e-20			)			)
                     UpperCamelCase					 =		torch.exp(0.5 * variance			)
              elif variance_type == "learned_range":
                     # NOTE difference with DDPM scheduler
                     UpperCamelCase					 =		variance.log()
                     UpperCamelCase					 =		beta.log()

                     UpperCamelCase					 =		(predicted_variance + 1) / 2
                     UpperCamelCase					 =		frac * max_log + (1 - frac) * min_log

              return variance






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]       ,      UpperCAmelCase_ :							torch.FloatTensor       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							torch.FloatTensor       ,      UpperCAmelCase_ :							Optional[int] = None       ,      UpperCAmelCase_ :							List[str]=None       ,      UpperCAmelCase_ :							bool = True       ,      )->  Union[UnCLIPSchedulerOutput, Tuple]:







              """simple docstring"""
              UpperCamelCase					 =		timestep

              if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
                     UpperCamelCase ,       UpperCamelCase					 =		torch.split(UpperCAmelCase_       ,      sample.shape[1]       ,      dim=1			)
              else:
                     UpperCamelCase					 =		None

              # 1. compute alphas, betas
              if prev_timestep is None:
                     UpperCamelCase					 =		t - 1

              UpperCamelCase					 =		self.alphas_cumprod[t]
              UpperCamelCase					 =		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
              UpperCamelCase					 =		1 - alpha_prod_t
              UpperCamelCase					 =		1 - alpha_prod_t_prev

              if prev_timestep == t - 1:
                     UpperCamelCase					 =		self.betas[t]
                     UpperCamelCase					 =		self.alphas[t]
              else:
                     UpperCamelCase					 =		1 - alpha_prod_t / alpha_prod_t_prev
                     UpperCamelCase					 =		1 - beta

              # 2. compute predicted original sample from predicted noise also called
              # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
              if self.config.prediction_type == "epsilon":
                     UpperCamelCase					 =		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
              elif self.config.prediction_type == "sample":
                     UpperCamelCase					 =		model_output
              else:
                     raise ValueError(
                         f"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
                         " for the UnCLIPScheduler."			)

              # 3. Clip "predicted x_0"
              if self.config.clip_sample:
                     UpperCamelCase					 =		torch.clamp(
                         UpperCAmelCase_       ,      -self.config.clip_sample_range       ,      self.config.clip_sample_range			)

              # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
              # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
              UpperCamelCase					 =		(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
              UpperCamelCase					 =		alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

              # 5. Compute predicted previous sample µ_t
              # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
              UpperCamelCase					 =		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

              # 6. Add noise
              UpperCamelCase					 =		0
              if t > 0:
                     UpperCamelCase					 =		randn_tensor(
                         model_output.shape       ,      dtype=model_output.dtype       ,      generator=UpperCAmelCase_       ,      device=model_output.device			)

                     UpperCamelCase					 =		self._get_variance(
                         UpperCAmelCase_       ,      predicted_variance=UpperCAmelCase_       ,      prev_timestep=UpperCAmelCase_       ,      )

                     if self.variance_type == "fixed_small_log":
                            UpperCamelCase					 =		variance
                     elif self.variance_type == "learned_range":
                            UpperCamelCase					 =		(0.5 * variance).exp()
                     else:
                            raise ValueError(
                                f"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
                                " for the UnCLIPScheduler."			)

                     UpperCamelCase					 =		variance * variance_noise

              UpperCamelCase					 =		pred_prev_sample + variance

              if not return_dict:
                     return (pred_prev_sample,)

              return UnCLIPSchedulerOutput(prev_sample=UpperCAmelCase_       ,      pred_original_sample=UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any       ,      UpperCAmelCase_ :							torch.FloatTensor       ,      UpperCAmelCase_ :							torch.FloatTensor       ,      UpperCAmelCase_ :							torch.IntTensor       ,      )->  torch.FloatTensor:







              """simple docstring"""
              # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
              UpperCamelCase					 =		self.alphas_cumprod.to(device=original_samples.device       ,      dtype=original_samples.dtype			)
              UpperCamelCase					 =		timesteps.to(original_samples.device			)

              UpperCamelCase					 =		alphas_cumprod[timesteps] ** 0.5
              UpperCamelCase					 =		sqrt_alpha_prod.flatten()
              while len(sqrt_alpha_prod.shape			) < len(original_samples.shape			):
                     UpperCamelCase					 =		sqrt_alpha_prod.unsqueeze(-1			)

              UpperCamelCase					 =		(1 - alphas_cumprod[timesteps]) ** 0.5
              UpperCamelCase					 =		sqrt_one_minus_alpha_prod.flatten()
              while len(sqrt_one_minus_alpha_prod.shape			) < len(original_samples.shape			):
                     UpperCamelCase					 =		sqrt_one_minus_alpha_prod.unsqueeze(-1			)

              UpperCamelCase					 =		sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
              return noisy_samples







"""simple docstring"""


from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE      =     {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     ["""MBartTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     ["""MBartTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MBartForCausalLM""",
          """MBartForConditionalGeneration""",
          """MBartForQuestionAnswering""",
          """MBartForSequenceClassification""",
          """MBartModel""",
          """MBartPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """TFMBartForConditionalGeneration""",
          """TFMBartModel""",
          """TFMBartPreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """FlaxMBartForConditionalGeneration""",
          """FlaxMBartForQuestionAnswering""",
          """FlaxMBartForSequenceClassification""",
          """FlaxMBartModel""",
          """FlaxMBartPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart import MBartTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart_fast import MBartTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mbart import (
                MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                MBartForCausalLM,
                MBartForConditionalGeneration,
                MBartForQuestionAnswering,
                MBartForSequenceClassification,
                MBartModel,
                MBartPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_mbart import (
                FlaxMBartForConditionalGeneration,
                FlaxMBartForQuestionAnswering,
                FlaxMBartForSequenceClassification,
                FlaxMBartModel,
                FlaxMBartPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE      =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





