


'''simple docstring'''

# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)





@flax.struct.dataclass
class   snake_case					:





  """simple docstring"""




  _lowerCamelCase						=						42

  # setable values
  _lowerCamelCase						=						42
  _lowerCamelCase						=						42
  _lowerCamelCase						=						None






  @classmethod
  def 						snake_case	(					cls    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   return cls(common=UpperCamelCase    ,						init_noise_sigma=UpperCamelCase    ,						timesteps=UpperCamelCase  )





@dataclass
class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						42





class   snake_case					(					lowercase    ,		lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						[e.name for e in FlaxKarrasDiffusionSchedulers]

  _lowerCamelCase						=						42






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return True






  @register_to_config
  def __init__(					self    ,						UpperCamelCase = 1000    ,						UpperCamelCase = 0.0_001    ,						UpperCamelCase = 0.02    ,						UpperCamelCase = "linear"    ,						UpperCamelCase = None    ,						UpperCamelCase = "fixed_small"    ,						UpperCamelCase = True    ,						UpperCamelCase = "epsilon"    ,						UpperCamelCase = jnp.floataa    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						dtype






  def 						snake_case	(					self    ,						UpperCamelCase = None  ):







   """simple docstring"""
   if common is None:
    lowerCamelCase_  				=						CommonSchedulerState.create(self  )

   # standard deviation of the initial noise distribution
   lowerCamelCase_  				=						jnp.array(1.0    ,						dtype=self.dtype  )

   lowerCamelCase_  				=						jnp.arange(0    ,						self.config.num_train_timesteps  ).round()[::-1]

   return DDPMSchedulerState.create(
       common=UpperCamelCase    ,						init_noise_sigma=UpperCamelCase    ,						timesteps=UpperCamelCase    ,						)






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase = None  ):







   """simple docstring"""
   return sample






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase = ()  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.config.num_train_timesteps // num_inference_steps
   # creates integer timesteps by multiplying by ratio
   # rounding to avoid issues when num_inference_step is power of 3
   lowerCamelCase_  				=						(jnp.arange(0    ,						UpperCamelCase  ) * step_ratio).round()[::-1]

   return state.replace(
       num_inference_steps=UpperCamelCase    ,						timesteps=UpperCamelCase    ,						)






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase=None    ,						UpperCamelCase=None  ):







   """simple docstring"""
   lowerCamelCase_  				=						state.common.alphas_cumprod[t]
   lowerCamelCase_  				=						jnp.where(t > 0    ,						state.common.alphas_cumprod[t - 1]    ,						jnp.array(1.0    ,						dtype=self.dtype  )  )

   # 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
   lowerCamelCase_  				=						(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

   # hacks - were probably added for training stability
   if variance_type == "fixed_small":
    lowerCamelCase_  				=						jnp.clip(UpperCamelCase    ,						a_min=1e-20  )
   # for rl-diffuser https://arxiv.org/abs/2205.09991
   elif variance_type == "fixed_small_log":
    lowerCamelCase_  				=						jnp.log(jnp.clip(UpperCamelCase    ,						a_min=1e-20  )  )
   elif variance_type == "fixed_large":
    lowerCamelCase_  				=						state.common.betas[t]
   elif variance_type == "fixed_large_log":
    # Glide max_log
    lowerCamelCase_  				=						jnp.log(state.common.betas[t]  )
   elif variance_type == "learned":
    return predicted_variance
   elif variance_type == "learned_range":
    lowerCamelCase_  				=						variance
    lowerCamelCase_  				=						state.common.betas[t]
    lowerCamelCase_  				=						(predicted_variance + 1) / 2
    lowerCamelCase_  				=						frac * max_log + (1 - frac) * min_log

   return variance






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase = None    ,						UpperCamelCase = True    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						timestep

   if key is None:
    lowerCamelCase_  				=						jax.random.PRNGKey(0  )

   if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
    lowerCamelCase_				,lowerCamelCase_  				=						jnp.split(UpperCamelCase    ,						sample.shape[1]    ,						axis=1  )
   else:
    lowerCamelCase_  				=						None

   # 1. compute alphas, betas
   lowerCamelCase_  				=						state.common.alphas_cumprod[t]
   lowerCamelCase_  				=						jnp.where(t > 0    ,						state.common.alphas_cumprod[t - 1]    ,						jnp.array(1.0    ,						dtype=self.dtype  )  )
   lowerCamelCase_  				=						1 - alpha_prod_t
   lowerCamelCase_  				=						1 - alpha_prod_t_prev

   # 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":
    lowerCamelCase_  				=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
   elif self.config.prediction_type == "sample":
    lowerCamelCase_  				=						model_output
   elif self.config.prediction_type == "v_prediction":
    lowerCamelCase_  				=						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
   else:
    raise ValueError(
        f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
        " for the FlaxDDPMScheduler."  )

   # 3. Clip "predicted x_0"
   if self.config.clip_sample:
    lowerCamelCase_  				=						jnp.clip(UpperCamelCase    ,						-1    ,						1  )

   # 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
   lowerCamelCase_  				=						(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
   lowerCamelCase_  				=						state.common.alphas[t] ** 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
   lowerCamelCase_  				=						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

   # 6. Add noise
   def random_variance():
    lowerCamelCase_  				=						jax.random.split(UpperCamelCase    ,						num=1  )
    lowerCamelCase_  				=						jax.random.normal(UpperCamelCase    ,						shape=model_output.shape    ,						dtype=self.dtype  )
    return (self._get_variance(UpperCamelCase    ,						UpperCamelCase    ,						predicted_variance=UpperCamelCase  ) ** 0.5) * noise

   lowerCamelCase_  				=						jnp.where(t > 0    ,						random_variance()    ,						jnp.zeros(model_output.shape    ,						dtype=self.dtype  )  )

   lowerCamelCase_  				=						pred_prev_sample + variance

   if not return_dict:
    return (pred_prev_sample, state)

   return FlaxDDPMSchedulerOutput(prev_sample=UpperCamelCase    ,						state=UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						):







   """simple docstring"""
   return add_noise_common(state.common    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						):







   """simple docstring"""
   return get_velocity_common(state.common    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  )







  def __len__(					self  ):







   """simple docstring"""
   return self.config.num_train_timesteps







'''simple docstring'''

import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         Adafactor,
         AdamW,
         get_constant_schedule,
         get_constant_schedule_with_warmup,
         get_cosine_schedule_with_warmup,
         get_cosine_with_hard_restarts_schedule_with_warmup,
         get_inverse_sqrt_schedule,
         get_linear_schedule_with_warmup,
         get_polynomial_decay_schedule_with_warmup,
     )





def __snake_case      (   UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		Union[str, Any]=10	):
 lowerCamelCase_  				=						[]
 for _ in range(UpperCAmelCase_	):
  lrs.append(scheduler.get_lr()[0]	)
  scheduler.step()
 return lrs





def __snake_case      (   UpperCAmelCase_		:		Tuple      ,       UpperCAmelCase_		:		List[Any]=10	):
 lowerCamelCase_  				=						[]
 for step in range(UpperCAmelCase_	):
  lrs.append(scheduler.get_lr()[0]	)
  scheduler.step()
  if step == num_steps // 2:
   with tempfile.TemporaryDirectory() as tmpdirname:
    lowerCamelCase_  				=						os.path.join(UpperCAmelCase_      ,       "schedule.bin"	)
    torch.save(scheduler.state_dict()      ,       UpperCAmelCase_	)

    lowerCamelCase_  				=						torch.load(UpperCAmelCase_	)
    scheduler.load_state_dict(UpperCAmelCase_	)
 return lrs





@require_torch
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   self.assertEqual(len(UpperCamelCase  )    ,						len(UpperCamelCase  )  )
   for a, b in zip(UpperCamelCase    ,						UpperCamelCase  ):
    self.assertAlmostEqual(UpperCamelCase    ,						UpperCamelCase    ,						delta=UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						torch.tensor([0.1, -0.2, -0.1]    ,						requires_grad=UpperCamelCase  )
   lowerCamelCase_  				=						torch.tensor([0.4, 0.2, -0.5]  )
   lowerCamelCase_  				=						nn.MSELoss()
   # No warmup, constant schedule, no gradient clipping
   lowerCamelCase_  				=						AdamW(params=[w]    ,						lr=2e-1    ,						weight_decay=0.0  )
   for _ in range(100  ):
    lowerCamelCase_  				=						criterion(UpperCamelCase    ,						UpperCamelCase  )
    loss.backward()
    optimizer.step()
    w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
    w.grad.zero_()
   self.assertListAlmostEqual(w.tolist()    ,						[0.4, 0.2, -0.5]    ,						tol=1e-2  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						torch.tensor([0.1, -0.2, -0.1]    ,						requires_grad=UpperCamelCase  )
   lowerCamelCase_  				=						torch.tensor([0.4, 0.2, -0.5]  )
   lowerCamelCase_  				=						nn.MSELoss()
   # No warmup, constant schedule, no gradient clipping
   lowerCamelCase_  				=						Adafactor(
       params=[w]    ,						lr=1e-2    ,						eps=(1e-30, 1e-3)    ,						clip_threshold=1.0    ,						decay_rate=-0.8    ,						betaa=UpperCamelCase    ,						weight_decay=0.0    ,						relative_step=UpperCamelCase    ,						scale_parameter=UpperCamelCase    ,						warmup_init=UpperCamelCase    ,						)
   for _ in range(1000  ):
    lowerCamelCase_  				=						criterion(UpperCamelCase    ,						UpperCamelCase  )
    loss.backward()
    optimizer.step()
    w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
    w.grad.zero_()
   self.assertListAlmostEqual(w.tolist()    ,						[0.4, 0.2, -0.5]    ,						tol=1e-2  )





@require_torch
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						nn.Linear(50    ,		50    ) if is_torch_available() else None
  _lowerCamelCase						=						AdamW(m.parameters()    ,		lr=10.0    ) if is_torch_available() else None
  _lowerCamelCase						=						10






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase=None  ):







   """simple docstring"""
   self.assertEqual(len(UpperCamelCase  )    ,						len(UpperCamelCase  )  )
   for a, b in zip(UpperCamelCase    ,						UpperCamelCase  ):
    self.assertAlmostEqual(UpperCamelCase    ,						UpperCamelCase    ,						delta=UpperCamelCase    ,						msg=UpperCamelCase  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						{"num_warmup_steps": 2, "num_training_steps": 10}
   # schedulers doct format
   # function: (sched_args_dict, expected_learning_rates)
   lowerCamelCase_  				=						{
       get_constant_schedule: ({}, [10.0] * self.num_steps),
       get_constant_schedule_with_warmup: (
           {"num_warmup_steps": 4},
           [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
       ),
       get_linear_schedule_with_warmup: (
           {**common_kwargs},
           [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
       ),
       get_cosine_schedule_with_warmup: (
           {**common_kwargs},
           [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
       ),
       get_cosine_with_hard_restarts_schedule_with_warmup: (
           {**common_kwargs, "num_cycles": 2},
           [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
       ),
       get_polynomial_decay_schedule_with_warmup: (
           {**common_kwargs, "power": 2.0, "lr_end": 1e-7},
           [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
       ),
       get_inverse_sqrt_schedule: (
           {"num_warmup_steps": 2},
           [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
       ),
   }

   for scheduler_func, data in scheds.items():
    lowerCamelCase_				,lowerCamelCase_  				=						data

    lowerCamelCase_  				=						scheduler_func(self.optimizer    ,						**UpperCamelCase  )
    self.assertEqual(len([scheduler.get_lr()[0]]  )    ,						1  )
    lowerCamelCase_  				=						unwrap_schedule(UpperCamelCase    ,						self.num_steps  )
    self.assertListAlmostEqual(
        UpperCamelCase    ,						UpperCamelCase    ,						tol=1e-2    ,						msg=f'''failed for {scheduler_func} in normal scheduler'''    ,						)

    lowerCamelCase_  				=						scheduler_func(self.optimizer    ,						**UpperCamelCase  )
    if scheduler_func.__name__ != "get_constant_schedule":
     LambdaScheduleWrapper.wrap_scheduler(UpperCamelCase  )  # wrap to test picklability of the schedule
    lowerCamelCase_  				=						unwrap_and_save_reload_schedule(UpperCamelCase    ,						self.num_steps  )
    self.assertListEqual(UpperCamelCase    ,						UpperCamelCase    ,						msg=f'''failed for {scheduler_func} in save and reload'''  )





class   snake_case					:





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						fn






  def __call__(					self    ,						*UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   return self.fn(*UpperCamelCase    ,						**UpperCamelCase  )







  @classmethod
  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						list(map(self    ,						scheduler.lr_lambdas  )  )



