# 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      A							:
   __UpperCAmelCase : CommonSchedulerState

   # setable values
   __UpperCAmelCase : jnp.ndarray
   __UpperCAmelCase : jnp.ndarray
   __UpperCAmelCase : Optional[int]      =			None


   @classmethod
   def 			lowercase_      (cls     :						Any       ,  __UpperCAmelCase     :						CommonSchedulerState       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						jnp.ndarray		)						->				List[str]:
         """simple docstring"""



         return cls(common=__UpperCAmelCase       ,  init_noise_sigma=__UpperCAmelCase       ,  timesteps=__UpperCAmelCase		)





@dataclass
class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : DDPMSchedulerState





class      A							(				UpperCAmelCase_		,				UpperCAmelCase_     ):
   __UpperCAmelCase : Any      =			[e.name for e in FlaxKarrasDiffusionSchedulers]

   __UpperCAmelCase : jnp.dtype


   @property
   def 			lowercase_      (self     :						Any		)						->				Union[str, Any]:
         """simple docstring"""



         return True


   @register_to_config
   def __init__(self     :						Optional[Any]       ,  __UpperCAmelCase     :						int = 1_0_0_0       ,  __UpperCAmelCase     :						float = 0.0001       ,  __UpperCAmelCase     :						float = 0.02       ,  __UpperCAmelCase     :						str = "linear"       ,  __UpperCAmelCase     :						Optional[jnp.ndarray] = None       ,  __UpperCAmelCase     :						str = "fixed_small"       ,  __UpperCAmelCase     :						bool = True       ,  __UpperCAmelCase     :						str = "epsilon"       ,  __UpperCAmelCase     :						jnp.dtype = jnp.floataa       ,  )						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  dtype


   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						Optional[CommonSchedulerState] = None		)						->				DDPMSchedulerState:
         """simple docstring"""



         if common is None:
               UpperCAmelCase__								=  CommonSchedulerState.create(self		)

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

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

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


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						DDPMSchedulerState       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						Optional[int] = None		)						->				jnp.ndarray:
         """simple docstring"""



         return sample


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						DDPMSchedulerState       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						Tuple = ()		)						->				DDPMSchedulerState:
         """simple docstring"""



         UpperCAmelCase__								=  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
         UpperCAmelCase__								=  (jnp.arange(0       ,  __UpperCAmelCase		) * step_ratio).round()[::-1]

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


   def 			lowercase_      (self     :						str       ,  __UpperCAmelCase     :						DDPMSchedulerState       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						Union[str, Any]=None       ,  __UpperCAmelCase     :						Optional[int]=None		)						->				Optional[Any]:
         """simple docstring"""



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

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

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

         return variance


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						DDPMSchedulerState       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						Optional[jax.random.KeyArray] = None       ,  __UpperCAmelCase     :						bool = True       ,  )						->				Union[FlaxDDPMSchedulerOutput, Tuple]:
         """simple docstring"""



         UpperCAmelCase__								=  timestep

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

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

         # 1. compute alphas, betas
         UpperCAmelCase__								=  state.common.alphas_cumprod[t]
         UpperCAmelCase__								=  jnp.where(t > 0       ,  state.common.alphas_cumprod[t - 1]       ,  jnp.array(1.0       ,  dtype=self.dtype		)		)
         UpperCAmelCase__								=  1 - alpha_prod_t
         UpperCAmelCase__								=  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":
               UpperCAmelCase__								=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
               UpperCAmelCase__								=  model_output
         elif self.config.prediction_type == "v_prediction":
               UpperCAmelCase__								=  (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:
               UpperCAmelCase__								=  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
         UpperCAmelCase__								=  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
         UpperCAmelCase__								=  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
         UpperCAmelCase__								=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

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

         UpperCAmelCase__								=  pred_prev_sample + variance

         if not return_dict:
               return (pred_prev_sample, state)

         return FlaxDDPMSchedulerOutput(prev_sample=__UpperCAmelCase       ,  state=__UpperCAmelCase		)


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						DDPMSchedulerState       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						jnp.ndarray       ,  )						->				jnp.ndarray:
         """simple docstring"""



         return add_noise_common(state.common       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase		)


   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						DDPMSchedulerState       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						jnp.ndarray       ,  )						->				jnp.ndarray:
         """simple docstring"""



         return get_velocity_common(state.common       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase		)





   def __len__(self     :						int		)						->				Union[str, Any]:
         """simple docstring"""



         return self.config.num_train_timesteps











import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


__UpperCAmelCase								=						importlib.util.find_spec('s3fs') is not None

if _has_safs:
		from .safilesystem import SaFileSystem  # noqa: F401

__UpperCAmelCase								=						[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
		if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
				warnings.warn(F'A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.')
		fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)




def      lowercase__      (     __snake_case    :							str  ):


					'''simple docstring'''


					if "://" in dataset_path:
										UpperCAmelCase_    :      int            =						dataset_path.split('://'  )[1]
					return dataset_path




def      lowercase__      (     __snake_case    :							fsspec.AbstractFileSystem  ):


					'''simple docstring'''


					if fs is not None and fs.protocol != "file":
										return True
					else:
										return False




def      lowercase__      (     __snake_case    :							fsspec.AbstractFileSystem     ,       __snake_case    :							str     ,       __snake_case    :							str  ):


					'''simple docstring'''


					UpperCAmelCase_    :      List[str]            =						not is_remote_filesystem(__snake_case  )
					if is_local:
										# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
										shutil.move(fs._strip_protocol(__snake_case  )     ,       fs._strip_protocol(__snake_case  )  )
					else:
										fs.mv(__snake_case     ,       __snake_case     ,       recursive=__snake_case  )






def      lowercase__      (     ):


					'''simple docstring'''


					if hasattr(fsspec.asyn     ,       'reset_lock'  ):
										# for future fsspec>2022.05.0
										fsspec.asyn.reset_lock()
					else:
										UpperCAmelCase_    :      Optional[Any]            =						None
										UpperCAmelCase_    :      Union[str, Any]            =						None
										UpperCAmelCase_    :      int            =						threading.Lock()






