
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
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  _lowerCamelCase			:
     lowercase__				  =			42
     lowercase__				  =			42

     # setable values
     lowercase__				  =			42
     lowercase__				  =			42
     lowercase__				  =			None

     @classmethod
     def 						__lowerCamelCase				(  cls							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       ):
            return cls(
                common=SCREAMING_SNAKE_CASE_							,       final_alpha_cumprod=SCREAMING_SNAKE_CASE_							,       init_noise_sigma=SCREAMING_SNAKE_CASE_							,       timesteps=SCREAMING_SNAKE_CASE_							,       )







@dataclass
class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			42







class  _lowerCamelCase			(lowerCamelCase							,					lowerCamelCase					):
     lowercase__				  =			[e.name for e in FlaxKarrasDiffusionSchedulers]

     lowercase__				  =			42

     @property
     def 						__lowerCamelCase				(  self    ):
            return True

     @register_to_config
     def __init__(  self							,       SCREAMING_SNAKE_CASE_ = 1_000							,       SCREAMING_SNAKE_CASE_ = 0.0_0_0_1							,       SCREAMING_SNAKE_CASE_ = 0.0_2							,       SCREAMING_SNAKE_CASE_ = "linear"							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = 0							,       SCREAMING_SNAKE_CASE_ = "epsilon"							,       SCREAMING_SNAKE_CASE_ = jnp.floataa							,       ):
            __snake_case						       =   dtype

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_ = None    ):
            if common is None:
                   __snake_case						       =   CommonSchedulerState.create(self    )

            # At every step in ddim, we are looking into the previous alphas_cumprod
            # For the final step, there is no previous alphas_cumprod because we are already at 0
            # `set_alpha_to_one` decides whether we set this parameter simply to one or
            # whether we use the final alpha of the "non-previous" one.
            __snake_case						       =   (
                jnp.array(1.0							,       dtype=self.dtype    ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
            )

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

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

            return DDIMSchedulerState.create(
                common=SCREAMING_SNAKE_CASE_							,       final_alpha_cumprod=SCREAMING_SNAKE_CASE_							,       init_noise_sigma=SCREAMING_SNAKE_CASE_							,       timesteps=SCREAMING_SNAKE_CASE_							,       )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None    ):
            return sample

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = ()    ):
            __snake_case						       =   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
            __snake_case						       =   (jnp.arange(0							,       SCREAMING_SNAKE_CASE_    ) * step_ratio).round()[::-1] + self.config.steps_offset

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

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   state.common.alphas_cumprod[timestep]
            __snake_case						       =   jnp.where(
                prev_timestep >= 0							,       state.common.alphas_cumprod[prev_timestep]							,       state.final_alpha_cumprod    )
            __snake_case						       =   1 - alpha_prod_t
            __snake_case						       =   1 - alpha_prod_t_prev

            __snake_case						       =   (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

            return variance

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = 0.0							,       SCREAMING_SNAKE_CASE_ = True							,       ):
            if state.num_inference_steps is None:
                   raise ValueError(
                       'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'    )

            # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
            # Ideally, read DDIM paper in-detail understanding

            # Notation (<variable name> -> <name in paper>
            # - pred_noise_t -> e_theta(x_t, t)
            # - pred_original_sample -> f_theta(x_t, t) or x_0
            # - std_dev_t -> sigma_t
            # - eta -> η
            # - pred_sample_direction -> "direction pointing to x_t"
            # - pred_prev_sample -> "x_t-1"

            # 1. get previous step value (=t-1)
            __snake_case						       =   timestep - self.config.num_train_timesteps // state.num_inference_steps

            __snake_case						       =   state.common.alphas_cumprod
            __snake_case						       =   state.final_alpha_cumprod

            # 2. compute alphas, betas
            __snake_case						       =   alphas_cumprod[timestep]
            __snake_case						       =   jnp.where(prev_timestep >= 0							,       alphas_cumprod[prev_timestep]							,       SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   1 - alpha_prod_t

            # 3. compute predicted original sample from predicted noise also called
            # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            if self.config.prediction_type == "epsilon":
                   __snake_case						       =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                   __snake_case						       =   model_output
            elif self.config.prediction_type == "sample":
                   __snake_case						       =   model_output
                   __snake_case						       =   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
            elif self.config.prediction_type == "v_prediction":
                   __snake_case						       =   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                   __snake_case						       =   (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
            else:
                   raise ValueError(
                       f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
                       ' `v_prediction`'    )

            # 4. compute variance: "sigma_t(η)" -> see formula (16)
            # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
            __snake_case						       =   self._get_variance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   eta * variance ** 0.5

            # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            __snake_case						       =   (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

            # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            __snake_case						       =   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

            if not return_dict:
                   return (prev_sample, state)

            return FlaxDDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_							,       state=SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       ):
            return add_noise_common(state.common							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       ):
            return get_velocity_common(state.common							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

     def __len__(  self    ):
            return self.config.num_train_timesteps


import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


lowerCamelCase_				:							Any       =    logging.get_logger(__name__)

if is_vision_available():
 import PIL.Image
 from PIL import Image
 from PIL.Image import Image as ImageType
else:
 lowerCamelCase_				:							Union[str, Any]       =    object

if is_torch_available():
 import torch

if is_soundfile_availble():
 import soundfile as sf







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   value

     def __str__(  self    ):
            return self.to_string()

     def 						__lowerCamelCase				(  self    ):
            logger.error(
                'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'    )
            return self._value

     def 						__lowerCamelCase				(  self    ):
            logger.error(
                'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'    )
            return str(self._value    )







class  _lowerCamelCase			(lowerCamelCase							,					lowerCamelCase					):

     def 						__lowerCamelCase				(  self    ):
            return self._value

     def 						__lowerCamelCase				(  self    ):
            return self._value







class  _lowerCamelCase			(lowerCamelCase							,					lowerCamelCase					):

     def __init__(  self							,       SCREAMING_SNAKE_CASE_    ):
            super().__init__(SCREAMING_SNAKE_CASE_    )

            if not is_vision_available():
                   raise ImportError('PIL must be installed in order to handle images.'    )

            __snake_case						       =   None
            __snake_case						       =   None
            __snake_case						       =   None

            if isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
                   __snake_case						       =   value
            elif isinstance(SCREAMING_SNAKE_CASE_							,       (str, pathlib.Path)    ):
                   __snake_case						       =   value
            elif isinstance(SCREAMING_SNAKE_CASE_							,       torch.Tensor    ):
                   __snake_case						       =   value
            else:
                   raise ValueError(f'''Unsupported type for {self.__class__.__name__}: {type(SCREAMING_SNAKE_CASE_    )}'''    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=None    ):
            from IPython.display import Image, display

            display(Image(self.to_string()    )    )

     def 						__lowerCamelCase				(  self    ):
            if self._raw is not None:
                   return self._raw

            if self._path is not None:
                   __snake_case						       =   Image.open(self._path    )
                   return self._raw

     def 						__lowerCamelCase				(  self    ):
            if self._path is not None:
                   return self._path

            if self._raw is not None:
                   __snake_case						       =   tempfile.mkdtemp()
                   __snake_case						       =   os.path.join(SCREAMING_SNAKE_CASE_							,       str(uuid.uuida()    ) + '.png'    )
                   self._raw.save(self._path    )

                   return self._path

            if self._tensor is not None:
                   __snake_case						       =   self._tensor.cpu().detach().numpy()

                   # There is likely simpler than load into image into save
                   __snake_case						       =   Image.fromarray((array * 255).astype(np.uinta    )    )

                   __snake_case						       =   tempfile.mkdtemp()
                   __snake_case						       =   os.path.join(SCREAMING_SNAKE_CASE_							,       str(uuid.uuida()    ) + '.png'    )

                   img.save(self._path    )

                   return self._path







class  _lowerCamelCase			(lowerCamelCase					):

     def __init__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=16_000    ):
            super().__init__(SCREAMING_SNAKE_CASE_    )

            if not is_soundfile_availble():
                   raise ImportError('soundfile must be installed in order to handle audio.'    )

            __snake_case						       =   None
            __snake_case						       =   None

            __snake_case						       =   samplerate

            if isinstance(SCREAMING_SNAKE_CASE_							,       (str, pathlib.Path)    ):
                   __snake_case						       =   value
            elif isinstance(SCREAMING_SNAKE_CASE_							,       torch.Tensor    ):
                   __snake_case						       =   value
            else:
                   raise ValueError(f'''Unsupported audio type: {type(SCREAMING_SNAKE_CASE_    )}'''    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=None    ):
            from IPython.display import Audio, display

            display(Audio(self.to_string()							,       rate=self.samplerate    )    )

     def 						__lowerCamelCase				(  self    ):
            if self._tensor is not None:
                   return self._tensor

            if self._path is not None:
                   __snake_case       ,       __snake_case						       =   sf.read(self._path    )
                   __snake_case						       =   torch.tensor(SCREAMING_SNAKE_CASE_    )
                   return self._tensor

     def 						__lowerCamelCase				(  self    ):
            if self._path is not None:
                   return self._path

            if self._tensor is not None:
                   __snake_case						       =   tempfile.mkdtemp()
                   __snake_case						       =   os.path.join(SCREAMING_SNAKE_CASE_							,       str(uuid.uuida()    ) + '.wav'    )
                   sf.write(self._path							,       self._tensor							,       samplerate=self.samplerate    )
                   return self._path





lowerCamelCase_				:							Optional[Any]       =    {"text": AgentText, "image": AgentImage, "audio": AgentAudio}
lowerCamelCase_				:							Any       =    {str: AgentText}

if is_vision_available():
 lowerCamelCase_				:							Optional[int]       =    AgentImage
def        __lowercase(							*__snake_case				:		Optional[Any]    ,**__snake_case				:		str					)       ->   Tuple:
       __snake_case						       =   [(arg.to_raw() if isinstance(__snake_case    ,__snake_case					) else arg) for arg in args]
       __snake_case						       =   {k: (v.to_raw() if isinstance(__snake_case    ,__snake_case					) else v) for k, v in kwargs.items()}
       return args, kwargs
def        __lowercase(							__snake_case				:		Optional[int]    ,__snake_case				:		List[str]=None					)       ->   Tuple:
       if isinstance(__snake_case    ,__snake_case					):
              __snake_case						       =   {}
              for i, (k, v) in enumerate(outputs.items()					):
                     if output_types is not None:
                            # If the class has defined outputs, we can map directly according to the class definition
                            if output_types[i] in AGENT_TYPE_MAPPING:
                                   __snake_case						       =   AGENT_TYPE_MAPPING[output_types[i]](__snake_case					)
                            else:
                                   __snake_case						       =   AgentType(__snake_case					)

                     else:
                            # If the class does not have defined output, then we map according to the type
                            for _k, _v in INSTANCE_TYPE_MAPPING.items():
                                   if isinstance(__snake_case    ,_k					):
                                          __snake_case						       =   _v(__snake_case					)
                            if k not in decoded_outputs:
                                   __snake_case						       =   AgentType[v]

       elif isinstance(__snake_case    ,(list, tuple)					):
              __snake_case						       =   type(__snake_case					)()
              for i, v in enumerate(__snake_case					):
                     if output_types is not None:
                            # If the class has defined outputs, we can map directly according to the class definition
                            if output_types[i] in AGENT_TYPE_MAPPING:
                                   decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](__snake_case					)					)
                            else:
                                   decoded_outputs.append(AgentType(__snake_case					)					)
                     else:
                            # If the class does not have defined output, then we map according to the type
                            __snake_case						       =   False
                            for _k, _v in INSTANCE_TYPE_MAPPING.items():
                                   if isinstance(__snake_case    ,_k					):
                                          decoded_outputs.append(_v(__snake_case					)					)
                                          __snake_case						       =   True

                            if not found:
                                   decoded_outputs.append(AgentType(__snake_case					)					)

       else:
              if output_types[0] in AGENT_TYPE_MAPPING:
                     # If the class has defined outputs, we can map directly according to the class definition
                     __snake_case						       =   AGENT_TYPE_MAPPING[output_types[0]](__snake_case					)

              else:
                     # If the class does not have defined output, then we map according to the type
                     for _k, _v in INSTANCE_TYPE_MAPPING.items():
                            if isinstance(__snake_case    ,_k					):
                                   return _v(__snake_case					)
                     return AgentType(__snake_case					)

       return decoded_outputs
