





# 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       lowerCAmelCase__:






       '''simple docstring'''


       __snake_case			      =     4_2

       # setable values
       __snake_case			      =     4_2
       __snake_case			      =     4_2
       __snake_case			      =     None




       @classmethod
       def      UpperCamelCase_      (   cls     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase				)      ->  List[str]:
            return cls(common=_lowerCamelCase     ,    init_noise_sigma=_lowerCamelCase     ,    timesteps=_lowerCamelCase				)





@dataclass
class       lowerCAmelCase__(			_snake_case     ):






       '''simple docstring'''


       __snake_case			      =     4_2





class       lowerCAmelCase__(			_snake_case	,  _snake_case     ):






       '''simple docstring'''


       __snake_case			      =     [e.name for e in FlaxKarrasDiffusionSchedulers]

       __snake_case			      =     4_2




       @property
       def      UpperCamelCase_      (   self				)      ->  Dict:
            return True




       @register_to_config
       def __init__(   self     ,    __lowerCamelCase = 1_0_0_0     ,    __lowerCamelCase = 0.0001     ,    __lowerCamelCase = 0.02     ,    __lowerCamelCase = "linear"     ,    __lowerCamelCase = None     ,    __lowerCamelCase = "fixed_small"     ,    __lowerCamelCase = True     ,    __lowerCamelCase = "epsilon"     ,    __lowerCamelCase = jnp.floataa     ,    )      ->  Optional[int]:
            _SCREAMING_SNAKE_CASE							:						List[Any]         =				dtype




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase = None				)      ->  Optional[int]:
            if common is None:
                 _SCREAMING_SNAKE_CASE							:						Tuple         =				CommonSchedulerState.create(self				)

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

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

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




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase = None				)      ->  int:
            return sample




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase = ()				)      ->  Tuple:
            _SCREAMING_SNAKE_CASE							:						Optional[int]         =				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
            _SCREAMING_SNAKE_CASE							:						List[str]         =				(jnp.arange(0     ,    _lowerCamelCase				) * step_ratio).round()[::-1]

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




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase=None     ,    __lowerCamelCase=None				)      ->  str:
            _SCREAMING_SNAKE_CASE							:						List[str]         =				state.common.alphas_cumprod[t]
            _SCREAMING_SNAKE_CASE							:						Tuple         =				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
            _SCREAMING_SNAKE_CASE							:						List[Any]         =				(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

            if variance_type is None:
                 _SCREAMING_SNAKE_CASE							:						int         =				self.config.variance_type

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

            return variance




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase = None     ,    __lowerCamelCase = True     ,    )      ->  Optional[int]:
            _SCREAMING_SNAKE_CASE							:						Tuple         =				timestep

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

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

            # 1. compute alphas, betas
            _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				state.common.alphas_cumprod[t]
            _SCREAMING_SNAKE_CASE							:						List[Any]         =				jnp.where(t > 0     ,    state.common.alphas_cumprod[t - 1]     ,    jnp.array(1.0     ,    dtype=self.dtype				)				)
            _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				1 - alpha_prod_t
            _SCREAMING_SNAKE_CASE							:						Dict         =				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":
                 _SCREAMING_SNAKE_CASE							:						Any         =				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
            elif self.config.prediction_type == "sample":
                 _SCREAMING_SNAKE_CASE							:						List[str]         =				model_output
            elif self.config.prediction_type == "v_prediction":
                 _SCREAMING_SNAKE_CASE							:						List[Any]         =				(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:
                 _SCREAMING_SNAKE_CASE							:						str         =				jnp.clip(_lowerCamelCase     ,    -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
            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
            _SCREAMING_SNAKE_CASE							:						Tuple         =				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
            _SCREAMING_SNAKE_CASE							:						Tuple         =				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

            # 6. Add noise
            def random_variance():
                 _SCREAMING_SNAKE_CASE							:						str         =				jax.random.split(_lowerCamelCase     ,    num=1				)
                 _SCREAMING_SNAKE_CASE							:						List[str]         =				jax.random.normal(_lowerCamelCase     ,    shape=model_output.shape     ,    dtype=self.dtype				)
                 return (self._get_variance(_lowerCamelCase     ,    _lowerCamelCase     ,    predicted_variance=_lowerCamelCase				) ** 0.5) * noise

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

            _SCREAMING_SNAKE_CASE							:						Tuple         =				pred_prev_sample + variance

            if not return_dict:
                 return (pred_prev_sample, state)

            return FlaxDDPMSchedulerOutput(prev_sample=_lowerCamelCase     ,    state=_lowerCamelCase				)




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    )      ->  Optional[Any]:
            return add_noise_common(state.common     ,    _lowerCamelCase     ,    _lowerCamelCase     ,    _lowerCamelCase				)




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    )      ->  Optional[Any]:
            return get_velocity_common(state.common     ,    _lowerCamelCase     ,    _lowerCamelCase     ,    _lowerCamelCase				)







       def __len__(   self				)      ->  Any:
            return self.config.num_train_timesteps



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


snake_case							:       Any										=	{
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case							:       List[Any]										=	[
         '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTNeoForCausalLM''',
         '''GPTNeoForQuestionAnswering''',
         '''GPTNeoForSequenceClassification''',
         '''GPTNeoForTokenClassification''',
         '''GPTNeoModel''',
         '''GPTNeoPreTrainedModel''',
         '''load_tf_weights_in_gpt_neo''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case							:       Any										=	[
         '''FlaxGPTNeoForCausalLM''',
         '''FlaxGPTNeoModel''',
         '''FlaxGPTNeoPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_neo import (
              GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTNeoForCausalLM,
              GPTNeoForQuestionAnswering,
              GPTNeoForSequenceClassification,
              GPTNeoForTokenClassification,
              GPTNeoModel,
              GPTNeoPreTrainedModel,
              load_tf_weights_in_gpt_neo,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
     import sys

     snake_case							:       Optional[int]										=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





