"""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      UpperCAmelCase_	:
      lowerCamelCase__		:    CommonSchedulerState

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


      @classmethod
      def 		_UpperCAmelCase       (   cls		,			a		,			a		,			a							)       ->					Optional[int]:
             return cls(common=a		,			init_noise_sigma=a		,			timesteps=a							)





@dataclass
class      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    DDPMSchedulerState





class      UpperCAmelCase_	(    _a ,							_a):
      lowerCamelCase__		:    Tuple   =  [e.name for e in FlaxKarrasDiffusionSchedulers]

      lowerCamelCase__		:    jnp.dtype


      @property
      def 		_UpperCAmelCase       (   self							)       ->					str:
             return True


      @register_to_config
      def __init__(   self		,			a = 1_0_0_0		,			a = 0.0_001		,			a = 0.02		,			a = "linear"		,			a = None		,			a = "fixed_small"		,			a = True		,			a = "epsilon"		,			a = jnp.floataa		,			)       ->					str:
             lowercase__			:		List[Any]       =	dtype


      def 		_UpperCAmelCase       (   self		,			a = None							)       ->					DDPMSchedulerState:
             if common is None:
                    lowercase__			:		str       =	CommonSchedulerState.create(self							)

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

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

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


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a = None							)       ->					jnp.ndarray:
             return sample


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a = ()							)       ->					DDPMSchedulerState:
             lowercase__			:		Tuple       =	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
             lowercase__			:		Optional[int]       =	(jnp.arange(0		,			a							) * step_ratio).round()[::-1]

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


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a=None		,			a=None							)       ->					Optional[int]:
             lowercase__			:		Any       =	state.common.alphas_cumprod[t]
             lowercase__			:		Optional[int]       =	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
             lowercase__			:		List[str]       =	(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

             if variance_type is None:
                    lowercase__			:		Optional[int]       =	self.config.variance_type

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

             return variance


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a		,			a		,			a = None		,			a = True		,			)       ->					Union[FlaxDDPMSchedulerOutput, Tuple]:
             lowercase__			:		Dict       =	timestep

             if key is None:
                    lowercase__			:		Optional[int]       =	jax.random.PRNGKey(0							)

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

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

             # 6. Add noise
             def random_variance():
                    lowercase__			:		Optional[int]       =	jax.random.split(a		,			num=1							)
                    lowercase__			:		List[Any]       =	jax.random.normal(a		,			shape=model_output.shape		,			dtype=self.dtype							)
                    return (self._get_variance(a		,			a		,			predicted_variance=a							) ** 0.5) * noise

             lowercase__			:		List[str]       =	jnp.where(t > 0		,			random_variance()		,			jnp.zeros(model_output.shape		,			dtype=self.dtype							)							)

             lowercase__			:		Optional[Any]       =	pred_prev_sample + variance

             if not return_dict:
                    return (pred_prev_sample, state)

             return FlaxDDPMSchedulerOutput(prev_sample=a		,			state=a							)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a		,			a		,			)       ->					jnp.ndarray:
             return add_noise_common(state.common		,			a		,			a		,			a							)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a		,			a		,			)       ->					jnp.ndarray:
             return get_velocity_common(state.common		,			a		,			a		,			a							)


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








'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__    :      List[Any]						=    logging.get_logger(__name__)

lowercase__    :      str						=    {}


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       '''llama'''
     lowerCAmelCase					=       ['''past_key_values''']


     def __init__(						self						,	_UpperCAmelCase=3_2000						,	_UpperCAmelCase=4096						,	_UpperCAmelCase=1_1008						,	_UpperCAmelCase=32						,	_UpperCAmelCase=32						,	_UpperCAmelCase=None						,	_UpperCAmelCase="silu"						,	_UpperCAmelCase=2048						,	_UpperCAmelCase=0.02						,	_UpperCAmelCase=1e-6						,	_UpperCAmelCase=True						,	_UpperCAmelCase=0						,	_UpperCAmelCase=1						,	_UpperCAmelCase=2						,	_UpperCAmelCase=1						,	_UpperCAmelCase=False						,	_UpperCAmelCase=None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          __A						: Union[str, Any]      =       vocab_size
          __A						: Union[str, Any]      =       max_position_embeddings
          __A						: Any      =       hidden_size
          __A						: Optional[Any]      =       intermediate_size
          __A						: str      =       num_hidden_layers
          __A						: Optional[Any]      =       num_attention_heads

          # for backward compatibility
          if num_key_value_heads is None:
               __A						: List[Any]      =       num_attention_heads

          __A						: int      =       num_key_value_heads
          __A						: List[Any]      =       hidden_act
          __A						: Union[str, Any]      =       initializer_range
          __A						: List[Any]      =       rms_norm_eps
          __A						: Any      =       pretraining_tp
          __A						: Optional[Any]      =       use_cache
          __A						: Dict      =       rope_scaling
          self._rope_scaling_validation()

          super().__init__(
              pad_token_id=_UpperCAmelCase						,	bos_token_id=_UpperCAmelCase						,	eos_token_id=_UpperCAmelCase						,	tie_word_embeddings=_UpperCAmelCase						,	**_UpperCAmelCase						,	)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if self.rope_scaling is None:
               return

          if not isinstance(self.rope_scaling						,	_UpperCAmelCase) or len(self.rope_scaling) != 2:
               raise ValueError(
                   '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
                   F'got {self.rope_scaling}')
          __A						: Optional[Any]      =       self.rope_scaling.get('type'						,	_UpperCAmelCase)
          __A						: Tuple      =       self.rope_scaling.get('factor'						,	_UpperCAmelCase)
          if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
               raise ValueError(
                   F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}')
          if rope_scaling_factor is None or not isinstance(_UpperCAmelCase						,	_UpperCAmelCase) or rope_scaling_factor <= 1.0:
               raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}')