
# 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      SCREAMING_SNAKE_CASE__  :





	'''simple docstring'''



	_UpperCAmelCase :	CommonSchedulerState
	_UpperCAmelCase :	jnp.ndarray

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



	@classmethod
	def 				A							(						cls			:					Any    ,						lowercase			:					CommonSchedulerState    ,						lowercase			:					jnp.ndarray    ,						lowercase			:					jnp.ndarray    ,						lowercase			:					jnp.ndarray    ,						):


				'''simple docstring'''


				return cls(
				    common=lowercase    ,						final_alpha_cumprod=lowercase    ,						init_noise_sigma=lowercase    ,						timesteps=lowercase    ,						)




@dataclass
class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	DDIMSchedulerState




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase							,UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Any					     =      [e.name for e in FlaxKarrasDiffusionSchedulers]

	_UpperCAmelCase :	jnp.dtype



	@property
	def 				A							(						self			:					Optional[int]			):


				'''simple docstring'''


				return True



	@register_to_config
	def __init__(						self			:					List[Any]    ,						lowercase			:					int = 1_000    ,						lowercase			:					float = 0.0001    ,						lowercase			:					float = 0.02    ,						lowercase			:					str = "linear"    ,						lowercase			:					Optional[jnp.ndarray] = None    ,						lowercase			:					bool = True    ,						lowercase			:					int = 0    ,						lowercase			:					str = "epsilon"    ,						lowercase			:					jnp.dtype = jnp.floataa    ,						):


				'''simple docstring'''


				_snake_case						=    dtype



	def 				A							(						self			:					Tuple    ,						lowercase			:					Optional[CommonSchedulerState] = None			):


				'''simple docstring'''


				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=lowercase    ,						final_alpha_cumprod=lowercase    ,						init_noise_sigma=lowercase    ,						timesteps=lowercase    ,						)



	def 				A							(						self			:					Optional[int]    ,						lowercase			:					DDIMSchedulerState    ,						lowercase			:					jnp.ndarray    ,						lowercase			:					Optional[int] = None			):


				'''simple docstring'''


				return sample



	def 				A							(						self			:					Union[str, Any]    ,						lowercase			:					DDIMSchedulerState    ,						lowercase			:					int    ,						lowercase			:					Tuple = ()			):


				'''simple docstring'''


				_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    ,						lowercase			) * step_ratio).round()[::-1] + self.config.steps_offset

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



	def 				A							(						self			:					Union[str, Any]    ,						lowercase			:					DDIMSchedulerState    ,						lowercase			:					List[Any]    ,						lowercase			:					Tuple			):


				'''simple docstring'''


				_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 				A							(						self			:					Any    ,						lowercase			:					DDIMSchedulerState    ,						lowercase			:					jnp.ndarray    ,						lowercase			:					int    ,						lowercase			:					jnp.ndarray    ,						lowercase			:					float = 0.0    ,						lowercase			:					bool = True    ,						):


				'''simple docstring'''


				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]    ,						lowercase			)

				_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(lowercase    ,						lowercase    ,						lowercase			)
				_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=lowercase    ,						state=lowercase			)



	def 				A							(						self			:					int    ,						lowercase			:					DDIMSchedulerState    ,						lowercase			:					jnp.ndarray    ,						lowercase			:					jnp.ndarray    ,						lowercase			:					jnp.ndarray    ,						):


				'''simple docstring'''


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



	def 				A							(						self			:					Optional[Any]    ,						lowercase			:					DDIMSchedulerState    ,						lowercase			:					jnp.ndarray    ,						lowercase			:					jnp.ndarray    ,						lowercase			:					jnp.ndarray    ,						):


				'''simple docstring'''


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





	def __len__(						self			:					Any			):


				'''simple docstring'''


				return self.config.num_train_timesteps

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_lowerCamelCase :       Optional[int]     					=    {}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       List[str]     					=    ['''MBart50Tokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       Optional[int]     					=    ['''MBart50TokenizerFast''']


if TYPE_CHECKING:
			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_mbartaa import MBartaaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
			import sys

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