




# 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_       :
	A    							= 42

	# setable values
	A    							= 42
	A    							= 42
	A    							= None





	@classmethod
	def 						A_(							cls      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					int:




				"""simple docstring"""



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




@dataclass
class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= 42




class       a_       (     SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__     ):
	A    							= [e.name for e in FlaxKarrasDiffusionSchedulers]

	A    							= 42





	@property
	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				return True





	@register_to_config
	def __init__(							self      ,     SCREAMING_SNAKE_CASE = 1000      ,     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 = "fixed_small"      ,     SCREAMING_SNAKE_CASE = True      ,     SCREAMING_SNAKE_CASE = "epsilon"      ,     SCREAMING_SNAKE_CASE = jnp.floataa      ,     )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						dtype





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE = None      )  ->					DDPMSchedulerState:




				"""simple docstring"""



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

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

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

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





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = None      )  ->					jnp.ndarray:




				"""simple docstring"""



				return sample





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = ()      )  ->					DDPMSchedulerState:




				"""simple docstring"""



				SCREAMING_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
				SCREAMING_SNAKE_CASE_								=						(jnp.arange(0      ,     SCREAMING_SNAKE_CASE      ) * step_ratio).round()[::-1]

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





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      )  ->					Any:




				"""simple docstring"""



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

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

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

				return variance





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = True      ,     )  ->					Union[FlaxDDPMSchedulerOutput, Tuple]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						timestep

				if key is None:
							SCREAMING_SNAKE_CASE_								=						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_						,		SCREAMING_SNAKE_CASE_								=						jnp.split(SCREAMING_SNAKE_CASE      ,     sample.shape[1]      ,     axis=1      )
				else:
							SCREAMING_SNAKE_CASE_								=						None

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

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

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

				SCREAMING_SNAKE_CASE_								=						pred_prev_sample + variance

				if not return_dict:
							return (pred_prev_sample, state)

				return FlaxDDPMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE      ,     state=SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     )  ->					jnp.ndarray:




				"""simple docstring"""



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





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     )  ->					jnp.ndarray:




				"""simple docstring"""



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





	def __len__(							self      )  ->					List[str]:




				"""simple docstring"""



				return self.config.num_train_timesteps






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__     :     str		    =       {
    "configuration_altclip": [
        "ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AltCLIPConfig",
        "AltCLIPTextConfig",
        "AltCLIPVisionConfig",
    ],
    "processing_altclip": ["AltCLIPProcessor"],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				SCREAMING_SNAKE_CASE__     :     Any		    =       [
				    "ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "AltCLIPPreTrainedModel",
				    "AltCLIPModel",
				    "AltCLIPTextModel",
				    "AltCLIPVisionModel",
				]


if TYPE_CHECKING:
				from .configuration_altclip import (
				    ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    AltCLIPConfig,
				    AltCLIPTextConfig,
				    AltCLIPVisionConfig,
				)
				from .processing_altclip import AltCLIPProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_altclip import (
								    ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AltCLIPModel,
								    AltCLIPPreTrainedModel,
								    AltCLIPTextModel,
								    AltCLIPVisionModel,
								)


else:
				import sys

				SCREAMING_SNAKE_CASE__     :     Tuple		    =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
