
"""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   _lowercase						:


     '''simple docstring'''





     _A								=							42

     # setable values
     _A								=							42
     _A								=							42
     _A								=							None



     @classmethod
     def  lowerCAmelCase__       (      cls ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Union[str, Any]:
          return cls(common=__UpperCamelCase ,			init_noise_sigma=__UpperCamelCase ,			timesteps=__UpperCamelCase  )







@dataclass
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							42







class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							[e.name for e in FlaxKarrasDiffusionSchedulers]

     _A								=							42



     @property
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          return True



     @register_to_config
     def __init__(      self ,			__UpperCamelCase = 10_00 ,			__UpperCamelCase = 0.0001 ,			__UpperCamelCase = 0.02 ,			__UpperCamelCase = "linear" ,			__UpperCamelCase = None ,			__UpperCamelCase = "fixed_small" ,			__UpperCamelCase = True ,			__UpperCamelCase = "epsilon" ,			__UpperCamelCase = jnp.floataa ,			)->					List[str]:
          UpperCAmelCase__							: int 							=						dtype



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase = None  )->					DDPMSchedulerState:
          if common is None:
               UpperCAmelCase__							: int 							=						CommonSchedulerState.create(self  )

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

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

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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					jnp.ndarray:
          return sample



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = ()  )->					DDPMSchedulerState:
          UpperCAmelCase__							: Dict 							=						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
          UpperCAmelCase__							: Optional[int] 							=						(jnp.arange(0 ,			__UpperCamelCase  ) * step_ratio).round()[::-1]

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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase=None ,			__UpperCamelCase=None  )->					Union[str, Any]:
          UpperCAmelCase__							: Optional[Any] 							=						state.common.alphas_cumprod[t]
          UpperCAmelCase__							: 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
          UpperCAmelCase__							: Any 							=						(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

          if variance_type is None:
               UpperCAmelCase__							: Union[str, Any] 							=						self.config.variance_type

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

          return variance



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = True ,			)->					Union[FlaxDDPMSchedulerOutput, Tuple]:
          UpperCAmelCase__							: List[str] 							=						timestep

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

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

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

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

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

          UpperCAmelCase__							: Any 							=						pred_prev_sample + variance

          if not return_dict:
               return (pred_prev_sample, state)

          return FlaxDDPMSchedulerOutput(prev_sample=__UpperCamelCase ,			state=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					jnp.ndarray:
          return add_noise_common(state.common ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					jnp.ndarray:
          return get_velocity_common(state.common ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



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





"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
    import PIL

A__      :	Dict          =    logging.get_logger(__name__)



def 							a__ (				lowerCAmelCase	:       Optional[Any]		):






     '''simple docstring'''
     if isinstance(lowerCAmelCase       ,    (list, tuple)		) and isinstance(videos[0]       ,    (list, tuple)		) and is_valid_image(videos[0][0]		):
          return videos

     elif isinstance(lowerCAmelCase       ,    (list, tuple)		) and is_valid_image(videos[0]		):
          return [videos]

     elif is_valid_image(lowerCAmelCase		):
          return [[videos]]

     raise ValueError(F"Could not make batched video from {videos}"		)







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							['pixel_values']



     def __init__(      self ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			__UpperCamelCase = PILImageResampling.BILINEAR ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			__UpperCamelCase = True ,			__UpperCamelCase = 1 / 2_55 ,			__UpperCamelCase = True ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					None:
          super().__init__(**__UpperCamelCase  )
          UpperCAmelCase__							: Optional[Any] 							=						size if size is not None else {"shortest_edge": 2_56}
          UpperCAmelCase__							: List[Any] 							=						get_size_dict(__UpperCamelCase ,			default_to_square=__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
          UpperCAmelCase__							: int 							=						get_size_dict(__UpperCamelCase ,			param_name="crop_size"  )

          UpperCAmelCase__							: Dict 							=						do_resize
          UpperCAmelCase__							: Optional[int] 							=						size
          UpperCAmelCase__							: List[Any] 							=						do_center_crop
          UpperCAmelCase__							: str 							=						crop_size
          UpperCAmelCase__							: Optional[int] 							=						resample
          UpperCAmelCase__							: int 							=						do_rescale
          UpperCAmelCase__							: Union[str, Any] 							=						rescale_factor
          UpperCAmelCase__							: Union[str, Any] 							=						offset
          UpperCAmelCase__							: Dict 							=						do_normalize
          UpperCAmelCase__							: int 							=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCAmelCase__							: Union[str, Any] 							=						image_std if image_std is not None else IMAGENET_STANDARD_STD



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = PILImageResampling.BILINEAR ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					np.ndarray:
          UpperCAmelCase__							: Optional[int] 							=						get_size_dict(__UpperCamelCase ,			default_to_square=__UpperCamelCase  )
          if "shortest_edge" in size:
               UpperCAmelCase__							: Union[str, Any] 							=						get_resize_output_image_size(__UpperCamelCase ,			size["shortest_edge"] ,			default_to_square=__UpperCamelCase  )
          elif "height" in size and "width" in size:
               UpperCAmelCase__							: Any 							=						(size["height"], size["width"])
          else:
               raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"  )
          return resize(__UpperCamelCase ,			size=__UpperCamelCase ,			resample=__UpperCamelCase ,			data_format=__UpperCamelCase ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					np.ndarray:
          UpperCAmelCase__							: Optional[Any] 							=						get_size_dict(__UpperCamelCase  )
          if "height" not in size or "width" not in size:
               raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}"  )
          return center_crop(__UpperCamelCase ,			size=(size["height"], size["width"]) ,			data_format=__UpperCamelCase ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					Tuple:
          UpperCAmelCase__							: str 							=						image.astype(np.floataa  )
          if offset:
               UpperCAmelCase__							: Tuple 							=						image - (scale / 2)
          return rescale(__UpperCamelCase ,			scale=__UpperCamelCase ,			data_format=__UpperCamelCase ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					np.ndarray:
          return normalize(__UpperCamelCase ,			mean=__UpperCamelCase ,			std=__UpperCamelCase ,			data_format=__UpperCamelCase ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = ChannelDimension.FIRST ,			)->					np.ndarray:
          if do_resize and size is None or resample is None:
               raise ValueError("Size and resample must be specified if do_resize is True."  )

          if do_center_crop and crop_size is None:
               raise ValueError("Crop size must be specified if do_center_crop is True."  )

          if do_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True."  )

          if do_normalize and (image_mean is None or image_std is None):
               raise ValueError("Image mean and std must be specified if do_normalize is True."  )

          if offset and not do_rescale:
               raise ValueError("For offset, do_rescale must also be set to True."  )

          # All transformations expect numpy arrays.
          UpperCAmelCase__							: Optional[Any] 							=						to_numpy_array(__UpperCamelCase  )

          if do_resize:
               UpperCAmelCase__							: Union[str, Any] 							=						self.resize(image=__UpperCamelCase ,			size=__UpperCamelCase ,			resample=__UpperCamelCase  )

          if do_center_crop:
               UpperCAmelCase__							: int 							=						self.center_crop(__UpperCamelCase ,			size=__UpperCamelCase  )

          if do_rescale:
               UpperCAmelCase__							: List[str] 							=						self.rescale(image=__UpperCamelCase ,			scale=__UpperCamelCase ,			offset=__UpperCamelCase  )

          if do_normalize:
               UpperCAmelCase__							: List[Any] 							=						self.normalize(image=__UpperCamelCase ,			mean=__UpperCamelCase ,			std=__UpperCamelCase  )

          UpperCAmelCase__							: Dict 							=						to_channel_dimension_format(__UpperCamelCase ,			__UpperCamelCase  )
          return image



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = ChannelDimension.FIRST ,			**__UpperCamelCase ,			)->					PIL.Image.Image:
          UpperCAmelCase__							: Optional[int] 							=						do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase__							: int 							=						resample if resample is not None else self.resample
          UpperCAmelCase__							: Tuple 							=						do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCAmelCase__							: int 							=						do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase__							: Any 							=						rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase__							: Optional[int] 							=						offset if offset is not None else self.offset
          UpperCAmelCase__							: Dict 							=						do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase__							: Dict 							=						image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase__							: Optional[int] 							=						image_std if image_std is not None else self.image_std

          UpperCAmelCase__							: List[str] 							=						size if size is not None else self.size
          UpperCAmelCase__							: Optional[int] 							=						get_size_dict(__UpperCamelCase ,			default_to_square=__UpperCamelCase  )
          UpperCAmelCase__							: Dict 							=						crop_size if crop_size is not None else self.crop_size
          UpperCAmelCase__							: Tuple 							=						get_size_dict(__UpperCamelCase ,			param_name="crop_size"  )

          if not valid_images(__UpperCamelCase  ):
               raise ValueError(
                   "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                   "torch.Tensor, tf.Tensor or jax.ndarray."  )

          UpperCAmelCase__							: List[str] 							=						make_batched(__UpperCamelCase  )

          UpperCAmelCase__							: Optional[Any] 							=						[
              [
                  self._preprocess_image(
                      image=__UpperCamelCase ,			do_resize=__UpperCamelCase ,			size=__UpperCamelCase ,			resample=__UpperCamelCase ,			do_center_crop=__UpperCamelCase ,			crop_size=__UpperCamelCase ,			do_rescale=__UpperCamelCase ,			rescale_factor=__UpperCamelCase ,			offset=__UpperCamelCase ,			do_normalize=__UpperCamelCase ,			image_mean=__UpperCamelCase ,			image_std=__UpperCamelCase ,			data_format=__UpperCamelCase ,			)
                  for img in video
              ]
              for video in videos
          ]

          UpperCAmelCase__							: Dict 							=						{"pixel_values": videos}
          return BatchFeature(data=__UpperCamelCase ,			tensor_type=__UpperCamelCase  )



