



'''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 __A			:



 '''simple docstring'''







 __lowerCamelCase    :       CommonSchedulerState

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



 @classmethod
 def 					a__  (cls ,   A ,   A ,   A       )    ->     str:


      """simple docstring"""



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






@dataclass
class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       DDPMSchedulerState






class __A			(  A   ,   A						):



 '''simple docstring'''







 __lowerCamelCase    :       Dict          =       [e.name for e in FlaxKarrasDiffusionSchedulers]

 __lowerCamelCase    :       jnp.dtype



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


      """simple docstring"""



      return True



 @register_to_config
 def __init__(self ,   A = 1_000 ,   A = 0.0001 ,   A = 0.02 ,   A = "linear" ,   A = None ,   A = "fixed_small" ,   A = True ,   A = "epsilon" ,   A = jnp.floataa ,   )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  dtype



 def 					a__  (self ,   A = None       )    ->     DDPMSchedulerState:


      """simple docstring"""



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

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

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

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



 def 					a__  (self ,   A ,   A ,   A = None       )    ->     jnp.ndarray:


      """simple docstring"""



      return sample



 def 					a__  (self ,   A ,   A ,   A = ()       )    ->     DDPMSchedulerState:


      """simple docstring"""



      _a      =  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
      _a      =  (jnp.arange(0 ,   A       ) * step_ratio).round()[::-1]

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



 def 					a__  (self ,   A ,   A ,   A=None ,   A=None       )    ->     int:


      """simple docstring"""



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

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

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

      return variance



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


      """simple docstring"""



      _a      =  timestep

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

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

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

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

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

      _a      =  pred_prev_sample + variance

      if not return_dict:
           return (pred_prev_sample, state)

      return FlaxDDPMSchedulerOutput(prev_sample=A ,   state=A       )



 def 					a__  (self ,   A ,   A ,   A ,   A ,   )    ->     jnp.ndarray:


      """simple docstring"""



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



 def 					a__  (self ,   A ,   A ,   A ,   A ,   )    ->     jnp.ndarray:


      """simple docstring"""



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



 def __len__(self       )    ->     Tuple:


      """simple docstring"""



      return self.config.num_train_timesteps








'''simple docstring'''





from typing import Dict, Optional

import numpy as np

import datasets


lowercase_		       =  "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n"

lowercase_		       =  "\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric(\"mean_iou\")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), 'per_category_accuracy': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n"

lowercase_		       =  "\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}"




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A = None  ,	__A = False  ,	):



     """simple docstring"""



     if label_map is not None:
          for old_id, new_id in label_map.items():
               _a      =  new_id

    # turn into Numpy arrays
     _a      =  np.array(__A)
     _a      =  np.array(__A)

     if reduce_labels:
          _a      =  255
          _a      =  label - 1
          _a      =  255

     _a      =  label != ignore_index
     _a      =  np.not_equal(__A  ,	__A)
     _a      =  pred_label[mask]
     _a      =  np.array(__A)[mask]

     _a      =  pred_label[pred_label == label]

     _a      =  np.histogram(__A  ,	bins=__A  ,	range=(0, num_labels - 1))[0]
     _a      =  np.histogram(__A  ,	bins=__A  ,	range=(0, num_labels - 1))[0]
     _a      =  np.histogram(__A  ,	bins=__A  ,	range=(0, num_labels - 1))[0]

     _a      =  area_pred_label + area_label - area_intersect

     return area_intersect, area_union, area_pred_label, area_label




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A = None  ,	__A = False  ,	):



     """simple docstring"""



     _a      =  np.zeros((num_labels,)  ,	dtype=np.floataa)
     _a      =  np.zeros((num_labels,)  ,	dtype=np.floataa)
     _a      =  np.zeros((num_labels,)  ,	dtype=np.floataa)
     _a      =  np.zeros((num_labels,)  ,	dtype=np.floataa)
     for result, gt_seg_map in zip(__A  ,	__A):
          _a  ,    _a  ,    _a  ,    _a      =  intersect_and_union(
              __A  ,	__A  ,	__A  ,	__A  ,	__A  ,	__A)
          total_area_intersect += area_intersect
          total_area_union += area_union
          total_area_pred_label += area_pred_label
          total_area_label += area_label
     return total_area_intersect, total_area_union, total_area_pred_label, total_area_label




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A = None  ,	__A = None  ,	__A = False  ,	):



     """simple docstring"""



     _a  ,    _a  ,    _a  ,    _a      =  total_intersect_and_union(
         __A  ,	__A  ,	__A  ,	__A  ,	__A  ,	__A)

     # compute metrics
     _a      =  {}

     _a      =  total_area_intersect.sum() / total_area_label.sum()
     _a      =  total_area_intersect / total_area_union
     _a      =  total_area_intersect / total_area_label

     _a      =  np.nanmean(__A)
     _a      =  np.nanmean(__A)
     _a      =  all_acc
     _a      =  iou
     _a      =  acc

     if nan_to_num is not None:
          _a      =  {metric: np.nan_to_num(__A  ,	nan=__A) for metric, metric_value in metrics.items()}

     return metrics






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,   _KWARGS_DESCRIPTION						)
class __A			(  datasets.Metric						):



 '''simple docstring'''







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


      """simple docstring"""



      return datasets.MetricInfo(
          description=_DESCRIPTION ,   citation=_CITATION ,   inputs_description=_KWARGS_DESCRIPTION ,   features=datasets.Features(
              # 1st Seq - height dim, 2nd - width dim
              {
                  '''predictions''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16'''       )       )       ),
                  '''references''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16'''       )       )       ),
              }       ) ,   reference_urls=[
              '''https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'''
          ] ,   )



 def 					a__  (self ,   A ,   A ,   A ,   A ,   A = None ,   A = None ,   A = False ,   )    ->     List[Any]:


      """simple docstring"""



      _a      =  mean_iou(
          results=A ,   gt_seg_maps=A ,   num_labels=A ,   ignore_index=A ,   nan_to_num=A ,   label_map=A ,   reduce_labels=A ,   )
      return iou_result



