



"""simple docstring"""






from typing import List

import numpy as np






def        snake_case_  (      A_					:		List[Any]						):


       '''simple docstring'''





       _lowerCamelCase						:				Dict    			= {key: len(A__						) for key, value in gen_kwargs.items() if isinstance(A__,				A__						)}
       if len(set(lists_lengths.values()						)						) > 1:
              raise RuntimeError(
                  (
                      '''Sharding is ambiguous for this dataset: '''
                      + '''we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'''
                      + '''\n'''.join(F'''\t- key {key} has length {length}''' for key, length in lists_lengths.items()						)
                      + '''\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '''
                      + '''and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'''
                  )						)
       _lowerCamelCase						:				Any    			= max(lists_lengths.values(),				default=0						)
       return max(1,				A__						)






def        snake_case_  (      A_					:		Dict,				A_					:		Union[str, Any]						):


       '''simple docstring'''





       _lowerCamelCase						:				Optional[Any]    			= []
       for group_idx in range(A__						):
              _lowerCamelCase						:				Tuple    			= num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
              if num_shards_to_add == 0:
                     break
              _lowerCamelCase						:				Optional[int]    			= shards_indices_per_group[-1].stop if shards_indices_per_group else 0
              _lowerCamelCase						:				Any    			= range(A__,				start + num_shards_to_add						)
              shards_indices_per_group.append(A__						)
       return shards_indices_per_group






def        snake_case_  (      A_					:		List[Any],				A_					:		List[Any]						):


       '''simple docstring'''





       _lowerCamelCase						:				List[Any]    			= _number_of_shards_in_gen_kwargs(A__						)
       if num_shards == 1:
              return [dict(A__						)]
       else:
              _lowerCamelCase						:				Any    			= _distribute_shards(num_shards=A__,				max_num_jobs=A__						)
              return [
                  {
                      key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                      if isinstance(A__,				A__						)
                      else value
                      for key, value in gen_kwargs.items()
                  }
                  for group_idx in range(len(A__						)						)
              ]






def        snake_case_  (      A_					:		Any						):


       '''simple docstring'''





       return {
           key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
           if isinstance(gen_kwargs_list[0][key],				A__						)
           else gen_kwargs_list[0][key]
           for key in gen_kwargs_list[0]
       }







def        snake_case_  (      A_					:		Optional[int],				A_					:		Any						):


       '''simple docstring'''





       _lowerCamelCase						:				List[Any]    			= {len(A__						) for value in gen_kwargs.values() if isinstance(A__,				A__						)}
       _lowerCamelCase						:				Any    			= {}
       for size in list_sizes:
              _lowerCamelCase						:				List[Any]    			= list(range(A__						)						)
              rng.shuffle(indices_per_size[size]						)
       # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
       _lowerCamelCase						:				List[str]    			= dict(A__						)
       for key, value in shuffled_kwargs.items():
              if isinstance(A__,				A__						):
                     _lowerCamelCase						:				Any    			= [value[i] for i in indices_per_size[len(A__						)]]
       return shuffled_kwargs







'''simple docstring'''


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  None

class  SCREAMING_SNAKE_CASE (  _a   ,						_a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  2






    @register_to_config
    def __init__(  self      :					Union[str, Any]  ,						UpperCamelCase__      :					float = 0.0_2  ,						UpperCamelCase__      :					float = 1_0_0  ,						UpperCamelCase__      :					float = 1.0_0_7  ,						UpperCamelCase__      :					float = 8_0  ,						UpperCamelCase__      :					float = 0.0_5  ,						UpperCamelCase__      :					float = 5_0  ,						):


       """simple docstring"""







       UpperCamelCase					=  sigma_max

       # setable values
       UpperCamelCase					=  None
       UpperCamelCase					=  None
       UpperCamelCase					=  None  # sigma(t_i)






    def  A       (  self      :					str  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					Optional[int] = None ):


       """simple docstring"""







       return sample






    def  A       (  self      :					Union[str, Any]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Union[str, torch.device] = None ):


       """simple docstring"""







       UpperCamelCase					=  num_inference_steps
       UpperCamelCase					=  np.arange(0  ,						self.num_inference_steps )[::-1].copy()
       UpperCamelCase					=  torch.from_numpy(UpperCamelCase__ ).to(UpperCamelCase__ )
       UpperCamelCase					=  [
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       UpperCamelCase					=  torch.tensor(UpperCamelCase__  ,						dtype=torch.floataa  ,						device=UpperCamelCase__ )






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					Optional[torch.Generator] = None ):


       """simple docstring"""







       if self.config.s_min <= sigma <= self.config.s_max:
          UpperCamelCase					=  min(self.config.s_churn / self.num_inference_steps  ,						2**0.5 - 1 )
       else:
          UpperCamelCase					=  0

       # sample eps ~ N(0, S_noise^2 * I)
       UpperCamelCase					=  self.config.s_noise * randn_tensor(sample.shape  ,						generator=UpperCamelCase__ ).to(sample.device )
       UpperCamelCase					=  sigma + gamma * sigma
       UpperCamelCase					=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat






    def  A       (  self      :					str  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					bool = True  ,						):


       """simple docstring"""







       UpperCamelCase					=  sample_hat + sigma_hat * model_output
       UpperCamelCase					=  (sample_hat - pred_original_sample) / sigma_hat
       UpperCamelCase					=  sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
          return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=UpperCamelCase__  ,						derivative=UpperCamelCase__  ,						pred_original_sample=UpperCamelCase__ )






    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					bool = True  ,						):


       """simple docstring"""







       UpperCamelCase					=  sample_prev + sigma_prev * model_output
       UpperCamelCase					=  (sample_prev - pred_original_sample) / sigma_prev
       UpperCamelCase					=  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
          return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=UpperCamelCase__  ,						derivative=UpperCamelCase__  ,						pred_original_sample=UpperCamelCase__ )






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					str ):


       """simple docstring"""







       raise NotImplementedError()

