


'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput




class      lowerCamelCase_  (      __a ):






      def __init__(   self							:       Union[str, Any]		,		_A							:       int		,		_A							:       List[str]		,		_A							:       Optional[int]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[int]						=							dataset
          UpperCAmelCase__     :						List[Any]						=							process
          UpperCAmelCase__     :						List[Any]						=							params






      def __len__(   self							:       List[Any]					):


          '''simple docstring'''


          return len(self.dataset					)






      def __getitem__(   self							:       Tuple		,		_A							:       int					):


          '''simple docstring'''


          UpperCAmelCase__     :						Dict						=							self.dataset[i]
          UpperCAmelCase__     :						str						=							self.process(_A		,		**self.params					)
          return processed




class      lowerCamelCase_  (      __a ):






      def __init__(   self							:       Tuple		,		_A							:       int		,		_A							:       Tuple		,		_A							:       Optional[int]		,		_A							:       Union[str, Any]=None					):


          '''simple docstring'''


          UpperCAmelCase__     :						Dict						=							loader
          UpperCAmelCase__     :						Tuple						=							infer
          UpperCAmelCase__     :						Tuple						=							params
          if loader_batch_size == 1:
              # Let's spare some time by deactivating altogether
              UpperCAmelCase__     :						Optional[Any]						=							None
          UpperCAmelCase__     :						List[Any]						=							loader_batch_size

          # Internal bookkeeping
          UpperCAmelCase__     :						Optional[Any]						=							None
          UpperCAmelCase__     :						str						=							None






      def __len__(   self							:       Tuple					):


          '''simple docstring'''


          return len(self.loader					)






      def __iter__(   self							:       List[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[str]						=							iter(self.loader					)
          return self






      def lowercase_				(   self							:       Tuple					):


          '''simple docstring'''


          if isinstance(self._loader_batch_data		,		torch.Tensor					):
              # Batch data is simple tensor, just fetch the slice
              UpperCAmelCase__     :						Dict						=							self._loader_batch_data[self._loader_batch_index]
          else:
              # Batch data is assumed to be BaseModelOutput (or dict)
              UpperCAmelCase__     :						Optional[int]						=							{}
              for k, element in self._loader_batch_data.items():
                  if isinstance(_A		,		_A					):
                      # Convert ModelOutput to tuple first
                      UpperCAmelCase__     :						Dict						=							element.to_tuple()
                      if isinstance(element[0]		,		torch.Tensor					):
                          UpperCAmelCase__     :						Optional[int]						=							tuple(el[self._loader_batch_index].unsqueeze(0					) for el in element					)
                      elif isinstance(element[0]		,		np.ndarray					):
                          UpperCAmelCase__     :						Dict						=							tuple(np.expand_dims(el[self._loader_batch_index]		,		0					) for el in element					)
                      continue
                  if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(_A		,		_A					):
                      # Those are stored as lists of tensors so need specific unbatching.
                      if isinstance(element[0]		,		torch.Tensor					):
                          UpperCAmelCase__     :						Any						=							tuple(el[self._loader_batch_index].unsqueeze(0					) for el in element					)
                      elif isinstance(element[0]		,		np.ndarray					):
                          UpperCAmelCase__     :						int						=							tuple(np.expand_dims(el[self._loader_batch_index]		,		0					) for el in element					)
                      continue
                  if element is None:
                      # This can happen for optional data that get passed around
                      UpperCAmelCase__     :						Any						=							None
                  elif isinstance(element[self._loader_batch_index]		,		torch.Tensor					):
                      # Take correct batch data, but make it looked like batch_size=1
                      # For compatibility with other methods within transformers

                      UpperCAmelCase__     :						Union[str, Any]						=							element[self._loader_batch_index].unsqueeze(0					)
                  elif isinstance(element[self._loader_batch_index]		,		np.ndarray					):
                      # Take correct batch data, but make it looked like batch_size=1
                      # For compatibility with other methods within transformers
                      UpperCAmelCase__     :						Any						=							np.expand_dims(element[self._loader_batch_index]		,		0					)
                  else:
                      # This is typically a list, so no need to `unsqueeze`.
                      UpperCAmelCase__     :						str						=							element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
              UpperCAmelCase__     :						Optional[int]						=							self._loader_batch_data.__class__(_A					)
          self._loader_batch_index += 1
          return result






      def lowercase_				(   self							:       str					):


          '''simple docstring'''


          if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
              # We are currently unrolling a batch so we just need to return
              # the current item within a batch
              return self.loader_batch_item()

          # We're out of items within a batch
          UpperCAmelCase__     :						Dict						=							next(self.iterator					)
          UpperCAmelCase__     :						Tuple						=							self.infer(_A		,		**self.params					)
          # We now have a batch of "inferred things".
          if self.loader_batch_size is not None:
              # Try to infer the size of the batch
              if isinstance(_A		,		torch.Tensor					):
                  UpperCAmelCase__     :						str						=							processed
              else:
                  UpperCAmelCase__     :						Dict						=							list(processed.keys()					)[0]
                  UpperCAmelCase__     :						List[Any]						=							processed[key]
              if isinstance(_A		,		_A					):
                  UpperCAmelCase__     :						Optional[Any]						=							len(_A					)
              else:
                  UpperCAmelCase__     :						Dict						=							first_tensor.shape[0]
              if 0 < observed_batch_size < self.loader_batch_size:
                  # could be last batch so we can't unroll as many
                  # elements.
                  UpperCAmelCase__     :						Dict						=							observed_batch_size
              # Setting internal index to unwrap the batch
              UpperCAmelCase__     :						List[str]						=							processed
              UpperCAmelCase__     :						List[str]						=							0
              return self.loader_batch_item()
          else:
              # We're not unrolling batches
              return processed




class      lowerCamelCase_  (      __a ):






      def __init__(   self							:       Union[str, Any]		,		_A							:       Tuple		,		_A							:       Union[str, Any]		,		_A							:       Optional[Any]		,		_A							:       str=None					):


          '''simple docstring'''


          super().__init__(_A		,		_A		,		_A					)






      def __iter__(   self							:       Optional[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						int						=							iter(self.loader					)
          UpperCAmelCase__     :						Union[str, Any]						=							None
          return self






      def lowercase_				(   self							:       str					):


          '''simple docstring'''


          if self.subiterator is None:
              UpperCAmelCase__     :						Optional[Any]						=							self.infer(next(self.iterator					)		,		**self.params					)
          try:
              # Try to return next item
              UpperCAmelCase__     :						Any						=							next(self.subiterator					)
          except StopIteration:
              # When a preprocess iterator ends, we can start lookig at the next item
              # ChunkIterator will keep feeding until ALL elements of iterator
              # all have created their subiterator and have been iterating against.
              #
              # Another way to look at it, is we're basically flattening lists of lists
              # into a single list, but with generators
              UpperCAmelCase__     :						Optional[Any]						=							self.infer(next(self.iterator					)		,		**self.params					)
              UpperCAmelCase__     :						List[Any]						=							next(self.subiterator					)
          return processed




class      lowerCamelCase_  (      __a ):






      def __iter__(   self							:       Optional[int]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Dict						=							iter(self.loader					)
          return self






      def lowercase_				(   self							:       int					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[str]						=							False
          UpperCAmelCase__     :						str						=							[]
          if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
              while self._loader_batch_index < self.loader_batch_size:
                  UpperCAmelCase__     :						Optional[Any]						=							self.loader_batch_item()
                  UpperCAmelCase__     :						Dict						=							item.pop('''is_last'''					)
                  accumulator.append(_A					)
                  if is_last:
                      return accumulator

          while not is_last:
              UpperCAmelCase__     :						List[str]						=							self.infer(next(self.iterator					)		,		**self.params					)
              if self.loader_batch_size is not None:
                  if isinstance(_A		,		torch.Tensor					):
                      UpperCAmelCase__     :						Union[str, Any]						=							processed
                  else:
                      UpperCAmelCase__     :						List[str]						=							list(processed.keys()					)[0]
                      UpperCAmelCase__     :						List[Any]						=							processed[key]
                  if isinstance(_A		,		_A					):
                      UpperCAmelCase__     :						Tuple						=							len(_A					)
                  else:
                      UpperCAmelCase__     :						List[Any]						=							first_tensor.shape[0]
                  if 0 < observed_batch_size < self.loader_batch_size:
                      # could be last batch so we can't unroll as many
                      # elements.
                      UpperCAmelCase__     :						int						=							observed_batch_size
                  UpperCAmelCase__     :						str						=							processed
                  UpperCAmelCase__     :						List[str]						=							0
                  while self._loader_batch_index < self.loader_batch_size:
                      UpperCAmelCase__     :						Tuple						=							self.loader_batch_item()
                      UpperCAmelCase__     :						List[Any]						=							item.pop('''is_last'''					)
                      accumulator.append(_A					)
                      if is_last:
                          return accumulator
              else:
                  UpperCAmelCase__     :						int						=							processed
                  UpperCAmelCase__     :						List[str]						=							item.pop('''is_last'''					)
                  accumulator.append(_A					)
          return accumulator




class      lowerCamelCase_  (      __a ):






      def __init__(   self							:       List[Any]		,		_A							:       Dataset		,		_A							:       str					):


          '''simple docstring'''


          UpperCAmelCase__     :						Dict						=							dataset
          UpperCAmelCase__     :						str						=							key






      def __len__(   self							:       List[Any]					):


          '''simple docstring'''


          return len(self.dataset					)






      def __getitem__(   self							:       Dict		,		_A							:       Union[str, Any]					):


          '''simple docstring'''


          return self.dataset[i][self.key]




class      lowerCamelCase_  (      __a ):






      def __init__(   self							:       Optional[int]		,		_A							:       Dataset		,		_A							:       str		,		_A							:       str					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[Any]						=							dataset
          UpperCAmelCase__     :						List[str]						=							keya
          UpperCAmelCase__     :						int						=							keya






      def __len__(   self							:       Tuple					):


          '''simple docstring'''


          return len(self.dataset					)






      def __getitem__(   self							:       Optional[int]		,		_A							:       Optional[int]					):


          '''simple docstring'''


          return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

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




class 		__A   (      UpperCamelCase__       ,    UpperCamelCase__      ):
						UpperCamelCase      =       1

						@register_to_config
						def __init__(		self :Any     ,			__snake_case :Tuple=20_00     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :Any=20     ,			__snake_case :Optional[int]=1E-3			):
										'''simple docstring'''

										__magic_name__						:						Dict										=None
										__magic_name__						:						List[str]										=None
										__magic_name__						:						str										=None

						def      A__    (		self :Dict     ,			__snake_case :Optional[int]     ,			__snake_case :Union[str, torch.device] = None			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=torch.linspace(1     ,			self.config.sampling_eps     ,			__snake_case     ,			device=__snake_case			)

						def      A__    (		self :List[str]     ,			__snake_case :List[str]     ,			__snake_case :int     ,			__snake_case :int     ,			__snake_case :List[str]=None			):
										'''simple docstring'''

										if self.timesteps is None:
														raise ValueError(
														    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""			)

										# TODO(Patrick) better comments + non-PyTorch
										# postprocess model score
										__magic_name__						:						int										=(
										    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
										)
										__magic_name__						:						Optional[int]										=torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff			)			)
										__magic_name__						:						str										=std.flatten()
										while len(std.shape			) < len(score.shape			):
														__magic_name__						:						List[str]										=std.unsqueeze(-1			)
										__magic_name__						:						Union[str, Any]										=-score / std

										# compute
										__magic_name__						:						Tuple										=-1.0 / len(self.timesteps			)

										__magic_name__						:						int										=self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
										__magic_name__						:						Dict										=beta_t.flatten()
										while len(beta_t.shape			) < len(x.shape			):
														__magic_name__						:						Any										=beta_t.unsqueeze(-1			)
										__magic_name__						:						Dict										=-0.5 * beta_t * x

										__magic_name__						:						Optional[int]										=torch.sqrt(__snake_case			)
										__magic_name__						:						int										=drift - diffusion**2 * score
										__magic_name__						:						List[str]										=x + drift * dt

										# add noise
										__magic_name__						:						Optional[int]										=randn_tensor(x.shape     ,			layout=x.layout     ,			generator=__snake_case     ,			device=x.device     ,			dtype=x.dtype			)
										__magic_name__						:						Optional[Any]										=x_mean + diffusion * math.sqrt(-dt			) * noise

										return x, x_mean


						def __len__(		self :List[Any]			):
										'''simple docstring'''

										return self.config.num_train_timesteps




