

"""simple docstring"""






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

from ..utils.generic import ModelOutput
class        _lowercase					(     UpperCamelCase_     ):







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      dataset
      __magic_name__     =      process
      __magic_name__     =      params







     def __len__(      self       ):
      return len(self.dataset       )







     def __getitem__(      self ,						UpperCamelCase_       ):
      __magic_name__     =      self.dataset[i]
      __magic_name__     =      self.process(UpperCamelCase__ ,						**self.params       )
      return processed


class        _lowercase					(     UpperCamelCase_     ):







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_=None       ):
      __magic_name__     =      loader
      __magic_name__     =      infer
      __magic_name__     =      params
      if loader_batch_size == 1:
       # Let's spare some time by deactivating altogether
       __magic_name__     =      None
      __magic_name__     =      loader_batch_size

      # Internal bookkeeping
      __magic_name__     =      None
      __magic_name__     =      None







     def __len__(      self       ):
      return len(self.loader       )







     def __iter__(      self       ):
      __magic_name__     =      iter(self.loader       )
      return self







     def 	lowerCAmelCase__    (      self       ):





      if isinstance(self._loader_batch_data ,						torch.Tensor       ):
       # Batch data is simple tensor, just fetch the slice
       __magic_name__     =      self._loader_batch_data[self._loader_batch_index]
      else:
       # Batch data is assumed to be BaseModelOutput (or dict)
       __magic_name__     =      {}
       for k, element in self._loader_batch_data.items():
        if isinstance(UpperCamelCase__ ,						UpperCamelCase__       ):
         # Convert ModelOutput to tuple first
         __magic_name__     =      element.to_tuple()
         if isinstance(element[0] ,						torch.Tensor       ):
          __magic_name__     =      tuple(el[self._loader_batch_index].unsqueeze(0       ) for el in element       )
         elif isinstance(element[0] ,						np.ndarray       ):
          __magic_name__     =      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(UpperCamelCase__ ,						UpperCamelCase__       ):
         # Those are stored as lists of tensors so need specific unbatching.
         if isinstance(element[0] ,						torch.Tensor       ):
          __magic_name__     =      tuple(el[self._loader_batch_index].unsqueeze(0       ) for el in element       )
         elif isinstance(element[0] ,						np.ndarray       ):
          __magic_name__     =      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
         __magic_name__     =      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

         __magic_name__     =      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
         __magic_name__     =      np.expand_dims(element[self._loader_batch_index] ,						0       )
        else:
         # This is typically a list, so no need to `unsqueeze`.
         __magic_name__     =      element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
       __magic_name__     =      self._loader_batch_data.__class__(UpperCamelCase__       )
      self._loader_batch_index += 1
      return result







     def 	lowerCAmelCase__    (      self       ):





      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
      __magic_name__     =      next(self.iterator       )
      __magic_name__     =      self.infer(UpperCamelCase__ ,						**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(UpperCamelCase__ ,						torch.Tensor       ):
        __magic_name__     =      processed
       else:
        __magic_name__     =      list(processed.keys()       )[0]
        __magic_name__     =      processed[key]
       if isinstance(UpperCamelCase__ ,						UpperCamelCase__       ):
        __magic_name__     =      len(UpperCamelCase__       )
       else:
        __magic_name__     =      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.
        __magic_name__     =      observed_batch_size
       # Setting internal index to unwrap the batch
       __magic_name__     =      processed
       __magic_name__     =      0
       return self.loader_batch_item()
      else:
       # We're not unrolling batches
       return processed


class        _lowercase					(     UpperCamelCase_     ):







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_=None       ):
      super().__init__(UpperCamelCase__ ,						UpperCamelCase__ ,						UpperCamelCase__       )







     def __iter__(      self       ):
      __magic_name__     =      iter(self.loader       )
      __magic_name__     =      None
      return self







     def 	lowerCAmelCase__    (      self       ):





      if self.subiterator is None:
       __magic_name__     =      self.infer(next(self.iterator       ) ,						**self.params       )
      try:
       # Try to return next item
       __magic_name__     =      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
       __magic_name__     =      self.infer(next(self.iterator       ) ,						**self.params       )
       __magic_name__     =      next(self.subiterator       )
      return processed


class        _lowercase					(     UpperCamelCase_     ):







     def __iter__(      self       ):
      __magic_name__     =      iter(self.loader       )
      return self







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      False
      __magic_name__     =      []
      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:
        __magic_name__     =      self.loader_batch_item()
        __magic_name__     =      item.pop('''is_last'''       )
        accumulator.append(UpperCamelCase__       )
        if is_last:
         return accumulator

      while not is_last:
       __magic_name__     =      self.infer(next(self.iterator       ) ,						**self.params       )
       if self.loader_batch_size is not None:
        if isinstance(UpperCamelCase__ ,						torch.Tensor       ):
         __magic_name__     =      processed
        else:
         __magic_name__     =      list(processed.keys()       )[0]
         __magic_name__     =      processed[key]
        if isinstance(UpperCamelCase__ ,						UpperCamelCase__       ):
         __magic_name__     =      len(UpperCamelCase__       )
        else:
         __magic_name__     =      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.
         __magic_name__     =      observed_batch_size
        __magic_name__     =      processed
        __magic_name__     =      0
        while self._loader_batch_index < self.loader_batch_size:
         __magic_name__     =      self.loader_batch_item()
         __magic_name__     =      item.pop('''is_last'''       )
         accumulator.append(UpperCamelCase__       )
         if is_last:
          return accumulator
       else:
        __magic_name__     =      processed
        __magic_name__     =      item.pop('''is_last'''       )
        accumulator.append(UpperCamelCase__       )
      return accumulator


class        _lowercase					(     UpperCamelCase_     ):







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      dataset
      __magic_name__     =      key







     def __len__(      self       ):
      return len(self.dataset       )







     def __getitem__(      self ,						UpperCamelCase_       ):
      return self.dataset[i][self.key]


class        _lowercase					(     UpperCamelCase_     ):







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      dataset
      __magic_name__     =      keya
      __magic_name__     =      keya







     def __len__(      self       ):
      return len(self.dataset       )







     def __getitem__(      self ,						UpperCamelCase_       ):
      return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}




"""simple docstring"""






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       (EulerDiscreteScheduler,)
     _lowerCamelCase     =       10







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      __magic_name__     =      {
          '''num_train_timesteps''': 1100,
          '''beta_start''': 0.0_0_0_1,
          '''beta_end''': 0.0_2,
          '''beta_schedule''': '''linear''',
      }

      config.update(**UpperCamelCase_       )
      return config







     def 	lowerCAmelCase__    (      self       ):
      for timesteps in [10, 50, 100, 1000]:
       self.check_over_configs(num_train_timesteps=UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1] ,						[0.0_0_0_2, 0.0_0_2, 0.0_2]       ):
       self.check_over_configs(beta_start=UpperCamelCase_ ,						beta_end=UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config()
      __magic_name__     =      scheduler_class(**UpperCamelCase_       )

      scheduler.set_timesteps(self.num_inference_steps       )

      __magic_name__     =      torch.manual_seed(0       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for i, t in enumerate(scheduler.timesteps       ):
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						generator=UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      assert abs(result_sum.item() - 1_0.0_8_0_7       ) < 1E-2
      assert abs(result_mean.item() - 0.0_1_3_1       ) < 1E-3







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config(prediction_type='''v_prediction'''       )
      __magic_name__     =      scheduler_class(**UpperCamelCase_       )

      scheduler.set_timesteps(self.num_inference_steps       )

      __magic_name__     =      torch.manual_seed(0       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for i, t in enumerate(scheduler.timesteps       ):
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						generator=UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      assert abs(result_sum.item() - 0.0_0_0_2       ) < 1E-2
      assert abs(result_mean.item() - 2.2_6_7_6E-0_6       ) < 1E-3







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config()
      __magic_name__     =      scheduler_class(**UpperCamelCase_       )

      scheduler.set_timesteps(self.num_inference_steps ,						device=UpperCamelCase_       )

      __magic_name__     =      torch.manual_seed(0       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for t in scheduler.timesteps:
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						generator=UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      assert abs(result_sum.item() - 1_0.0_8_0_7       ) < 1E-2
      assert abs(result_mean.item() - 0.0_1_3_1       ) < 1E-3







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config()
      __magic_name__     =      scheduler_class(**UpperCamelCase_ ,						use_karras_sigmas=UpperCamelCase_       )

      scheduler.set_timesteps(self.num_inference_steps ,						device=UpperCamelCase_       )

      __magic_name__     =      torch.manual_seed(0       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for t in scheduler.timesteps:
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						generator=UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9       ) < 1E-2
      assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3       ) < 1E-3

