


'''simple docstring'''






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

from ..utils.generic import ModelOutput




class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Union[str, Any]:
      _a			      =							dataset
      _a			      =							process
      _a			      =							params
 def __len__( self						)					->							Dict:
      return len(self.dataset						)




 def __getitem__( self      ,				__magic_name__						)					->							Optional[Any]:
      _a			      =							self.dataset[i]
      _a			      =							self.process(__magic_name__      ,				**self.params						)
      return processed





class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__=None						)					->							str:
      _a			      =							loader
      _a			      =							infer
      _a			      =							params
      if loader_batch_size == 1:
           # Let's spare some time by deactivating altogether
           _a			      =							None
      _a			      =							loader_batch_size

      # Internal bookkeeping
      _a			      =							None
      _a			      =							None
 def __len__( self						)					->							Tuple:
      return len(self.loader						)
 def __iter__( self						)					->							Union[str, Any]:
      _a			      =							iter(self.loader						)
      return self
 def 							__UpperCAmelCase		( self						)					->							Dict:
      if isinstance(self._loader_batch_data      ,				torch.Tensor						):
           # Batch data is simple tensor, just fetch the slice
           _a			      =							self._loader_batch_data[self._loader_batch_index]
      else:
           # Batch data is assumed to be BaseModelOutput (or dict)
           _a			      =							{}
           for k, element in self._loader_batch_data.items():
                if isinstance(__magic_name__      ,				__magic_name__						):
                     # Convert ModelOutput to tuple first
                     _a			      =							element.to_tuple()
                     if isinstance(element[0]      ,				torch.Tensor						):
                          _a			      =							tuple(el[self._loader_batch_index].unsqueeze(0						) for el in element						)
                     elif isinstance(element[0]      ,				np.ndarray						):
                          _a			      =							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(__magic_name__      ,				__magic_name__						):
                     # Those are stored as lists of tensors so need specific unbatching.
                     if isinstance(element[0]      ,				torch.Tensor						):
                          _a			      =							tuple(el[self._loader_batch_index].unsqueeze(0						) for el in element						)
                     elif isinstance(element[0]      ,				np.ndarray						):
                          _a			      =							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
                     _a			      =							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

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




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





class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__=None						)					->							Dict:
      super().__init__(__magic_name__      ,				__magic_name__      ,				__magic_name__						)
 def __iter__( self						)					->							List[Any]:
      _a			      =							iter(self.loader						)
      _a			      =							None
      return self




 def 							__UpperCAmelCase		( self						)					->							Tuple:
      if self.subiterator is None:
           _a			      =							self.infer(next(self.iterator						)      ,				**self.params						)
      try:
           # Try to return next item
           _a			      =							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
           _a			      =							self.infer(next(self.iterator						)      ,				**self.params						)
           _a			      =							next(self.subiterator						)
      return processed





class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __iter__( self						)					->							Union[str, Any]:
      _a			      =							iter(self.loader						)
      return self




 def 							__UpperCAmelCase		( self						)					->							List[str]:
      # Extremely similar to PipelineIterator in its unpacking mechanism
      # BUT, we have an extra required item which is the presence of `is_last`
      # That is because everything is flattened by `PipelineChunkIterator` we
      # need to keep track of how to regroup here in the original `process`
      # boundaries so that `process` and `postprocess` see the same data.

      # This iterator accumulates items (possibly while unbatching) until it
      # its a `is_last` and then just passes it on to the caller.
      _a			      =							False
      _a			      =							[]
      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:
                _a			      =							self.loader_batch_item()
                _a			      =							item.pop('is_last'						)
                accumulator.append(__magic_name__						)
                if is_last:
                     return accumulator

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





class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self      ,				__magic_name__      ,				__magic_name__						)					->							str:
      _a			      =							dataset
      _a			      =							key
 def __len__( self						)					->							int:
      return len(self.dataset						)




 def __getitem__( self      ,				__magic_name__						)					->							List[Any]:
      return self.dataset[i][self.key]





class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Dict:
      _a			      =							dataset
      _a			      =							keya
      _a			      =							keya
 def __len__( self						)					->							Dict:
      return len(self.dataset						)




 def __getitem__( self      ,				__magic_name__						)					->							Optional[Any]:
      return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}





'''simple docstring'''






import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict






@pytest.mark.parametrize(
    'split_dict'				,						[
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'				,						num_bytes=13_37				,						num_examples=42				,						dataset_name='my_dataset'		)}		),
        SplitDict({'train': SplitInfo(name='train'				,						num_bytes=13_37				,						num_examples=42		)}		),
        SplitDict({'train': SplitInfo()}		),
    ]				,						)
def 	_A				(lowerCAmelCase__  :SplitDict		)   ->  Any:




     '''simple docstring'''



     _a			      =							split_dict._to_yaml_list()
     assert len(lowerCAmelCase__		) == len(lowerCAmelCase__		)
     _a			      =							SplitDict._from_yaml_list(lowerCAmelCase__		)
     for split_name, split_info in split_dict.items():
          # dataset_name field is deprecated, and is therefore not part of the YAML dump
          _a			      =							None
          # the split name of split_dict takes over the name of the split info object
          _a			      =							split_name
     assert split_dict == reloaded






@pytest.mark.parametrize(
    'split_info'				,						[SplitInfo(), SplitInfo(dataset_name=lowerCAmelCase__		), SplitInfo(dataset_name='my_dataset'		)]		)
def 	_A				(lowerCAmelCase__  :Optional[Any]		)   ->  List[str]:




     '''simple docstring'''



     _a			      =							asdict(SplitDict({'train': split_info}		)		)
     assert "dataset_name" in split_dict_asdict["train"]
     assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name

