






'''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__						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Tuple					    =							dataset
       UpperCamelCase_				:   Dict					    =							process
       UpperCamelCase_				:   List[str]					    =							params
 def __len__(							self ):
       return len(self.dataset )





 def __getitem__(							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   int					    =							self.dataset[i]
       UpperCamelCase_				:   List[Any]					    =							self.process(__lowerCAmelCase			,     **self.params )
       return processed






class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase=None ):
       UpperCamelCase_				:   Dict					    =							loader
       UpperCamelCase_				:   int					    =							infer
       UpperCamelCase_				:   Union[str, Any]					    =							params
       if loader_batch_size == 1:
             # Let's spare some time by deactivating altogether
             UpperCamelCase_				:   str					    =							None
       UpperCamelCase_				:   Union[str, Any]					    =							loader_batch_size

       # Internal bookkeeping
       UpperCamelCase_				:   Optional[Any]					    =							None
       UpperCamelCase_				:   Optional[int]					    =							None
 def __len__(							self ):
       return len(self.loader )
 def __iter__(							self ):
       UpperCamelCase_				:   Optional[int]					    =							iter(self.loader )
       return self
 def   _UpperCAmelCase       (							self ):
       if isinstance(self._loader_batch_data			,     torch.Tensor ):
             # Batch data is simple tensor, just fetch the slice
             UpperCamelCase_				:   Tuple					    =							self._loader_batch_data[self._loader_batch_index]
       else:
             # Batch data is assumed to be BaseModelOutput (or dict)
             UpperCamelCase_				:   List[Any]					    =							{}
             for k, element in self._loader_batch_data.items():
                   if isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
                         # Convert ModelOutput to tuple first
                         UpperCamelCase_				:   Optional[Any]					    =							element.to_tuple()
                         if isinstance(element[0]			,     torch.Tensor ):
                               UpperCamelCase_				:   Dict					    =							tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
                         elif isinstance(element[0]			,     np.ndarray ):
                               UpperCamelCase_				:   List[Any]					    =							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(__lowerCAmelCase			,     __lowerCAmelCase ):
                         # 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_				:   str					    =							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_				:   str					    =							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_				:   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_				:   Tuple					    =							np.expand_dims(element[self._loader_batch_index]			,     0 )
                   else:
                         # This is typically a list, so no need to `unsqueeze`.
                         UpperCamelCase_				:   List[str]					    =							element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
             UpperCamelCase_				:   Tuple					    =							self._loader_batch_data.__class__(__lowerCAmelCase )
       self._loader_batch_index += 1
       return result





 def   _UpperCAmelCase       (							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
       UpperCamelCase_				:   List[Any]					    =							next(self.iterator )
       UpperCamelCase_				:   Dict					    =							self.infer(__lowerCAmelCase			,     **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(__lowerCAmelCase			,     torch.Tensor ):
                   UpperCamelCase_				:   Optional[int]					    =							processed
             else:
                   UpperCamelCase_				:   Optional[int]					    =							list(processed.keys() )[0]
                   UpperCamelCase_				:   Optional[Any]					    =							processed[key]
             if isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
                   UpperCamelCase_				:   Any					    =							len(__lowerCAmelCase )
             else:
                   UpperCamelCase_				:   List[str]					    =							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_				:   Optional[int]					    =							observed_batch_size
             # Setting internal index to unwrap the batch
             UpperCamelCase_				:   Tuple					    =							processed
             UpperCamelCase_				:   Union[str, Any]					    =							0
             return self.loader_batch_item()
       else:
             # We're not unrolling batches
             return processed






class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase=None ):
       super().__init__(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )
 def __iter__(							self ):
       UpperCamelCase_				:   Optional[int]					    =							iter(self.loader )
       UpperCamelCase_				:   List[str]					    =							None
       return self





 def   _UpperCAmelCase       (							self ):
       if self.subiterator is None:
             UpperCamelCase_				:   Optional[Any]					    =							self.infer(next(self.iterator )			,     **self.params )
       try:
             # Try to return next item
             UpperCamelCase_				:   int					    =							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_				:   Dict					    =							self.infer(next(self.iterator )			,     **self.params )
             UpperCamelCase_				:   Optional[int]					    =							next(self.subiterator )
       return processed






class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __iter__(							self ):
       UpperCamelCase_				:   str					    =							iter(self.loader )
       return self





 def   _UpperCAmelCase       (							self ):
       # 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.
       UpperCamelCase_				:   Union[str, Any]					    =							False
       UpperCamelCase_				:   Optional[int]					    =							[]
       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_				:   Any					    =							self.loader_batch_item()
                   UpperCamelCase_				:   Optional[Any]					    =							item.pop("""is_last""" )
                   accumulator.append(__lowerCAmelCase )
                   if is_last:
                         return accumulator

       while not is_last:
             UpperCamelCase_				:   Optional[Any]					    =							self.infer(next(self.iterator )			,     **self.params )
             if self.loader_batch_size is not None:
                   if isinstance(__lowerCAmelCase			,     torch.Tensor ):
                         UpperCamelCase_				:   Optional[int]					    =							processed
                   else:
                         UpperCamelCase_				:   Any					    =							list(processed.keys() )[0]
                         UpperCamelCase_				:   Tuple					    =							processed[key]
                   if isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
                         UpperCamelCase_				:   Optional[Any]					    =							len(__lowerCAmelCase )
                   else:
                         UpperCamelCase_				:   Optional[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_				:   Dict					    =							observed_batch_size
                   UpperCamelCase_				:   Optional[Any]					    =							processed
                   UpperCamelCase_				:   Any					    =							0
                   while self._loader_batch_index < self.loader_batch_size:
                         UpperCamelCase_				:   List[Any]					    =							self.loader_batch_item()
                         UpperCamelCase_				:   int					    =							item.pop("""is_last""" )
                         accumulator.append(__lowerCAmelCase )
                         if is_last:
                               return accumulator
             else:
                   UpperCamelCase_				:   str					    =							processed
                   UpperCamelCase_				:   int					    =							item.pop("""is_last""" )
                   accumulator.append(__lowerCAmelCase )
       return accumulator






class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Tuple					    =							dataset
       UpperCamelCase_				:   Optional[Any]					    =							key
 def __len__(							self ):
       return len(self.dataset )





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






class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Optional[Any]					    =							dataset
       UpperCamelCase_				:   Union[str, Any]					    =							keya
       UpperCamelCase_				:   Tuple					    =							keya
 def __len__(							self ):
       return len(self.dataset )





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














'''simple docstring'''

import pytest

import datasets


# Import fixture modules as plugins
UpperCamelCase							=["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]



def 							snake_case					(     a_							: List[str]    ,   a_							: Optional[Any]	)       ->				Union[str, Any]:





      """simple docstring"""

      for item in items:
            if any(marker in item.keywords for marker in ["""integration""", """unit"""]	):
                  continue
            item.add_marker(pytest.mark.unit	)



def 							snake_case					(     a_							: Optional[int]	)       ->				Optional[Any]:





      """simple docstring"""

      config.addinivalue_line("""markers"""    ,   """torchaudio_latest: mark test to run with torchaudio>=0.12"""	)



@pytest.fixture(autouse=a_	)
def 							snake_case					(     a_							: Any    ,   a_							: Optional[int]	)       ->				Tuple:





      """simple docstring"""

      UpperCamelCase_				:   Optional[Any]					    =							tmp_path_factory.getbasetemp() / """cache"""
      UpperCamelCase_				:   str					    =							test_hf_cache_home / """datasets"""
      UpperCamelCase_				:   Any					    =							test_hf_cache_home / """metrics"""
      UpperCamelCase_				:   List[Any]					    =							test_hf_cache_home / """modules"""
      monkeypatch.setattr("""datasets.config.HF_DATASETS_CACHE"""    ,   str(a_	)	)
      monkeypatch.setattr("""datasets.config.HF_METRICS_CACHE"""    ,   str(a_	)	)
      monkeypatch.setattr("""datasets.config.HF_MODULES_CACHE"""    ,   str(a_	)	)
      UpperCamelCase_				:   List[Any]					    =							test_hf_datasets_cache / """downloads"""
      monkeypatch.setattr("""datasets.config.DOWNLOADED_DATASETS_PATH"""    ,   str(a_	)	)
      UpperCamelCase_				:   Any					    =							test_hf_datasets_cache / """downloads""" / """extracted"""
      monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH"""    ,   str(a_	)	)



@pytest.fixture(autouse=a_    ,   scope="""session"""	)
def 							snake_case					(     )       ->				Any:





      """simple docstring"""

      datasets.disable_progress_bar()



@pytest.fixture(autouse=a_	)
def 							snake_case					(     a_							: Optional[int]	)       ->				Union[str, Any]:





      """simple docstring"""

      monkeypatch.setattr("""datasets.config.HF_UPDATE_DOWNLOAD_COUNTS"""    ,   a_	)



@pytest.fixture
def 							snake_case					(     a_							: int	)       ->				Union[str, Any]:





      """simple docstring"""

      monkeypatch.setattr("""sqlalchemy.util.deprecations.SILENCE_UBER_WARNING"""    ,   a_	)






