
"""simple docstring"""





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

from ..utils.generic import ModelOutput


class A_	(lowercase__      ):




  '''simple docstring'''







  def __init__(		self				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    dataset
     UpperCAmelCase_					:					Dict			     =    process
     UpperCAmelCase_					:					List[Any]			     =    params







  def __len__(		self  ):

     """simple docstring"""
     return len(self.dataset  )







  def __getitem__(		self				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    self.dataset[i]
     UpperCAmelCase_					:					Tuple			     =    self.process(lowercase_				,   **self.params  )
     return processed




class A_	(lowercase__      ):




  '''simple docstring'''







  def __init__(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_=None  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    loader
     UpperCAmelCase_					:					Optional[int]			     =    infer
     UpperCAmelCase_					:					List[str]			     =    params
     if loader_batch_size == 1:
        # Let's spare some time by deactivating altogether
        UpperCAmelCase_					:					List[Any]			     =    None
     UpperCAmelCase_					:					str			     =    loader_batch_size

     # Internal bookkeeping
     UpperCAmelCase_					:					List[Any]			     =    None
     UpperCAmelCase_					:					Union[str, Any]			     =    None







  def __len__(		self  ):

     """simple docstring"""
     return len(self.loader  )







  def __iter__(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    iter(self.loader  )
     return self







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     if isinstance(self._loader_batch_data				,   torch.Tensor  ):
        # Batch data is simple tensor, just fetch the slice
        UpperCAmelCase_					:					str			     =    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(lowercase_				,   lowercase_  ):
              # Convert ModelOutput to tuple first
              UpperCAmelCase_					:					List[str]			     =    element.to_tuple()
              if isinstance(element[0]				,   torch.Tensor  ):
                 UpperCAmelCase_					:					int			     =    tuple(el[self._loader_batch_index].unsqueeze(0  ) for el in element  )
              elif isinstance(element[0]				,   np.ndarray  ):
                 UpperCAmelCase_					:					Union[str, 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(lowercase_				,   lowercase_  ):
              # Those are stored as lists of tensors so need specific unbatching.
              if isinstance(element[0]				,   torch.Tensor  ):
                 UpperCAmelCase_					:					Tuple			     =    tuple(el[self._loader_batch_index].unsqueeze(0  ) for el in element  )
              elif isinstance(element[0]				,   np.ndarray  ):
                 UpperCAmelCase_					:					Union[str, Any]			     =    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_					:					int			     =    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_					:					Optional[int]			     =    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_					:					str			     =    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_					:					Union[str, Any]			     =    self._loader_batch_data.__class__(lowercase_  )
     self._loader_batch_index += 1
     return result







  def        UpperCamelCase__					(		self  ):

     """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_					:					Union[str, Any]			     =    self.infer(lowercase_				,   **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(lowercase_				,   torch.Tensor  ):
           UpperCAmelCase_					:					Union[str, Any]			     =    processed
        else:
           UpperCAmelCase_					:					str			     =    list(processed.keys()  )[0]
           UpperCAmelCase_					:					Optional[Any]			     =    processed[key]
        if isinstance(lowercase_				,   lowercase_  ):
           UpperCAmelCase_					:					str			     =    len(lowercase_  )
        else:
           UpperCAmelCase_					:					Union[str, 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_					:					str			     =    observed_batch_size
        # Setting internal index to unwrap the batch
        UpperCAmelCase_					:					Dict			     =    processed
        UpperCAmelCase_					:					Union[str, Any]			     =    0
        return self.loader_batch_item()
     else:
        # We're not unrolling batches
        return processed




class A_	(lowercase__      ):




  '''simple docstring'''







  def __init__(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_=None  ):

     """simple docstring"""
     super().__init__(lowercase_				,   lowercase_				,   lowercase_  )







  def __iter__(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    iter(self.loader  )
     UpperCAmelCase_					:					Dict			     =    None
     return self







  def        UpperCamelCase__					(		self  ):

     """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_					:					Tuple			     =    self.infer(next(self.iterator  )				,   **self.params  )
        UpperCAmelCase_					:					Dict			     =    next(self.subiterator  )
     return processed




class A_	(lowercase__      ):




  '''simple docstring'''







  def __iter__(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    iter(self.loader  )
     return self







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # 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_					:					List[Any]			     =    False
     UpperCAmelCase_					:					Optional[Any]			     =    []
     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_					:					Union[str, Any]			     =    self.loader_batch_item()
           UpperCAmelCase_					:					Dict			     =    item.pop("is_last"  )
           accumulator.append(lowercase_  )
           if is_last:
              return accumulator

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




class A_	(lowercase__      ):




  '''simple docstring'''







  def __init__(		self				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    dataset
     UpperCAmelCase_					:					Dict			     =    key







  def __len__(		self  ):

     """simple docstring"""
     return len(self.dataset  )







  def __getitem__(		self				,   lowercase_  ):

     """simple docstring"""
     return self.dataset[i][self.key]




class A_	(lowercase__      ):




  '''simple docstring'''







  def __init__(		self				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    dataset
     UpperCAmelCase_					:					Any			     =    keya
     UpperCAmelCase_					:					str			     =    keya







  def __len__(		self  ):

     """simple docstring"""
     return len(self.dataset  )







  def __getitem__(		self				,   lowercase_  ):

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




"""simple docstring"""





from diffusers.utils.testing_utils import require_onnxruntime


@require_onnxruntime
class A_	:




  '''simple docstring'''
  pass


