





'''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	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase						)			->							Dict:
     _lowerCAmelCase			=					dataset
     _lowerCAmelCase			=					process
     _lowerCAmelCase			=					params




 def __len__(		self						)			->							int:
     return len(self.dataset						)





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







class       lowerCAmelCase_						(						a_						):




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

     # Internal bookkeeping
     _lowerCAmelCase			=					None
     _lowerCAmelCase			=					None




 def __len__(		self						)			->							Optional[int]:
     return len(self.loader						)




 def __iter__(		self						)			->							Optional[int]:
     _lowerCAmelCase			=					iter(self.loader						)
     return self




 def 							_snake_case      (		self						)			->							int:
     if isinstance(self._loader_batch_data	,   torch.Tensor						):
         # Batch data is simple tensor, just fetch the slice
         _lowerCAmelCase			=					self._loader_batch_data[self._loader_batch_index]
     else:
         # Batch data is assumed to be BaseModelOutput (or dict)
         _lowerCAmelCase			=					{}
         for k, element in self._loader_batch_data.items():
             if isinstance(lowercase_	,   lowercase_						):
                 # Convert ModelOutput to tuple first
                 _lowerCAmelCase			=					element.to_tuple()
                 if isinstance(element[0]	,   torch.Tensor						):
                     _lowerCAmelCase			=					tuple(el[self._loader_batch_index].unsqueeze(0						) for el in element						)
                 elif isinstance(element[0]	,   np.ndarray						):
                     _lowerCAmelCase			=					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						):
                     _lowerCAmelCase			=					tuple(el[self._loader_batch_index].unsqueeze(0						) for el in element						)
                 elif isinstance(element[0]	,   np.ndarray						):
                     _lowerCAmelCase			=					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
                 _lowerCAmelCase			=					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

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





 def 							_snake_case      (		self						)			->							Optional[int]:
     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
     _lowerCAmelCase			=					next(self.iterator						)
     _lowerCAmelCase			=					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						):
             _lowerCAmelCase			=					processed
         else:
             _lowerCAmelCase			=					list(processed.keys()						)[0]
             _lowerCAmelCase			=					processed[key]
         if isinstance(lowercase_	,   lowercase_						):
             _lowerCAmelCase			=					len(lowercase_						)
         else:
             _lowerCAmelCase			=					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.
             _lowerCAmelCase			=					observed_batch_size
         # Setting internal index to unwrap the batch
         _lowerCAmelCase			=					processed
         _lowerCAmelCase			=					0
         return self.loader_batch_item()
     else:
         # We're not unrolling batches
         return processed







class       lowerCAmelCase_						(						a_						):




 def __init__(		self	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase=None						)			->							Optional[int]:
     super().__init__(lowercase_	,   lowercase_	,   lowercase_						)




 def __iter__(		self						)			->							List[str]:
     _lowerCAmelCase			=					iter(self.loader						)
     _lowerCAmelCase			=					None
     return self





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







class       lowerCAmelCase_						(						a_						):




 def __iter__(		self						)			->							List[str]:
     _lowerCAmelCase			=					iter(self.loader						)
     return self





 def 							_snake_case      (		self						)			->							Dict:
     # 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.
     _lowerCAmelCase			=					False
     _lowerCAmelCase			=					[]
     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:
             _lowerCAmelCase			=					self.loader_batch_item()
             _lowerCAmelCase			=					item.pop("is_last"						)
             accumulator.append(lowercase_						)
             if is_last:
                 return accumulator

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







class       lowerCAmelCase_						(						a_						):




 def __init__(		self	,   _lowerCAmelCase	,   _lowerCAmelCase						)			->							Optional[Any]:
     _lowerCAmelCase			=					dataset
     _lowerCAmelCase			=					key




 def __len__(		self						)			->							Tuple:
     return len(self.dataset						)





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







class       lowerCAmelCase_						(						a_						):




 def __init__(		self	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase						)			->							Tuple:
     _lowerCAmelCase			=					dataset
     _lowerCAmelCase			=					keya
     _lowerCAmelCase			=					keya




 def __len__(		self						)			->							Optional[Any]:
     return len(self.dataset						)





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





"""simple docstring"""





from ..utils import DummyObject, requires_backends







class 				SCREAMING_SNAKE_CASE					(		metaclass=a_       ):






					"""simple docstring"""



					lowercase__				 =       ["speech"]







					def __init__(       self			:				Tuple   ,*lowercase_			:				Tuple   ,**lowercase_			:				List[str]		):
						requires_backends(self   ,['''speech''']		)







class 				SCREAMING_SNAKE_CASE					(		metaclass=a_       ):






					"""simple docstring"""



					lowercase__				 =       ["speech"]







					def __init__(       self			:				Union[str, Any]   ,*lowercase_			:				List[str]   ,**lowercase_			:				Any		):
						requires_backends(self   ,['''speech''']		)




