





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

from ..utils.generic import ModelOutput



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 def __init__(self				:   Any       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   Any)       ->str:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =dataset
   lowerCamelCase__:      List[str]			  =process
   lowerCamelCase__:      Any			  =params

 def __len__(self				:   Optional[int])       ->Any:



   '''simple docstring'''



   return len(self.dataset)







 def __getitem__(self				:   Any       , UpperCAmelCase_				:   Dict)       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =self.dataset[i]
   lowerCamelCase__:      int			  =self.process(UpperCAmelCase_       , **self.params)
   return processed



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 def __init__(self				:   Optional[int]       , UpperCAmelCase_				:   Union[str, Any]       , UpperCAmelCase_				:   Any       , UpperCAmelCase_				:   List[str]       , UpperCAmelCase_				:   List[str]=None)       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      int			  =loader
   lowerCamelCase__:      int			  =infer
   lowerCamelCase__:      List[str]			  =params
   if loader_batch_size == 1:
     # Let's spare some time by deactivating altogether
     lowerCamelCase__:      List[Any]			  =None
   lowerCamelCase__:      Tuple			  =loader_batch_size

   # Internal bookkeeping
   lowerCamelCase__:      Optional[int]			  =None
   lowerCamelCase__:      List[Any]			  =None

 def __len__(self				:   Dict)       ->Any:



   '''simple docstring'''



   return len(self.loader)

 def __iter__(self				:   Optional[Any])       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =iter(self.loader)
   return self

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Union[str, Any]:



   '''simple docstring'''



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

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







 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Dict:



   '''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
   lowerCamelCase__:      int			  =next(self.iterator)
   lowerCamelCase__:      List[str]			  =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):
       lowerCamelCase__:      List[Any]			  =processed
     else:
       lowerCamelCase__:      Optional[int]			  =list(processed.keys())[0]
       lowerCamelCase__:      Dict			  =processed[key]
     if isinstance(UpperCAmelCase_       , UpperCAmelCase_):
       lowerCamelCase__:      Optional[int]			  =len(UpperCAmelCase_)
     else:
       lowerCamelCase__:      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.
       lowerCamelCase__:      str			  =observed_batch_size
     # Setting internal index to unwrap the batch
     lowerCamelCase__:      List[Any]			  =processed
     lowerCamelCase__:      List[Any]			  =0
     return self.loader_batch_item()
   else:
     # We're not unrolling batches
     return processed



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 def __init__(self				:   Optional[Any]       , UpperCAmelCase_				:   List[str]       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   List[str]       , UpperCAmelCase_				:   Union[str, Any]=None)       ->Dict:



   '''simple docstring'''



   super().__init__(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_)

 def __iter__(self				:   Any)       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =iter(self.loader)
   lowerCamelCase__:      int			  =None
   return self







 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->List[Any]:



   '''simple docstring'''



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



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 def __iter__(self				:   Dict)       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =iter(self.loader)
   return self







 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      str			  =False
   lowerCamelCase__:      str			  =[]
   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__:      List[str]			  =self.loader_batch_item()
       lowerCamelCase__:      Union[str, Any]			  =item.pop("is_last")
       accumulator.append(UpperCAmelCase_)
       if is_last:
         return accumulator

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



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 def __init__(self				:   str       , UpperCAmelCase_				:   Dataset       , UpperCAmelCase_				:   str)       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      int			  =dataset
   lowerCamelCase__:      int			  =key

 def __len__(self				:   List[Any])       ->List[str]:



   '''simple docstring'''



   return len(self.dataset)







 def __getitem__(self				:   Dict       , UpperCAmelCase_				:   str)       ->Union[str, Any]:



   '''simple docstring'''



   return self.dataset[i][self.key]



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 def __init__(self				:   Any       , UpperCAmelCase_				:   Dataset       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   str)       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =dataset
   lowerCamelCase__:      Union[str, Any]			  =keya
   lowerCamelCase__:      str			  =keya

 def __len__(self				:   List[str])       ->int:



   '''simple docstring'''



   return len(self.dataset)







 def __getitem__(self				:   Optional[Any]       , UpperCAmelCase_				:   Dict)       ->List[Any]:



   '''simple docstring'''



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








import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
__A									=				"."


if __name__ == "__main__":
     __A									=				os.path.join(REPO_PATH, "utils/documentation_tests.txt")
     __A									=				[]
     __A									=				[]
     with open(doctest_file_path) as fp:
          for line in fp:
               __A									=				line.strip()
               __A									=				os.path.join(REPO_PATH, line)
               if not (os.path.isfile(path) or os.path.isdir(path)):
                    non_existent_paths.append(line)
               all_paths.append(path)
     if len(non_existent_paths) > 0:
          __A									=				"\n".join(non_existent_paths)
          raise ValueError(f'`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}')
     if all_paths != sorted(all_paths):
          raise ValueError("Files in `utils/documentation_tests.txt` are not in alphabetical order.")

