






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 :       Dict							,       SCREAMING_SNAKE_CASE :       Tuple							,       SCREAMING_SNAKE_CASE :       Tuple							,       SCREAMING_SNAKE_CASE :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  dataset
      UpperCamelCase__      :       Optional[int]	    =  process
      UpperCamelCase__      :       Optional[int]	    =  params







 def __len__(		self :       List[Any]						):


      '''simple docstring'''






      return len(self.dataset						)







 def __getitem__(		self :       int							,       SCREAMING_SNAKE_CASE :       Tuple						):


      '''simple docstring'''






      UpperCamelCase__      :       Dict	    =  self.dataset[i]
      UpperCamelCase__      :       Dict	    =  self.process(UpperCamelCase__							,       **self.params						)
      return processed







class       __a  (   __SCREAMING_SNAKE_CASE	):







 def __init__(		self :       Tuple							,       SCREAMING_SNAKE_CASE :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       Any							,       SCREAMING_SNAKE_CASE :       List[Any]=None						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  loader
      UpperCamelCase__      :       Optional[Any]	    =  infer
      UpperCamelCase__      :       Optional[Any]	    =  params
      if loader_batch_size == 1:
           # Let's spare some time by deactivating altogether
           UpperCamelCase__      :       Optional[int]	    =  None
      UpperCamelCase__      :       int	    =  loader_batch_size

      # Internal bookkeeping
      UpperCamelCase__      :       Dict	    =  None
      UpperCamelCase__      :       Optional[int]	    =  None







 def __len__(		self :       Any						):


      '''simple docstring'''






      return len(self.loader						)







 def __iter__(		self :       int						):


      '''simple docstring'''






      UpperCamelCase__      :       Any	    =  iter(self.loader						)
      return self







 def  __lowercase (		self :       Tuple						):


      '''simple docstring'''






      if isinstance(self._loader_batch_data							,       torch.Tensor						):
           # Batch data is simple tensor, just fetch the slice
           UpperCamelCase__      :       List[Any]	    =  self._loader_batch_data[self._loader_batch_index]
      else:
           # Batch data is assumed to be BaseModelOutput (or dict)
           UpperCamelCase__      :       Dict	    =  {}
           for k, element in self._loader_batch_data.items():
                if isinstance(UpperCamelCase__							,       UpperCamelCase__						):
                     # Convert ModelOutput to tuple first
                     UpperCamelCase__      :       Dict	    =  element.to_tuple()
                     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__      :       int	    =  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						):
                          UpperCamelCase__      :       Dict	    =  tuple(el[self._loader_batch_index].unsqueeze(0						) for el in element						)
                     elif isinstance(element[0]							,       np.ndarray						):
                          UpperCamelCase__      :       Dict	    =  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__      :       Optional[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__      :       Union[str, 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__      :       int	    =  np.expand_dims(element[self._loader_batch_index]							,       0						)
                else:
                     # This is typically a list, so no need to `unsqueeze`.
                     UpperCamelCase__      :       Union[str, Any]	    =  element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
           UpperCamelCase__      :       Any	    =  self._loader_batch_data.__class__(UpperCamelCase__						)
      self._loader_batch_index += 1
      return result







 def  __lowercase (		self :       List[str]						):


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







class       __a  (   __SCREAMING_SNAKE_CASE	):







 def __init__(		self :       Dict							,       SCREAMING_SNAKE_CASE :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       Dict							,       SCREAMING_SNAKE_CASE :       Dict							,       SCREAMING_SNAKE_CASE :       str=None						):


      '''simple docstring'''






      super().__init__(UpperCamelCase__							,       UpperCamelCase__							,       UpperCamelCase__						)







 def __iter__(		self :       Optional[int]						):


      '''simple docstring'''






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







 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      if self.subiterator is None:
           UpperCamelCase__      :       Optional[int]	    =  self.infer(next(self.iterator						)							,       **self.params						)
      try:
           # Try to return next item
           UpperCamelCase__      :       Union[str, 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__      :       List[Any]	    =  self.infer(next(self.iterator						)							,       **self.params						)
           UpperCamelCase__      :       List[Any]	    =  next(self.subiterator						)
      return processed







class       __a  (   __SCREAMING_SNAKE_CASE	):







 def __iter__(		self :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  iter(self.loader						)
      return self







 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  False
      UpperCamelCase__      :       List[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:
                UpperCamelCase__      :       List[str]	    =  self.loader_batch_item()
                UpperCamelCase__      :       Optional[Any]	    =  item.pop("is_last"						)
                accumulator.append(UpperCamelCase__						)
                if is_last:
                     return accumulator

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







class       __a  (   __SCREAMING_SNAKE_CASE	):







 def __init__(		self :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       Dataset							,       SCREAMING_SNAKE_CASE :       str						):


      '''simple docstring'''






      UpperCamelCase__      :       List[Any]	    =  dataset
      UpperCamelCase__      :       Tuple	    =  key







 def __len__(		self :       Optional[Any]						):


      '''simple docstring'''






      return len(self.dataset						)







 def __getitem__(		self :       Optional[int]							,       SCREAMING_SNAKE_CASE :       Dict						):


      '''simple docstring'''






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







class       __a  (   __SCREAMING_SNAKE_CASE	):







 def __init__(		self :       Tuple							,       SCREAMING_SNAKE_CASE :       Dataset							,       SCREAMING_SNAKE_CASE :       str							,       SCREAMING_SNAKE_CASE :       str						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  dataset
      UpperCamelCase__      :       Dict	    =  keya
      UpperCamelCase__      :       int	    =  keya







 def __len__(		self :       Tuple						):


      '''simple docstring'''






      return len(self.dataset						)







 def __getitem__(		self :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       Any						):


      '''simple docstring'''






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







import re







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     bool:
     UpperCamelCase__      :       Union[str, Any]	    =  re.compile(
         R"^(?:0|94|\+94|0{2}94)" R"7(0|1|2|4|5|6|7|8)" R"(-| |)" R"\d{7}$"      )

     return bool(re.search(__lowerCAmelCase ,   __lowerCAmelCase      )      )


if __name__ == "__main__":
 lowerCamelCase     :							Union[str, Any]    ='''0094702343221'''

 print(is_sri_lankan_phone_number(phone))