


'''simple docstring'''







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

from ..utils.generic import ModelOutput
class 	UpperCamelCase__					( __lowercase	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       dataset
            _lowerCAmelCase    :							List[str]   			=       process
            _lowerCAmelCase    :							List[Any]   			=       params







     def __len__( self   ):

            '''simple docstring'''

            return len(self.dataset   )







     def __getitem__( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       self.dataset[i]
            _lowerCAmelCase    :							Union[str, Any]   			=       self.process(snake_case__      ,	**self.params   )
            return processed
class 	UpperCamelCase__					( __lowercase	):



     """simple docstring"""






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

            '''simple docstring'''

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

            # Internal bookkeeping
            _lowerCAmelCase    :							Optional[Any]   			=       None
            _lowerCAmelCase    :							Union[str, Any]   			=       None







     def __len__( self   ):

            '''simple docstring'''

            return len(self.loader   )







     def __iter__( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       iter(self.loader   )
            return self







     def a	( self   ):

            '''simple docstring'''

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







     def a	( 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
            _lowerCAmelCase    :							Any   			=       next(self.iterator   )
            _lowerCAmelCase    :							Optional[int]   			=       self.infer(snake_case__      ,	**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(snake_case__      ,	torch.Tensor   ):
                          _lowerCAmelCase    :							Optional[Any]   			=       processed
                   else:
                          _lowerCAmelCase    :							Tuple   			=       list(processed.keys()   )[0]
                          _lowerCAmelCase    :							List[str]   			=       processed[key]
                   if isinstance(snake_case__      ,	snake_case__   ):
                          _lowerCAmelCase    :							str   			=       len(snake_case__   )
                   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
                   # Setting internal index to unwrap the batch
                   _lowerCAmelCase    :							Tuple   			=       processed
                   _lowerCAmelCase    :							List[Any]   			=       0
                   return self.loader_batch_item()
            else:
                   # We're not unrolling batches
                   return processed
class 	UpperCamelCase__					( __lowercase	):



     """simple docstring"""






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

            '''simple docstring'''

            super().__init__(snake_case__      ,	snake_case__      ,	snake_case__   )







     def __iter__( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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



     """simple docstring"""






     def __iter__( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       iter(self.loader   )
            return self







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       False
            _lowerCAmelCase    :							List[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:
                          _lowerCAmelCase    :							Optional[Any]   			=       self.loader_batch_item()
                          _lowerCAmelCase    :							Union[str, Any]   			=       item.pop('is_last'   )
                          accumulator.append(snake_case__   )
                          if is_last:
                                 return accumulator

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



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       dataset
            _lowerCAmelCase    :							Union[str, Any]   			=       key







     def __len__( self   ):

            '''simple docstring'''

            return len(self.dataset   )







     def __getitem__( self      ,	snake_case__   ):

            '''simple docstring'''

            return self.dataset[i][self.key]
class 	UpperCamelCase__					( __lowercase	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       dataset
            _lowerCAmelCase    :							Optional[int]   			=       keya
            _lowerCAmelCase    :							Dict   			=       keya







     def __len__( self   ):

            '''simple docstring'''

            return len(self.dataset   )







     def __getitem__( self      ,	snake_case__   ):

            '''simple docstring'''

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







'''simple docstring'''







import socket






def 			lowercase				():







       """simple docstring"""







       _lowerCAmelCase    :							Tuple   			=       socket.socket(socket.AF_INET   ,						socket.SOCK_STREAM					)
       _lowerCAmelCase    :							Optional[int]   			=       socket.gethostname()
       _lowerCAmelCase    :							Any   			=       1_2_3_1_2

       sock.connect((host, port)					)
       sock.send(B'Hello server!'					)

       with open('Received_file'   ,						'wb'					) as out_file:
              print('File opened'					)
              print('Receiving data...'					)
              while True:
                     _lowerCAmelCase    :							Union[str, Any]   			=       sock.recv(1_0_2_4					)
                     if not data:
                            break
                     out_file.write(_A					)

       print('Successfully received the file'					)
       sock.close()
       print('Connection closed'					)


if __name__ == "__main__":
       main()

