



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

from ..utils.generic import ModelOutput






class __a					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Union[str, Any]										=       dataset
        A			:	List[str]										=       process
        A			:	List[str]										=       params






    def __len__(	self		):
        return len(self.dataset		)






    def __getitem__(	self,		lowerCamelCase__		):
        A			:	Union[str, Any]										=       self.dataset[i]
        A			:	Dict										=       self.process(lowerCamelCase__,		**self.params		)
        return processed






class __a					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=None		):
        A			:	str										=       loader
        A			:	int										=       infer
        A			:	int										=       params
        if loader_batch_size == 1:
            # Let's spare some time by deactivating altogether
            A			:	Union[str, Any]										=       None
        A			:	Optional[Any]										=       loader_batch_size

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






    def __len__(	self		):
        return len(self.loader		)






    def __iter__(	self		):
        A			:	Dict										=       iter(self.loader		)
        return self






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

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






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






class __a					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=None		):
        super().__init__(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)






    def __iter__(	self		):
        A			:	Optional[Any]										=       iter(self.loader		)
        A			:	Union[str, Any]										=       None
        return self






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






class __a					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    def __iter__(	self		):
        A			:	Tuple										=       iter(self.loader		)
        return self






    def       _lowerCAmelCase (	self		):
        # 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.
        A			:	Optional[int]										=       False
        A			:	int										=       []
        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:
                A			:	int										=       self.loader_batch_item()
                A			:	Union[str, Any]										=       item.pop("""is_last"""		)
                accumulator.append(lowerCamelCase__		)
                if is_last:
                    return accumulator

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






class __a					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	str										=       dataset
        A			:	Optional[Any]										=       key






    def __len__(	self		):
        return len(self.dataset		)






    def __getitem__(	self,		lowerCamelCase__		):
        return self.dataset[i][self.key]






class __a					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Optional[Any]										=       dataset
        A			:	Optional[Any]										=       keya
        A			:	Optional[Any]										=       keya






    def __len__(	self		):
        return len(self.dataset		)






    def __getitem__(	self,		lowerCamelCase__		):
        return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}







import socket




def      __UpperCamelCase     (  )	->  Optional[int]:



    """simple docstring"""






    A			:	str										=       socket.socket(socket.AF_INET				,					socket.SOCK_STREAM		)
    A			:	Union[str, Any]										=       socket.gethostname()
    A			:	Dict										=       1_2312

    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:
            A			:	Optional[int]										=       sock.recv(1024		)
            if not data:
                break
            out_file.write(_lowerCAmelCase		)

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


if __name__ == "__main__":
     main()


