




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

from ..utils.generic import ModelOutput
class 			_UpperCamelCase      (				lowerCAmelCase		):
      def __init__(			self   :int ,      lowerCamelCase   :List[str] ,      lowerCamelCase   :Union[str, Any] ,      lowerCamelCase   :Tuple      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						dataset
       UpperCAmelCase__							 =						process
       UpperCAmelCase__							 =						params
      def __len__(			self   :Tuple      )     ->			Optional[Any]:
       return len(self.dataset      )







      def __getitem__(			self   :Optional[Any] ,      lowerCamelCase   :Tuple      )     ->			str:
       UpperCAmelCase__							 =						self.dataset[i]
       UpperCAmelCase__							 =						self.process(lowerCamelCase ,      **self.params      )
       return processed



class 			_UpperCamelCase      (				lowerCAmelCase		):
      def __init__(			self   :List[Any] ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :Optional[Any] ,      lowerCamelCase   :Optional[Any] ,      lowerCamelCase   :List[Any]=None      )     ->			str:
       UpperCAmelCase__							 =						loader
       UpperCAmelCase__							 =						infer
       UpperCAmelCase__							 =						params
       if loader_batch_size == 1:
        # Let's spare some time by deactivating altogether
        UpperCAmelCase__							 =						None
       UpperCAmelCase__							 =						loader_batch_size

       # Internal bookkeeping
       UpperCAmelCase__							 =						None
       UpperCAmelCase__							 =						None
      def __len__(			self   :Dict      )     ->			List[Any]:
       return len(self.loader      )
      def __iter__(			self   :List[str]      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						iter(self.loader      )
       return self
      def 	UpperCAmelCase_						(			self   :Any      )     ->			List[str]:
       if isinstance(self._loader_batch_data ,      torch.Tensor      ):
        # Batch data is simple tensor, just fetch the slice
        UpperCAmelCase__							 =						self._loader_batch_data[self._loader_batch_index]
       else:
        # Batch data is assumed to be BaseModelOutput (or dict)
        UpperCAmelCase__							 =						{}
        for k, element in self._loader_batch_data.items():
         if isinstance(lowerCamelCase ,      lowerCamelCase      ):
          # Convert ModelOutput to tuple first
          UpperCAmelCase__							 =						element.to_tuple()
          if isinstance(element[0] ,      torch.Tensor      ):
           UpperCAmelCase__							 =						tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
          elif isinstance(element[0] ,      np.ndarray      ):
           UpperCAmelCase__							 =						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      ):
           UpperCAmelCase__							 =						tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
          elif isinstance(element[0] ,      np.ndarray      ):
           UpperCAmelCase__							 =						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__							 =						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__							 =						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__							 =						np.expand_dims(element[self._loader_batch_index] ,      0      )
         else:
          # This is typically a list, so no need to `unsqueeze`.
          UpperCAmelCase__							 =						element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
        UpperCAmelCase__							 =						self._loader_batch_data.__class__(lowerCamelCase      )
       self._loader_batch_index += 1
       return result







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



class 			_UpperCamelCase      (				lowerCAmelCase		):
      def __init__(			self   :Dict ,      lowerCamelCase   :Any ,      lowerCamelCase   :int ,      lowerCamelCase   :Dict ,      lowerCamelCase   :Union[str, Any]=None      )     ->			Union[str, Any]:
       super().__init__(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      )
      def __iter__(			self   :Union[str, Any]      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						iter(self.loader      )
       UpperCAmelCase__							 =						None
       return self







      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			List[str]:
       if self.subiterator is None:
        UpperCAmelCase__							 =						self.infer(next(self.iterator      ) ,      **self.params      )
       try:
        # Try to return next item
        UpperCAmelCase__							 =						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__							 =						self.infer(next(self.iterator      ) ,      **self.params      )
        UpperCAmelCase__							 =						next(self.subiterator      )
       return processed



class 			_UpperCamelCase      (				lowerCAmelCase		):
      def __iter__(			self   :Any      )     ->			Dict:
       UpperCAmelCase__							 =						iter(self.loader      )
       return self







      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			Union[str, Any]:
       # 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.
       UpperCAmelCase__							 =						False
       UpperCAmelCase__							 =						[]
       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__							 =						self.loader_batch_item()
         UpperCAmelCase__							 =						item.pop("is_last"      )
         accumulator.append(lowerCamelCase      )
         if is_last:
          return accumulator

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



class 			_UpperCamelCase      (				lowerCAmelCase		):
      def __init__(			self   :Union[str, Any] ,      lowerCamelCase   :Dataset ,      lowerCamelCase   :str      )     ->			List[Any]:
       UpperCAmelCase__							 =						dataset
       UpperCAmelCase__							 =						key
      def __len__(			self   :str      )     ->			int:
       return len(self.dataset      )







      def __getitem__(			self   :Union[str, Any] ,      lowerCamelCase   :Optional[int]      )     ->			Optional[int]:
       return self.dataset[i][self.key]



class 			_UpperCamelCase      (				lowerCAmelCase		):
      def __init__(			self   :List[Any] ,      lowerCamelCase   :Dataset ,      lowerCamelCase   :str ,      lowerCamelCase   :str      )     ->			Any:
       UpperCAmelCase__							 =						dataset
       UpperCAmelCase__							 =						keya
       UpperCAmelCase__							 =						keya
      def __len__(			self   :int      )     ->			Optional[Any]:
       return len(self.dataset      )







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











import torch

from transformers import AutoModel
class 			_UpperCamelCase      (				torch.nn.Module		):
      def __init__(			self   :str ,      lowerCamelCase   :Tuple="sayef/fsner-bert-base-uncased"      )     ->			int:
       super(lowerCamelCase ,      self      ).__init__()

       UpperCAmelCase__							 =						AutoModel.from_pretrained(lowerCamelCase ,      return_dict=lowerCamelCase      )
       UpperCAmelCase__							 =						torch.nn.CosineSimilarity(3 ,      1e-08      )
       UpperCAmelCase__							 =						torch.nn.Softmax(dim=1      )
      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      **lowerCamelCase   :Tuple      )     ->			Dict:
       return self.bert(**lowerCamelCase      ).last_hidden_state
      def 	UpperCAmelCase_						(			self   :Any ,      lowerCamelCase   :Union[str, Any]      )     ->			Union[str, Any]:
       return token_embeddings.sum(2 ,      keepdim=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Dict ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :int ,      lowerCamelCase   :Union[str, Any]=1      )     ->			Dict:
       return self.softmax(T * self.cos(lowerCamelCase ,      lowerCamelCase      )      )







      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :Any ,      lowerCamelCase   :Any      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						W_supports["sizes"].tolist()
       UpperCAmelCase__							 =						W_supports["start_token_id"].item()
       UpperCAmelCase__							 =						W_supports["end_token_id"].item()

       del W_supports["sizes"]
       del W_supports["start_token_id"]
       del W_supports["end_token_id"]

       UpperCAmelCase__							 =						self.BERT(**lowerCamelCase      )
       UpperCAmelCase__							 =						self.BERT(**lowerCamelCase      )

       UpperCAmelCase__							 =						None
       UpperCAmelCase__							 =						None

       UpperCAmelCase__							 =						W_supports["input_ids"] == start_token_id
       UpperCAmelCase__							 =						W_supports["input_ids"] == end_token_id

       for i, size in enumerate(lowerCamelCase      ):
        if i == 0:
         UpperCAmelCase__							 =						0
        else:
         UpperCAmelCase__							 =						support_sizes[i - 1]

        UpperCAmelCase__							 =						S[s : s + size][start_token_masks[s : s + size]]
        UpperCAmelCase__							 =						S[s : s + size][end_token_masks[s : s + size]]

        UpperCAmelCase__							 =						torch.matmul(q[i] ,      s_start.T      ).sum(1      ).softmax(0      )
        UpperCAmelCase__							 =						torch.matmul(q[i] ,      s_end.T      ).sum(1      ).softmax(0      )

        if p_starts is not None:
         UpperCAmelCase__							 =						torch.vstack((p_starts, p_start)      )
         UpperCAmelCase__							 =						torch.vstack((p_ends, p_end)      )
        else:
         UpperCAmelCase__							 =						p_start
         UpperCAmelCase__							 =						p_end

       return p_starts, p_ends





