




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

from ..utils.generic import ModelOutput



class       a					(       _A      ):

     '''simple docstring'''





     def __init__(     self       :      Dict ,   __snake_case       :      List[Any] ,   __snake_case       :      str ,   __snake_case       :      Tuple    ):
         UpperCAmelCase_										=					dataset
         UpperCAmelCase_										=					process
         UpperCAmelCase_										=					params
     def __len__(     self       :      int    ):
         return len(self.dataset    )




     def __getitem__(     self       :      int ,   __snake_case       :      int    ):
         UpperCAmelCase_										=					self.dataset[i]
         UpperCAmelCase_										=					self.process(__snake_case ,   **self.params    )
         return processed



class       a					(       _A      ):

     '''simple docstring'''





     def __init__(     self       :      Dict ,   __snake_case       :      Optional[int] ,   __snake_case       :      Dict ,   __snake_case       :      Optional[int] ,   __snake_case       :      List[str]=None    ):
         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    ):
         return len(self.loader    )
     def __iter__(     self       :      Dict    ):
         UpperCAmelCase_										=					iter(self.loader    )
         return self
     def   lowerCamelCase_       (     self       :      Any    ):
         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(__snake_case ,   __snake_case    ):
                     # 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(__snake_case ,   __snake_case    ):
                     # 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__(__snake_case    )
         self._loader_batch_index += 1
         return result




     def   lowerCamelCase_       (     self       :      str    ):
         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(__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    ):
                 UpperCAmelCase_										=					processed
             else:
                 UpperCAmelCase_										=					list(processed.keys()    )[0]
                 UpperCAmelCase_										=					processed[key]
             if isinstance(__snake_case ,   __snake_case    ):
                 UpperCAmelCase_										=					len(__snake_case    )
             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       a					(       _A      ):

     '''simple docstring'''





     def __init__(     self       :      Union[str, Any] ,   __snake_case       :      Any ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      Tuple ,   __snake_case       :      Union[str, Any]=None    ):
         super().__init__(__snake_case ,   __snake_case ,   __snake_case    )
     def __iter__(     self       :      List[Any]    ):
         UpperCAmelCase_										=					iter(self.loader    )
         UpperCAmelCase_										=					None
         return self




     def   lowerCamelCase_       (     self       :      Any    ):
         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       a					(       _A      ):

     '''simple docstring'''





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




     def   lowerCamelCase_       (     self       :      List[str]    ):
         # 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(__snake_case    )
                 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(__snake_case ,   torch.Tensor    ):
                     UpperCAmelCase_										=					processed
                 else:
                     UpperCAmelCase_										=					list(processed.keys()    )[0]
                     UpperCAmelCase_										=					processed[key]
                 if isinstance(__snake_case ,   __snake_case    ):
                     UpperCAmelCase_										=					len(__snake_case    )
                 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(__snake_case    )
                     if is_last:
                         return accumulator
             else:
                 UpperCAmelCase_										=					processed
                 UpperCAmelCase_										=					item.pop('''is_last'''    )
                 accumulator.append(__snake_case    )
         return accumulator



class       a					(       _A      ):

     '''simple docstring'''





     def __init__(     self       :      Optional[int] ,   __snake_case       :      Dataset ,   __snake_case       :      str    ):
         UpperCAmelCase_										=					dataset
         UpperCAmelCase_										=					key
     def __len__(     self       :      Tuple    ):
         return len(self.dataset    )




     def __getitem__(     self       :      List[str] ,   __snake_case       :      Optional[int]    ):
         return self.dataset[i][self.key]



class       a					(       _A      ):

     '''simple docstring'''





     def __init__(     self       :      Union[str, Any] ,   __snake_case       :      Dataset ,   __snake_case       :      str ,   __snake_case       :      str    ):
         UpperCAmelCase_										=					dataset
         UpperCAmelCase_										=					keya
         UpperCAmelCase_										=					keya
     def __len__(     self       :      Any    ):
         return len(self.dataset    )




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











from __future__ import annotations

import os
from collections.abc import Mapping

_lowerCamelCase      			=						tuple[int, int]



class       a					:

     '''simple docstring'''





     def __init__(     self       :      str ,   __snake_case       :      set[int] ,   __snake_case       :      Mapping[EdgeT, int]    ):
         UpperCAmelCase_										=					vertices
         UpperCAmelCase_										=					{
             (min(__snake_case    ), max(__snake_case    )): weight for edge, weight in edges.items()
         }
     def   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      EdgeT ,   __snake_case       :      int    ):
         self.vertices.add(edge[0]    )
         self.vertices.add(edge[1]    )
         UpperCAmelCase_										=					weight




     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					Graph({min(self.vertices    )} ,   {}    )
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42

         while len(subgraph.vertices    ) < len(self.vertices    ):
             UpperCAmelCase_										=					max(self.edges.values()    ) + 1
             for edge, weight in self.edges.items():
                 if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                     if weight < min_weight:
                         UpperCAmelCase_										=					edge
                         UpperCAmelCase_										=					weight

             subgraph.add_edge(__snake_case ,   __snake_case    )

         return subgraph




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str = "p107_network.txt"				)  ->							int:
    UpperCAmelCase_										=					os.path.abspath(os.path.dirname(__UpperCamelCase				)				)
    UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					__UpperCamelCase				)
    UpperCAmelCase_										=					{}
    UpperCAmelCase_										=					42
    UpperCAmelCase_										=					42
    UpperCAmelCase_										=					42

    with open(__UpperCamelCase				) as f:
        UpperCAmelCase_										=					f.read().strip().split('''\n'''				)

    UpperCAmelCase_										=					[line.split(''','''				) for line in data]

    for edgea in range(1	,					len(__UpperCamelCase				)				):
        for edgea in range(__UpperCamelCase				):
            if adjaceny_matrix[edgea][edgea] != "-":
                UpperCAmelCase_										=					int(adjaceny_matrix[edgea][edgea]				)

    UpperCAmelCase_										=					Graph(set(range(len(__UpperCamelCase				)				)				)	,					__UpperCamelCase				)

    UpperCAmelCase_										=					graph.prims_algorithm()

    UpperCAmelCase_										=					sum(graph.edges.values()				)
    UpperCAmelCase_										=					sum(subgraph.edges.values()				)

    return initial_total - optimal_total


if __name__ == "__main__":
 print(F"{solution() = }")





