"""simple docstring"""


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

from ..utils.generic import ModelOutput




class  UpperCamelCase					(				lowerCAmelCase__   ):







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)      ->      List[Any]:
            snake_case_					       =					dataset
            snake_case_					       =					process
            snake_case_					       =					params







       def __len__( self)      ->      Optional[Any]:
            return len(self.dataset)







       def __getitem__( self,							lowerCAmelCase__)      ->      Optional[int]:
            snake_case_					       =					self.dataset[i]
            snake_case_					       =					self.process(lowerCAmelCase__,							**self.params)
            return processed




class  UpperCamelCase					(				lowerCAmelCase__   ):







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__=None)      ->      Optional[Any]:
            snake_case_					       =					loader
            snake_case_					       =					infer
            snake_case_					       =					params
            if loader_batch_size == 1:
                 # Let's spare some time by deactivating altogether
                 snake_case_					       =					None
            snake_case_					       =					loader_batch_size

            # Internal bookkeeping
            snake_case_					       =					None
            snake_case_					       =					None







       def __len__( self)      ->      str:
            return len(self.loader)







       def __iter__( self)      ->      List[Any]:
            snake_case_					       =					iter(self.loader)
            return self







       def 		a_						( self)      ->      Dict:
            if isinstance(self._loader_batch_data,							torch.Tensor):
                 # Batch data is simple tensor, just fetch the slice
                 snake_case_					       =					self._loader_batch_data[self._loader_batch_index]
            else:
                 # Batch data is assumed to be BaseModelOutput (or dict)
                 snake_case_					       =					{}
                 for k, element in self._loader_batch_data.items():
                      if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                           # Convert ModelOutput to tuple first
                           snake_case_					       =					element.to_tuple()
                           if isinstance(element[0],							torch.Tensor):
                                snake_case_					       =					tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
                           elif isinstance(element[0],							np.ndarray):
                                snake_case_					       =					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):
                                snake_case_					       =					tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
                           elif isinstance(element[0],							np.ndarray):
                                snake_case_					       =					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
                           snake_case_					       =					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

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







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




class  UpperCamelCase					(				lowerCAmelCase__   ):







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__=None)      ->      Any:
            super().__init__(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)







       def __iter__( self)      ->      str:
            snake_case_					       =					iter(self.loader)
            snake_case_					       =					None
            return self







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




class  UpperCamelCase					(				lowerCAmelCase__   ):







       def __iter__( self)      ->      Any:
            snake_case_					       =					iter(self.loader)
            return self







       def 		a_						( self)      ->      Tuple:
            # 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.
            snake_case_					       =					False
            snake_case_					       =					[]
            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:
                      snake_case_					       =					self.loader_batch_item()
                      snake_case_					       =					item.pop('is_last')
                      accumulator.append(lowerCAmelCase__)
                      if is_last:
                           return accumulator

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




class  UpperCamelCase					(				lowerCAmelCase__   ):







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__)      ->      Tuple:
            snake_case_					       =					dataset
            snake_case_					       =					key







       def __len__( self)      ->      str:
            return len(self.dataset)







       def __getitem__( self,							lowerCAmelCase__)      ->      List[Any]:
            return self.dataset[i][self.key]




class  UpperCamelCase					(				lowerCAmelCase__   ):







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)      ->      List[str]:
            snake_case_					       =					dataset
            snake_case_					       =					keya
            snake_case_					       =					keya







       def __len__( self)      ->      str:
            return len(self.dataset)







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



def 						UpperCAmelCase			(	a_						)		->	Optional[int]:







				"""simple docstring"""



				__A   			= [0] * len(a_						)
				__A   			= []
				__A   			= [1] * len(a_						)

				for values in graph.values():
								for i in values:
												indegree[i] += 1

				for i in range(len(a_						)						):
								if indegree[i] == 0:
												queue.append(a_						)

				while queue:
								__A   			= queue.pop(0						)
								for x in graph[vertex]:
												indegree[x] -= 1

												if long_dist[vertex] + 1 > long_dist[x]:
																__A   			= long_dist[vertex] + 1

												if indegree[x] == 0:
																queue.append(a_						)

				print(max(a_						)						)


# Adjacency list of Graph
SCREAMING_SNAKE_CASE							:List[Any]							 =				{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
