


'''simple docstring'''






from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


a_   :      List[Any]				    =      logging.get_logger(__name__)






def 		_A			(lowerCAmelCase__					:Union[tf.Tensor, np.ndarray]						)				->					List[int]:




     '''simple docstring'''



     if isinstance(UpperCAmelCase__			,  np.ndarray						):
          return list(tensor.shape						)

     _a      =							tf.shape(UpperCAmelCase__						)

     if tensor.shape == tf.TensorShape(UpperCAmelCase__						):
          return dynamic

     _a      =							tensor.shape.as_list()

     return [dynamic[i] if s is None else s for i, s in enumerate(UpperCAmelCase__						)]






def 		_A			(lowerCAmelCase__					:tf.Tensor			,  lowerCAmelCase__					:Optional[int] = None			,  lowerCAmelCase__					:Optional[str] = None						)				->					tf.Tensor:




     '''simple docstring'''



     return tf.nn.softmax(logits=logits + 1E-9			,  axis=UpperCAmelCase__			,  name=UpperCAmelCase__						)






def 		_A			(lowerCAmelCase__					:Any			,  lowerCAmelCase__					:List[str]			,  lowerCAmelCase__					:Dict			,  lowerCAmelCase__					:Tuple=1E-5			,  lowerCAmelCase__					:List[str]=-1						)				->					List[str]:




     '''simple docstring'''



     if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCAmelCase__			,  UpperCAmelCase__						):
          raise NotImplementedError('Only 1D weight and bias tensors are supported for now, with only a single axis.'						)

     # Get mean and variance on the axis to be normalized
     _a      =							tf.nn.moments(UpperCAmelCase__			,  axes=[axis]			,  keepdims=UpperCAmelCase__						)

     if axis != -1:
          # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
          # on every dimension except axis
          _a      =							[1] * inputs.shape.rank
          _a      =							shape_list(UpperCAmelCase__						)[axis]
          _a      =							tf.reshape(UpperCAmelCase__			,  UpperCAmelCase__						)
          _a      =							tf.reshape(UpperCAmelCase__			,  UpperCAmelCase__						)

     # Compute layer normalization using the batch_normalization
     # function.
     _a      =							tf.nn.batch_normalization(
         UpperCAmelCase__			,  UpperCAmelCase__			,  UpperCAmelCase__			,  offset=UpperCAmelCase__			,  scale=UpperCAmelCase__			,  variance_epsilon=UpperCAmelCase__			,  )
     return outputs






def 		_A			(lowerCAmelCase__					:Dict			,  lowerCAmelCase__					:Tuple=0			,  lowerCAmelCase__					:Any=-1						)				->					Dict:




     '''simple docstring'''



     if end_dim < 0:
          end_dim += input.shape.rank
     if start_dim < 0:
          start_dim += input.shape.rank

     if start_dim == end_dim:
          return input

     _a      =							tf.shape(UpperCAmelCase__						)
     _a      =							tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]						)
     _a      =							tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]			,  axis=0						)
     return tf.reshape(UpperCAmelCase__			,  UpperCAmelCase__						)






def 		_A			(lowerCAmelCase__					:tf.Tensor						)				->					tf.Tensor:




     '''simple docstring'''



     if not isinstance(UpperCAmelCase__			,  tf.Tensor						):
          _a      =							tf.convert_to_tensor(UpperCAmelCase__						)  # Catches stray NumPy inputs
     if encoder_attention_mask.shape.rank == 3:
          _a      =							encoder_attention_mask[:, None, :, :]
     if encoder_attention_mask.shape.rank == 2:
          _a      =							encoder_attention_mask[:, None, None, :]
     # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
     # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
     # /transformer/transformer_layers.py#L270
     # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
     # encoder_extended_attention_mask.transpose(-1, -2))
     _a      =							(
         tf.cast(1			,  encoder_attention_mask.dtype						) - encoder_extended_attention_mask
     ) * encoder_extended_attention_mask.dtype.min

     return encoder_extended_attention_mask






def 		_A			(lowerCAmelCase__					:tf.Tensor			,  lowerCAmelCase__					:int			,  lowerCAmelCase__					:str = "input_ids"						)				->					None:




     '''simple docstring'''



     tf.debugging.assert_less(
         UpperCAmelCase__			,  tf.cast(UpperCAmelCase__			,  dtype=tensor.dtype						)			,  message=(
             f'The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCAmelCase__						)}) must be smaller than the embedding '
             f'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
         )			,  )






def 		_A			(lowerCAmelCase__					:List[Any]			,  lowerCAmelCase__					:str			,  lowerCAmelCase__					:List[str]						)				->					Any:




     '''simple docstring'''



     _a      =							6_45_12
     # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
     # because in that case even chunking the array would not make the saving
     # possible.
     _a      =							[x for x in data if len(UpperCAmelCase__						) > HDF5_OBJECT_HEADER_LIMIT]

     # Expecting this to never be true.
     if bad_attributes:
          raise RuntimeError(
              'The following attributes cannot be saved to HDF5 file because '
              f'they are larger than {HDF5_OBJECT_HEADER_LIMIT} '
              f'bytes: {bad_attributes}'						)

     _a      =							np.asarray(UpperCAmelCase__						)

     _a      =							1
     _a      =							np.array_split(UpperCAmelCase__			,  UpperCAmelCase__						)

     # This will never loop forever thanks to the test above.
     while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data						):
          num_chunks += 1
          _a      =							np.array_split(UpperCAmelCase__			,  UpperCAmelCase__						)

     if num_chunks > 1:
          for chunk_id, chunk_data in enumerate(UpperCAmelCase__						):
               _a      =							chunk_data
     else:
          _a      =							data






def 		_A			(lowerCAmelCase__					:str			,  lowerCAmelCase__					:Union[str, Any]						)				->					str:




     '''simple docstring'''



     if name in group.attrs:
          _a      =							[n.decode('utf8'						) if hasattr(UpperCAmelCase__			,  'decode'						) else n for n in group.attrs[name]]
     else:
          _a      =							[]
          _a      =							0
          while "%s%d" % (name, chunk_id) in group.attrs:
               data.extend(
                   [n.decode('utf8'						) if hasattr(UpperCAmelCase__			,  'decode'						) else n for n in group.attrs['%s%d' % (name, chunk_id)]]						)
               chunk_id += 1
     return data






def 		_A			(lowerCAmelCase__					:Optional[Any]						)				->					Any:




     '''simple docstring'''



     def _expand_single_ad_tensor(lowerCAmelCase__					:Optional[Any]						):
          if isinstance(UpperCAmelCase__			,  tf.Tensor						) and t.shape.rank == 1:
               return tf.expand_dims(UpperCAmelCase__			,  axis=-1						)
          return t

     return tf.nest.map_structure(_expand_single_ad_tensor			,  UpperCAmelCase__						)





'''simple docstring'''






from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

a_   :      Optional[Any]				    =      TypeVar("T")






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     return (position - 1) // 2






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     return (2 * position) + 1






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     return (2 * position) + 2




class 	a       (       Generic[T]    ):
 def __init__(	self  )   ->							None:
      _a      =							[]
      _a      =							{}
      _a      =							0
 def __len__(	self  )   ->							int:
      return self.elements
 def __repr__(	self  )   ->							str:
      return str(self.heap  )
 def 							__UpperCAmelCase						(	self  )   ->							bool:
      # Check if the priority queue is empty
      return self.elements == 0
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Add an element with given priority to the queue
      self.heap.append((elem, weight)  )
      _a      =							self.elements
      self.elements += 1
      self._bubble_up(__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							T:
      # Remove and return the element with lowest weight (highest priority)
      if self.elements > 1:
           self._swap_nodes(0 ,			self.elements - 1  )
      _a ,				_a      =							self.heap.pop()
      del self.position_map[elem]
      self.elements -= 1
      if self.elements > 0:
           _a ,				_a      =							self.heap[0]
           self._bubble_down(__magic_name__  )
      return elem
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Update the weight of the given key
      _a      =							self.position_map[elem]
      _a      =							(elem, weight)
      if position > 0:
           _a      =							get_parent_position(__magic_name__  )
           _a ,				_a      =							self.heap[parent_position]
           if parent_weight > weight:
                self._bubble_up(__magic_name__  )
           else:
                self._bubble_down(__magic_name__  )
      else:
           self._bubble_down(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							None:
      # Place a node at the proper position (upward movement) [to be used internally
      # only]
      _a      =							self.position_map[elem]
      if curr_pos == 0:
           return None
      _a      =							get_parent_position(__magic_name__  )
      _a ,				_a      =							self.heap[curr_pos]
      _a ,				_a      =							self.heap[parent_position]
      if parent_weight > weight:
           self._swap_nodes(__magic_name__ ,			__magic_name__  )
           return self._bubble_up(__magic_name__  )
      return None
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							None:
      # Place a node at the proper position (downward movement) [to be used
      # internally only]
      _a      =							self.position_map[elem]
      _a ,				_a      =							self.heap[curr_pos]
      _a      =							get_child_left_position(__magic_name__  )
      _a      =							get_child_right_position(__magic_name__  )
      if child_left_position < self.elements and child_right_position < self.elements:
           _a ,				_a      =							self.heap[child_left_position]
           _a ,				_a      =							self.heap[child_right_position]
           if child_right_weight < child_left_weight and child_right_weight < weight:
                self._swap_nodes(__magic_name__ ,			__magic_name__  )
                return self._bubble_down(__magic_name__  )
      if child_left_position < self.elements:
           _a ,				_a      =							self.heap[child_left_position]
           if child_left_weight < weight:
                self._swap_nodes(__magic_name__ ,			__magic_name__  )
                return self._bubble_down(__magic_name__  )
      else:
           return None
      if child_right_position < self.elements:
           _a ,				_a      =							self.heap[child_right_position]
           if child_right_weight < weight:
                self._swap_nodes(__magic_name__ ,			__magic_name__  )
                return self._bubble_down(__magic_name__  )
      return None




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Swap the nodes at the given positions
      _a      =							self.heap[nodea_pos][0]
      _a      =							self.heap[nodea_pos][0]
      _a ,				_a      =							(
          self.heap[nodea_pos],
          self.heap[nodea_pos],
      )
      _a      =							nodea_pos
      _a      =							nodea_pos





class 	a       (       Generic[T]    ):
 def __init__(	self  )   ->							None:
      _a      =							{}
      _a      =							0
 def __repr__(	self  )   ->							str:
      return str(self.connections  )
 def __len__(	self  )   ->							int:
      return self.nodes
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							None:
      # Add a node in the graph if it is not in the graph
      if node not in self.connections:
           _a      =							{}
           self.nodes += 1




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Add an edge between 2 nodes in the graph
      self.add_node(__magic_name__  )
      self.add_node(__magic_name__  )
      _a      =							weight
      _a      =							weight






def 		_A			(lowerCAmelCase__					:GraphUndirectedWeighted[T]			,  )				->					tuple[dict[T, int], dict[T, T | None]]:




     '''simple docstring'''



     _a      =							{node: maxsize for node in graph.connections}
     _a      =							{node: None for node in graph.connections}

     _a      =							MinPriorityQueue()
     for node, weight in dist.items():
          priority_queue.push(lowerCAmelCase__			,  lowerCAmelCase__						)

     if priority_queue.is_empty():
          return dist, parent

     # initialization
     _a      =							priority_queue.extract_min()
     _a      =							0
     for neighbour in graph.connections[node]:
          if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
               _a      =							dist[node] + graph.connections[node][neighbour]
               priority_queue.update_key(lowerCAmelCase__			,  dist[neighbour]						)
               _a      =							node

    # running prim's algorithm
     while not priority_queue.is_empty():
          _a      =							priority_queue.extract_min()
          for neighbour in graph.connections[node]:
               if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                    _a      =							dist[node] + graph.connections[node][neighbour]
                    priority_queue.update_key(lowerCAmelCase__			,  dist[neighbour]						)
                    _a      =							node
     return dist, parent

