




from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[Any]  ):


      '''simple docstring'''



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

      lowerCamelCase_	  =					tf.shape(lowerCamelCase__  )

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

      lowerCamelCase_	  =					tensor.shape.as_list()

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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]			,							lowercase			: Optional[Any] = None			,							lowercase			: List[str] = None  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: Dict			,							lowercase			: Tuple			,							lowercase			: Dict=1e-5			,							lowercase			: Any=-1  ):


      '''simple docstring'''



      if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(lowerCamelCase__			,							lowerCamelCase__  ):
            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
      lowerCamelCase_	  =					tf.nn.moments(lowerCamelCase__			,							axes=[axis]			,							keepdims=lowerCamelCase__  )

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

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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]			,							lowercase			: List[str]=0			,							lowercase			: Any=-1  ):


      '''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

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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Union[str, Any]  ):


      '''simple docstring'''



      if not isinstance(lowerCamelCase__			,							tf.Tensor  ):
            lowerCamelCase_	  =					tf.convert_to_tensor(lowerCamelCase__  )  # Catches stray NumPy inputs
      if encoder_attention_mask.shape.rank == 3:
            lowerCamelCase_	  =					encoder_attention_mask[:, None, :, :]
      if encoder_attention_mask.shape.rank == 2:
            lowerCamelCase_	  =					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))
      lowerCamelCase_	  =					(
          tf.cast(1			,							encoder_attention_mask.dtype  ) - encoder_extended_attention_mask
      ) * encoder_extended_attention_mask.dtype.min

      return encoder_extended_attention_mask

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: int			,							lowercase			: Optional[int] = "input_ids"  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Dict			,							lowercase			: int			,							lowercase			: Union[str, Any]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					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.
      lowerCamelCase_	  =					[x for x in data if len(lowerCamelCase__  ) > 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}"""  )

      lowerCamelCase_	  =					np.asarray(lowerCamelCase__  )

      lowerCamelCase_	  =					1
      lowerCamelCase_	  =					np.array_split(lowerCamelCase__			,							lowerCamelCase__  )

      # 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
            lowerCamelCase_	  =					np.array_split(lowerCamelCase__			,							lowerCamelCase__  )

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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Tuple			,							lowercase			: Optional[Any]  ):


      '''simple docstring'''



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






def     _SCREAMING_SNAKE_CASE    (							lowercase			: Tuple  ):


      '''simple docstring'''



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

      return tf.nest.map_structure(_expand_single_ad_tensor			,							lowerCamelCase__  )









class 						A:



 '''simple docstring'''


 def __init__(       self      :						Dict						)     ->			Optional[int]:

       """simple docstring"""






       lowerCamelCase_	  =					0
       lowerCamelCase_	  =					0
       lowerCamelCase_	  =					{}

 def        a__				(       self      :						Union[str, Any]		,							A_      :						List[Any]						)     ->			int:

       """simple docstring"""






       if vertex not in self.adjacency:
             lowerCamelCase_	  =					{}
             self.num_vertices += 1

 def        a__				(       self      :						int		,							A_      :						int		,							A_      :						Optional[Any]		,							A_      :						List[str]						)     ->			Tuple:

       """simple docstring"""






       self.add_vertex(A_						)
       self.add_vertex(A_						)

       if head == tail:
             return

       lowerCamelCase_	  =					weight
       lowerCamelCase_	  =					weight

 def        a__				(       self      :						Optional[int]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_edges()
       for edge in edges:
             lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					edge
             edges.remove((tail, head, weight)						)
       for i in range(len(A_						)						):
             lowerCamelCase_	  =					list(edges[i]						)

       edges.sort(key=lambda A_						: e[2]						)
       for i in range(len(A_						) - 1						):
             if edges[i][2] >= edges[i + 1][2]:
                   lowerCamelCase_	  =					edges[i][2] + 1
       for edge in edges:
             lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					edge
             lowerCamelCase_	  =					weight
             lowerCamelCase_	  =					weight

 def __str__(       self      :						str						)     ->			Optional[int]:

       """simple docstring"""






       lowerCamelCase_	  =					''
       for tail in self.adjacency:
             for head in self.adjacency[tail]:
                   lowerCamelCase_	  =					self.adjacency[head][tail]
                   string += f"""{head} -> {tail} == {weight}\n"""
       return string.rstrip('\n'						)

 def        a__				(       self      :						Union[str, Any]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					[]
       for tail in self.adjacency:
             for head in self.adjacency[tail]:
                   output.append((tail, head, self.adjacency[head][tail])						)
       return output

 def        a__				(       self      :						List[str]						)     ->			int:

       """simple docstring"""






       return self.adjacency.keys()

 @staticmethod
 def        a__				(       A_      :						Optional[Any]=None		,							A_      :						List[str]=None						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					Graph()
       if vertices is None:
             lowerCamelCase_	  =					[]
       if edges is None:
             lowerCamelCase_	  =					[]
       for vertex in vertices:
             g.add_vertex(A_						)
       for edge in edges:
             g.add_edge(*A_						)
       return g







 class 						A:



  '''simple docstring'''


  def __init__(       self      :						Optional[int]						)     ->			int:

        """simple docstring"""






        lowerCamelCase_	  =					{}
        lowerCamelCase_	  =					{}

  def __len__(       self      :						Any						)     ->			List[str]:

        """simple docstring"""






        return len(self.parent						)

  def        a__				(       self      :						List[str]		,							A_      :						Any						)     ->			Dict:

        """simple docstring"""






        if item in self.parent:
              return self.find(A_						)

        lowerCamelCase_	  =					item
        lowerCamelCase_	  =					0
        return item

  def        a__				(       self      :						List[str]		,							A_      :						Tuple						)     ->			Optional[int]:

        """simple docstring"""






        if item not in self.parent:
              return self.make_set(A_						)
        if item != self.parent[item]:
              lowerCamelCase_	  =					self.find(self.parent[item]						)
        return self.parent[item]





  def        a__				(       self      :						Any		,							A_      :						int		,							A_      :						Tuple						)     ->			Union[str, Any]:

        """simple docstring"""






        lowerCamelCase_	  =					self.find(A_						)
        lowerCamelCase_	  =					self.find(A_						)

        if roota == roota:
              return roota

        if self.rank[roota] > self.rank[roota]:
              lowerCamelCase_	  =					roota
              return roota

        if self.rank[roota] < self.rank[roota]:
              lowerCamelCase_	  =					roota
              return roota

        if self.rank[roota] == self.rank[roota]:
              self.rank[roota] += 1
              lowerCamelCase_	  =					roota
              return roota
        return None

 @staticmethod
 def        a__				(       A_      :						int						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					graph.num_vertices

       lowerCamelCase_	  =					Graph.UnionFind()
       lowerCamelCase_	  =					[]
       while num_components > 1:
             lowerCamelCase_	  =					{}
             for vertex in graph.get_vertices():
                   lowerCamelCase_	  =					-1

             lowerCamelCase_	  =					graph.get_edges()
             for edge in edges:
                   lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					edge
                   edges.remove((tail, head, weight)						)
             for edge in edges:
                   lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					edge
                   lowerCamelCase_	  =					union_find.find(A_						)
                   lowerCamelCase_	  =					union_find.find(A_						)
                   if seta != seta:
                         if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                               lowerCamelCase_	  =					[head, tail, weight]

                         if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                               lowerCamelCase_	  =					[head, tail, weight]
             for vertex in cheap_edge:
                   if cheap_edge[vertex] != -1:
                         lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					cheap_edge[vertex]
                         if union_find.find(A_						) != union_find.find(A_						):
                               union_find.union(A_		,							A_						)
                               mst_edges.append(cheap_edge[vertex]						)
                               lowerCamelCase_	  =					num_components - 1
       lowerCamelCase_	  =					Graph.build(edges=A_						)
       return mst



