




from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


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




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[int]:
   if isinstance(lowercase						,np.ndarray					):
      return list(tensor.shape					)

   snake_case				:    Dict           =	tf.shape(lowercase					)

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

   snake_case				:    Union[str, Any]           =	tensor.shape.as_list()

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




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase = None						,lowercase = None					)  ->       tf.Tensor:
   return tf.nn.softmax(logits=logits + 1E-9						,axis=lowercase						,name=lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase=1E-5						,lowercase=-1					)  ->       Optional[Any]:
   # This is a very simplified functional layernorm, designed to duplicate
   # the functionality of PyTorch nn.functional.layer_norm when this is needed to port
   # models in Transformers.

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

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

   # Compute layer normalization using the batch_normalization
   # function.
   snake_case				:    str           =	tf.nn.batch_normalization(
       lowercase						,lowercase						,lowercase						,offset=lowercase						,scale=lowercase						,variance_epsilon=lowercase						,)
   return outputs




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase=0						,lowercase=-1					)  ->       List[str]:
   # Replicates the behavior of torch.flatten in TF

   # If end_dim or start_dim is negative, count them from the end
   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

   snake_case				:    Optional[Any]           =	tf.shape(lowercase					)
   snake_case				:    str           =	tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]					)
   snake_case				:    int           =	tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]						,axis=0					)
   return tf.reshape(lowercase						,lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       tf.Tensor:
   if not isinstance(lowercase						,tf.Tensor					):
      snake_case				:    Any           =	tf.convert_to_tensor(lowercase					)  # Catches stray NumPy inputs
   if encoder_attention_mask.shape.rank == 3:
      snake_case				:    Optional[Any]           =	encoder_attention_mask[:, None, :, :]
   if encoder_attention_mask.shape.rank == 2:
      snake_case				:    Any           =	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))
   snake_case				:    Union[str, Any]           =	(
       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						,lowercase						,lowercase = "input_ids"					)  ->       None:
   tf.debugging.assert_less(
       lowercase						,tf.cast(lowercase						,dtype=tensor.dtype					)						,message=(
           f"""The maximum value of {tensor_name} ({tf.math.reduce_max(lowercase					)}) 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						,lowercase						,lowercase					)  ->       Tuple:
   snake_case				:    Optional[int]           =	64512
   # 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.
   snake_case				:    Dict           =	[x for x in data if len(lowercase					) > 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}"""					)

   snake_case				:    str           =	np.asarray(lowercase					)

   snake_case				:    Union[str, Any]           =	1
   snake_case				:    List[str]           =	np.array_split(lowercase						,lowercase					)

   # 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
      snake_case				:    str           =	np.array_split(lowercase						,lowercase					)

   if num_chunks > 1:
      for chunk_id, chunk_data in enumerate(lowercase					):
         snake_case				:    Optional[Any]           =	chunk_data
   else:
      snake_case				:    Tuple           =	data




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Tuple:
   if name in group.attrs:
      snake_case				:    List[Any]           =	[n.decode("""utf8"""					) if hasattr(lowercase						,"""decode"""					) else n for n in group.attrs[name]]
   else:
      snake_case				:    Tuple           =	[]
      snake_case				:    List[str]           =	0
      while "%s%d" % (name, chunk_id) in group.attrs:
         data.extend(
             [n.decode("""utf8"""					) if hasattr(lowercase						,"""decode"""					) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]]					)
         chunk_id += 1
   return data




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[int]:

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

   return tf.nest.map_structure(_expand_single_ad_tensor						,lowercase					)







def 						SCREAMING_SNAKE_CASE__ (   )  ->       Dict:
   snake_case				:    Optional[int]           =	[]
   snake_case				:    Tuple           =	1

   while len(lowercase					) < 1E6:
      constant.append(str(lowercase					)					)
      i += 1

   snake_case				:    int           =	"""""".join(lowercase					)

   return (
       int(constant[0]					)
       * int(constant[9]					)
       * int(constant[99]					)
       * int(constant[999]					)
       * int(constant[9999]					)
       * int(constant[99999]					)
       * int(constant[999999]					)
   )


if __name__ == "__main__":
       print(solution())





