


from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


UpperCAmelCase      =   logging.get_logger(__name__)






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  List[int]:




      """simple docstring"""

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

      snake_case_	=				tf.shape(SCREAMING_SNAKE_CASE )

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

      snake_case_	=				tensor.shape.as_list()

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None )->  tf.Tensor:




      """simple docstring"""

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=1E-5 , SCREAMING_SNAKE_CASE=-1 )->  Any:




      """simple docstring"""

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

      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_	=				[1] * inputs.shape.rank
            snake_case_	=				shape_list(SCREAMING_SNAKE_CASE )[axis]
            snake_case_	=				tf.reshape(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
            snake_case_	=				tf.reshape(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=0 , SCREAMING_SNAKE_CASE=-1 )->  Tuple:




      """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

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  tf.Tensor:




      """simple docstring"""

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

      return encoder_extended_attention_mask






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = "input_ids" )->  None:




      """simple docstring"""

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      snake_case_	=				6_4512
      # 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_	=				[x for x in data if len(SCREAMING_SNAKE_CASE ) > 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_	=				np.asarray(SCREAMING_SNAKE_CASE )

      snake_case_	=				1
      snake_case_	=				np.array_split(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )

      # 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_	=				np.array_split(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

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

      return tf.nest.map_structure(_expand_single_ad_tensor , SCREAMING_SNAKE_CASE )



import os






def 	__lowerCAmelCase   ()->  List[Any]:




      """simple docstring"""

      snake_case_	=				os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE ) , '''num.txt''' )
      with open(SCREAMING_SNAKE_CASE ) as file_hand:
            return str(sum(int(SCREAMING_SNAKE_CASE ) for line in file_hand ) )[:10]


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