'''simple docstring'''





from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


SCREAMING_SNAKE_CASE_:     Optional[int]  =logging.get_logger(__name__)





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


     '''simple docstring'''




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

     UpperCAmelCase_           =   tf.shape(snake_case_			)

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

     UpperCAmelCase_           =   tensor.shape.as_list()

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





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


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : List[str]					,    snake_case_       : int					,    snake_case_       : List[str]					,    snake_case_       : Optional[Any]=1E-5					,    snake_case_       : str=-1			)    ->      int:


     '''simple docstring'''




     if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(snake_case_					,    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
     UpperCAmelCase_    , UpperCAmelCase_           =   tf.nn.moments(snake_case_					,    axes=[axis]					,    keepdims=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
          UpperCAmelCase_           =   [1] * inputs.shape.rank
          UpperCAmelCase_           =   shape_list(snake_case_			)[axis]
          UpperCAmelCase_           =   tf.reshape(snake_case_					,    snake_case_			)
          UpperCAmelCase_           =   tf.reshape(snake_case_					,    snake_case_			)

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





def 	lowerCAmelCase_				(					snake_case_       : Optional[int]					,    snake_case_       : Optional[Any]=0					,    snake_case_       : Any=-1			)    ->      List[Any]:


     '''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

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





def 	lowerCAmelCase_				(					snake_case_       : tf.Tensor			)    ->      tf.Tensor:


     '''simple docstring'''




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

     return encoder_extended_attention_mask





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


     '''simple docstring'''




     tf.debugging.assert_less(
         snake_case_					,    tf.cast(snake_case_					,    dtype=tensor.dtype			)					,    message=(
             f"""The maximum value of {tensor_name} ({tf.math.reduce_max(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_				(					snake_case_       : List[Any]					,    snake_case_       : List[str]					,    snake_case_       : Union[str, Any]			)    ->      Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   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.
     UpperCAmelCase_           =   [x for x in data if len(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}"""			)

     UpperCAmelCase_           =   np.asarray(snake_case_			)

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

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





def 	lowerCAmelCase_				(					snake_case_       : Union[str, Any]					,    snake_case_       : str			)    ->      Tuple:


     '''simple docstring'''




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






def 	lowerCAmelCase_				(					snake_case_       : Dict			)    ->      Optional[int]:


     '''simple docstring'''




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

     return tf.nest.map_structure(_expand_single_ad_tensor					,    snake_case_			)





'''simple docstring'''





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : int			)    ->      str:


     '''simple docstring'''




     if a < 0 or b < 0:
          raise ValueError("the value of both inputs must be positive"			)
     UpperCAmelCase_           =   str(bin(snake_case_			)			)[2:]  # remove the leading "0b"
     UpperCAmelCase_           =   str(bin(snake_case_			)			)[2:]
     UpperCAmelCase_           =   max(len(snake_case_			)					,    len(snake_case_			)			)
     return "0b" + "".join(
         str(int("1" in (char_a, char_b)			)			)
         for char_a, char_b in zip(a_binary.zfill(snake_case_			)					,    b_binary.zfill(snake_case_			)			)			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()




