






from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


lowerCamelCase__       		=  logging.get_logger(__name__)






def 	UpperCamelCase	(							snake_case__  : Union[tf.Tensor, np.ndarray]	):




     '''simple docstring'''






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

     __snake_case      :List[Any] 							=    tf.shape(snake_case__	)

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

     __snake_case      :str 							=    tensor.shape.as_list()

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






def 	UpperCamelCase	(							snake_case__  : tf.Tensor						,snake_case__  : Optional[int] = None						,snake_case__  : Optional[str] = None	):




     '''simple docstring'''






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






def 	UpperCamelCase	(							snake_case__  : List[Any]						,snake_case__  : int						,snake_case__  : Dict						,snake_case__  : List[Any]=1e-5						,snake_case__  : Tuple=-1	):




     '''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
     __snake_case							,			__snake_case      :Tuple 							=    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
          __snake_case      :int 							=    [1] * inputs.shape.rank
          __snake_case      :str 							=    shape_list(snake_case__	)[axis]
          __snake_case      :int 							=    tf.reshape(snake_case__						,snake_case__	)
          __snake_case      :Optional[int] 							=    tf.reshape(snake_case__						,snake_case__	)

     # Compute layer normalization using the batch_normalization
     # function.
     __snake_case      :Optional[int] 							=    tf.nn.batch_normalization(
         snake_case__						,snake_case__						,snake_case__						,offset=snake_case__						,scale=snake_case__						,variance_epsilon=snake_case__						,)
     return outputs






def 	UpperCamelCase	(							snake_case__  : Optional[int]						,snake_case__  : Tuple=0						,snake_case__  : List[str]=-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

     __snake_case      :int 							=    tf.shape(snake_case__	)
     __snake_case      :Any 							=    tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]	)
     __snake_case      :Dict 							=    tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]						,axis=0	)
     return tf.reshape(snake_case__						,snake_case__	)






def 	UpperCamelCase	(							snake_case__  : tf.Tensor	):




     '''simple docstring'''






     if not isinstance(snake_case__						,tf.Tensor	):
          __snake_case      :Union[str, Any] 							=    tf.convert_to_tensor(snake_case__	)  # Catches stray NumPy inputs
     if encoder_attention_mask.shape.rank == 3:
          __snake_case      :Tuple 							=    encoder_attention_mask[:, None, :, :]
     if encoder_attention_mask.shape.rank == 2:
          __snake_case      :List[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      :Any 							=    (
         tf.cast(1						,encoder_attention_mask.dtype	) - encoder_extended_attention_mask
     ) * encoder_extended_attention_mask.dtype.min

     return encoder_extended_attention_mask






def 	UpperCamelCase	(							snake_case__  : tf.Tensor						,snake_case__  : int						,snake_case__  : str = "input_ids"	):




     '''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 	UpperCamelCase	(							snake_case__  : List[Any]						,snake_case__  : List[str]						,snake_case__  : Tuple	):




     '''simple docstring'''






     __snake_case      :Optional[Any] 							=    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      :str 							=    [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}'''	)

     __snake_case      :List[str] 							=    np.asarray(snake_case__	)

     __snake_case      :Optional[Any] 							=    1
     __snake_case      :Any 							=    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
          __snake_case      :Union[str, Any] 							=    np.array_split(snake_case__						,snake_case__	)

     if num_chunks > 1:
          for chunk_id, chunk_data in enumerate(snake_case__	):
               __snake_case      :List[str] 							=    chunk_data
     else:
          __snake_case      :List[Any] 							=    data






def 	UpperCamelCase	(							snake_case__  : Dict						,snake_case__  : Dict	):




     '''simple docstring'''






     if name in group.attrs:
          __snake_case      :List[str] 							=    [n.decode("""utf8"""	) if hasattr(snake_case__						,"""decode"""	) else n for n in group.attrs[name]]
     else:
          __snake_case      :Dict 							=    []
          __snake_case      :Dict 							=    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 	UpperCamelCase	(							snake_case__  : int	):




     '''simple docstring'''






     def _expand_single_ad_tensor(snake_case__  : Optional[Any]	):
          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__	)










lowerCamelCase__       		=  8.3_1_4_4_5_9_8






def 	UpperCamelCase	(							snake_case__  : float						,snake_case__  : float	):




     '''simple docstring'''






     if temperature < 0:
          raise Exception("""Temperature cannot be less than 0 K"""	)
     if molar_mass <= 0:
          raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol"""	)
     else:
          return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
     import doctest

     # run doctest
     doctest.testmod()

     # example
     lowerCamelCase__       		=  300
     lowerCamelCase__       		=  28
     lowerCamelCase__       		=  rms_speed_of_molecule(temperature, molar_mass)
     print(f'''Vrms of Nitrogen gas at 300 K is {vrms} m/s''')


