'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


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







def __UpperCamelCase(   _A					:				Optional[int]    ):
   '''simple docstring'''






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

   UpperCAmelCase__							:				Optional[Any]   				=  tf.shape(_snake_case    )

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

   UpperCAmelCase__							:				Dict   				=  tensor.shape.as_list()

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







def __UpperCamelCase(   _A					:				Optional[int]  ,			_A					:				List[Any] = None  ,			_A					:				List[str] = None    ):
   '''simple docstring'''
   return tf.nn.softmax(logits=logits + 1e-9  ,			axis=_snake_case  ,			name=_snake_case    )







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				int  ,			_A					:				Dict  ,			_A					:				Any=1e-5  ,			_A					:				int=-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
   UpperCAmelCase__				,      UpperCAmelCase__							:				List[Any]   				=  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__							:				Union[str, Any]   				=  [1] * inputs.shape.rank
      UpperCAmelCase__							:				str   				=  shape_list(_snake_case    )[axis]
      UpperCAmelCase__							:				Dict   				=  tf.reshape(_snake_case  ,			_snake_case    )
      UpperCAmelCase__							:				int   				=  tf.reshape(_snake_case  ,			_snake_case    )

   # Compute layer normalization using the batch_normalization
   # function.
   UpperCAmelCase__							:				Dict   				=  tf.nn.batch_normalization(
       _snake_case  ,			_snake_case  ,			_snake_case  ,			offset=_snake_case  ,			scale=_snake_case  ,			variance_epsilon=_snake_case  ,			)
   return outputs







def __UpperCamelCase(   _A					:				List[Any]  ,			_A					:				List[Any]=0  ,			_A					:				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

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







def __UpperCamelCase(   _A					:				List[Any]    ):
   '''simple docstring'''






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

   return encoder_extended_attention_mask







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				Optional[Any]  ,			_A					:				int = "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(   _A					:				List[Any]  ,			_A					:				str  ,			_A					:				Any    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Any   				=  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__							:				Optional[int]   				=  [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__							:				List[str]   				=  np.asarray(_snake_case    )

   UpperCAmelCase__							:				List[Any]   				=  1
   UpperCAmelCase__							:				Dict   				=  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__							:				Tuple   				=  np.array_split(_snake_case  ,			_snake_case    )

   if num_chunks > 1:
      for chunk_id, chunk_data in enumerate(_snake_case    ):
         UpperCAmelCase__							:				Union[str, Any]   				=  chunk_data
   else:
      UpperCAmelCase__							:				Tuple   				=  data







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				Tuple    ):
   '''simple docstring'''






   if name in group.attrs:
      UpperCAmelCase__							:				Optional[int]   				=  [n.decode('''utf8'''    ) if hasattr(_snake_case  ,			'''decode'''    ) else n for n in group.attrs[name]]
   else:
      UpperCAmelCase__							:				List[Any]   				=  []
      UpperCAmelCase__							:				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(   _A					:				Union[str, Any]    ):
   '''simple docstring'''






   def _expand_single_ad_tensor(_A					:				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    )





'''simple docstring'''

from __future__ import annotations

from collections import namedtuple







def __UpperCamelCase(   _A					:				float  ,			_A					:				float  ,			_A					:				float    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  namedtuple('''result'''  ,			'''name value'''    )
   if (voltage, current, power).count(0    ) != 1:
      raise ValueError('''Only one argument must be 0'''    )
   elif power < 0:
      raise ValueError(
          '''Power cannot be negative in any electrical/electronics system'''    )
   elif voltage == 0:
      return result('''voltage'''  ,			power / current    )
   elif current == 0:
      return result('''current'''  ,			power / voltage    )
   elif power == 0:
      return result('''power'''  ,			float(round(abs(voltage * current    )  ,			2    )    )    )
   else:
      raise ValueError('''Exactly one argument must be 0'''    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()




