




"""simple docstring"""

from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


a         =      logging.get_logger(__name__)






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






      '''simple docstring'''


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

      _A        =       tf.shape(_snake_case							)

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

      _A        =       tensor.shape.as_list()

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






def 						_snake_case					( _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 						_snake_case					( _snake_case							: Dict    ,  _snake_case							: Any    ,  _snake_case							: List[str]    ,  _snake_case							: List[str]=1E-5    ,  _snake_case							: Optional[Any]=-1							)   -> Any:






      '''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
      _A       ,   _A        =       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
            _A        =       [1] * inputs.shape.rank
            _A        =       shape_list(_snake_case							)[axis]
            _A        =       tf.reshape(_snake_case    ,  _snake_case							)
            _A        =       tf.reshape(_snake_case    ,  _snake_case							)

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






def 						_snake_case					( _snake_case							: Any    ,  _snake_case							: Union[str, Any]=0    ,  _snake_case							: int=-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

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






def 						_snake_case					( _snake_case							: tf.Tensor							)   -> tf.Tensor:






      '''simple docstring'''


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

      return encoder_extended_attention_mask






def 						_snake_case					( _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 						_snake_case					( _snake_case							: Union[str, Any]    ,  _snake_case							: Optional[Any]    ,  _snake_case							: Tuple							)   -> List[Any]:






      '''simple docstring'''


      _A        =       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.
      _A        =       [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}'''							)

      _A        =       np.asarray(_snake_case							)

      _A        =       1
      _A        =       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
            _A        =       np.array_split(_snake_case    ,  _snake_case							)

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






def 						_snake_case					( _snake_case							: Any    ,  _snake_case							: Tuple							)   -> Dict:






      '''simple docstring'''


      if name in group.attrs:
            _A        =       [n.decode('utf8'							) if hasattr(_snake_case    ,  'decode'							) else n for n in group.attrs[name]]
      else:
            _A        =       []
            _A        =       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 						_snake_case					( _snake_case							: List[Any]							)   -> Optional[Any]:






      '''simple docstring'''


      def _expand_single_ad_tensor(_snake_case							: str							):
            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

import time

import numpy as np

a         =      [8, 5, 9, 7]
a         =      [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
a         =      [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]





class     lowercase_	:



 '''simple docstring'''

 def __init__(			self	:	str   ,							_UpperCAmelCase	:	list[int]   ,							_UpperCAmelCase	:	list[list[int]]   ,							_UpperCAmelCase	:	list[list[int]]   ,							):
       _A        =       claim_vector
       _A        =       allocated_resources_table
       _A        =       maximum_claim_table

 def       lowerCAmelCase_							(			self	:	Tuple		):
       return [
           sum(p_item[i] for p_item in self.__allocated_resources_table		)
           for i in range(len(self.__allocated_resources_table[0]		)		)
       ]

 def       lowerCAmelCase_							(			self	:	Tuple		):
       return np.array(self.__claim_vector		) - np.array(
           self.__processes_resource_summation()		)

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       return [
           list(np.array(self.__maximum_claim_table[i]		) - np.array(_UpperCAmelCase		)		)
           for i, allocated_resource in enumerate(self.__allocated_resources_table		)
       ]

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       return {self.__need().index(_UpperCAmelCase		): i for i in self.__need()}

 def       lowerCAmelCase_							(			self	:	List[str]   ,							**_UpperCAmelCase	:	int		):
       _A        =       self.__need()
       _A        =       self.__allocated_resources_table
       _A        =       self.__available_resources()
       _A        =       self.__need_index_manager()
       for kw, val in kwargs.items():
             if kw and val is True:
                   self.__pretty_data()
       print('_' * 50 + '\n'		)
       while need_list:
             _A        =       False
             for each_need in need_list:
                   _A        =       True
                   for index, need in enumerate(_UpperCAmelCase		):
                         if need > available_resources[index]:
                               _A        =       False
                               break
                   if execution:
                         _A        =       True
                         # get the original index of the process from ind_ctrl db
                         for original_need_index, need_clone in need_index_manager.items():
                               if each_need == need_clone:
                                     _A        =       original_need_index
                         print(F'''Process {process_number + 1} is executing.'''		)
                         # remove the process run from stack
                         need_list.remove(_UpperCAmelCase		)
                         # update available/freed resources stack
                         _A        =       np.array(_UpperCAmelCase		) + np.array(
                             alloc_resources_table[process_number]		)
                         print(
                             'Updated available resource stack for processes: '
                             + ' '.join([str(_UpperCAmelCase		) for x in available_resources]		)		)
                         break
             if safe:
                   print('The process is in a safe state.\n'		)
             else:
                   print('System in unsafe state. Aborting...\n'		)
                   break



 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       print(' ' * 9 + 'Allocated Resource Table'		)
       for item in self.__allocated_resources_table:
             print(
                 F'''P{self.__allocated_resources_table.index(_UpperCAmelCase		) + 1}'''
                 + ' '.join(F'''{it:>8}''' for it in item		)
                 + '\n'		)
       print(' ' * 9 + 'System Resource Table'		)
       for item in self.__maximum_claim_table:
             print(
                 F'''P{self.__maximum_claim_table.index(_UpperCAmelCase		) + 1}'''
                 + ' '.join(F'''{it:>8}''' for it in item		)
                 + '\n'		)
       print(
           'Current Usage by Active Processes: '
           + ' '.join(str(_UpperCAmelCase		) for x in self.__claim_vector		)		)
       print(
           'Initial Available Resources:       '
           + ' '.join(str(_UpperCAmelCase		) for x in self.__available_resources()		)		)
       time.sleep(1		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

