



"""simple docstring"""
from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


UpperCAmelCase  :						Optional[Any]	=       logging.get_logger(__name__)


def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase							)       ->  int:







  '''simple docstring'''


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

  lowercase_	    =       tf.shape(UpperCamelCase__							)

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

  lowercase_	    =       tensor.shape.as_list()

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


def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase       ,     __lowerCAmelCase = None       ,     __lowerCAmelCase = None							)       ->  Dict:







  '''simple docstring'''


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


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







  '''simple docstring'''


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

  if axis != -1:
    # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
    # on every dimension except axis
    lowercase_	    =       [1] * inputs.shape.rank
    lowercase_	    =       shape_list(UpperCamelCase__							)[axis]
    lowercase_	    =       tf.reshape(UpperCamelCase__       ,     UpperCamelCase__							)
    lowercase_	    =       tf.reshape(UpperCamelCase__       ,     UpperCamelCase__							)

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


def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase       ,     __lowerCAmelCase=0       ,     __lowerCAmelCase=-1							)       ->  str:







  '''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

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


def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase							)       ->  Optional[Any]:







  '''simple docstring'''


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

  return encoder_extended_attention_mask


def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase = "input_ids"							)       ->  Union[str, Any]:







  '''simple docstring'''


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


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







  '''simple docstring'''


  lowercase_	    =       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.
  lowercase_	    =       [x for x in data if len(UpperCamelCase__							) > 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}'''							)

  lowercase_	    =       np.asarray(UpperCamelCase__							)

  lowercase_	    =       1
  lowercase_	    =       np.array_split(UpperCamelCase__       ,     UpperCamelCase__							)

  # 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
    lowercase_	    =       np.array_split(UpperCamelCase__       ,     UpperCamelCase__							)

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


def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase       ,     __lowerCAmelCase							)       ->  str:







  '''simple docstring'''


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


def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase							)       ->  str:







  '''simple docstring'''


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

  return tf.nest.map_structure(_expand_single_ad_tensor       ,     UpperCamelCase__							)









"""simple docstring"""




from __future__ import annotations

import time

import numpy as np

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






class        UpperCamelCase			:


 def __init__(     self		:						List[Any]							,						UpperCAmelCase__		:						list[int]							,						UpperCAmelCase__		:						list[list[int]]							,						UpperCAmelCase__		:						list[list[int]]							,						)					->       None:
   _a  :       List[str]             =	claim_vector
   _a  :       List[Any]             =	allocated_resources_table
   _a  :       Union[str, Any]             =	maximum_claim_table


 def _lowercase	(     self		:						Tuple							)					->       list[int]:
   return [
       sum(p_item[i] for p_item in self.__allocated_resources_table							)
       for i in range(len(self.__allocated_resources_table[0]							)							)
   ]


 def _lowercase	(     self		:						int							)					->       list[int]:
   return np.array(self.__claim_vector							) - np.array(
       self.__processes_resource_summation()							)


 def _lowercase	(     self		:						List[str]							)					->       list[list[int]]:
   return [
       list(np.array(self.__maximum_claim_table[i]							) - np.array(UpperCAmelCase__							)							)
       for i, allocated_resource in enumerate(self.__allocated_resources_table							)
   ]


 def _lowercase	(     self		:						Optional[Any]							)					->       dict[int, list[int]]:
   return {self.__need().index(UpperCAmelCase__							): i for i in self.__need()}


 def _lowercase	(     self		:						Dict							,						**UpperCAmelCase__		:						Optional[Any]							)					->       None:
   _a  :       List[Any]             =	self.__need()
   _a  :       Optional[int]             =	self.__allocated_resources_table
   _a  :       str             =	self.__available_resources()
   _a  :       Optional[Any]             =	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  :       int             =	False
     for each_need in need_list:
       _a  :       Optional[int]             =	True
       for index, need in enumerate(UpperCAmelCase__							):
         if need > available_resources[index]:
           _a  :       List[Any]             =	False
           break
       if execution:
         _a  :       str             =	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  :       Any             =	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  :       Union[str, Any]             =	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 _lowercase	(     self		:						Any							)					->       Optional[int]:
   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()

