


from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


A   :					List[str]	=      logging.get_logger(__name__)

def    lowercase_	(	_A    :					Union[tf.Tensor, np.ndarray]		):

      """simple docstring"""




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

      lowerCamelCase__	:       Union[str, Any]					=						tf.shape(_A		)

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

      lowerCamelCase__	:       Optional[int]					=						tensor.shape.as_list()

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

def    lowercase_	(	_A    :					tf.Tensor					,      _A    :					Optional[int] = None					,      _A    :					Optional[str] = None		):

      """simple docstring"""




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

def    lowercase_	(	_A    :					Tuple					,      _A    :					Dict					,      _A    :					Any					,      _A    :					str=1E-5					,      _A    :					Optional[Any]=-1		):

      """simple docstring"""




      if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(_A					,      _A		):
            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
      lowerCamelCase__					,     lowerCamelCase__	:       Union[str, Any]					=						tf.nn.moments(_A					,      axes=[axis]					,      keepdims=_A		)

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

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

def    lowercase_	(	_A    :					int					,      _A    :					Optional[Any]=0					,      _A    :					Optional[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

      lowerCamelCase__	:       Dict					=						tf.shape(_A		)
      lowerCamelCase__	:       List[str]					=						tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]		)
      lowerCamelCase__	:       Optional[int]					=						tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]					,      axis=0		)
      return tf.reshape(_A					,      _A		)

def    lowercase_	(	_A    :					tf.Tensor		):

      """simple docstring"""




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

      return encoder_extended_attention_mask

def    lowercase_	(	_A    :					tf.Tensor					,      _A    :					int					,      _A    :					str = "input_ids"		):

      """simple docstring"""




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

def    lowercase_	(	_A    :					int					,      _A    :					List[Any]					,      _A    :					List[str]		):

      """simple docstring"""




      lowerCamelCase__	:       Tuple					=						64512
      # 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.
      lowerCamelCase__	:       Union[str, Any]					=						[x for x in data if len(_A		) > 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}"		)

      lowerCamelCase__	:       List[Any]					=						np.asarray(_A		)

      lowerCamelCase__	:       Any					=						1
      lowerCamelCase__	:       Dict					=						np.array_split(_A					,      _A		)

      # 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
            lowerCamelCase__	:       Optional[Any]					=						np.array_split(_A					,      _A		)

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

def    lowercase_	(	_A    :					str					,      _A    :					Any		):

      """simple docstring"""




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


def    lowercase_	(	_A    :					Dict		):

      """simple docstring"""




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

      return tf.nest.map_structure(_expand_single_ad_tensor					,      _A		)






from __future__ import annotations

import time

import numpy as np

A   :					Dict	=      [8, 5, 9, 7]
A   :					Optional[Any]	=      [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
A   :					Any	=      [
    [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      ,   __lowerCamelCase							:    list[int]      ,   __lowerCamelCase							:    list[list[int]]      ,   __lowerCamelCase							:    list[list[int]]      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						claim_vector
             lowerCamelCase__	:       str					=						allocated_resources_table
             lowerCamelCase__	:       int					=						maximum_claim_table





       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             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							:    Optional[int] ):


             '''simple docstring'''



             return np.array(self.__claim_vector ) - np.array(
                 self.__processes_resource_summation() )





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             return [
                 list(np.array(self.__maximum_claim_table[i] ) - np.array(__lowerCamelCase ) )
                 for i, allocated_resource in enumerate(self.__allocated_resources_table )
             ]





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             return {self.__need().index(__lowerCamelCase ): i for i in self.__need()}





       def 		lowerCAmelCase  (     self							:    List[str]      ,   **__lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						self.__need()
             lowerCamelCase__	:       str					=						self.__allocated_resources_table
             lowerCamelCase__	:       List[Any]					=						self.__available_resources()
             lowerCamelCase__	:       str					=						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:
                   lowerCamelCase__	:       int					=						False
                   for each_need in need_list:
                         lowerCamelCase__	:       Dict					=						True
                         for index, need in enumerate(__lowerCamelCase ):
                               if need > available_resources[index]:
                                     lowerCamelCase__	:       str					=						False
                                     break
                         if execution:
                               lowerCamelCase__	:       Tuple					=						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:
                                           lowerCamelCase__	:       Any					=						original_need_index
                               print(f"Process {process_number + 1} is executing." )
                               # remove the process run from stack
                               need_list.remove(__lowerCamelCase )
                               # update available/freed resources stack
                               lowerCamelCase__	:       Union[str, Any]					=						np.array(__lowerCamelCase ) + np.array(
                                   alloc_resources_table[process_number] )
                               print(
                                   "Updated available resource stack for processes: "
                                   + " ".join([str(__lowerCamelCase ) 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							:    Optional[int] ):


             '''simple docstring'''



             print(" " * 9 + "Allocated Resource Table" )
             for item in self.__allocated_resources_table:
                   print(
                       f"P{self.__allocated_resources_table.index(__lowerCamelCase ) + 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(__lowerCamelCase ) + 1}"
                       + " ".join(f"{it:>8}" for it in item )
                       + "\n" )
             print(
                 "Current Usage by Active Processes: "
                 + " ".join(str(__lowerCamelCase ) for x in self.__claim_vector ) )
             print(
                 "Initial Available Resources:       "
                 + " ".join(str(__lowerCamelCase ) for x in self.__available_resources() ) )
             time.sleep(1 )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


