


import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
    import jax.numpy as jnp







class     _lowercase				(							lowercase__):


       """simple docstring"""




       def __get__(     self							:    Union[str, Any]      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    Any=None ):


             '''simple docstring'''



             if obj is None:
                   return self
             if self.fget is None:
                   raise AttributeError("unreadable attribute" )
             lowerCamelCase__	:       Optional[int]					=						"__cached_" + self.fget.__name__
             lowerCamelCase__	:       Optional[Any]					=						getattr(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )
             if cached is None:
                   lowerCamelCase__	:       List[Any]					=						self.fget(__lowerCamelCase )
                   setattr(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )
             return cached



def    lowercase_	(	_A    :					List[str]		):

      """simple docstring"""




      lowerCamelCase__	:       List[Any]					=						val.lower()
      if val in {"y", "yes", "t", "true", "on", "1"}:
            return 1
      if val in {"n", "no", "f", "false", "off", "0"}:
            return 0
      raise ValueError(F"invalid truth value {val!r}"		)

def    lowercase_	(	_A    :					str		):

      """simple docstring"""




      if is_torch_fx_proxy(_A		):
            return True
      if is_torch_available():
            import torch

            if isinstance(_A					,      torch.Tensor		):
                  return True
      if is_tf_available():
            import tensorflow as tf

            if isinstance(_A					,      tf.Tensor		):
                  return True

      if is_flax_available():
            import jax.numpy as jnp
            from jax.core import Tracer

            if isinstance(_A					,      (jnp.ndarray, Tracer)		):
                  return True

      return isinstance(_A					,      np.ndarray		)

def    lowercase_	(	_A    :					int		):

      """simple docstring"""




      return isinstance(_A					,      np.ndarray		)

def    lowercase_	(	_A    :					Dict		):

      """simple docstring"""




      return _is_numpy(_A		)

def    lowercase_	(	_A    :					List[str]		):

      """simple docstring"""




      import torch

      return isinstance(_A					,      torch.Tensor		)

def    lowercase_	(	_A    :					Any		):

      """simple docstring"""




      return False if not is_torch_available() else _is_torch(_A		)

def    lowercase_	(	_A    :					str		):

      """simple docstring"""




      import torch

      return isinstance(_A					,      torch.device		)

def    lowercase_	(	_A    :					Any		):

      """simple docstring"""




      return False if not is_torch_available() else _is_torch_device(_A		)

def    lowercase_	(	_A    :					int		):

      """simple docstring"""




      import torch

      if isinstance(_A					,      _A		):
            if hasattr(_A					,      _A		):
                  lowerCamelCase__	:       List[Any]					=						getattr(_A					,      _A		)
            else:
                  return False
      return isinstance(_A					,      torch.dtype		)

def    lowercase_	(	_A    :					Tuple		):

      """simple docstring"""




      return False if not is_torch_available() else _is_torch_dtype(_A		)

def    lowercase_	(	_A    :					str		):

      """simple docstring"""




      import tensorflow as tf

      return isinstance(_A					,      tf.Tensor		)

def    lowercase_	(	_A    :					Union[str, Any]		):

      """simple docstring"""




      return False if not is_tf_available() else _is_tensorflow(_A		)

def    lowercase_	(	_A    :					Dict		):

      """simple docstring"""




      import tensorflow as tf

      # the `is_symbolic_tensor` predicate is only available starting with TF 2.14
      if hasattr(_A					,      "is_symbolic_tensor"		):
            return tf.is_symbolic_tensor(_A		)
      return type(_A		) == tf.Tensor

def    lowercase_	(	_A    :					Optional[Any]		):

      """simple docstring"""




      return False if not is_tf_available() else _is_tf_symbolic_tensor(_A		)

def    lowercase_	(	_A    :					Optional[int]		):

      """simple docstring"""




      import jax.numpy as jnp  # noqa: F811

      return isinstance(_A					,      jnp.ndarray		)

def    lowercase_	(	_A    :					List[Any]		):

      """simple docstring"""




      return False if not is_flax_available() else _is_jax(_A		)

def    lowercase_	(	_A    :					Any		):

      """simple docstring"""




      if isinstance(_A					,      (dict, UserDict)		):
            return {k: to_py_obj(_A		) for k, v in obj.items()}
      elif isinstance(_A					,      (list, tuple)		):
            return [to_py_obj(_A		) for o in obj]
      elif is_tf_tensor(_A		):
            return obj.numpy().tolist()
      elif is_torch_tensor(_A		):
            return obj.detach().cpu().tolist()
      elif is_jax_tensor(_A		):
            return np.asarray(_A		).tolist()
      elif isinstance(_A					,      (np.ndarray, np.number)		):  # tolist also works on 0d np arrays
            return obj.tolist()
      else:
            return obj

def    lowercase_	(	_A    :					List[Any]		):

      """simple docstring"""




      if isinstance(_A					,      (dict, UserDict)		):
            return {k: to_numpy(_A		) for k, v in obj.items()}
      elif isinstance(_A					,      (list, tuple)		):
            return np.array(_A		)
      elif is_tf_tensor(_A		):
            return obj.numpy()
      elif is_torch_tensor(_A		):
            return obj.detach().cpu().numpy()
      elif is_jax_tensor(_A		):
            return np.asarray(_A		)
      else:
            return obj







class     _lowercase				(							lowercase__):


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						fields(self )

             # Safety and consistency checks
             if not len(__lowerCamelCase ):
                   raise ValueError(f"{self.__class__.__name__} has no fields." )
             if not all(field.default is None for field in class_fields[1:] ):
                   raise ValueError(f"{self.__class__.__name__} should not have more than one required field." )

             lowerCamelCase__	:       str					=						getattr(self      ,   class_fields[0].name )
             lowerCamelCase__	:       List[Any]					=						all(getattr(self      ,   field.name ) is None for field in class_fields[1:] )

             if other_fields_are_none and not is_tensor(__lowerCamelCase ):
                   if isinstance(__lowerCamelCase      ,   __lowerCamelCase ):
                         lowerCamelCase__	:       Dict					=						first_field.items()
                         lowerCamelCase__	:       List[str]					=						True
                   else:
                         try:
                               lowerCamelCase__	:       Dict					=						iter(__lowerCamelCase )
                               lowerCamelCase__	:       Dict					=						True
                         except TypeError:
                               lowerCamelCase__	:       Optional[int]					=						False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
                   if first_field_iterator:
                         for idx, element in enumerate(__lowerCamelCase ):
                               if (
                                   not isinstance(__lowerCamelCase      ,   (list, tuple) )
                                   or not len(__lowerCamelCase ) == 2
                                   or not isinstance(element[0]      ,   __lowerCamelCase )
                               ):
                                     if idx == 0:
                                           # If we do not have an iterator of key/values, set it as attribute
                                           lowerCamelCase__	:       int					=						first_field
                                     else:
                                           # If we have a mixed iterator, raise an error
                                           raise ValueError(
                                               f"Cannot set key/value for {element}. It needs to be a tuple (key, value)." )
                                     break
                               setattr(self      ,   element[0]      ,   element[1] )
                               if element[1] is not None:
                                     lowerCamelCase__	:       Optional[int]					=						element[1]
                   elif first_field is not None:
                         lowerCamelCase__	:       str					=						first_field
             else:
                   for field in class_fields:
                         lowerCamelCase__	:       str					=						getattr(self      ,   field.name )
                         if v is not None:
                               lowerCamelCase__	:       Any					=						v





       def __delitem__(     self							:    Tuple      ,   *__lowerCamelCase							:    str      ,   **__lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance." )





       def 		lowerCAmelCase  (     self							:    Optional[int]      ,   *__lowerCamelCase							:    Tuple      ,   **__lowerCamelCase							:    Optional[int] ):


             '''simple docstring'''



             raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance." )





       def 		lowerCAmelCase  (     self							:    Optional[int]      ,   *__lowerCamelCase							:    Any      ,   **__lowerCamelCase							:    int ):


             '''simple docstring'''



             raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance." )





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


             '''simple docstring'''



             raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance." )





       def __getitem__(     self							:    int      ,   __lowerCamelCase							:    Any ):


             '''simple docstring'''



             if isinstance(__lowerCamelCase      ,   __lowerCamelCase ):
                   lowerCamelCase__	:       List[str]					=						dict(self.items() )
                   return inner_dict[k]
             else:
                   return self.to_tuple()[k]





       def __setattr__(     self							:    List[str]      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             if name in self.keys() and value is not None:
                   # Don't call self.__setitem__ to avoid recursion errors
                   super().__setitem__(__lowerCamelCase      ,   __lowerCamelCase )
             super().__setattr__(__lowerCamelCase      ,   __lowerCamelCase )





       def __setitem__(     self							:    Tuple      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    List[str] ):


             '''simple docstring'''



             super().__setitem__(__lowerCamelCase      ,   __lowerCamelCase )
             # Don't call self.__setattr__ to avoid recursion errors
             super().__setattr__(__lowerCamelCase      ,   __lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             return tuple(self[k] for k in self.keys() )







class     _lowercase				(							lowercase__							,						lowercase__):


       """simple docstring"""




       @classmethod
       def 		lowerCAmelCase  (     cls							:    List[Any]      ,   __lowerCamelCase							:    List[str] ):


             '''simple docstring'''



             raise ValueError(
                 f"{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}" )







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "longest"
       A__      		=    "max_length"
       A__      		=    "do_not_pad"







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "pt"
       A__      		=    "tf"
       A__      		=    "np"
       A__      		=    "jax"







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    Tuple      ,   __lowerCamelCase							:    List[ContextManager] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						context_managers
             lowerCamelCase__	:       Dict					=						ExitStack()





       def __enter__(     self							:    Dict ):


             '''simple docstring'''



             for context_manager in self.context_managers:
                   self.stack.enter_context(__lowerCamelCase )





       def __exit__(     self							:    List[str]      ,   *__lowerCamelCase							:    Any      ,   **__lowerCamelCase							:    int ):


             '''simple docstring'''



             self.stack.__exit__(*__lowerCamelCase      ,   **__lowerCamelCase )



def    lowercase_	(	_A    :					List[Any]		):

      """simple docstring"""




      lowerCamelCase__	:       str					=						infer_framework(_A		)
      if framework == "tf":
            lowerCamelCase__	:       Union[str, Any]					=						inspect.signature(model_class.call		)  # TensorFlow models
      elif framework == "pt":
            lowerCamelCase__	:       Union[str, Any]					=						inspect.signature(model_class.forward		)  # PyTorch models
      else:
            lowerCamelCase__	:       Optional[Any]					=						inspect.signature(model_class.__call__		)  # Flax models

      for p in signature.parameters:
            if p == "return_loss" and signature.parameters[p].default is True:
                  return True

      return False

def    lowercase_	(	_A    :					Any		):

      """simple docstring"""




      lowerCamelCase__	:       Any					=						model_class.__name__
      lowerCamelCase__	:       Optional[Any]					=						infer_framework(_A		)
      if framework == "tf":
            lowerCamelCase__	:       Optional[int]					=						inspect.signature(model_class.call		)  # TensorFlow models
      elif framework == "pt":
            lowerCamelCase__	:       Optional[Any]					=						inspect.signature(model_class.forward		)  # PyTorch models
      else:
            lowerCamelCase__	:       Optional[Any]					=						inspect.signature(model_class.__call__		)  # Flax models

      if "QuestionAnswering" in model_name:
            return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
      else:
            return [p for p in signature.parameters if "label" in p]

def    lowercase_	(	_A    :					MutableMapping					,      _A    :					str = ""					,      _A    :					str = "."		):

      """simple docstring"""




      def _flatten_dict(_A    :					Optional[int]					,      _A    :					Any=""					,      _A    :					Any="."		):
            for k, v in d.items():
                  lowerCamelCase__	:       List[str]					=						str(_A		) + delimiter + str(_A		) if parent_key else k
                  if v and isinstance(_A					,      _A		):
                        yield from flatten_dict(_A					,      _A					,      delimiter=_A		).items()
                  else:
                        yield key, v

      return dict(_flatten_dict(_A					,      _A					,      _A		)		)

@contextmanager
def    lowercase_	(	_A    :					Dict					,      _A    :					bool = False		):

      """simple docstring"""




      if use_temp_dir:
            with tempfile.TemporaryDirectory() as tmp_dir:
                  yield tmp_dir
      else:
            yield working_dir

def    lowercase_	(	_A    :					Optional[int]					,      _A    :					Optional[int]=None		):

      """simple docstring"""




      if is_numpy_array(_A		):
            return np.transpose(_A					,      axes=_A		)
      elif is_torch_tensor(_A		):
            return array.T if axes is None else array.permute(*_A		)
      elif is_tf_tensor(_A		):
            import tensorflow as tf

            return tf.transpose(_A					,      perm=_A		)
      elif is_jax_tensor(_A		):
            return jnp.transpose(_A					,      axes=_A		)
      else:
            raise ValueError(F"Type not supported for transpose: {type(_A		)}."		)

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

      """simple docstring"""




      if is_numpy_array(_A		):
            return np.reshape(_A					,      _A		)
      elif is_torch_tensor(_A		):
            return array.reshape(*_A		)
      elif is_tf_tensor(_A		):
            import tensorflow as tf

            return tf.reshape(_A					,      _A		)
      elif is_jax_tensor(_A		):
            return jnp.reshape(_A					,      _A		)
      else:
            raise ValueError(F"Type not supported for reshape: {type(_A		)}."		)

def    lowercase_	(	_A    :					Dict					,      _A    :					int=None		):

      """simple docstring"""




      if is_numpy_array(_A		):
            return np.squeeze(_A					,      axis=_A		)
      elif is_torch_tensor(_A		):
            return array.squeeze() if axis is None else array.squeeze(dim=_A		)
      elif is_tf_tensor(_A		):
            import tensorflow as tf

            return tf.squeeze(_A					,      axis=_A		)
      elif is_jax_tensor(_A		):
            return jnp.squeeze(_A					,      axis=_A		)
      else:
            raise ValueError(F"Type not supported for squeeze: {type(_A		)}."		)

def    lowercase_	(	_A    :					Any					,      _A    :					Union[str, Any]		):

      """simple docstring"""




      if is_numpy_array(_A		):
            return np.expand_dims(_A					,      _A		)
      elif is_torch_tensor(_A		):
            return array.unsqueeze(dim=_A		)
      elif is_tf_tensor(_A		):
            import tensorflow as tf

            return tf.expand_dims(_A					,      axis=_A		)
      elif is_jax_tensor(_A		):
            return jnp.expand_dims(_A					,      axis=_A		)
      else:
            raise ValueError(F"Type not supported for expand_dims: {type(_A		)}."		)

def    lowercase_	(	_A    :					int		):

      """simple docstring"""




      if is_numpy_array(_A		):
            return np.size(_A		)
      elif is_torch_tensor(_A		):
            return array.numel()
      elif is_tf_tensor(_A		):
            import tensorflow as tf

            return tf.size(_A		)
      elif is_jax_tensor(_A		):
            return array.size
      else:
            raise ValueError(F"Type not supported for expand_dims: {type(_A		)}."		)

def    lowercase_	(	_A    :					Tuple					,      _A    :					Tuple		):

      """simple docstring"""




      for key, value in auto_map.items():
            if isinstance(_A					,      (tuple, list)		):
                  lowerCamelCase__	:       Optional[Any]					=						[F"{repo_id}--{v}" if (v is not None and "--" not in v) else v for v in value]
            elif value is not None and "--" not in value:
                  lowerCamelCase__	:       Tuple					=						F"{repo_id}--{value}"

      return auto_map


def    lowercase_	(	_A    :					Dict		):

      """simple docstring"""




      for base_class in inspect.getmro(_A		):
            lowerCamelCase__	:       Optional[int]					=						base_class.__module__
            lowerCamelCase__	:       List[str]					=						base_class.__name__
            if module.startswith("tensorflow"		) or module.startswith("keras"		) or name == "TFPreTrainedModel":
                  return "tf"
            elif module.startswith("torch"		) or name == "PreTrainedModel":
                  return "pt"
            elif module.startswith("flax"		) or module.startswith("jax"		) or name == "FlaxPreTrainedModel":
                  return "flax"
      else:
            raise TypeError(F"Could not infer framework from class {model_class}."		)






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()


