


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 						_UpperCAmelCase      (     lowercase							):






		def __get__(						self				:     List[Any]   ,  UpperCAmelCase				:     Optional[Any]   ,  UpperCAmelCase				:     List[Any]=None):
				# See docs.python.org/3/howto/descriptor.html#properties
				if obj is None:
						return self
				if self.fget is None:
						raise AttributeError("unreadable attribute")
				SCREAMING_SNAKE_CASE_				:List[str]				     =						"__cached_" + self.fget.__name__
				SCREAMING_SNAKE_CASE_				:Dict				     =						getattr(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase)
				if cached is None:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self.fget(UpperCAmelCase)
						setattr(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase)
				return cached




def     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				: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 ):



		'''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 ):



		'''simple docstring'''




		return isinstance(a     , np.ndarray )


def     lowercase	(    a ):



		'''simple docstring'''




		return _is_numpy(a )


def     lowercase	(    a ):



		'''simple docstring'''




		import torch

		return isinstance(a     , torch.Tensor )


def     lowercase	(    a ):



		'''simple docstring'''




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


def     lowercase	(    a ):



		'''simple docstring'''




		import torch

		return isinstance(a     , torch.device )


def     lowercase	(    a ):



		'''simple docstring'''




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


def     lowercase	(    a ):



		'''simple docstring'''




		import torch

		if isinstance(a     , a ):
				if hasattr(a     , a ):
						SCREAMING_SNAKE_CASE_				:List[str]				     =						getattr(a     , a )
				else:
						return False
		return isinstance(a     , torch.dtype )


def     lowercase	(    a ):



		'''simple docstring'''




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


def     lowercase	(    a ):



		'''simple docstring'''




		import tensorflow as tf

		return isinstance(a     , tf.Tensor )


def     lowercase	(    a ):



		'''simple docstring'''




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


def     lowercase	(    a ):



		'''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 ):



		'''simple docstring'''




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


def     lowercase	(    a ):



		'''simple docstring'''




		import jax.numpy as jnp  # noqa: F811

		return isinstance(a     , jnp.ndarray )


def     lowercase	(    a ):



		'''simple docstring'''




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


def     lowercase	(    a ):



		'''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 ):



		'''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 						_UpperCAmelCase      (     lowercase							):






		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						fields(self)

				# Safety and consistency checks
				if not len(UpperCAmelCase):
						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.")

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

				if other_fields_are_none and not is_tensor(UpperCAmelCase):
						if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
								SCREAMING_SNAKE_CASE_				:str				     =						first_field.items()
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						True
						else:
								try:
										SCREAMING_SNAKE_CASE_				:List[str]				     =						iter(UpperCAmelCase)
										SCREAMING_SNAKE_CASE_				:Tuple				     =						True
								except TypeError:
										SCREAMING_SNAKE_CASE_				: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(UpperCAmelCase):
										if (
										    not isinstance(UpperCAmelCase   ,  (list, tuple))
										    or not len(UpperCAmelCase) == 2
										    or not isinstance(element[0]   ,  UpperCAmelCase)
										):
												if idx == 0:
														# If we do not have an iterator of key/values, set it as attribute
														SCREAMING_SNAKE_CASE_				:Optional[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:
												SCREAMING_SNAKE_CASE_				:List[str]				     =						element[1]
						elif first_field is not None:
								SCREAMING_SNAKE_CASE_				:Tuple				     =						first_field
				else:
						for field in class_fields:
								SCREAMING_SNAKE_CASE_				:Any				     =						getattr(self   ,  field.name)
								if v is not None:
										SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						v






		def __delitem__(						self				:     str   ,  *UpperCAmelCase				:     List[Any]   ,  **UpperCAmelCase				:     Optional[Any]):
				raise Exception(F"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")






		def _snake_case		(						self				:     Tuple   ,  *UpperCAmelCase				:     int   ,  **UpperCAmelCase				:     Dict):
				raise Exception(F"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")






		def _snake_case		(						self				:     Optional[int]   ,  *UpperCAmelCase				:     Optional[int]   ,  **UpperCAmelCase				:     Optional[Any]):
				raise Exception(F"You cannot use ``pop`` on a {self.__class__.__name__} instance.")






		def _snake_case		(						self				:     Dict   ,  *UpperCAmelCase				:     List[str]   ,  **UpperCAmelCase				:     Union[str, Any]):
				raise Exception(F"You cannot use ``update`` on a {self.__class__.__name__} instance.")






		def __getitem__(						self				:     Dict   ,  UpperCAmelCase				:     Optional[int]):
				if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						dict(self.items())
						return inner_dict[k]
				else:
						return self.to_tuple()[k]






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






		def __setitem__(						self				:     int   ,  UpperCAmelCase				:     List[Any]   ,  UpperCAmelCase				:     List[str]):
				# Will raise a KeyException if needed
				super().__setitem__(UpperCAmelCase   ,  UpperCAmelCase)
				# Don't call self.__setattr__ to avoid recursion errors
				super().__setattr__(UpperCAmelCase   ,  UpperCAmelCase)







		def _snake_case		(						self				:     str):
				return tuple(self[k] for k in self.keys())




class 						_UpperCAmelCase      (     lowercase		,		lowercase							):






		@classmethod
		def _snake_case		(						cls				:     Dict   ,  UpperCAmelCase				:     List[Any]):
				raise ValueError(
				    F"{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys())}")




class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			Any       			=  """longest"""
		lowerCamelCase_	:			Tuple       			=  """max_length"""
		lowerCamelCase_	:			Union[str, Any]       			=  """do_not_pad"""




class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			Dict       			=  """pt"""
		lowerCamelCase_	:			Optional[Any]       			=  """tf"""
		lowerCamelCase_	:			int       			=  """np"""
		lowerCamelCase_	:			Union[str, Any]       			=  """jax"""




class 						_UpperCAmelCase      :






		def __init__(						self				:     str   ,  UpperCAmelCase				:     List[ContextManager]):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						context_managers
				SCREAMING_SNAKE_CASE_				:int				     =						ExitStack()






		def __enter__(						self				:     Optional[Any]):
				for context_manager in self.context_managers:
						self.stack.enter_context(UpperCAmelCase)







		def __exit__(						self				:     Tuple   ,  *UpperCAmelCase				:     Optional[int]   ,  **UpperCAmelCase				:     int):
				self.stack.__exit__(*UpperCAmelCase   ,  **UpperCAmelCase)




def     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Tuple				     =						infer_framework(a )
		if framework == "tf":
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						inspect.signature(model_class.call )  # TensorFlow models
		elif framework == "pt":
				SCREAMING_SNAKE_CASE_				:Tuple				     =						inspect.signature(model_class.forward )  # PyTorch models
		else:
				SCREAMING_SNAKE_CASE_				:List[str]				     =						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 ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						model_class.__name__
		SCREAMING_SNAKE_CASE_				:Tuple				     =						infer_framework(a )
		if framework == "tf":
				SCREAMING_SNAKE_CASE_				:Any				     =						inspect.signature(model_class.call )  # TensorFlow models
		elif framework == "pt":
				SCREAMING_SNAKE_CASE_				:Tuple				     =						inspect.signature(model_class.forward )  # PyTorch models
		else:
				SCREAMING_SNAKE_CASE_				:List[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     , a = ""     , a = "." ):



		'''simple docstring'''




		def _flatten_dict(a     , a=""     , a="." ):
				for k, v in d.items():
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						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     , a = False ):



		'''simple docstring'''




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


def     lowercase	(    a     , a=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     , a ):



		'''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     , a=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     , a ):



		'''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 ):



		'''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     , a ):



		'''simple docstring'''




		for key, value in auto_map.items():
				if isinstance(a     , (tuple, list) ):
						SCREAMING_SNAKE_CASE_				:List[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:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						F"{repo_id}--{value}"

		return auto_map


def     lowercase	(    a ):



		'''simple docstring'''




		for base_class in inspect.getmro(a ):
				SCREAMING_SNAKE_CASE_				:Any				     =						base_class.__module__
				SCREAMING_SNAKE_CASE_				: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}." )










def     lowercase	(    a     , a     , a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						[False] * len(a )
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						[]
		queue.append(a )
		SCREAMING_SNAKE_CASE_				:int				     =						True

		while queue:
				SCREAMING_SNAKE_CASE_				:int				     =						queue.pop(0 )
				for ind in range(len(graph[u] ) ):
						if visited[ind] is False and graph[u][ind] > 0:
								queue.append(a )
								SCREAMING_SNAKE_CASE_				:Tuple				     =						True
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						u

		return visited[t]


def     lowercase	(    a     , a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Any				     =						[-1] * (len(a ))
		SCREAMING_SNAKE_CASE_				:Tuple				     =						0
		while bfs(a     , a     , a     , a ):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						float("Inf" )
				SCREAMING_SNAKE_CASE_				:str				     =						sink

				while s != source:
						# Find the minimum value in select path
						SCREAMING_SNAKE_CASE_				:str				     =						min(a     , graph[parent[s]][s] )
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						parent[s]

				max_flow += path_flow
				SCREAMING_SNAKE_CASE_				:Dict				     =						sink

				while v != source:
						SCREAMING_SNAKE_CASE_				:int				     =						parent[v]
						graph[u][v] -= path_flow
						graph[v][u] += path_flow
						SCREAMING_SNAKE_CASE_				:Any				     =						parent[v]
		return max_flow


SCREAMING_SNAKE_CASE__     					=			[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

SCREAMING_SNAKE_CASE__					,    SCREAMING_SNAKE_CASE__     					=			0, 5
print(ford_fulkerson(graph, source, sink))






