





import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
		import torch
		from torch import nn

		from transformers import (
		    Adafactor,
		    AdamW,
		    get_constant_schedule,
		    get_constant_schedule_with_warmup,
		    get_cosine_schedule_with_warmup,
		    get_cosine_with_hard_restarts_schedule_with_warmup,
		    get_inverse_sqrt_schedule,
		    get_linear_schedule_with_warmup,
		    get_polynomial_decay_schedule_with_warmup,
		)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=10							)		->    Dict:
		__lowercase											=     []
		for _ in range(snake_case							):
				lrs.append(scheduler.get_lr()[0]							)
				scheduler.step()
		return lrs



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=10							)		->    Optional[int]:
		__lowercase											=     []
		for step in range(snake_case							):
				lrs.append(scheduler.get_lr()[0]							)
				scheduler.step()
				if step == num_steps // 2:
						with tempfile.TemporaryDirectory() as tmpdirname:
								__lowercase											=     os.path.join(snake_case						,   'schedule.bin'							)
								torch.save(scheduler.state_dict()						,   snake_case							)

								__lowercase											=     torch.load(snake_case							)
								scheduler.load_state_dict(snake_case							)
		return lrs

@require_torch
class 		snake_case_   (							unittest.TestCase						):







	'''simple docstring'''




	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     Any					,      __lowerCamelCase							:     List[str]					,      __lowerCamelCase							:     Optional[int]   )       -> Tuple:

			'''simple docstring'''




			self.assertEqual(len(__lowerCamelCase   )					,      len(__lowerCamelCase   )   )
			for a, b in zip(__lowerCamelCase					,      __lowerCamelCase   ):
					self.assertAlmostEqual(__lowerCamelCase					,      __lowerCamelCase					,      delta=__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     List[Any]   )       -> str:

			'''simple docstring'''




			__lowercase											=     torch.tensor([0.1, -0.2, -0.1]					,      requires_grad=__lowerCamelCase   )
			__lowercase											=     torch.tensor([0.4, 0.2, -0.5]   )
			__lowercase											=     nn.MSELoss()
			# No warmup, constant schedule, no gradient clipping
			__lowercase											=     AdamW(params=[w]					,      lr=2E-1					,      weight_decay=0.0   )
			for _ in range(100   ):
					__lowercase											=     criterion(__lowerCamelCase					,      __lowerCamelCase   )
					loss.backward()
					optimizer.step()
					w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
					w.grad.zero_()
			self.assertListAlmostEqual(w.tolist()					,      [0.4, 0.2, -0.5]					,      tol=1E-2   )






	def    UpperCAmelCase			(						self							:     Dict   )       -> Tuple:

			'''simple docstring'''




			__lowercase											=     torch.tensor([0.1, -0.2, -0.1]					,      requires_grad=__lowerCamelCase   )
			__lowercase											=     torch.tensor([0.4, 0.2, -0.5]   )
			__lowercase											=     nn.MSELoss()
			# No warmup, constant schedule, no gradient clipping
			__lowercase											=     Adafactor(
			    params=[w]					,      lr=1E-2					,      eps=(1E-3_0, 1E-3)					,      clip_threshold=1.0					,      decay_rate=-0.8					,      betaa=__lowerCamelCase					,      weight_decay=0.0					,      relative_step=__lowerCamelCase					,      scale_parameter=__lowerCamelCase					,      warmup_init=__lowerCamelCase					,      )
			for _ in range(1_000   ):
					__lowercase											=     criterion(__lowerCamelCase					,      __lowerCamelCase   )
					loss.backward()
					optimizer.step()
					w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
					w.grad.zero_()
			self.assertListAlmostEqual(w.tolist()					,      [0.4, 0.2, -0.5]					,      tol=1E-2   )





@require_torch
class 		snake_case_   (							unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		nn.Linear(50     ,     50						) if is_torch_available() else None
	__UpperCamelCase   =		AdamW(m.parameters()     ,     lr=1_0.0						) if is_torch_available() else None
	__UpperCamelCase   =		10




	def    UpperCAmelCase			(						self							:     List[str]					,      __lowerCamelCase							:     List[str]					,      __lowerCamelCase							:     Union[str, Any]					,      __lowerCamelCase							:     str					,      __lowerCamelCase							:     Tuple=None   )       -> Tuple:

			'''simple docstring'''




			self.assertEqual(len(__lowerCamelCase   )					,      len(__lowerCamelCase   )   )
			for a, b in zip(__lowerCamelCase					,      __lowerCamelCase   ):
					self.assertAlmostEqual(__lowerCamelCase					,      __lowerCamelCase					,      delta=__lowerCamelCase					,      msg=__lowerCamelCase   )






	def    UpperCAmelCase			(						self							:     Union[str, Any]   )       -> Any:

			'''simple docstring'''




			__lowercase											=     {'num_warmup_steps': 2, 'num_training_steps': 10}
			# schedulers doct format
			# function: (sched_args_dict, expected_learning_rates)
			__lowercase											=     {
			    get_constant_schedule: ({}, [10.0] * self.num_steps),
			    get_constant_schedule_with_warmup: (
			        {'num_warmup_steps': 4},
			        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
			    ),
			    get_linear_schedule_with_warmup: (
			        {**common_kwargs},
			        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
			    ),
			    get_cosine_schedule_with_warmup: (
			        {**common_kwargs},
			        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
			    ),
			    get_cosine_with_hard_restarts_schedule_with_warmup: (
			        {**common_kwargs, 'num_cycles': 2},
			        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
			    ),
			    get_polynomial_decay_schedule_with_warmup: (
			        {**common_kwargs, 'power': 2.0, 'lr_end': 1E-7},
			        [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
			    ),
			    get_inverse_sqrt_schedule: (
			        {'num_warmup_steps': 2},
			        [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
			    ),
			}

			for scheduler_func, data in scheds.items():
					__lowercase						,      __lowercase											=     data

					__lowercase											=     scheduler_func(self.optimizer					,      **__lowerCamelCase   )
					self.assertEqual(len([scheduler.get_lr()[0]]   )					,      1   )
					__lowercase											=     unwrap_schedule(__lowerCamelCase					,      self.num_steps   )
					self.assertListAlmostEqual(
					    __lowerCamelCase					,      __lowerCamelCase					,      tol=1E-2					,      msg=F"failed for {scheduler_func} in normal scheduler"					,      )

					__lowercase											=     scheduler_func(self.optimizer					,      **__lowerCamelCase   )
					if scheduler_func.__name__ != "get_constant_schedule":
							LambdaScheduleWrapper.wrap_scheduler(__lowerCamelCase   )  # wrap to test picklability of the schedule
					__lowercase											=     unwrap_and_save_reload_schedule(__lowerCamelCase					,      self.num_steps   )
					self.assertListEqual(__lowerCamelCase					,      __lowerCamelCase					,      msg=F"failed for {scheduler_func} in save and reload"   )





class 		snake_case_   :







	'''simple docstring'''




	def __init__(						self							:     int					,      __lowerCamelCase							:     Dict   )       -> List[str]:

			'''simple docstring'''




			__lowercase											=     fn




	def __call__(						self							:     List[Any]					,      *__lowerCamelCase							:     Tuple					,      **__lowerCamelCase							:     str   )       -> Optional[int]:

			'''simple docstring'''




			return self.fn(*__lowerCamelCase					,      **__lowerCamelCase   )






	@classmethod
	def    UpperCAmelCase			(						self							:     Dict					,      __lowerCamelCase							:     Dict   )       -> Dict:

			'''simple docstring'''




			__lowercase											=     list(map(self					,      scheduler.lr_lambdas   )   )











def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    int:
		# Return True if there is node that has not iterated.
		__lowercase											=     [False] * len(snake_case							)
		__lowercase											=     []
		queue.append(snake_case							)
		__lowercase											=     True

		while queue:
				__lowercase											=     queue.pop(0							)
				for ind in range(len(graph[u]							)							):
						if visited[ind] is False and graph[u][ind] > 0:
								queue.append(snake_case							)
								__lowercase											=     True
								__lowercase											=     u

		return visited[t]



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Dict:
		# This array is filled by BFS and to store path
		__lowercase											=     [-1] * (len(snake_case							))
		__lowercase											=     0
		while bfs(snake_case						,   snake_case						,   snake_case						,   snake_case							):
				__lowercase											=     float('Inf'							)
				__lowercase											=     sink

				while s != source:
						# Find the minimum value in select path
						__lowercase											=     min(snake_case						,   graph[parent[s]][s]							)
						__lowercase											=     parent[s]

				max_flow += path_flow
				__lowercase											=     sink

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


SCREAMING_SNAKE_CASE_   :      Any			=   [
    [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_   :      Optional[Any]			=   0, 5


print(ford_fulkerson(graph, source, sink))




