

'''simple docstring'''



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       snake_case   (	snake_case      : Tuple					,		snake_case      : Optional[int]=10			) ->  int:


    """simple docstring"""


    lowerCAmelCase 						=							[]
    for _ in range(snake_case			):
        lrs.append(scheduler.get_lr()[0]			)
        scheduler.step()
    return lrs


def       snake_case   (	snake_case      : str					,		snake_case      : Optional[int]=10			) ->  Any:


    """simple docstring"""


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

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



@require_torch
class 				_snake_case					(   unittest.TestCase			):

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							torch.tensor([0.1, -0.2, -0.1]      , requires_grad=_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							torch.tensor([0.4, 0.2, -0.5]	)
      lowerCAmelCase 						=							nn.MSELoss()
      # No warmup, constant schedule, no gradient clipping
      lowerCAmelCase 						=							AdamW(params=[w]      , lr=2e-1      , weight_decay=0.0	)
      for _ in range(1_00	):
          lowerCAmelCase 						=							criterion(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)
          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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							torch.tensor([0.1, -0.2, -0.1]      , requires_grad=_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							torch.tensor([0.4, 0.2, -0.5]	)
      lowerCAmelCase 						=							nn.MSELoss()
      # No warmup, constant schedule, no gradient clipping
      lowerCAmelCase 						=							Adafactor(
          params=[w]      , lr=1e-2      , eps=(1e-30, 1e-3)      , clip_threshold=1.0      , decay_rate=-0.8      , betaa=_SCREAMING_SNAKE_CASE      , weight_decay=0.0      , relative_step=_SCREAMING_SNAKE_CASE      , scale_parameter=_SCREAMING_SNAKE_CASE      , warmup_init=_SCREAMING_SNAKE_CASE      , )
      for _ in range(10_00	):
          lowerCAmelCase 						=							criterion(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)
          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			):
  SCREAMING_SNAKE_CASE			:       List[Any]				=     nn.Linear(50     , 50			) if is_torch_available() else None
  SCREAMING_SNAKE_CASE			:       Union[str, Any]				=     AdamW(m.parameters()     , lr=10.0			) if is_torch_available() else None
  SCREAMING_SNAKE_CASE			:       List[Any]				=     10

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE=None	):





      '''simple docstring'''

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


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							{'num_warmup_steps': 2, 'num_training_steps': 10}
      # schedulers doct format
      # function: (sched_args_dict, expected_learning_rates)
      lowerCAmelCase 						=							{
          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():
          lowerCAmelCase						,   lowerCAmelCase 						=							data

          lowerCAmelCase 						=							scheduler_func(self.optimizer      , **_SCREAMING_SNAKE_CASE	)
          self.assertEqual(len([scheduler.get_lr()[0]]	)      , 1	)
          lowerCAmelCase 						=							unwrap_schedule(_SCREAMING_SNAKE_CASE      , self.num_steps	)
          self.assertListAlmostEqual(
              _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , tol=1e-2      , msg=F'failed for {scheduler_func} in normal scheduler'      , )

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



class 				_snake_case					:

  def __init__(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							fn

  def __call__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

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


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

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








'''simple docstring'''



def       snake_case   (	snake_case      : dict			) ->  set:


    """simple docstring"""


    lowerCAmelCase 						=							set()
    # edges = list of graph's edges
    lowerCAmelCase 						=							get_edges(snake_case			)

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        lowerCAmelCase						,   lowerCAmelCase 						=							edges.pop()
        chosen_vertices.add(snake_case			)
        chosen_vertices.add(snake_case			)
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(snake_case			)
    return chosen_vertices


def       snake_case   (	snake_case      : dict			) ->  set:


    """simple docstring"""


    lowerCAmelCase 						=							set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node)			)
    return edges


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
     # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")





