

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 	__a     (      A__			:  str			,  A__			:  Any=10       ):
    SCREAMING_SNAKE_CASE										=   []
    for _ in range(A__       ):
        lrs.append(scheduler.get_lr()[0]       )
        scheduler.step()
    return lrs





def 	__a     (      A__			:  List[Any]			,  A__			:  Optional[Any]=10       ):
    SCREAMING_SNAKE_CASE										=   []
    for step in range(A__       ):
        lrs.append(scheduler.get_lr()[0]       )
        scheduler.step()
        if step == num_steps // 2:
            with tempfile.TemporaryDirectory() as tmpdirname:
                SCREAMING_SNAKE_CASE										=   os.path.join(A__			,  "schedule.bin"       )
                torch.save(scheduler.state_dict()			,  A__       )

                SCREAMING_SNAKE_CASE										=   torch.load(A__       )
                scheduler.load_state_dict(A__       )
    return lrs





@require_torch
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			Tuple			):
       self.assertEqual(len(__lowerCamelCase			)     , len(__lowerCamelCase			)			)
       for a, b in zip(__lowerCamelCase     , __lowerCamelCase			):
           self.assertAlmostEqual(__lowerCamelCase     , __lowerCamelCase     , delta=__lowerCamelCase			)





   def      _snake_case		(       self					:			List[str]			):
       SCREAMING_SNAKE_CASE										=   torch.tensor([0.1, -0.2, -0.1]     , requires_grad=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   torch.tensor([0.4, 0.2, -0.5]			)
       SCREAMING_SNAKE_CASE										=   nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       SCREAMING_SNAKE_CASE										=   AdamW(params=[w]     , lr=2e-1     , weight_decay=0.0			)
       for _ in range(100			):
           SCREAMING_SNAKE_CASE										=   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      _snake_case		(       self					:			Optional[int]			):
       SCREAMING_SNAKE_CASE										=   torch.tensor([0.1, -0.2, -0.1]     , requires_grad=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   torch.tensor([0.4, 0.2, -0.5]			)
       SCREAMING_SNAKE_CASE										=   nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       SCREAMING_SNAKE_CASE										=   Adafactor(
           params=[w]     , lr=1e-2     , eps=(1e-30, 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(1000			):
           SCREAMING_SNAKE_CASE										=   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 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      nn.Linear(5_0					,       5_0		) if is_torch_available() else None
   lowerCamelCase__				=      AdamW(m.parameters()					,       lr=10.0		) if is_torch_available() else None
   lowerCamelCase__				=      1_0





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			List[str]     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			Tuple     , __lowerCamelCase					:			Any=None			):
       self.assertEqual(len(__lowerCamelCase			)     , len(__lowerCamelCase			)			)
       for a, b in zip(__lowerCamelCase     , __lowerCamelCase			):
           self.assertAlmostEqual(__lowerCamelCase     , __lowerCamelCase     , delta=__lowerCamelCase     , msg=__lowerCamelCase			)





   def      _snake_case		(       self					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   {"num_warmup_steps": 2, "num_training_steps": 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       SCREAMING_SNAKE_CASE										=   {
           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():
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   data

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

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





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			str     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   fn





   def __call__(       self					:			List[Any]     , *__lowerCamelCase					:			Optional[Any]     , **__lowerCamelCase					:			List[Any]			):
       return self.fn(*__lowerCamelCase     , **__lowerCamelCase			)





   @classmethod
   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			Dict			):
       SCREAMING_SNAKE_CASE										=   list(map(self     , scheduler.lr_lambdas			)			)



def    a__    (		snake_case__					:							Tuple     ):  # noqa: E741
  _UpperCAmelCase		:							Dict							=     len(snake_case__     )
  _UpperCAmelCase		:							Tuple							=     0
  _UpperCAmelCase		:							Union[str, Any]							=     [0] * n
  _UpperCAmelCase		:							Union[str, Any]							=     [False] * n
  _UpperCAmelCase		:							Tuple							=     [False] * n

  def dfs(snake_case__					:							List[str]						,   snake_case__					:							int						,   snake_case__					:							str						,   snake_case__					:							int     ):
    if parent == root:
      out_edge_count += 1
    _UpperCAmelCase		:							List[Any]							=     True
    _UpperCAmelCase		:							List[str]							=     at

    for to in l[at]:
      if to == parent:
        pass
      elif not visited[to]:
        _UpperCAmelCase		:							Optional[Any]							=     dfs(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__     )
        _UpperCAmelCase		:							Optional[Any]							=     min(low[at]						,   low[to]     )

        # AP found via bridge
        if at < low[to]:
          _UpperCAmelCase		:							List[str]							=     True
        # AP found via cycle
        if at == low[to]:
          _UpperCAmelCase		:							Optional[Any]							=     True
      else:
        _UpperCAmelCase		:							str							=     min(low[at]						,   snake_case__     )
    return out_edge_count

  for i in range(snake_case__     ):
    if not visited[i]:
      _UpperCAmelCase		:							List[Any]							=     0
      _UpperCAmelCase		:							List[str]							=     dfs(snake_case__						,   snake_case__						,   -1						,   snake_case__     )
      _UpperCAmelCase		:							Optional[Any]							=     out_edge_count > 1

  for x in range(len(snake_case__     )     ):
    if is_art[x] is True:
      print(snake_case__     )


# Adjacency list of graph
SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}





compute_ap(data)





