

"""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    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=1_0			)     ->     int:
		__lowerCAmelCase:	Optional[Any]   				=      []
		for _ in range(__SCREAMING_SNAKE_CASE			):
				lrs.append(scheduler.get_lr()[0]			)
				scheduler.step()
		return lrs






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=1_0			)     ->     Optional[int]:
		__lowerCAmelCase:	List[Any]   				=      []
		for step in range(__SCREAMING_SNAKE_CASE			):
				lrs.append(scheduler.get_lr()[0]			)
				scheduler.step()
				if step == num_steps // 2:
						with tempfile.TemporaryDirectory() as tmpdirname:
								__lowerCAmelCase:	str   				=      os.path.join(__SCREAMING_SNAKE_CASE ,			"schedule.bin"			)
								torch.save(scheduler.state_dict() ,			__SCREAMING_SNAKE_CASE			)

								__lowerCAmelCase:	Optional[int]   				=      torch.load(__SCREAMING_SNAKE_CASE			)
								scheduler.load_state_dict(__SCREAMING_SNAKE_CASE			)
		return lrs





@require_torch
class    snake_case     (    unittest.TestCase  ):

				def 							lowercase_					(     self							:    Optional[Any]					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    List[Any])->       Dict:



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    List[str])->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      torch.tensor([0.1, -0.2, -0.1]					,  requires_grad=UpperCamelCase__)
						__lowerCAmelCase:	Union[str, Any]   				=      torch.tensor([0.4, 0.2, -0.5])
						__lowerCAmelCase:	str   				=      nn.MSELoss()
						# No warmup, constant schedule, no gradient clipping
						__lowerCAmelCase:	Optional[int]   				=      AdamW(params=[w]					,  lr=2e-1					,  weight_decay=0.0)
						for _ in range(1_0_0):
								__lowerCAmelCase:	Optional[Any]   				=      criterion(UpperCamelCase__					,  UpperCamelCase__)
								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 							lowercase_					(     self							:    str)->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      torch.tensor([0.1, -0.2, -0.1]					,  requires_grad=UpperCamelCase__)
						__lowerCAmelCase:	Tuple   				=      torch.tensor([0.4, 0.2, -0.5])
						__lowerCAmelCase:	str   				=      nn.MSELoss()
						# No warmup, constant schedule, no gradient clipping
						__lowerCAmelCase:	Any   				=      Adafactor(
						    params=[w]					,  lr=1e-2					,  eps=(1e-30, 1e-3)					,  clip_threshold=1.0					,  decay_rate=-0.8					,  betaa=UpperCamelCase__					,  weight_decay=0.0					,  relative_step=UpperCamelCase__					,  scale_parameter=UpperCamelCase__					,  warmup_init=UpperCamelCase__					,  )
						for _ in range(1_0_0_0):
								__lowerCAmelCase:	Optional[int]   				=      criterion(UpperCamelCase__					,  UpperCamelCase__)
								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[str]       				=  nn.Linear(50,							50  ) if is_torch_available() else None
				SCREAMING_SNAKE_CASE_       :				Tuple       				=  AdamW(m.parameters(),							lr=10.0  ) if is_torch_available() else None
				SCREAMING_SNAKE_CASE_       :				Dict       				=  10

				def 							lowercase_					(     self							:    Union[str, Any]					,  UpperCamelCase__							:    List[Any]					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    Optional[int]=None)->       Dict:



						'''simple docstring'''


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




				def 							lowercase_					(     self							:    Dict)->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      {"num_warmup_steps": 2, "num_training_steps": 1_0}
						# schedulers doct format
						# function: (sched_args_dict, expected_learning_rates)
						__lowerCAmelCase:	Tuple   				=      {
						    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:	Union[str, Any]   				=      data

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

								__lowerCAmelCase:	Union[str, Any]   				=      scheduler_func(self.optimizer					,  **UpperCamelCase__)
								if scheduler_func.__name__ != "get_constant_schedule":
										LambdaScheduleWrapper.wrap_scheduler(UpperCamelCase__)  # wrap to test picklability of the schedule
								__lowerCAmelCase:	Dict   				=      unwrap_and_save_reload_schedule(UpperCamelCase__					,  self.num_steps)
								self.assertListEqual(UpperCamelCase__					,  UpperCamelCase__					,  msg=f"failed for {scheduler_func} in save and reload")





class    snake_case     :

				def __init__(     self							:    Tuple					,  UpperCamelCase__							:    Union[str, Any])->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      fn

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



						'''simple docstring'''


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




				@classmethod
				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    Dict)->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      list(map(self					,  scheduler.lr_lambdas))





"""simple docstring"""


from math import ceil






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	Tuple   				=      list(range(0 ,			__SCREAMING_SNAKE_CASE			)			)

		__lowerCAmelCase:	Optional[Any]   				=      [item for sublist in list(device_map.values()			) for item in sublist]

		# Duplicate check
		__lowerCAmelCase:	List[Any]   				=      []
		for i in device_map_blocks:
				if device_map_blocks.count(__SCREAMING_SNAKE_CASE			) > 1 and i not in duplicate_blocks:
						duplicate_blocks.append(__SCREAMING_SNAKE_CASE			)
    # Missing blocks
		__lowerCAmelCase:	Optional[Any]   				=      [i for i in blocks if i not in device_map_blocks]
		__lowerCAmelCase:	List[Any]   				=      [i for i in device_map_blocks if i not in blocks]

		if len(__SCREAMING_SNAKE_CASE			) != 0:
				raise ValueError(
				    "Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
				    " These attention blocks were specified more than once: " + str(__SCREAMING_SNAKE_CASE			)			)
		if len(__SCREAMING_SNAKE_CASE			) != 0:
				raise ValueError(
				    "There are attention blocks for this model that are not specified in the device_map. Add these attention "
				    "blocks to a device on the device_map: " + str(__SCREAMING_SNAKE_CASE			)			)
		if len(__SCREAMING_SNAKE_CASE			) != 0:
				raise ValueError(
				    "The device_map contains more attention blocks than this model has. Remove these from the device_map:"
				    + str(__SCREAMING_SNAKE_CASE			)			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	List[Any]   				=      list(range(__SCREAMING_SNAKE_CASE			)			)
		__lowerCAmelCase:	Dict   				=      int(ceil(n_layers / len(__SCREAMING_SNAKE_CASE			)			)			)
		__lowerCAmelCase:	Union[str, Any]   				=      [layers[i : i + n_blocks] for i in range(0 ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)]

		return dict(zip(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)			)


