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 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase=10						):
					__a		 =       []
					for _ in range(__lowerCamelCase						):
										lrs.append(scheduler.get_lr()[0]						)
										scheduler.step()
					return lrs
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase=10						):
					__a		 =       []
					for step in range(__lowerCamelCase						):
										lrs.append(scheduler.get_lr()[0]						)
										scheduler.step()
										if step == num_steps // 2:
															with tempfile.TemporaryDirectory() as tmpdirname:
																				__a		 =       os.path.join(__lowerCamelCase				,    'schedule.bin'						)
																				torch.save(scheduler.state_dict()				,    __lowerCamelCase						)

																				__a		 =       torch.load(__lowerCamelCase						)
																				scheduler.load_state_dict(__lowerCamelCase						)
					return lrs


@require_torch
class 						a__							(       unittest.TestCase		):



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Tuple:
								self.assertEqual(len(UpperCAmelCase   )				,						len(UpperCAmelCase   )   )
								for a, b in zip(UpperCAmelCase				,						UpperCAmelCase   ):
													self.assertAlmostEqual(UpperCAmelCase				,						UpperCAmelCase				,						delta=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
								__a		 =       torch.tensor([0.1, -0.2, -0.1]				,						requires_grad=UpperCAmelCase   )
								__a		 =       torch.tensor([0.4, 0.2, -0.5]   )
								__a		 =       nn.MSELoss()
								# No warmup, constant schedule, no gradient clipping
								__a		 =       AdamW(params=[w]				,						lr=2e-1				,						weight_decay=0.0   )
								for _ in range(1_0_0   ):
													__a		 =       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 __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       torch.tensor([0.1, -0.2, -0.1]				,						requires_grad=UpperCAmelCase   )
								__a		 =       torch.tensor([0.4, 0.2, -0.5]   )
								__a		 =       nn.MSELoss()
								# No warmup, constant schedule, no gradient clipping
								__a		 =       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   ):
													__a		 =       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 						a__							(       unittest.TestCase		):
			A__    :       Union[str, Any]        =      nn.Linear(50				,						50		) if is_torch_available() else None
			A__    :       Tuple        =      AdamW(m.parameters()				,						lr=10.0		) if is_torch_available() else None
			A__    :       int        =      10



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase=None   )			->   Optional[int]:
								self.assertEqual(len(UpperCAmelCase   )				,						len(UpperCAmelCase   )   )
								for a, b in zip(UpperCAmelCase				,						UpperCAmelCase   ):
													self.assertAlmostEqual(UpperCAmelCase				,						UpperCAmelCase				,						delta=UpperCAmelCase				,						msg=UpperCAmelCase   )







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       {'num_warmup_steps': 2, 'num_training_steps': 1_0}
								# schedulers doct format
								# function: (sched_args_dict, expected_learning_rates)
								__a		 =       {
								    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():
													__a ,				__a		 =       data

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

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






class 						a__							:



			def __init__(						self				,						UpperCAmelCase   )			->   Optional[int]:
								__a		 =       fn



			def __call__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Tuple:
								return self.fn(*UpperCAmelCase				,						**UpperCAmelCase   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   str:
								__a		 =       list(map(self				,						scheduler.lr_lambdas   )   )





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase_    :		Optional[Any]							=	{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Dict							=	[
	    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RobertaPreLayerNormForCausalLM""",
	    """RobertaPreLayerNormForMaskedLM""",
	    """RobertaPreLayerNormForMultipleChoice""",
	    """RobertaPreLayerNormForQuestionAnswering""",
	    """RobertaPreLayerNormForSequenceClassification""",
	    """RobertaPreLayerNormForTokenClassification""",
	    """RobertaPreLayerNormModel""",
	    """RobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFRobertaPreLayerNormForCausalLM""",
	    """TFRobertaPreLayerNormForMaskedLM""",
	    """TFRobertaPreLayerNormForMultipleChoice""",
	    """TFRobertaPreLayerNormForQuestionAnswering""",
	    """TFRobertaPreLayerNormForSequenceClassification""",
	    """TFRobertaPreLayerNormForTokenClassification""",
	    """TFRobertaPreLayerNormMainLayer""",
	    """TFRobertaPreLayerNormModel""",
	    """TFRobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """FlaxRobertaPreLayerNormForCausalLM""",
	    """FlaxRobertaPreLayerNormForMaskedLM""",
	    """FlaxRobertaPreLayerNormForMultipleChoice""",
	    """FlaxRobertaPreLayerNormForQuestionAnswering""",
	    """FlaxRobertaPreLayerNormForSequenceClassification""",
	    """FlaxRobertaPreLayerNormForTokenClassification""",
	    """FlaxRobertaPreLayerNormModel""",
	    """FlaxRobertaPreLayerNormPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase_    :		Optional[int]							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




