
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_   (      UpperCamelCase__			,		UpperCamelCase__=1_0		):
      UpperCamelCase__     : Any											=					[]
      for _ in range(UpperCamelCase__		):
            lrs.append(scheduler.get_lr()[0]		)
            scheduler.step()
      return lrs


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__=1_0		):
      UpperCamelCase__     : Any											=					[]
      for step in range(UpperCamelCase__		):
            lrs.append(scheduler.get_lr()[0]		)
            scheduler.step()
            if step == num_steps // 2:
                  with tempfile.TemporaryDirectory() as tmpdirname:
                        UpperCamelCase__     : int											=					os.path.join(UpperCamelCase__			,		'''schedule.bin'''		)
                        torch.save(scheduler.state_dict()			,		UpperCamelCase__		)

                        UpperCamelCase__     : Union[str, Any]											=					torch.load(UpperCamelCase__		)
                        scheduler.load_state_dict(UpperCamelCase__		)
      return lrs




@require_torch
class 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> int:




           """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	)	-> Tuple:




           """simple docstring"""







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




           """simple docstring"""







           UpperCamelCase__     : str											=					torch.tensor([0.1, -0.2, -0.1]    ,    requires_grad=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Tuple											=					torch.tensor([0.4, 0.2, -0.5]	)
           UpperCamelCase__     : int											=					nn.MSELoss()
           # No warmup, constant schedule, no gradient clipping
           UpperCamelCase__     : List[str]											=					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(1_0_0_0	):
                 UpperCamelCase__     : int											=					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 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	nn.Linear(50						,				50			) if is_torch_available() else None
     SCREAMING_SNAKE_CASE_		 =	AdamW(m.parameters()						,				lr=10.0			) if is_torch_available() else None
     SCREAMING_SNAKE_CASE_		 =	10






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=None	)	-> List[str]:




           """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	)	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					{'''num_warmup_steps''': 2, '''num_training_steps''': 1_0}
           # schedulers doct format
           # function: (sched_args_dict, expected_learning_rates)
           UpperCamelCase__     : Any											=					{
               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():
                 UpperCamelCase__					,UpperCamelCase__     : Tuple											=					data

                 UpperCamelCase__     : int											=					scheduler_func(self.optimizer    ,    **__SCREAMING_SNAKE_CASE	)
                 self.assertEqual(len([scheduler.get_lr()[0]]	)    ,    1	)
                 UpperCamelCase__     : Tuple											=					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'''    ,    )

                 UpperCamelCase__     : List[str]											=					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
                 UpperCamelCase__     : Any											=					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 					_lowerCamelCase			:





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					fn






     def __call__(				self    ,    *__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> Union[str, Any]:




           """simple docstring"""







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






     @classmethod
     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : str											=					list(map(self    ,    scheduler.lr_lambdas	)	)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase  ={
    "configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
    "tokenization_luke": ["LukeTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase  =[
        "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
        "LukeForEntityClassification",
        "LukeForEntityPairClassification",
        "LukeForEntitySpanClassification",
        "LukeForMultipleChoice",
        "LukeForQuestionAnswering",
        "LukeForSequenceClassification",
        "LukeForTokenClassification",
        "LukeForMaskedLM",
        "LukeModel",
        "LukePreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
    from .tokenization_luke import LukeTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_luke import (
            LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
            LukeForEntityClassification,
            LukeForEntityPairClassification,
            LukeForEntitySpanClassification,
            LukeForMaskedLM,
            LukeForMultipleChoice,
            LukeForQuestionAnswering,
            LukeForSequenceClassification,
            LukeForTokenClassification,
            LukeModel,
            LukePreTrainedModel,
        )

else:
    import sys

    lowerCamelCase  =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


