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						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=10			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	[]
    for _ in range(SCREAMING_SNAKE_CASE			):
        lrs.append(scheduler.get_lr()[0]			)
        scheduler.step()
    return lrs



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=10			):
    '''simple docstring'''







    __UpperCamelCase  :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:
                __UpperCamelCase  :str    						=	os.path.join(SCREAMING_SNAKE_CASE						,    '''schedule.bin'''			)
                torch.save(scheduler.state_dict()						,    SCREAMING_SNAKE_CASE			)

                __UpperCamelCase  :List[str]    						=	torch.load(SCREAMING_SNAKE_CASE			)
                scheduler.load_state_dict(SCREAMING_SNAKE_CASE			)
    return lrs


@require_torch
class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase) ->			Optional[int]:
        self.assertEqual(len(__lowercase)				,				len(__lowercase))
        for a, b in zip(__lowercase				,				__lowercase):
            self.assertAlmostEqual(__lowercase				,				__lowercase				,				delta=__lowercase)







    def     UpperCamelCase__     (			self) ->			Any:
        __UpperCamelCase  :Tuple    						=	torch.tensor([0.1, -0.2, -0.1]				,				requires_grad=__lowercase)
        __UpperCamelCase  :Tuple    						=	torch.tensor([0.4, 0.2, -0.5])
        __UpperCamelCase  :List[Any]    						=	nn.MSELoss()
        # No warmup, constant schedule, no gradient clipping
        __UpperCamelCase  :Dict    						=	AdamW(params=[w]				,				lr=2E-1				,				weight_decay=0.0)
        for _ in range(100):
            __UpperCamelCase  :Dict    						=	criterion(__lowercase				,				__lowercase)
            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     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :Tuple    						=	torch.tensor([0.1, -0.2, -0.1]				,				requires_grad=__lowercase)
        __UpperCamelCase  :List[str]    						=	torch.tensor([0.4, 0.2, -0.5])
        __UpperCamelCase  :List[Any]    						=	nn.MSELoss()
        # No warmup, constant schedule, no gradient clipping
        __UpperCamelCase  :Any    						=	Adafactor(
            params=[w]				,				lr=1E-2				,				eps=(1E-3_0, 1E-3)				,				clip_threshold=1.0				,				decay_rate=-0.8				,				betaa=__lowercase				,				weight_decay=0.0				,				relative_step=__lowercase				,				scale_parameter=__lowercase				,				warmup_init=__lowercase				,				)
        for _ in range(1_000):
            __UpperCamelCase  :int    						=	criterion(__lowercase				,				__lowercase)
            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'''






    a__			:		List[Any]      			=   nn.Linear(5_0		,			5_0				) if is_torch_available() else None
    a__			:		Union[str, Any]      			=   AdamW(m.parameters()		,			lr=1_0.0				) if is_torch_available() else None
    a__			:		Union[str, Any]      			=   1_0







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase=None) ->			int:
        self.assertEqual(len(__lowercase)				,				len(__lowercase))
        for a, b in zip(__lowercase				,				__lowercase):
            self.assertAlmostEqual(__lowercase				,				__lowercase				,				delta=__lowercase				,				msg=__lowercase)







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        __UpperCamelCase  :str    						=	{'''num_warmup_steps''': 2, '''num_training_steps''': 10}
        # schedulers doct format
        # function: (sched_args_dict, expected_learning_rates)
        __UpperCamelCase  :Optional[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, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
            ),
            get_inverse_sqrt_schedule: (
                {'''num_warmup_steps''': 2},
                [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
            ),
        }

        for scheduler_func, data in scheds.items():
            __UpperCamelCase  :List[Any]    						=	data

            __UpperCamelCase  :Optional[int]    						=	scheduler_func(self.optimizer				,				**__lowercase)
            self.assertEqual(len([scheduler.get_lr()[0]])				,				1)
            __UpperCamelCase  :List[str]    						=	unwrap_schedule(__lowercase				,				self.num_steps)
            self.assertListAlmostEqual(
                __lowercase				,				__lowercase				,				tol=1E-2				,				msg=f"""failed for {scheduler_func} in normal scheduler"""				,				)

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


class 			lowerCamelCase_  :






    '''simple docstring'''






    def __init__(			self				,				__lowercase) ->			Any:
        __UpperCamelCase  :Union[str, Any]    						=	fn







    def __call__(			self				,				*__lowercase				,				**__lowercase) ->			Dict:
        return self.fn(*__lowercase				,				**__lowercase)







    @classmethod
    def     UpperCamelCase__     (			self				,				__lowercase) ->			List[str]:
        __UpperCamelCase  :Optional[int]    						=	list(map(self				,				scheduler.lr_lambdas))







import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin


@require_jieba
class 			lowerCamelCase_  (     UpperCAmelCase_		,			unittest.TestCase				):






    '''simple docstring'''






    a__			:		Any      			=   CpmAntTokenizer
    a__			:		Optional[Any]      			=   False







    def     UpperCamelCase__     (			self) ->			Any:
        super().setUp()

        __UpperCamelCase  :Optional[int]    						=	[
            '''<d>''',
            '''</d>''',
            '''<s>''',
            '''</s>''',
            '''</_>''',
            '''<unk>''',
            '''<pad>''',
            '''</n>''',
            '''我''',
            '''是''',
            '''C''',
            '''P''',
            '''M''',
            '''A''',
            '''n''',
            '''t''',
        ]
        __UpperCamelCase  :Optional[int]    						=	os.path.join(self.tmpdirname				,				VOCAB_FILES_NAMES['''vocab_file'''])
        with open(self.vocab_file				,				'''w'''				,				encoding='''utf-8''') as vocab_writer:
            vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]))







    @tooslow
    def     UpperCamelCase__     (			self) ->			Dict:
        __UpperCamelCase  :Tuple    						=	CpmAntTokenizer.from_pretrained('''openbmb/cpm-ant-10b''')
        __UpperCamelCase  :Dict    						=	'''今天天气真好！'''
        __UpperCamelCase  :Tuple    						=	['''今天''', '''天气''', '''真''', '''好''', '''！''']
        __UpperCamelCase  :Optional[Any]    						=	tokenizer.tokenize(__lowercase)
        self.assertListEqual(__lowercase				,				__lowercase)
        __UpperCamelCase  :int    						=	'''今天天气真好！'''
        __UpperCamelCase  :List[str]    						=	[tokenizer.bos_token] + tokens

        __UpperCamelCase  :List[str]    						=	[6, 9_802, 14_962, 2_082, 831, 244]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase)				,				__lowercase)

        __UpperCamelCase  :Dict    						=	tokenizer.decode(__lowercase)
        self.assertEqual(__lowercase				,				__lowercase)





