




'''simple docstring'''







import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


A									=logging.get_logger(__name__)


class     _a						(			__a				):
       __a       :  str            =      """linear"""
       __a       :  Optional[int]            =      """cosine"""
       __a       :  Optional[Any]            =      """cosine_with_restarts"""
       __a       :  Optional[Any]            =      """polynomial"""
       __a       :  Tuple            =      """constant"""
       __a       :  Dict            =      """constant_with_warmup"""
       __a       :  List[str]            =      """piecewise_constant"""






def 		snake_case_							(_a			:							Optimizer					,     _a			:							int = -1  ):
    return LambdaLR(_a					,     lambda _a  : 1					,     last_epoch=_a  )





def 		snake_case_							(_a			:							Optimizer					,     _a			:							int					,     _a			:							int = -1  ):

    def lr_lambda(_a			:							int  ):
        if current_step < num_warmup_steps:
            return float(_a  ) / float(max(1.0					,     _a  )  )
        return 1.0

    return LambdaLR(_a					,     _a					,     last_epoch=_a  )





def 		snake_case_							(_a			:							Optimizer					,     _a			:							str					,     _a			:							int = -1  ):
    UpperCAmelCase     =	{}
    UpperCAmelCase     =	step_rules.split(''','''  )
    for rule_str in rule_list[:-1]:
        UpperCAmelCase ,  UpperCAmelCase     =	rule_str.split(''':'''  )
        UpperCAmelCase     =	int(_a  )
        UpperCAmelCase     =	float(_a  )
        UpperCAmelCase     =	value
    UpperCAmelCase     =	float(rule_list[-1]  )

    def create_rules_function(_a			:							Union[str, Any]					,     _a			:							List[Any]  ):
        def rule_func(_a			:							int  ) -> float:
            UpperCAmelCase     =	sorted(rules_dict.keys()  )
            for i, sorted_step in enumerate(_a  ):
                if steps < sorted_step:
                    return rules_dict[sorted_steps[i]]
            return last_lr_multiple

        return rule_func

    UpperCAmelCase     =	create_rules_function(_a					,     _a  )

    return LambdaLR(_a					,     _a					,     last_epoch=_a  )





def 		snake_case_							(_a			:							Optional[int]					,     _a			:							Tuple					,     _a			:							Union[str, Any]					,     _a			:							Tuple=-1  ):

    def lr_lambda(_a			:							int  ):
        if current_step < num_warmup_steps:
            return float(_a  ) / float(max(1					,     _a  )  )
        return max(
            0.0					,     float(num_training_steps - current_step  ) / float(max(1					,     num_training_steps - num_warmup_steps  )  )  )

    return LambdaLR(_a					,     _a					,     _a  )





def 		snake_case_							(_a			:							Optimizer					,     _a			:							int					,     _a			:							int					,     _a			:							float = 0.5					,     _a			:							int = -1  ):

    def lr_lambda(_a			:							Optional[int]  ):
        if current_step < num_warmup_steps:
            return float(_a  ) / float(max(1					,     _a  )  )
        UpperCAmelCase     =	float(current_step - num_warmup_steps  ) / float(max(1					,     num_training_steps - num_warmup_steps  )  )
        return max(0.0					,     0.5 * (1.0 + math.cos(math.pi * float(_a  ) * 2.0 * progress  ))  )

    return LambdaLR(_a					,     _a					,     _a  )





def 		snake_case_							(_a			:							Optimizer					,     _a			:							int					,     _a			:							int					,     _a			:							int = 1					,     _a			:							int = -1  ):

    def lr_lambda(_a			:							int  ):
        if current_step < num_warmup_steps:
            return float(_a  ) / float(max(1					,     _a  )  )
        UpperCAmelCase     =	float(current_step - num_warmup_steps  ) / float(max(1					,     num_training_steps - num_warmup_steps  )  )
        if progress >= 1.0:
            return 0.0
        return max(0.0					,     0.5 * (1.0 + math.cos(math.pi * ((float(_a  ) * progress) % 1.0)  ))  )

    return LambdaLR(_a					,     _a					,     _a  )





def 		snake_case_							(_a			:							Union[str, Any]					,     _a			:							List[Any]					,     _a			:							Any					,     _a			:							Optional[int]=1E-7					,     _a			:							str=1.0					,     _a			:							Any=-1  ):
    UpperCAmelCase     =	optimizer.defaults['''lr''']
    if not (lr_init > lr_end):
        raise ValueError(F"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"  )

    def lr_lambda(_a			:							int  ):
        if current_step < num_warmup_steps:
            return float(_a  ) / float(max(1					,     _a  )  )
        elif current_step > num_training_steps:
            return lr_end / lr_init  # as LambdaLR multiplies by lr_init
        else:
            UpperCAmelCase     =	lr_init - lr_end
            UpperCAmelCase     =	num_training_steps - num_warmup_steps
            UpperCAmelCase     =	1 - (current_step - num_warmup_steps) / decay_steps
            UpperCAmelCase     =	lr_range * pct_remaining**power + lr_end
            return decay / lr_init  # as LambdaLR multiplies by lr_init

    return LambdaLR(_a					,     _a					,     _a  )


A									={
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def 		snake_case_							(_a			:							Union[str, SchedulerType]					,     _a			:							Optimizer					,     _a			:							Optional[str] = None					,     _a			:							Optional[int] = None					,     _a			:							Optional[int] = None					,     _a			:							int = 1					,     _a			:							float = 1.0					,     _a			:							int = -1					,     ):
    UpperCAmelCase     =	SchedulerType(_a  )
    UpperCAmelCase     =	TYPE_TO_SCHEDULER_FUNCTION[name]
    if name == SchedulerType.CONSTANT:
        return schedule_func(_a					,     last_epoch=_a  )

    if name == SchedulerType.PIECEWISE_CONSTANT:
        return schedule_func(_a					,     step_rules=_a					,     last_epoch=_a  )

    # All other schedulers require `num_warmup_steps`
    if num_warmup_steps is None:
        raise ValueError(F"{name} requires `num_warmup_steps`, please provide that argument."  )

    if name == SchedulerType.CONSTANT_WITH_WARMUP:
        return schedule_func(_a					,     num_warmup_steps=_a					,     last_epoch=_a  )

    # All other schedulers require `num_training_steps`
    if num_training_steps is None:
        raise ValueError(F"{name} requires `num_training_steps`, please provide that argument."  )

    if name == SchedulerType.COSINE_WITH_RESTARTS:
        return schedule_func(
            _a					,     num_warmup_steps=_a					,     num_training_steps=_a					,     num_cycles=_a					,     last_epoch=_a					,     )

    if name == SchedulerType.POLYNOMIAL:
        return schedule_func(
            _a					,     num_warmup_steps=_a					,     num_training_steps=_a					,     power=_a					,     last_epoch=_a					,     )

    return schedule_func(
        _a					,     num_warmup_steps=_a					,     num_training_steps=_a					,     last_epoch=_a  )










'''simple docstring'''







import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class     _a						(			__a							,							unittest.TestCase				):
       __a       :  Any            =      MgpstrTokenizer
       __a       :  Optional[Any]            =      False
       __a       :  str            =      {}
       __a       :  Optional[int]            =      False
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           super().setUp()

           # fmt: off
           UpperCAmelCase     =	['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
           # fmt: on
           UpperCAmelCase     =	dict(zip(lowercase     ,       range(len(lowercase       )       )       )       )

           UpperCAmelCase     =	os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['''vocab_file''']       )
           with open(self.vocab_file     ,       '''w'''     ,       encoding='''utf-8'''       ) as fp:
               fp.write(json.dumps(lowercase       ) + '''\n'''       )
       def 					A   (			self		:			int     ,       **lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           return MgpstrTokenizer.from_pretrained(self.tmpdirname     ,       **lowercase       )
       def 					A   (			self		:			int     ,       lowercase		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	'''tester'''
           UpperCAmelCase     =	'''tester'''
           return input_text, output_text
       @unittest.skip('''MGP-STR always lower cases letters.'''       )
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           pass
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_tokenizers(do_lower_case=lowercase       )
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"       ):
                   UpperCAmelCase     =	'''[SPECIAL_TOKEN]'''

                   tokenizer.add_special_tokens({'''cls_token''': special_token}       )
                   UpperCAmelCase     =	tokenizer.encode([special_token]     ,       add_special_tokens=lowercase       )
                   self.assertEqual(len(lowercase       )     ,       1       )

                   UpperCAmelCase     =	tokenizer.decode(lowercase     ,       skip_special_tokens=lowercase       )
                   self.assertTrue(special_token not in decoded       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_tokenizers()
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"       ):
                   UpperCAmelCase ,  UpperCAmelCase     =	self.get_input_output_texts(lowercase       )

                   UpperCAmelCase     =	tokenizer.tokenize(lowercase       )
                   UpperCAmelCase     =	tokenizer.convert_tokens_to_ids(lowercase       )
                   UpperCAmelCase     =	tokenizer.encode(lowercase     ,       add_special_tokens=lowercase       )
                   self.assertListEqual(lowercase     ,       lowercase       )

                   UpperCAmelCase     =	tokenizer.convert_ids_to_tokens(lowercase       )
                   self.assertNotEqual(len(lowercase       )     ,       0       )
                   UpperCAmelCase     =	tokenizer.decode(lowercase       )
                   self.assertIsInstance(lowercase     ,       lowercase       )

                   self.assertEqual(text_a.replace(''' '''     ,       ''''''       )     ,       lowercase       )
       @unittest.skip('''MGP-STR tokenizer only handles one sequence.'''       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           pass







       @unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''       )
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           pass




