



'''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


__lowerCAmelCase		     =  logging.get_logger(__name__)

class       __magic_name__		(       _UpperCamelCase  ):
							lowerCAmelCase			:       str  							=   'linear'
							lowerCAmelCase			:       List[str]  							=   'cosine'
							lowerCAmelCase			:       int  							=   'cosine_with_restarts'
							lowerCAmelCase			:       List[Any]  							=   'polynomial'
							lowerCAmelCase			:       str  							=   'constant'
							lowerCAmelCase			:       Optional[Any]  							=   'constant_with_warmup'
							lowerCAmelCase			:       int  							=   'piecewise_constant'



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_ = -1    )     -> Union[str, Any]:
		return LambdaLR(lowerCAmelCase_		,     lambda lowerCAmelCase_    : 1		,     last_epoch=lowerCAmelCase_    )



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_ = -1    )     -> Union[str, Any]:

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

		return LambdaLR(lowerCAmelCase_		,     lowerCAmelCase_		,     last_epoch=lowerCAmelCase_    )



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_ = -1    )     -> Optional[Any]:
		_a	: List[Any]								=				{}
		_a	: Tuple								=				step_rules.split(','    )
		for rule_str in rule_list[:-1]:
				_a			,   _a	: List[str]								=				rule_str.split(':'    )
				_a	: Any								=				int(lowerCAmelCase_    )
				_a	: Tuple								=				float(lowerCAmelCase_    )
				_a	: List[str]								=				value
		_a	: List[str]								=				float(rule_list[-1]    )

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

				return rule_func

		_a	: Any								=				create_rules_function(lowerCAmelCase_		,     lowerCAmelCase_    )

		return LambdaLR(lowerCAmelCase_		,     lowerCAmelCase_		,     last_epoch=lowerCAmelCase_    )



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_=-1    )     -> Union[str, Any]:

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

		return LambdaLR(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_ = 0.5		,     lowerCAmelCase_ = -1    )     -> Any:

		def lr_lambda(lowerCAmelCase_    ):
				if current_step < num_warmup_steps:
						return float(lowerCAmelCase_    ) / float(max(1		,     lowerCAmelCase_    )    )
				_a	: List[str]								=				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(lowerCAmelCase_    ) * 2.0 * progress    ))    )

		return LambdaLR(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_ = 1		,     lowerCAmelCase_ = -1    )     -> Dict:

		def lr_lambda(lowerCAmelCase_    ):
				if current_step < num_warmup_steps:
						return float(lowerCAmelCase_    ) / float(max(1		,     lowerCAmelCase_    )    )
				_a	: Optional[Any]								=				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(lowerCAmelCase_    ) * progress) % 1.0)    ))    )

		return LambdaLR(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_=1E-7		,     lowerCAmelCase_=1.0		,     lowerCAmelCase_=-1    )     -> Dict:
		_a	: List[Any]								=				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(lowerCAmelCase_    ):
				if current_step < num_warmup_steps:
						return float(lowerCAmelCase_    ) / float(max(1		,     lowerCAmelCase_    )    )
				elif current_step > num_training_steps:
						return lr_end / lr_init  # as LambdaLR multiplies by lr_init
				else:
						_a	: Any								=				lr_init - lr_end
						_a	: Dict								=				num_training_steps - num_warmup_steps
						_a	: Union[str, Any]								=				1 - (current_step - num_warmup_steps) / decay_steps
						_a	: List[str]								=				lr_range * pct_remaining**power + lr_end
						return decay / lr_init  # as LambdaLR multiplies by lr_init

		return LambdaLR(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )


__lowerCAmelCase		     =  {
    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 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_ = None		,     lowerCAmelCase_ = None		,     lowerCAmelCase_ = None		,     lowerCAmelCase_ = 1		,     lowerCAmelCase_ = 1.0		,     lowerCAmelCase_ = -1		,     )     -> Optional[Any]:
		_a	: Optional[int]								=				SchedulerType(lowerCAmelCase_    )
		_a	: Optional[int]								=				TYPE_TO_SCHEDULER_FUNCTION[name]
		if name == SchedulerType.CONSTANT:
				return schedule_func(lowerCAmelCase_		,     last_epoch=lowerCAmelCase_    )

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

		# 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(lowerCAmelCase_		,     num_warmup_steps=lowerCAmelCase_		,     last_epoch=lowerCAmelCase_    )

		# 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(
				    lowerCAmelCase_		,     num_warmup_steps=lowerCAmelCase_		,     num_training_steps=lowerCAmelCase_		,     num_cycles=lowerCAmelCase_		,     last_epoch=lowerCAmelCase_		,     )

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

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









'''simple docstring'''

import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase		     =  get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')

@require_sentencepiece
@require_tokenizers
class       __magic_name__		(       _UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Tuple  							=   GPTSwaTokenizer
							lowerCAmelCase			:       Tuple  							=   False
							lowerCAmelCase			:       Optional[Any]  							=   True
							lowerCAmelCase			:       Any  							=   False







							def 				__lowercase     (				self   :			Any						):
									super().setUp()

									# We have a SentencePiece fixture for testing
									_a	: int								=				GPTSwaTokenizer(_UpperCAmelCase      ,eos_token='<unk>'      ,bos_token='<unk>'      ,pad_token='<unk>'						)

									tokenizer.save_pretrained(self.tmpdirname						)







							def 				__lowercase     (				self   :			Optional[Any]      ,_UpperCAmelCase   :			int						):
									_a	: Any								=				'This is a test'
									_a	: Any								=				'This is a test'
									return input_text, output_text







							def 				__lowercase     (				self   :			str						):
									_a	: Optional[Any]								=				'<s>'
									_a	: str								=				1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase						)      ,_UpperCAmelCase						)
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase						)      ,_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Union[str, Any]						):
									_a	: Dict								=				list(self.get_tokenizer().get_vocab().keys()						)

									self.assertEqual(vocab_keys[0]      ,'<unk>'						)
									self.assertEqual(vocab_keys[1]      ,'<s>'						)
									self.assertEqual(vocab_keys[-1]      ,'j'						)
									self.assertEqual(len(_UpperCAmelCase						)      ,2000						)







							def 				__lowercase     (				self   :			Optional[int]						):
									self.assertEqual(self.get_tokenizer().vocab_size      ,2000						)







							def 				__lowercase     (				self   :			List[Any]						):
									_a	: Union[str, Any]								=				GPTSwaTokenizer(_UpperCAmelCase						)

									_a	: str								=				tokenizer.tokenize('This is a test'						)
									self.assertListEqual(_UpperCAmelCase      ,['▁This', '▁is', '▁a', '▁t', 'est']						)

									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)      ,[465, 287, 265, 631, 842]						)

									_a	: Any								=				tokenizer.tokenize('I was born in 92000, and this is falsé.'						)
									# fmt: off
									self.assertListEqual(
									    _UpperCAmelCase      ,['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']      ,)
									# fmt: on

									_a	: Optional[Any]								=				tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)
									self.assertListEqual(
									    _UpperCAmelCase      ,[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]      ,)

									_a	: List[str]								=				tokenizer.convert_ids_to_tokens(_UpperCAmelCase						)
									# fmt: off
									self.assertListEqual(
									    _UpperCAmelCase      ,['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']						)
									# fmt: on







							def 				__lowercase     (				self   :			str						):
									_a	: List[Any]								=				GPTSwaTokenizer(_UpperCAmelCase						)
									_a	: Optional[Any]								=				['This is a test', 'I was born in 92000, and this is falsé.']
									_a	: Dict								=				[
									    [465, 287, 265, 631, 842],
									    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
									]

									# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
									for text, expected_ids in zip(_UpperCAmelCase      ,_UpperCAmelCase						):
											self.assertListEqual(tokenizer.encode_fast(_UpperCAmelCase						)      ,_UpperCAmelCase						)

									# Test that decode_fast returns the input text
									for text, token_ids in zip(_UpperCAmelCase      ,_UpperCAmelCase						):
											self.assertEqual(tokenizer.decode_fast(_UpperCAmelCase						)      ,_UpperCAmelCase						)







							@slow
							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: Optional[int]								=				[
									    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
									    'Hey there, how are you doing this fine day?',
									    'This is a text with a trailing spaces followed by a dot     .',
									    'Häj sväjs lillebrör! =)',
									    'Det är inget fel på Mr. Cool',
									]

									# fmt: off
									_a	: Tuple								=				{'input_ids': [[63423, 5, 6811, 14954, 282, 816, 3821, 63466, 63425, 63462, 18, 63978, 678, 301, 1320, 63423, 63455, 63458, 18, 63982, 4246, 3940, 1901, 47789, 5547, 18994], [19630, 1100, 63446, 1342, 633, 544, 4488, 593, 5102, 2416, 63495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 58593, 22413, 9106, 546, 268, 33213, 63979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55130, 63450, 924, 63449, 2249, 4062, 1558, 318, 63504, 21498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 63443, 26801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
									# fmt: on
									self.tokenizer_integration_test_util(
									    expected_encoding=_UpperCAmelCase      ,model_name='AI-Sweden/gpt-sw3-126m'      ,sequences=_UpperCAmelCase      ,)




