



"""simple docstring"""

import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class SCREAMING_SNAKE_CASE__					(		__a     ,					unittest.TestCase      ):
						_lowerCAmelCase							=					LongformerTokenizer
						_lowerCAmelCase							=					True
						_lowerCAmelCase							=					LongformerTokenizerFast
						_lowerCAmelCase							=					True



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													super().setUp()

													# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
													__a		:		List[Any]									=       [
													    """l""",
													    """o""",
													    """w""",
													    """e""",
													    """r""",
													    """s""",
													    """t""",
													    """i""",
													    """d""",
													    """n""",
													    """\u0120""",
													    """\u0120l""",
													    """\u0120n""",
													    """\u0120lo""",
													    """\u0120low""",
													    """er""",
													    """\u0120lowest""",
													    """\u0120newer""",
													    """\u0120wider""",
													    """<unk>""",
													]
													__a		:		List[str]									=       dict(zip(a_						,     range(len(a_							)							)							)							)
													__a		:		List[Any]									=       ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
													__a		:		Any									=       {"""unk_token""": """<unk>"""}

													__a		:		int									=       os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["""vocab_file"""]							)
													__a		:		Tuple									=       os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["""merges_file"""]							)
													with open(self.vocab_file						,     """w"""						,     encoding="""utf-8"""							) as fp:
																				fp.write(json.dumps(a_							) + """\n"""							)
													with open(self.merges_file						,     """w"""						,     encoding="""utf-8"""							) as fp:
																				fp.write("""\n""".join(a_							)							)



						def     lowerCAmelCase__(self						,     **_lowercase							):





													'''simple docstring'''

													kwargs.update(self.special_tokens_map							)
													return self.tokenizer_class.from_pretrained(self.tmpdirname						,     **a_							)



						def     lowerCAmelCase__(self						,     **_lowercase							):





													'''simple docstring'''

													kwargs.update(self.special_tokens_map							)
													return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,     **a_							)



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													__a		:		int									=       """lower newer"""
													__a		:		Optional[int]									=       """lower newer"""
													return input_text, output_text



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Any									=       self.tokenizer_class(self.vocab_file						,     self.merges_file						,     **self.special_tokens_map							)
													__a		:		int									=       """lower newer"""
													__a		:		Union[str, Any]									=       ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
													__a		:		int									=       tokenizer.tokenize(a_							)  # , add_prefix_space=True)
													self.assertListEqual(a_						,     a_							)

													__a		:		Any									=       tokens + [tokenizer.unk_token]
													__a		:		str									=       [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(a_							)						,     a_							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		int									=       self.get_tokenizer()

													self.assertListEqual(tokenizer.encode("""Hello world!"""						,     add_special_tokens=a_							)						,     [0, 31414, 232, 328, 2]							)
													self.assertListEqual(
													    tokenizer.encode("""Hello world! cécé herlolip 418"""						,     add_special_tokens=a_							)						,     [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]						,     )



						@slow
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[int]									=       self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""							)

													__a		:		Union[str, Any]									=       tokenizer.encode("""sequence builders"""						,     add_special_tokens=a_							)
													__a		:		Tuple									=       tokenizer.encode("""multi-sequence build"""						,     add_special_tokens=a_							)

													__a		:		Tuple									=       tokenizer.encode(
													    """sequence builders"""						,     add_special_tokens=a_						,     add_prefix_space=a_							)
													__a		:		Optional[Any]									=       tokenizer.encode(
													    """sequence builders"""						,     """multi-sequence build"""						,     add_special_tokens=a_						,     add_prefix_space=a_							)

													__a		:		List[str]									=       tokenizer.build_inputs_with_special_tokens(a_							)
													__a		:		Union[str, Any]									=       tokenizer.build_inputs_with_special_tokens(a_						,     a_							)

													assert encoded_sentence == encoded_text_from_decode
													assert encoded_pair == encoded_pair_from_decode



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       self.get_tokenizer()

													__a		:		Dict									=       """Encode this sequence."""
													__a		:		Optional[Any]									=       tokenizer.byte_encoder[""" """.encode("""utf-8"""							)[0]]

													# Testing encoder arguments
													__a		:		Union[str, Any]									=       tokenizer.encode(a_						,     add_special_tokens=a_						,     add_prefix_space=a_							)
													__a		:		Optional[int]									=       tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
													self.assertNotEqual(a_						,     a_							)

													__a		:		Optional[int]									=       tokenizer.encode(a_						,     add_special_tokens=a_						,     add_prefix_space=a_							)
													__a		:		Optional[int]									=       tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
													self.assertEqual(a_						,     a_							)

													tokenizer.add_special_tokens({"""bos_token""": """<s>"""}							)
													__a		:		Union[str, Any]									=       tokenizer.encode(a_						,     add_special_tokens=a_							)
													__a		:		str									=       tokenizer.convert_ids_to_tokens(encoded[1]							)[0]
													self.assertNotEqual(a_						,     a_							)

													# Testing spaces after special tokens
													__a		:		List[str]									=       """<mask>"""
													tokenizer.add_special_tokens(
													    {"""mask_token""": AddedToken(a_						,     lstrip=a_						,     rstrip=a_							)}							)  # mask token has a left space
													__a		:		str									=       tokenizer.convert_tokens_to_ids(a_							)

													__a		:		int									=       """Encode <mask> sequence"""
													__a		:		Tuple									=       """Encode <mask>sequence"""

													__a		:		List[str]									=       tokenizer.encode(a_							)
													__a		:		Optional[int]									=       encoded.index(a_							)
													__a		:		Tuple									=       tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
													self.assertEqual(a_						,     a_							)

													__a		:		Union[str, Any]									=       tokenizer.encode(a_							)
													__a		:		Optional[int]									=       encoded.index(a_							)
													__a		:		Any									=       tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
													self.assertNotEqual(a_						,     a_							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													pass



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																				with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																											__a		:		str									=       self.rust_tokenizer_class.from_pretrained(a_						,     **a_							)
																											__a		:		Dict									=       self.tokenizer_class.from_pretrained(a_						,     **a_							)
																											__a		:		List[str]									=       """A, <mask> AllenNLP sentence."""
																											__a		:		Tuple									=       tokenizer_r.encode_plus(a_						,     add_special_tokens=a_						,     return_token_type_ids=a_							)
																											__a		:		Optional[int]									=       tokenizer_p.encode_plus(a_						,     add_special_tokens=a_						,     return_token_type_ids=a_							)

																											# token_type_ids should put 0 everywhere
																											self.assertEqual(sum(tokens_r["""token_type_ids"""]							)						,     sum(tokens_p["""token_type_ids"""]							)							)

																											# attention_mask should put 1 everywhere, so sum over length should be 1
																											self.assertEqual(
																											    sum(tokens_r["""attention_mask"""]							) / len(tokens_r["""attention_mask"""]							)						,     sum(tokens_p["""attention_mask"""]							) / len(tokens_p["""attention_mask"""]							)						,     )

																											__a		:		int									=       tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]							)
																											__a		:		int									=       tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]							)

																											# Rust correctly handles the space before the mask while python doesnt
																											self.assertSequenceEqual(tokens_p["""input_ids"""]						,     [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]							)
																											self.assertSequenceEqual(tokens_r["""input_ids"""]						,     [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]							)

																											self.assertSequenceEqual(
																											    a_						,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)
																											self.assertSequenceEqual(
																											    a_						,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													for trim_offsets, add_prefix_space in itertools.product([True, False]						,     repeat=2							):
																				__a		:		Any									=       self.rust_tokenizer_class.from_pretrained(
																				    self.tmpdirname						,     use_fast=a_						,     add_prefix_space=a_						,     trim_offsets=a_							)

																				__a		:		Optional[Any]									=       json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()							)
																				__a		:		Any									=       json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()							)

																				self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]						,     a_							)

																				self.assertEqual(post_processor_state["""add_prefix_space"""]						,     a_							)
																				self.assertEqual(post_processor_state["""trim_offsets"""]						,     a_							)





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																				with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																											__a		:		Optional[int]									=       """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																											__a		:		int									=       F'''{text_of_1_token} {text_of_1_token}'''

																											__a		:		Optional[int]									=       self.rust_tokenizer_class.from_pretrained(
																											    a_						,     use_fast=a_						,     add_prefix_space=a_						,     trim_offsets=a_							)
																											__a		:		Dict									=       tokenizer_r(a_						,     return_offsets_mapping=a_						,     add_special_tokens=a_							)
																											self.assertEqual(encoding.offset_mapping[0]						,     (0, len(a_							))							)
																											self.assertEqual(
																											    encoding.offset_mapping[1]						,     (len(a_							) + 1, len(a_							) + 1 + len(a_							))						,     )

																											__a		:		Union[str, Any]									=       self.rust_tokenizer_class.from_pretrained(
																											    a_						,     use_fast=a_						,     add_prefix_space=a_						,     trim_offsets=a_							)
																											__a		:		str									=       tokenizer_r(a_						,     return_offsets_mapping=a_						,     add_special_tokens=a_							)
																											self.assertEqual(encoding.offset_mapping[0]						,     (0, len(a_							))							)
																											self.assertEqual(
																											    encoding.offset_mapping[1]						,     (len(a_							) + 1, len(a_							) + 1 + len(a_							))						,     )

																											__a		:		Union[str, Any]									=       self.rust_tokenizer_class.from_pretrained(
																											    a_						,     use_fast=a_						,     add_prefix_space=a_						,     trim_offsets=a_							)
																											__a		:		Dict									=       tokenizer_r(a_						,     return_offsets_mapping=a_						,     add_special_tokens=a_							)
																											self.assertEqual(encoding.offset_mapping[0]						,     (0, len(a_							))							)
																											self.assertEqual(
																											    encoding.offset_mapping[1]						,     (len(a_							), len(a_							) + 1 + len(a_							))						,     )

																											__a		:		Tuple									=       self.rust_tokenizer_class.from_pretrained(
																											    a_						,     use_fast=a_						,     add_prefix_space=a_						,     trim_offsets=a_							)
																											__a		:		Union[str, Any]									=       tokenizer_r(a_						,     return_offsets_mapping=a_						,     add_special_tokens=a_							)
																											self.assertEqual(encoding.offset_mapping[0]						,     (0, len(a_							))							)
																											self.assertEqual(
																											    encoding.offset_mapping[1]						,     (len(a_							), len(a_							) + 1 + len(a_							))						,     )

																											__a		:		Tuple									=       F''' {text}'''

																											# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																											#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																											# )
																											# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																											# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																											# self.assertEqual(
																											#     encoding.offset_mapping[1],
																											#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																											# )

																											__a		:		Optional[Any]									=       self.rust_tokenizer_class.from_pretrained(
																											    a_						,     use_fast=a_						,     add_prefix_space=a_						,     trim_offsets=a_							)
																											__a		:		Union[str, Any]									=       tokenizer_r(a_						,     return_offsets_mapping=a_						,     add_special_tokens=a_							)
																											self.assertEqual(encoding.offset_mapping[0]						,     (1, 1 + len(a_							))							)
																											self.assertEqual(
																											    encoding.offset_mapping[1]						,     (1 + len(a_							) + 1, 1 + len(a_							) + 1 + len(a_							))						,     )

																											__a		:		Tuple									=       self.rust_tokenizer_class.from_pretrained(
																											    a_						,     use_fast=a_						,     add_prefix_space=a_						,     trim_offsets=a_							)
																											__a		:		Any									=       tokenizer_r(a_						,     return_offsets_mapping=a_						,     add_special_tokens=a_							)
																											self.assertEqual(encoding.offset_mapping[0]						,     (0, 1 + len(a_							))							)
																											self.assertEqual(
																											    encoding.offset_mapping[1]						,     (1 + len(a_							), 1 + len(a_							) + 1 + len(a_							))						,     )

																											__a		:		List[str]									=       self.rust_tokenizer_class.from_pretrained(
																											    a_						,     use_fast=a_						,     add_prefix_space=a_						,     trim_offsets=a_							)
																											__a		:		Optional[Any]									=       tokenizer_r(a_						,     return_offsets_mapping=a_						,     add_special_tokens=a_							)
																											self.assertEqual(encoding.offset_mapping[0]						,     (0, 1 + len(a_							))							)
																											self.assertEqual(
																											    encoding.offset_mapping[1]						,     (1 + len(a_							), 1 + len(a_							) + 1 + len(a_							))						,     )






"""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


lowercase__			   =  logging.get_logger(__name__)




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					"linear"
						_lowerCAmelCase							=					"cosine"
						_lowerCAmelCase							=					"cosine_with_restarts"
						_lowerCAmelCase							=					"polynomial"
						_lowerCAmelCase							=					"constant"
						_lowerCAmelCase							=					"constant_with_warmup"
						_lowerCAmelCase							=					"piecewise_constant"







def       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: int = -1					):
							return LambdaLR(_lowerCamelCase  ,       lambda _lowerCamelCase					: 1  ,       last_epoch=_lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int = -1					):

							def lr_lambda(_lowerCamelCase				: int					):
														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       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: int = -1					):
							__a		:		Optional[int]									=       {}
							__a		:		Any									=       step_rules.split(""","""					)
							for rule_str in rule_list[:-1]:
														__a							,     __a		:		int									=       rule_str.split(""":"""					)
														__a		:		Optional[int]									=       int(_lowerCamelCase					)
														__a		:		str									=       float(_lowerCamelCase					)
														__a		:		int									=       value
							__a		:		Dict									=       float(rule_list[-1]					)

							def create_rules_function(_lowerCamelCase				: str  ,       _lowerCamelCase				: Tuple					):
														def rule_func(_lowerCamelCase				: int					) -> float:
																					__a		:		Optional[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		:		Optional[int]									=       create_rules_function(_lowerCamelCase  ,       _lowerCamelCase					)

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       last_epoch=_lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: Any  ,       _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: str=-1					):

							def lr_lambda(_lowerCamelCase				: int					):
														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       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: float = 0.5  ,       _lowerCamelCase				: int = -1					):

							def lr_lambda(_lowerCamelCase				: Any					):
														if current_step < num_warmup_steps:
																					return float(_lowerCamelCase					) / float(max(1  ,       _lowerCamelCase					)					)
														__a		:		Dict									=       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       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int = 1  ,       _lowerCamelCase				: int = -1					):

							def lr_lambda(_lowerCamelCase				: Optional[int]					):
														if current_step < num_warmup_steps:
																					return float(_lowerCamelCase					) / float(max(1  ,       _lowerCamelCase					)					)
														__a		:		Dict									=       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       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Any  ,       _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: Optional[Any]=1E-7  ,       _lowerCamelCase				: Optional[int]=1.0  ,       _lowerCamelCase				: Optional[int]=-1					):
							__a		:		Union[str, 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				: int					):
														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		:		Tuple									=       lr_init - lr_end
																					__a		:		int									=       num_training_steps - num_warmup_steps
																					__a		:		Optional[int]									=       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					)


lowercase__			   =  {
    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       __magic_name__    (   _lowerCamelCase				: Union[str, SchedulerType]  ,       _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: Optional[str] = None  ,       _lowerCamelCase				: Optional[int] = None  ,       _lowerCamelCase				: Optional[int] = None  ,       _lowerCamelCase				: int = 1  ,       _lowerCamelCase				: float = 1.0  ,       _lowerCamelCase				: int = -1  ,       ):
							__a		:		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					)

