




"""simple docstring"""

import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__					:						int												=			get_tests_dir('''fixtures/test_sentencepiece.model''')




@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase (    __a					,  unittest.TestCase):
 _lowerCAmelCase     :    Optional[int]	      =	XGLMTokenizer
 _lowerCAmelCase     :    Dict	      =	XGLMTokenizerFast
 _lowerCAmelCase     :    List[Any]	      =	True
 _lowerCAmelCase     :    Tuple	      =	True

 def 		_snake_case      (  self	: List[Any]       ):
     super().setUp()

     # We have a SentencePiece fixture for testing
     snake_case_				:  Optional[int]       =			XGLMTokenizer(lowerCAmelCase_						,  keep_accents=lowerCAmelCase_       )
     tokenizer.save_pretrained(self.tmpdirname       )

 def 		_snake_case      (  self	: Dict       ):
     snake_case_				:  int       =			'''<pad>'''
     snake_case_				:  Tuple       =			1

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

 def 		_snake_case      (  self	: Optional[Any]       ):
     snake_case_				:  Tuple       =			list(self.get_tokenizer().get_vocab().keys()       )

     self.assertEqual(vocab_keys[0]						,  '''<s>'''       )
     self.assertEqual(vocab_keys[1]						,  '''<pad>'''       )
     self.assertEqual(len(lowerCAmelCase_       )						,  1008       )

 def 		_snake_case      (  self	: Dict       ):
     self.assertEqual(self.get_tokenizer().vocab_size						,  1008       )

 def 		_snake_case      (  self	: List[str]       ):
     snake_case_				:  Dict       =			XGLMTokenizer(lowerCAmelCase_						,  keep_accents=lowerCAmelCase_       )

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

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(lowerCAmelCase_       )						,  [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]						,  )

     snake_case_				:  List[Any]       =			tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
     self.assertListEqual(
         lowerCAmelCase_						,  [
             SPIECE_UNDERLINE + '''I''',
             SPIECE_UNDERLINE + '''was''',
             SPIECE_UNDERLINE + '''b''',
             '''or''',
             '''n''',
             SPIECE_UNDERLINE + '''in''',
             SPIECE_UNDERLINE + '''''',
             '''9''',
             '''2''',
             '''0''',
             '''0''',
             '''0''',
             ''',''',
             SPIECE_UNDERLINE + '''and''',
             SPIECE_UNDERLINE + '''this''',
             SPIECE_UNDERLINE + '''is''',
             SPIECE_UNDERLINE + '''f''',
             '''al''',
             '''s''',
             '''é''',
             '''.''',
         ]						,  )
     snake_case_				:  int       =			tokenizer.convert_tokens_to_ids(lowerCAmelCase_       )
     self.assertListEqual(
         lowerCAmelCase_						,  [
             value + tokenizer.fairseq_offset
             for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
         ]						,  )

     snake_case_				:  int       =			tokenizer.convert_ids_to_tokens(lowerCAmelCase_       )
     self.assertListEqual(
         lowerCAmelCase_						,  [
             SPIECE_UNDERLINE + '''I''',
             SPIECE_UNDERLINE + '''was''',
             SPIECE_UNDERLINE + '''b''',
             '''or''',
             '''n''',
             SPIECE_UNDERLINE + '''in''',
             SPIECE_UNDERLINE + '''''',
             '''<unk>''',
             '''2''',
             '''0''',
             '''0''',
             '''0''',
             ''',''',
             SPIECE_UNDERLINE + '''and''',
             SPIECE_UNDERLINE + '''this''',
             SPIECE_UNDERLINE + '''is''',
             SPIECE_UNDERLINE + '''f''',
             '''al''',
             '''s''',
             '''<unk>''',
             '''.''',
         ]						,  )

 @cached_property
 def 		_snake_case      (  self	: Any       ):
     return XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''       )

 def 		_snake_case      (  self	: List[Any]       ):







     with tempfile.NamedTemporaryFile() as f:
         shutil.copyfile(lowerCAmelCase_						,  f.name       )
         snake_case_				:  str       =			XGLMTokenizer(f.name						,  keep_accents=lowerCAmelCase_       )
         snake_case_				:  Dict       =			pickle.dumps(lowerCAmelCase_       )
     pickle.loads(lowerCAmelCase_       )

 def 		_snake_case      (  self	: Optional[Any]       ):







     if not self.test_rust_tokenizer:
         return

     snake_case_				:  Tuple       =			self.get_tokenizer()
     snake_case_				:  Optional[int]       =			self.get_rust_tokenizer()

     snake_case_				:  Optional[int]       =			'''I was born in 92000, and this is falsé.'''

     snake_case_				:  int       =			tokenizer.tokenize(lowerCAmelCase_       )
     snake_case_				:  List[str]       =			rust_tokenizer.tokenize(lowerCAmelCase_       )
     self.assertListEqual(lowerCAmelCase_						,  lowerCAmelCase_       )

     snake_case_				:  Optional[int]       =			tokenizer.encode(lowerCAmelCase_						,  add_special_tokens=lowerCAmelCase_       )
     snake_case_				:  Union[str, Any]       =			rust_tokenizer.encode(lowerCAmelCase_						,  add_special_tokens=lowerCAmelCase_       )
     self.assertListEqual(lowerCAmelCase_						,  lowerCAmelCase_       )

     snake_case_				:  str       =			self.get_rust_tokenizer()
     snake_case_				:  Any       =			tokenizer.encode(lowerCAmelCase_       )
     snake_case_				:  Dict       =			rust_tokenizer.encode(lowerCAmelCase_       )
     self.assertListEqual(lowerCAmelCase_						,  lowerCAmelCase_       )

 @slow
 def 		_snake_case      (  self	: List[str]       ):
     snake_case_				:  Any       =			'''Hello World!'''
     snake_case_				:  Dict       =			[2, 31227, 4447, 35]

     self.assertListEqual(lowerCAmelCase_						,  self.big_tokenizer.encode(lowerCAmelCase_       )       )

 @slow
 def 		_snake_case      (  self	: str       ):
     snake_case_				:  Union[str, Any]       =			(
         '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will'''
         ''' add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth'''
     )
     # fmt: off
     snake_case_				:  int       =			[2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 71630, 28085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 13675, 377, 652, 7580, 10341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 202277, 17892, 33, 60, 87, 4, 3234, 157, 61, 2667, 52376, 19, 88, 23, 735]
     # fmt: on

     self.assertListEqual(lowerCAmelCase_						,  self.big_tokenizer.encode(lowerCAmelCase_       )       )






 @slow
 def 		_snake_case      (  self	: int       ):
     snake_case_				:  Tuple       =			{
         '''input_ids''': [[2, 108825, 1163, 15, 88010, 473, 15898, 157, 13672, 1857, 312, 8, 238021, 1163, 53, 13672, 1857, 312, 8, 53283, 182396, 8, 18566, 16, 36733, 4101, 8, 230, 244017, 122553, 7, 15, 132597, 4, 293, 12511, 7610, 4, 3414, 132597, 9, 4, 32361, 362, 4, 734, 28512, 32569, 18, 4, 32361, 26096, 14982, 73, 18715, 21433, 235261, 15, 492, 12427, 16, 53, 18715, 21433, 65454, 15, 23659, 563, 16, 278, 597, 2843, 595, 7931, 182396, 64186, 22, 886, 595, 132981, 53, 25540, 3449, 43982, 39901, 5951, 878, 330, 4, 27694, 80269, 312, 53, 6517, 11780, 611, 20408, 5], [2, 6, 132597, 67, 42897, 33, 592, 8, 163729, 25540, 361, 136997, 109514, 173230, 7, 501, 60, 102913, 196, 5631, 235, 63243, 473, 6, 231757, 74, 5277, 7905, 53, 3095, 37317, 22, 454, 183874, 5], [2, 268, 31298, 46530, 6, 132935, 43831, 7, 597, 32, 24, 3688, 9865, 5]],
         '''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, 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, 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, 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]]
     }  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=lowerCAmelCase_						,  model_name='''facebook/xglm-564M'''						,  padding=lowerCAmelCase_						,  )










"""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__					:						List[Any]												=			logging.get_logger(__name__)




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Tuple	      =	"""linear"""
 _lowerCAmelCase     :    Optional[int]	      =	"""cosine"""
 _lowerCAmelCase     :    Optional[int]	      =	"""cosine_with_restarts"""
 _lowerCAmelCase     :    Union[str, Any]	      =	"""polynomial"""
 _lowerCAmelCase     :    Optional[Any]	      =	"""constant"""
 _lowerCAmelCase     :    Optional[Any]	      =	"""constant_with_warmup"""
 _lowerCAmelCase     :    Union[str, Any]	      =	"""piecewise_constant"""





def __lowercase    (							_a	,    _a = -1					):
    return LambdaLR(_a	,    lambda _a					: 1	,    last_epoch=_a					)




def __lowercase    (							_a	,    _a	,    _a = -1					):

    def lr_lambda(_a					):
        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 __lowercase    (							_a	,    _a	,    _a = -1					):
    snake_case_				:  Dict       =			{}
    snake_case_				:  Optional[Any]       =			step_rules.split(''','''					)
    for rule_str in rule_list[:-1]:
        snake_case_,			snake_case_				:  Union[str, Any]       =			rule_str.split(''':'''					)
        snake_case_				:  Dict       =			int(_a					)
        snake_case_				:  int       =			float(_a					)
        snake_case_				:  Any       =			value
    snake_case_				:  Any       =			float(rule_list[-1]					)

    def create_rules_function(_a	,    _a					):
        def rule_func(_a					) -> float:
            snake_case_				:  Union[str, Any]       =			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

    snake_case_				:  List[str]       =			create_rules_function(_a	,    _a					)

    return LambdaLR(_a	,    _a	,    last_epoch=_a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a=-1					):

    def lr_lambda(_a					):
        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 __lowercase    (							_a	,    _a	,    _a	,    _a = 0.5	,    _a = -1					):

    def lr_lambda(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1	,    _a					)					)
        snake_case_				:  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(_a					) * 2.0 * progress					))					)

    return LambdaLR(_a	,    _a	,    _a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a = 1	,    _a = -1					):

    def lr_lambda(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1	,    _a					)					)
        snake_case_				:  int       =			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 __lowercase    (							_a	,    _a	,    _a	,    _a=1E-7	,    _a=1.0	,    _a=-1					):
    snake_case_				:  List[str]       =			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					):
        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:
            snake_case_				:  Tuple       =			lr_init - lr_end
            snake_case_				:  List[str]       =			num_training_steps - num_warmup_steps
            snake_case_				:  str       =			1 - (current_step - num_warmup_steps) / decay_steps
            snake_case_				:  Optional[int]       =			lr_range * pct_remaining**power + lr_end
            return decay / lr_init  # as LambdaLR multiplies by lr_init

    return LambdaLR(_a	,    _a	,    _a					)


lowercase__					:						Dict												=			{
    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 __lowercase    (							_a	,    _a	,    _a = None	,    _a = None	,    _a = None	,    _a = 1	,    _a = 1.0	,    _a = -1	,    ):
    snake_case_				:  Union[str, Any]       =			SchedulerType(_a					)
    snake_case_				:  Any       =			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					)





