"""simple docstring"""





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_big_bird import BigBirdTokenizer
else:
 snake_case__		:     Optional[Any]    				=				None

snake_case__		:     Union[str, Any]    				=				logging.get_logger(__name__)
snake_case__		:     List[str]    				=				{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case__		:     Any    				=				{
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    },
    '''tokenizer_file''': {
        '''google/bigbird-roberta-base''': (
            '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
        ),
    },
}

snake_case__		:     int    				=				{
    '''google/bigbird-roberta-base''': 4_096,
    '''google/bigbird-roberta-large''': 4_096,
    '''google/bigbird-base-trivia-itc''': 4_096,
}


snake_case__		:     Optional[Any]    				=				'''▁'''



class 		snake_case_(       a__			):
     __UpperCamelCase  =					VOCAB_FILES_NAMES
     __UpperCamelCase  =					PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase  =					BigBirdTokenizer
     __UpperCamelCase  =					['''input_ids''', '''attention_mask''']
     __UpperCamelCase  =					[]






     def __init__(	self  :					Union[str, Any]					,				UpperCamelCase_  :					str=None					,				UpperCamelCase_  :					Any=None					,				UpperCamelCase_  :					str="<unk>"					,				UpperCamelCase_  :					str="<s>"					,				UpperCamelCase_  :					str="</s>"					,				UpperCamelCase_  :					int="<pad>"					,				UpperCamelCase_  :					List[Any]="[SEP]"					,				UpperCamelCase_  :					Dict="[MASK]"					,				UpperCamelCase_  :					Any="[CLS]"					,				**UpperCamelCase_  :					Any					,				):
           lowerCAmelCase					:	Tuple       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else bos_token
           lowerCAmelCase					:	int       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else eos_token
           lowerCAmelCase					:	List[Any]       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else unk_token
           lowerCAmelCase					:	List[str]       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else pad_token
           lowerCAmelCase					:	Any       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else cls_token
           lowerCAmelCase					:	Tuple       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else sep_token

           # Mask token behave like a normal word, i.e. include the space before it
           lowerCAmelCase					:	Optional[Any]       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else mask_token

           super().__init__(
               UpperCamelCase_					,				tokenizer_file=UpperCamelCase_					,				bos_token=UpperCamelCase_					,				eos_token=UpperCamelCase_					,				unk_token=UpperCamelCase_					,				sep_token=UpperCamelCase_					,				pad_token=UpperCamelCase_					,				cls_token=UpperCamelCase_					,				mask_token=UpperCamelCase_					,				**UpperCamelCase_					,				)

           lowerCAmelCase					:	Optional[int]       =   vocab_file
           lowerCAmelCase					:	Optional[int]       =   False if not self.vocab_file else True






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					List[int]					,				UpperCamelCase_  :					Optional[List[int]] = None				):
           lowerCAmelCase					:	str       =   [self.sep_token_id]
           lowerCAmelCase					:	Tuple       =   [self.cls_token_id]
           if token_ids_a is None:
                 return cls + token_ids_a + sep
           return cls + token_ids_a + sep + token_ids_a + sep






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					List[int]					,				UpperCamelCase_  :					Optional[List[int]] = None					,				UpperCamelCase_  :					bool = False				):

           if already_has_special_tokens:
                 if token_ids_a is not None:
                       raise ValueError(
                           '''You should not supply a second sequence if the provided sequence of '''
                           '''ids is already formatted with special tokens for the model.'''				)
                 return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

           if token_ids_a is None:
                 return [1] + ([0] * len(UpperCamelCase_				)) + [1]
           return [1] + ([0] * len(UpperCamelCase_				)) + [1] + ([0] * len(UpperCamelCase_				)) + [1]






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					List[int]					,				UpperCamelCase_  :					Optional[List[int]] = None				):
           lowerCAmelCase					:	Tuple       =   [self.sep_token_id]
           lowerCAmelCase					:	Tuple       =   [self.cls_token_id]

           if token_ids_a is None:
                 return len(cls + token_ids_a + sep				) * [0]
           return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Optional[str] = None				):
           if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                     '''tokenizer.'''				)

           if not os.path.isdir(UpperCamelCase_				):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
                 return
           lowerCAmelCase					:	Optional[int]       =   os.path.join(
               UpperCamelCase_					,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

           if os.path.abspath(self.vocab_file				) != os.path.abspath(UpperCamelCase_				):
                 copyfile(self.vocab_file					,				UpperCamelCase_				)

           return (out_vocab_file,)






"""simple docstring"""





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      _snake_case						(						_snake_case				:			Tuple ,   _snake_case				:			Union[str, Any]=10			):
      lowerCAmelCase					:	Dict       =   []
      for _ in range(_snake_case			):
            lrs.append(scheduler.get_lr()[0]			)
            scheduler.step()
      return lrs


def      _snake_case						(						_snake_case				:			Optional[int] ,   _snake_case				:			int=10			):
      lowerCAmelCase					:	Optional[int]       =   []
      for step in range(_snake_case			):
            lrs.append(scheduler.get_lr()[0]			)
            scheduler.step()
            if step == num_steps // 2:
                  with tempfile.TemporaryDirectory() as tmpdirname:
                        lowerCAmelCase					:	List[str]       =   os.path.join(_snake_case ,   '''schedule.bin'''			)
                        torch.save(scheduler.state_dict() ,   _snake_case			)

                        lowerCAmelCase					:	List[Any]       =   torch.load(_snake_case			)
                        scheduler.load_state_dict(_snake_case			)
      return lrs



@require_torch
class 		snake_case_(       unittest.TestCase			):






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					Optional[int]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Any				):
           self.assertEqual(len(UpperCamelCase_				)					,				len(UpperCamelCase_				)				)
           for a, b in zip(UpperCamelCase_					,				UpperCamelCase_				):
                 self.assertAlmostEqual(UpperCamelCase_					,				UpperCamelCase_					,				delta=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase					:	Any       =   torch.tensor([0.1, -0.2, -0.1]					,				requires_grad=UpperCamelCase_				)
           lowerCAmelCase					:	List[str]       =   torch.tensor([0.4, 0.2, -0.5]				)
           lowerCAmelCase					:	List[Any]       =   nn.MSELoss()
           # No warmup, constant schedule, no gradient clipping
           lowerCAmelCase					:	Union[str, Any]       =   AdamW(params=[w]					,				lr=2E-1					,				weight_decay=0.0				)
           for _ in range(1_0_0				):
                 lowerCAmelCase					:	Union[str, Any]       =   criterion(UpperCamelCase_					,				UpperCamelCase_				)
                 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      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Tuple       =   torch.tensor([0.1, -0.2, -0.1]					,				requires_grad=UpperCamelCase_				)
           lowerCAmelCase					:	Union[str, Any]       =   torch.tensor([0.4, 0.2, -0.5]				)
           lowerCAmelCase					:	Optional[int]       =   nn.MSELoss()
           # No warmup, constant schedule, no gradient clipping
           lowerCAmelCase					:	Any       =   Adafactor(
               params=[w]					,				lr=1E-2					,				eps=(1E-30, 1E-3)					,				clip_threshold=1.0					,				decay_rate=-0.8					,				betaa=UpperCamelCase_					,				weight_decay=0.0					,				relative_step=UpperCamelCase_					,				scale_parameter=UpperCamelCase_					,				warmup_init=UpperCamelCase_					,				)
           for _ in range(1_0_0_0				):
                 lowerCAmelCase					:	List[Any]       =   criterion(UpperCamelCase_					,				UpperCamelCase_				)
                 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 		snake_case_(       unittest.TestCase			):
     __UpperCamelCase  =					nn.Linear(50							,					50			) if is_torch_available() else None
     __UpperCamelCase  =					AdamW(m.parameters()							,					lr=10.0			) if is_torch_available() else None
     __UpperCamelCase  =					10






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					Union[str, Any]=None				):
           self.assertEqual(len(UpperCamelCase_				)					,				len(UpperCamelCase_				)				)
           for a, b in zip(UpperCamelCase_					,				UpperCamelCase_				):
                 self.assertAlmostEqual(UpperCamelCase_					,				UpperCamelCase_					,				delta=UpperCamelCase_					,				msg=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Tuple       =   {'''num_warmup_steps''': 2, '''num_training_steps''': 1_0}
           # schedulers doct format
           # function: (sched_args_dict, expected_learning_rates)
           lowerCAmelCase					:	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, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
               ),
               get_inverse_sqrt_schedule: (
                   {'''num_warmup_steps''': 2},
                   [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
               ),
           }

           for scheduler_func, data in scheds.items():
                 lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   data

                 lowerCAmelCase					:	List[Any]       =   scheduler_func(self.optimizer					,				**UpperCamelCase_				)
                 self.assertEqual(len([scheduler.get_lr()[0]]				)					,				1				)
                 lowerCAmelCase					:	str       =   unwrap_schedule(UpperCamelCase_					,				self.num_steps				)
                 self.assertListAlmostEqual(
                     UpperCamelCase_					,				UpperCamelCase_					,				tol=1E-2					,				msg=F'''failed for {scheduler_func} in normal scheduler'''					,				)

                 lowerCAmelCase					:	Optional[int]       =   scheduler_func(self.optimizer					,				**UpperCamelCase_				)
                 if scheduler_func.__name__ != "get_constant_schedule":
                       LambdaScheduleWrapper.wrap_scheduler(UpperCamelCase_				)  # wrap to test picklability of the schedule
                 lowerCAmelCase					:	List[Any]       =   unwrap_and_save_reload_schedule(UpperCamelCase_					,				self.num_steps				)
                 self.assertListEqual(UpperCamelCase_					,				UpperCamelCase_					,				msg=F'''failed for {scheduler_func} in save and reload'''				)



class 		snake_case_:






     def __init__(	self  :					List[Any]					,				UpperCamelCase_  :					Any				):
           lowerCAmelCase					:	Tuple       =   fn






     def __call__(	self  :					Union[str, Any]					,				*UpperCamelCase_  :					Optional[Any]					,				**UpperCamelCase_  :					List[Any]				):
           return self.fn(*UpperCamelCase_					,				**UpperCamelCase_				)






     @classmethod
     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					Optional[int]				):
           lowerCAmelCase					:	Union[str, Any]       =   list(map(self					,				scheduler.lr_lambdas				)				)


