






import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin




def    __A    (   _lowercase=2    ,	_lowercase=True    ,	_lowercase=False       ):

					'''simple docstring'''







					_A									=       Accelerator(step_scheduler_with_optimizer=_lowercase    ,	split_batches=_lowercase       )
					_A									=       torch.nn.Linear(2    ,	4       )
					_A									=       torch.optim.AdamW(model.parameters()    ,	lr=1.0       )
					_A									=       torch.optim.lr_scheduler.OneCycleLR(_lowercase    ,	max_lr=0.01    ,	steps_per_epoch=2    ,	epochs=1       )
					_A							,_A							,_A									=       accelerator.prepare(_lowercase    ,	_lowercase    ,	_lowercase       )

					# Optimizer has stepped
					scheduler.step()
					if step_scheduler_with_optimizer or (num_processes == 1):
										assert (
										    scheduler.scheduler.last_epoch == num_processes
										), f"""Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"""
					else:
										assert (
										    scheduler.scheduler.last_epoch != num_processes
										), f"""Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"""




def    __A    (   _lowercase=2    ,	_lowercase=True    ,	_lowercase=False       ):

					'''simple docstring'''







					_A									=       Accelerator(step_scheduler_with_optimizer=_lowercase    ,	split_batches=_lowercase       )
					_A									=       torch.nn.Linear(2    ,	4       )
					_A									=       torch.optim.AdamW(model.parameters()    ,	lr=1.0       )
					_A									=       torch.optim.lr_scheduler.LambdaLR(_lowercase    ,	lr_lambda=lambda _lowercase       : 1 - n / 10       )
					_A							,_A							,_A									=       accelerator.prepare(_lowercase    ,	_lowercase    ,	_lowercase       )

					# Optimizer has stepped
					_A									=       False
					scheduler.step()
					_A									=       1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
					assert (
					    scheduler.get_last_lr()[0] == expected_lr
					), f"""Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""

					# Optimizer has not stepped
					_A									=       True
					scheduler.step()
					if not step_scheduler_with_optimizer:
										_A									=       1 - 2 / 10
					assert (
					    scheduler.get_last_lr()[0] == expected_lr
					), f"""Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""




def    __A    (   _lowercase = 2       ):

					'''simple docstring'''







					from transformers import get_linear_schedule_with_warmup

					_A									=       [1, 2, 4]
					for num_steps in steps:
										_A									=       GradientAccumulationPlugin(num_steps=_lowercase    ,	adjust_scheduler=num_steps > 1       )
										_A									=       Accelerator(gradient_accumulation_plugin=_lowercase       )
										_A									=       torch.nn.Linear(2    ,	4       )
										_A									=       torch.optim.AdamW(model.parameters()    ,	lr=10.0       )
										_A									=       get_linear_schedule_with_warmup(optimizer=_lowercase    ,	num_warmup_steps=0    ,	num_training_steps=20       )

										_A							,_A							,_A									=       accelerator.prepare(_lowercase    ,	_lowercase    ,	_lowercase       )

										for i in range(10 * num_steps       ):
															with accelerator.accumulate(_lowercase       ):
																				optimizer.step()
																				scheduler.step()

															if i == (10 * num_steps - 2):
																				assert (
																				    scheduler.get_last_lr()[0] != 0
																				), f"""Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"""
										assert (
										    scheduler.get_last_lr()[0] == 0
										), f"""Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"""
										GradientState._reset_state()





@require_cpu
class   SCREAMING_SNAKE_CASE (       unittest.TestCase    ):





							"""simple docstring"""

							def 					__A       (     self:      Optional[int]      )			-> Any:
												debug_launcher(partial(__A						,   num_processes=1      )						,   num_processes=1      )
												debug_launcher(partial(__A						,   num_processes=1						,   split_batches=__A      )						,   num_processes=1      )

							def 					__A       (     self:      Tuple      )			-> Tuple:
												debug_launcher(partial(__A						,   num_processes=1      )						,   num_processes=1      )
												debug_launcher(partial(__A						,   num_processes=1						,   split_batches=__A      )						,   num_processes=1      )

							def 					__A       (     self:      List[str]      )			-> str:
												debug_launcher(partial(__A						,   num_processes=1						,   step_scheduler_with_optimizer=__A      )						,   num_processes=1      )

							def 					__A       (     self:      Optional[int]      )			-> str:
												debug_launcher(partial(__A						,   num_processes=1						,   step_scheduler_with_optimizer=__A      )						,   num_processes=1      )

							def 					__A       (     self:      List[str]      )			-> str:
												AcceleratorState._reset_state(__A      )
												debug_launcher(__A      )
												debug_launcher(partial(__A						,   num_processes=1						,   split_batches=__A      )						,   num_processes=1      )

							def 					__A       (     self:      Optional[int]      )			-> List[str]:
												AcceleratorState._reset_state(__A      )
												debug_launcher(__A      )
												debug_launcher(partial(__A						,   num_processes=1						,   split_batches=__A      )						,   num_processes=1      )

							def 					__A       (     self:      Optional[Any]      )			-> Optional[int]:
												AcceleratorState._reset_state(__A      )
												debug_launcher(partial(__A						,   step_scheduler_with_optimizer=__A      )      )

							def 					__A       (     self:      List[Any]      )			-> Tuple:
												AcceleratorState._reset_state(__A      )
												debug_launcher(partial(__A						,   step_scheduler_with_optimizer=__A      )      )



							@require_huggingface_suite
							def 					__A       (     self:      List[str]      )			-> int:
												AcceleratorState._reset_state(__A      )
												debug_launcher(partial(__A						,   num_processes=1      )      )
												debug_launcher(__A      )











import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


__A          = logging.get_logger(__name__)

__A          = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__A          = {
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

__A          = {
    'allenai/longformer-base-4096': 4096,
    'allenai/longformer-large-4096': 4096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4096,
}





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    VOCAB_FILES_NAMES
							A_       =    PRETRAINED_VOCAB_FILES_MAP
							A_       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							A_       =    ["input_ids", "attention_mask"]
							A_       =    LongformerTokenizer

							def __init__(     self:      List[str]						,   __A:      Dict=None						,   __A:      int=None						,   __A:      Tuple=None						,   __A:      Any="replace"						,   __A:      List[str]="<s>"						,   __A:      int="</s>"						,   __A:      Dict="</s>"						,   __A:      Tuple="<s>"						,   __A:      Optional[int]="<unk>"						,   __A:      Optional[int]="<pad>"						,   __A:      List[Any]="<mask>"						,   __A:      Tuple=False						,   __A:      Dict=True						,   **__A:      List[Any]						,   )			-> List[Any]:
												super().__init__(
												    __A						,   __A						,   tokenizer_file=__A						,   errors=__A						,   bos_token=__A						,   eos_token=__A						,   sep_token=__A						,   cls_token=__A						,   unk_token=__A						,   pad_token=__A						,   mask_token=__A						,   add_prefix_space=__A						,   trim_offsets=__A						,   **__A						,   )

												_A									=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
												if pre_tok_state.get('''add_prefix_space'''						,   __A      ) != add_prefix_space:
																	_A									=       getattr(__A						,   pre_tok_state.pop('''type'''      )      )
																	_A									=       add_prefix_space
																	_A									=       pre_tok_class(**__A      )

												_A									=       add_prefix_space

												_A									=       '''post_processor'''
												_A									=       getattr(self.backend_tokenizer						,   __A						,   __A      )
												if tokenizer_component_instance:
																	_A									=       json.loads(tokenizer_component_instance.__getstate__()      )

																	# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																	if "sep" in state:
																						_A									=       tuple(state['''sep''']      )
																	if "cls" in state:
																						_A									=       tuple(state['''cls''']      )

																	_A									=       False

																	if state.get('''add_prefix_space'''						,   __A      ) != add_prefix_space:
																						_A									=       add_prefix_space
																						_A									=       True

																	if state.get('''trim_offsets'''						,   __A      ) != trim_offsets:
																						_A									=       trim_offsets
																						_A									=       True

																	if changes_to_apply:
																						_A									=       getattr(__A						,   state.pop('''type'''      )      )
																						_A									=       component_class(**__A      )
																						setattr(self.backend_tokenizer						,   __A						,   __A      )

							@property
							def 					__A       (     self:      Union[str, Any]      )			-> str:
												if self._mask_token is None:
																	if self.verbose:
																						logger.error('''Using mask_token, but it is not set yet.'''      )
																	return None
												return str(self._mask_token      )

							@mask_token.setter
							def 					__A       (     self:      Optional[int]						,   __A:      str      )			-> Union[str, Any]:
												_A									=       AddedToken(__A						,   lstrip=__A						,   rstrip=__A      ) if isinstance(__A						,   __A      ) else value
												_A									=       value

							def 					__A       (     self:      Optional[Any]						,   *__A:      Any						,   **__A:      List[Any]      )			-> BatchEncoding:
												_A									=       kwargs.get('''is_split_into_words'''						,   __A      )
												assert self.add_prefix_space or not is_split_into_words, (
												    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs."
												)

												return super()._batch_encode_plus(*__A						,   **__A      )

							def 					__A       (     self:      Optional[Any]						,   *__A:      List[str]						,   **__A:      List[Any]      )			-> BatchEncoding:
												_A									=       kwargs.get('''is_split_into_words'''						,   __A      )

												assert self.add_prefix_space or not is_split_into_words, (
												    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs."
												)

												return super()._encode_plus(*__A						,   **__A      )

							def 					__A       (     self:      List[str]						,   __A:      str						,   __A:      Optional[str] = None      )			-> Tuple[str]:
												_A									=       self._tokenizer.model.save(__A						,   name=__A      )
												return tuple(__A      )

							def 					__A       (     self:      Tuple						,   __A:      Dict						,   __A:      Optional[Any]=None      )			-> Optional[int]:
												_A									=       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
												if token_ids_a is None:
																	return output

												return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



							def 					__A       (     self:      Optional[int]						,   __A:      List[int]						,   __A:      Optional[List[int]] = None      )			-> List[int]:
												_A									=       [self.sep_token_id]
												_A									=       [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 + sep + token_ids_a + sep      ) * [0]



