




import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm

def 			lowerCAmelCase_			(  lowerCamelCase:       int     ,				lowerCamelCase:       bool = False						)					->			str:


      '''simple docstring'''




      if device_specific:
            seed += AcceleratorState().process_index
      random.seed(lowerCamelCase						)
      np.random.seed(lowerCamelCase						)
      torch.manual_seed(lowerCamelCase						)
      if is_xpu_available():
            torch.xpu.manual_seed_all(lowerCamelCase						)
      elif is_npu_available():
            torch.npu.manual_seed_all(lowerCamelCase						)
      else:
            torch.cuda.manual_seed_all(lowerCamelCase						)
      # ^^ safe to call this function even if cuda is not available
      if is_tpu_available():
            xm.set_rng_state(lowerCamelCase						)

def 			lowerCAmelCase_			(  lowerCamelCase:       Optional[RNGType] = None     ,				lowerCamelCase:       Optional[torch.Generator] = None						)					->			Dict:


      '''simple docstring'''




      if rng_type == RNGType.TORCH:
            lowerCamelCase__        =				torch.get_rng_state()
      elif rng_type == RNGType.CUDA:
            lowerCamelCase__        =				torch.cuda.get_rng_state()
      elif rng_type == RNGType.XLA:
            assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
            lowerCamelCase__        =				torch.tensor(xm.get_rng_state()						)
      elif rng_type == RNGType.NPU:
            assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
            lowerCamelCase__        =				torch.npu.get_rng_state()
      elif rng_type == RNGType.XPU:
            assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
            lowerCamelCase__        =				torch.xpu.get_rng_state()
      elif rng_type == RNGType.GENERATOR:
            assert generator is not None, "Need a generator to synchronize its seed."
            lowerCamelCase__        =				generator.get_state()

      # Broadcast the rng state from device 0 to other devices
      lowerCamelCase__        =				AcceleratorState()
      if state.distributed_type == DistributedType.TPU:
            lowerCamelCase__        =				rng_state.to(xm.xla_device()						)
            xm.collective_broadcast([rng_state]						)
            xm.mark_step()
            lowerCamelCase__        =				rng_state.cpu()
      elif (
          state.distributed_type in CUDA_DISTRIBUTED_TYPES
          or state.distributed_type == DistributedType.MULTI_NPU
          or state.distributed_type == DistributedType.MULTI_XPU
      ):
            lowerCamelCase__        =				rng_state.to(state.device						)
            torch.distributed.broadcast(lowerCamelCase     ,				0						)
            lowerCamelCase__        =				rng_state.cpu()
      elif state.distributed_type == DistributedType.MULTI_CPU:
            torch.distributed.broadcast(lowerCamelCase     ,				0						)

      # Set the broadcast rng state
      if rng_type == RNGType.TORCH:
            torch.set_rng_state(lowerCamelCase						)
      elif rng_type == RNGType.CUDA:
            torch.cuda.set_rng_state(lowerCamelCase						)
      elif rng_type == RNGType.NPU:
            torch.npu.set_rng_state(lowerCamelCase						)
      elif rng_type == RNGType.XPU:
            torch.xpu.set_rng_state(lowerCamelCase						)
      elif rng_type == RNGType.XLA:
            xm.set_rng_state(rng_state.item()						)
      elif rng_type == RNGType.GENERATOR:
            generator.set_state(lowerCamelCase						)


def 			lowerCAmelCase_			(  lowerCamelCase:       List[Union[str, RNGType]]     ,				lowerCamelCase:       Optional[torch.Generator] = None						)					->			Optional[int]:


      '''simple docstring'''




      for rng_type in rng_types:
            synchronize_rng_state(RNGType(lowerCamelCase						)     ,				generator=lowerCamelCase						)





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


__SCREAMING_SNAKE_CASE    :   Dict       =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE    :   str       ={"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE    :   Dict       ={
    """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"""
        ),
    },
}

__SCREAMING_SNAKE_CASE    :   int       ={
    """allenai/longformer-base-4096""": 4_096,
    """allenai/longformer-large-4096""": 4_096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4_096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4_096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4_096,
}






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Optional[Any]     =VOCAB_FILES_NAMES
     _snake_case:	List[str]     =PRETRAINED_VOCAB_FILES_MAP
     _snake_case:	str     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _snake_case:	str     =["input_ids", "attention_mask"]
     _snake_case:	Union[str, Any]     =LongformerTokenizer




     def __init__(    self  :   str	,  A__  :   Optional[Any]=None	,  A__  :   Dict=None	,  A__  :   Any=None	,  A__  :   Optional[Any]="replace"	,  A__  :   Tuple="<s>"	,  A__  :   Any="</s>"	,  A__  :   Dict="</s>"	,  A__  :   Any="<s>"	,  A__  :   Optional[Any]="<unk>"	,  A__  :   Tuple="<pad>"	,  A__  :   Union[str, Any]="<mask>"	,  A__  :   List[str]=False	,  A__  :   List[str]=True	,  **A__  :   Optional[int]	,  )				->       List[str]:
           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__	,  )

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

           lowerCamelCase__        =				add_prefix_space

           lowerCamelCase__        =				'post_processor'
           lowerCamelCase__        =				getattr(self.backend_tokenizer	,  A__	,  A__			)
           if tokenizer_component_instance:
                 lowerCamelCase__        =				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:
                       lowerCamelCase__        =				tuple(state['sep']			)
                 if "cls" in state:
                       lowerCamelCase__        =				tuple(state['cls']			)

                 lowerCamelCase__        =				False

                 if state.get('add_prefix_space'	,  A__			) != add_prefix_space:
                       lowerCamelCase__        =				add_prefix_space
                       lowerCamelCase__        =				True

                 if state.get('trim_offsets'	,  A__			) != trim_offsets:
                       lowerCamelCase__        =				trim_offsets
                       lowerCamelCase__        =				True

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




     @property
     def  __snake_case   (    self  :   Tuple			)				->       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  __snake_case   (    self  :   Dict	,  A__  :   Tuple			)				->       int:
           lowerCamelCase__        =				AddedToken(A__	,  lstrip=A__	,  rstrip=A__			) if isinstance(A__	,  A__			) else value
           lowerCamelCase__        =				value




     def  __snake_case   (    self  :   Any	,  *A__  :   Any	,  **A__  :   Any			)				->       BatchEncoding:
           lowerCamelCase__        =				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  __snake_case   (    self  :   Tuple	,  *A__  :   str	,  **A__  :   int			)				->       BatchEncoding:
           lowerCamelCase__        =				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  __snake_case   (    self  :   Union[str, Any]	,  A__  :   str	,  A__  :   Optional[str] = None			)				->       Tuple[str]:
           lowerCamelCase__        =				self._tokenizer.model.save(A__	,  name=A__			)
           return tuple(A__			)




     def  __snake_case   (    self  :   Any	,  A__  :   int	,  A__  :   List[str]=None			)				->       Dict:
           lowerCamelCase__        =				[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  __snake_case   (    self  :   Union[str, Any]	,  A__  :   List[int]	,  A__  :   Optional[List[int]] = None			)				->       List[int]:
           lowerCamelCase__        =				[self.sep_token_id]
           lowerCamelCase__        =				[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]