


import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map


def 		lowercase_  (     _UpperCamelCase  ):


   '''simple docstring'''







   __lowercase 					=   []
   if isinstance(_UpperCamelCase       ,			_UpperCamelCase  ):
      for v in tree.values():
         shapes.extend(_fetch_dims(_UpperCamelCase  )  )
   elif isinstance(_UpperCamelCase       ,			(list, tuple)  ):
      for t in tree:
         shapes.extend(_fetch_dims(_UpperCamelCase  )  )
   elif isinstance(_UpperCamelCase       ,			torch.Tensor  ):
      shapes.append(tree.shape  )
   else:
      raise ValueError('''Not supported'''  )

   return shapes


@torch.jit.ignore
def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


   '''simple docstring'''







   __lowercase 					=   []
   for d in reversed(_UpperCamelCase  ):
      idx.append(flat_idx % d  )
      __lowercase 					=   flat_idx // d

   return tuple(reversed(_UpperCamelCase  )  )


@torch.jit.ignore
def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase = None       ,			_UpperCamelCase = None       ,			):


   '''simple docstring'''







   def reduce_edge_list(_UpperCamelCase  ) -> None:
      __lowercase 					=   True
      for i in range(len(_UpperCamelCase  )  ):
         __lowercase 					=   -1 * (i + 1)
         l[reversed_idx] &= tally
         __lowercase 					=   l[reversed_idx]

   if start_edges is None:
      __lowercase 					=   [s == 0 for s in start]
      reduce_edge_list(_UpperCamelCase  )
   if end_edges is None:
      __lowercase 					=   [e == (d - 1) for e, d in zip(_UpperCamelCase       ,			_UpperCamelCase  )]
      reduce_edge_list(_UpperCamelCase  )

   # Base cases. Either start/end are empty and we're done, or the final,
   # one-dimensional tensor can be simply sliced
   if len(_UpperCamelCase  ) == 0:
      return [()]
   elif len(_UpperCamelCase  ) == 1:
      return [(slice(start[0]       ,			end[0] + 1  ),)]

   __lowercase 					=   []
   __lowercase 					=   []

   # Dimensions common to start and end can be selected directly
   for s, e in zip(_UpperCamelCase       ,			_UpperCamelCase  ):
      if s == e:
         path_list.append(slice(_UpperCamelCase       ,			s + 1  )  )
      else:
         break

   __lowercase 					=   tuple(_UpperCamelCase  )
   __lowercase 					=   len(_UpperCamelCase  )

   # start == end, and we're done
   if divergence_idx == len(_UpperCamelCase  ):
      return [path]

   def upper() -> Tuple[Tuple[slice, ...], ...]:
      assert start_edges is not None
      assert end_edges is not None

      __lowercase 					=   start[divergence_idx]
      return tuple(
          path + (slice(_UpperCamelCase       ,			sdi + 1  ),) + s
          for s in _get_minimal_slice_set(
              start[divergence_idx + 1 :]       ,			[d - 1 for d in dims[divergence_idx + 1 :]]       ,			dims[divergence_idx + 1 :]       ,			start_edges=start_edges[divergence_idx + 1 :]       ,			end_edges=[True for _ in end_edges[divergence_idx + 1 :]]       ,			)  )

   def lower() -> Tuple[Tuple[slice, ...], ...]:
      assert start_edges is not None
      assert end_edges is not None

      __lowercase 					=   end[divergence_idx]
      return tuple(
          path + (slice(_UpperCamelCase       ,			edi + 1  ),) + s
          for s in _get_minimal_slice_set(
              [0 for _ in start[divergence_idx + 1 :]]       ,			end[divergence_idx + 1 :]       ,			dims[divergence_idx + 1 :]       ,			start_edges=[True for _ in start_edges[divergence_idx + 1 :]]       ,			end_edges=end_edges[divergence_idx + 1 :]       ,			)  )

   # If both start and end are at the edges of the subtree rooted at
   # divergence_idx, we can just select the whole subtree at once
   if start_edges[divergence_idx] and end_edges[divergence_idx]:
      slices.append(path + (slice(start[divergence_idx]       ,			end[divergence_idx] + 1  ),)  )
   # If just start is at the edge, we can grab almost all of the subtree,
   # treating only the ragged bottom edge as an edge case
   elif start_edges[divergence_idx]:
      slices.append(path + (slice(start[divergence_idx]       ,			end[divergence_idx]  ),)  )
      slices.extend(lower()  )
   # Analogous to the previous case, but the top is ragged this time
   elif end_edges[divergence_idx]:
      slices.extend(upper()  )
      slices.append(path + (slice(start[divergence_idx] + 1       ,			end[divergence_idx] + 1  ),)  )
   # If both sides of the range are ragged, we need to handle both sides
   # separately. If there's contiguous meat in between them, we can index it
   # in one big chunk
   else:
      slices.extend(upper()  )
      __lowercase 					=   end[divergence_idx] - start[divergence_idx]
      if middle_ground > 1:
         slices.append(path + (slice(start[divergence_idx] + 1       ,			end[divergence_idx]  ),)  )
      slices.extend(lower()  )

   return slices


@torch.jit.ignore
def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


   '''simple docstring'''







   __lowercase 					=   t.shape[:no_batch_dims]
   __lowercase 					=   list(_flat_idx_to_idx(_UpperCamelCase       ,			_UpperCamelCase  )  )
   # _get_minimal_slice_set is inclusive
   __lowercase 					=   list(_flat_idx_to_idx(flat_end - 1       ,			_UpperCamelCase  )  )

   # Get an ordered list of slices to perform
   __lowercase 					=   _get_minimal_slice_set(
       _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			)

   __lowercase 					=   [t[s] for s in slices]

   return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]  ) for s in sliced_tensors]  )


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase = False       ,			_UpperCamelCase = None       ,			_UpperCamelCase = False       ,			):


   '''simple docstring'''







   if not (len(_UpperCamelCase  ) > 0):
      raise ValueError('''Must provide at least one input'''  )

   __lowercase 					=   [shape[:no_batch_dims] for shape in _fetch_dims(_UpperCamelCase  )]
   __lowercase 					=   tuple([max(_UpperCamelCase  ) for s in zip(*_UpperCamelCase  )]  )

   def _prep_inputs(_UpperCamelCase  ) -> torch.Tensor:
      if not low_mem:
         if not sum(t.shape[:no_batch_dims]  ) == no_batch_dims:
            __lowercase 					=   t.expand(orig_batch_dims + t.shape[no_batch_dims:]  )
         __lowercase 					=   t.reshape(-1       ,			*t.shape[no_batch_dims:]  )
      else:
         __lowercase 					=   t.expand(orig_batch_dims + t.shape[no_batch_dims:]  )
      return t

   __lowercase 					=   tensor_tree_map(_prep_inputs       ,			_UpperCamelCase  )
   __lowercase 					=   None
   if _out is not None:
      __lowercase 					=   tensor_tree_map(lambda _UpperCamelCase  : t.view([-1] + list(t.shape[no_batch_dims:]  )  )       ,			_out  )

   __lowercase 					=   1
   for d in orig_batch_dims:
      flat_batch_dim *= d

   __lowercase 					=   flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

   def _select_chunk(_UpperCamelCase  ) -> torch.Tensor:
      return t[i : i + chunk_size] if t.shape[0] != 1 else t

   __lowercase 					=   0
   __lowercase 					=   prepped_outputs
   for _ in range(_UpperCamelCase  ):
      # Chunk the input
      if not low_mem:
         __lowercase 					=   _select_chunk
      else:
         __lowercase 					=   partial(
             _chunk_slice       ,			flat_start=_UpperCamelCase       ,			flat_end=min(_UpperCamelCase       ,			i + chunk_size  )       ,			no_batch_dims=len(_UpperCamelCase  )       ,			)

      __lowercase 					=   tensor_tree_map(_UpperCamelCase       ,			_UpperCamelCase  )

      # Run the layer on the chunk
      __lowercase 					=   layer(**_UpperCamelCase  )

      # Allocate space for the output
      if out is None:
         __lowercase 					=   tensor_tree_map(lambda _UpperCamelCase  : t.new_zeros((flat_batch_dim,) + t.shape[1:]  )       ,			_UpperCamelCase  )

      # Put the chunk in its pre-allocated space
      if isinstance(_UpperCamelCase       ,			_UpperCamelCase  ):

         def assign(_UpperCamelCase       ,			_UpperCamelCase  ) -> None:
            for k, v in da.items():
               if isinstance(_UpperCamelCase       ,			_UpperCamelCase  ):
                  assign(_UpperCamelCase       ,			da[k]  )
               else:
                  if _add_into_out:
                     v[i : i + chunk_size] += da[k]
                  else:
                     __lowercase 					=   da[k]

         assign(_UpperCamelCase       ,			_UpperCamelCase  )
      elif isinstance(_UpperCamelCase       ,			_UpperCamelCase  ):
         for xa, xa in zip(_UpperCamelCase       ,			_UpperCamelCase  ):
            if _add_into_out:
               xa[i : i + chunk_size] += xa
            else:
               __lowercase 					=   xa
      elif isinstance(_UpperCamelCase       ,			torch.Tensor  ):
         if _add_into_out:
            out[i : i + chunk_size] += output_chunk
         else:
            __lowercase 					=   output_chunk
      else:
         raise ValueError('''Not supported'''  )

      i += chunk_size

   __lowercase 					=   tensor_tree_map(lambda _UpperCamelCase  : t.view(orig_batch_dims + t.shape[1:]  )       ,			_UpperCamelCase  )

   return out





class 	lowerCamelCase_       :



     '''simple docstring'''







     def __init__(				self ,       snake_case_ = 5_1_2 ,       )   ->			int:







        '''simple docstring'''

        __lowercase 					=   max_chunk_size
        __lowercase 					=   None
        __lowercase 					=   None






     def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_						)   ->			int:







        '''simple docstring'''

        logging.info('''Tuning chunk size...'''						)

        if min_chunk_size >= self.max_chunk_size:
           return min_chunk_size

        __lowercase 					=   [2**l for l in range(int(math.log(self.max_chunk_size ,       2						)						) + 1						)]
        __lowercase 					=   [c for c in candidates if c > min_chunk_size]
        __lowercase 					=   [min_chunk_size] + candidates
        candidates[-1] += 4

        def test_chunk_size(snake_case_						) -> bool:
           try:
              with torch.no_grad():
                 fn(*snake_case_ ,       chunk_size=snake_case_						)
              return True
           except RuntimeError:
              return False

        __lowercase 					=   0
        __lowercase 					=   len(snake_case_						) - 1
        while i > min_viable_chunk_size_index:
           __lowercase 					=   test_chunk_size(candidates[i]						)
           if not viable:
              __lowercase 					=   (min_viable_chunk_size_index + i) // 2
           else:
              __lowercase 					=   i
              __lowercase 					=   (i + len(snake_case_						) - 1) // 2

        return candidates[min_viable_chunk_size_index]






     def A      (				self ,       snake_case_ ,       snake_case_						)   ->			bool:







        '''simple docstring'''

        __lowercase 					=   True
        for aa, aa in zip(snake_case_ ,       snake_case_						):
           assert type(snake_case_						) == type(snake_case_						)
           if isinstance(snake_case_ ,       (list, tuple)						):
              consistent &= self._compare_arg_caches(snake_case_ ,       snake_case_						)
           elif isinstance(snake_case_ ,       snake_case_						):
              __lowercase 					=   [v for _, v in sorted(aa.items() ,       key=lambda snake_case_						: x[0]						)]
              __lowercase 					=   [v for _, v in sorted(aa.items() ,       key=lambda snake_case_						: x[0]						)]
              consistent &= self._compare_arg_caches(snake_case_ ,       snake_case_						)
           else:
              consistent &= aa == aa

        return consistent






     def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       )   ->			int:







        '''simple docstring'''

        __lowercase 					=   True
        __lowercase 					=   tree_map(lambda snake_case_						: a.shape if isinstance(snake_case_ ,       torch.Tensor						) else a ,       snake_case_ ,       snake_case_						)
        if self.cached_arg_data is not None:
           # If args have changed shape/value, we need to re-tune
           assert len(self.cached_arg_data						) == len(snake_case_						)
           __lowercase 					=   self._compare_arg_caches(self.cached_arg_data ,       snake_case_						)
        else:
           # Otherwise, we can reuse the precomputed value
           __lowercase 					=   False

        if not consistent:
           __lowercase 					=   self._determine_favorable_chunk_size(
               snake_case_ ,       snake_case_ ,       snake_case_ ,       )
           __lowercase 					=   arg_data

        assert self.cached_chunk_size is not None

        return self.cached_chunk_size






import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


a : int		=				logging.get_logger(__name__)


a : Any		=				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
a : Dict		=				{
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

a : Optional[int]		=				{
    '''allenai/led-base-16384''': 16384,
}


@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 		lowercase_  (     ):


			'''simple docstring'''







			__lowercase 					=   (
			    list(range(ord('''!'''  )       ,			ord('''~'''  ) + 1  )  ) + list(range(ord('''¡'''  )       ,			ord('''¬'''  ) + 1  )  ) + list(range(ord('''®'''  )       ,			ord('''ÿ'''  ) + 1  )  )
			)
			__lowercase 					=   bs[:]
			__lowercase 					=   0
			for b in range(2**8  ):
						if b not in bs:
									bs.append(_UpperCamelCase  )
									cs.append(2**8 + n  )
									n += 1
			__lowercase 					=   [chr(_UpperCamelCase  ) for n in cs]
			return dict(zip(_UpperCamelCase       ,			_UpperCamelCase  )  )


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   set()
			__lowercase 					=   word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)  )
						__lowercase 					=   char
			return pairs





class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				VOCAB_FILES_NAMES
					__UpperCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
					__UpperCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__UpperCAmelCase            =				["input_ids", "attention_mask"]






					def __init__(				self ,       snake_case_ ,       snake_case_ ,       snake_case_="replace" ,       snake_case_="<s>" ,       snake_case_="</s>" ,       snake_case_="</s>" ,       snake_case_="<s>" ,       snake_case_="<unk>" ,       snake_case_="<pad>" ,       snake_case_="<mask>" ,       snake_case_=False ,       **snake_case_ ,       )   ->			List[Any]:







								'''simple docstring'''

								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else bos_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else eos_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else sep_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else cls_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else unk_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else pad_token

								# Mask token behave like a normal word, i.e. include the space before it
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else mask_token

								super().__init__(
								    errors=snake_case_ ,       bos_token=snake_case_ ,       eos_token=snake_case_ ,       unk_token=snake_case_ ,       sep_token=snake_case_ ,       cls_token=snake_case_ ,       pad_token=snake_case_ ,       mask_token=snake_case_ ,       add_prefix_space=snake_case_ ,       **snake_case_ ,       )

								with open(snake_case_ ,       encoding='''utf-8'''						) as vocab_handle:
											__lowercase 					=   json.load(snake_case_						)
								__lowercase 					=   {v: k for k, v in self.encoder.items()}
								__lowercase 					=   errors  # how to handle errors in decoding
								__lowercase 					=   bytes_to_unicode()
								__lowercase 					=   {v: k for k, v in self.byte_encoder.items()}
								with open(snake_case_ ,       encoding='''utf-8'''						) as merges_handle:
											__lowercase 					=   merges_handle.read().split('''\n'''						)[1:-1]
								__lowercase 					=   [tuple(merge.split()						) for merge in bpe_merges]
								__lowercase 					=   dict(zip(snake_case_ ,       range(len(snake_case_						)						)						)						)
								__lowercase 					=   {}
								__lowercase 					=   add_prefix_space

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								__lowercase 					=   re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''						)






					@property
					# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
					def A      (				self						)   ->			Union[str, Any]:







								'''simple docstring'''

								return len(self.encoder						)






					def A      (				self						)   ->			str:







								'''simple docstring'''

								return dict(self.encoder ,       **self.added_tokens_encoder						)






					def A      (				self ,       snake_case_						)   ->			Optional[int]:







								'''simple docstring'''

								if token in self.cache:
											return self.cache[token]
								__lowercase 					=   tuple(snake_case_						)
								__lowercase 					=   get_pairs(snake_case_						)

								if not pairs:
											return token

								while True:
											__lowercase 					=   min(snake_case_ ,       key=lambda snake_case_						: self.bpe_ranks.get(snake_case_ ,       float('''inf'''						)						)						)
											if bigram not in self.bpe_ranks:
														break
											__lowercase					, __lowercase 					=   bigram
											__lowercase 					=   []
											__lowercase 					=   0
											while i < len(snake_case_						):
														try:
																	__lowercase 					=   word.index(snake_case_ ,       snake_case_						)
														except ValueError:
																	new_word.extend(word[i:]						)
																	break
														else:
																	new_word.extend(word[i:j]						)
																	__lowercase 					=   j

														if word[i] == first and i < len(snake_case_						) - 1 and word[i + 1] == second:
																	new_word.append(first + second						)
																	i += 2
														else:
																	new_word.append(word[i]						)
																	i += 1
											__lowercase 					=   tuple(snake_case_						)
											__lowercase 					=   new_word
											if len(snake_case_						) == 1:
														break
											else:
														__lowercase 					=   get_pairs(snake_case_						)
								__lowercase 					=   ''' '''.join(snake_case_						)
								__lowercase 					=   word
								return word






					def A      (				self ,       snake_case_						)   ->			Tuple:







								'''simple docstring'''

								__lowercase 					=   []
								for token in re.findall(self.pat ,       snake_case_						):
											__lowercase 					=   ''''''.join(
											    self.byte_encoder[b] for b in token.encode('''utf-8'''						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
											bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case_						).split(''' '''						)						)
								return bpe_tokens






					def A      (				self ,       snake_case_						)   ->			List[Any]:







								'''simple docstring'''

								return self.encoder.get(snake_case_ ,       self.encoder.get(self.unk_token						)						)






					def A      (				self ,       snake_case_						)   ->			Tuple:







								'''simple docstring'''

								return self.decoder.get(snake_case_						)






					def A      (				self ,       snake_case_						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   ''''''.join(snake_case_						)
								__lowercase 					=   bytearray([self.byte_decoder[c] for c in text]						).decode('''utf-8''' ,       errors=self.errors						)
								return text






					def A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			Tuple[str]:







								'''simple docstring'''

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

								with open(snake_case_ ,       '''w''' ,       encoding='''utf-8'''						) as f:
											f.write(json.dumps(self.encoder ,       indent=2 ,       sort_keys=snake_case_ ,       ensure_ascii=snake_case_						) + '''\n'''						)

								__lowercase 					=   0
								with open(snake_case_ ,       '''w''' ,       encoding='''utf-8'''						) as writer:
											writer.write('''#version: 0.2\n'''						)
											for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,       key=lambda snake_case_						: kv[1]						):
														if index != token_index:
																	logger.warning(
																	    F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																	    ''' Please check that the tokenizer is not corrupted!'''						)
																	__lowercase 					=   token_index
														writer.write(''' '''.join(snake_case_						) + '''\n'''						)
														index += 1

								return vocab_file, merge_file






					def A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			List[int]:







								'''simple docstring'''

								if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								__lowercase 					=   [self.cls_token_id]
								__lowercase 					=   [self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep






					def A      (				self ,       snake_case_ ,       snake_case_ = None ,       snake_case_ = False						)   ->			List[int]:







								'''simple docstring'''

								if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=snake_case_ ,       token_ids_a=snake_case_ ,       already_has_special_tokens=snake_case_						)

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






					def A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			List[int]:







								'''simple docstring'''

								__lowercase 					=   [self.sep_token_id]
								__lowercase 					=   [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]






					def A      (				self ,       snake_case_ ,       snake_case_=False ,       **snake_case_						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   kwargs.pop('''add_prefix_space''' ,       self.add_prefix_space						)
								if (is_split_into_words or add_prefix_space) and (len(snake_case_						) > 0 and not text[0].isspace()):
											__lowercase 					=   ''' ''' + text
								return (text, kwargs)






					def A      (				self ,       snake_case_ ,       snake_case_ = None ,       snake_case_ = PaddingStrategy.DO_NOT_PAD ,       snake_case_ = None ,       snake_case_ = None ,       )   ->			dict:







								'''simple docstring'''

								__lowercase 					=   super()._pad(
								    encoded_inputs=snake_case_ ,       max_length=snake_case_ ,       padding_strategy=snake_case_ ,       pad_to_multiple_of=snake_case_ ,       return_attention_mask=snake_case_ ,       )

								# Load from model defaults
								if return_attention_mask is None:
											__lowercase 					=   '''attention_mask''' in self.model_input_names

								if return_attention_mask and "global_attention_mask" in encoded_inputs:
											__lowercase 					=   encoded_inputs[self.model_input_names[0]]
											# `global_attention_mask` need to have the same length as other (sequential) inputs.
											__lowercase 					=   len(encoded_inputs['''global_attention_mask''']						) != len(snake_case_						)

											if needs_to_be_padded:
														__lowercase 					=   len(snake_case_						) - len(encoded_inputs['''global_attention_mask''']						)

														if self.padding_side == "right":
																	# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																	__lowercase 					=   (
																	    encoded_inputs['''global_attention_mask'''] + [-1] * difference
																	)
														elif self.padding_side == "left":
																	__lowercase 					=   [-1] * difference + encoded_inputs[
																	    '''global_attention_mask'''
																	]
														else:
																	raise ValueError('''Invalid padding strategy:''' + str(self.padding_side						)						)

								return encoded_inputs


