






"""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_fnet import FNetTokenizer
else:
					SCREAMING_SNAKE_CASE						:						Any					   =       None

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

SCREAMING_SNAKE_CASE						:						Union[str, Any]					   =       {
    '''vocab_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/spiece.model''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE						:						int					   =       {
    '''google/fnet-base''': 512,
    '''google/fnet-large''': 512,
}

SCREAMING_SNAKE_CASE						:						Any					   =       '''▁'''





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							List[str]   			=					VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_:							List[str]   			=					PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_:							Optional[Any]   			=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_:							List[Any]   			=					["""input_ids""", """token_type_ids"""]
	SCREAMING_SNAKE_CASE_:							List[Any]   			=					FNetTokenizer


	def __init__(      self  ,					__a=None  ,					__a=None  ,					__a=False  ,					__a=True  ,					__a=True  ,					__a="<unk>"  ,					__a="[SEP]"  ,					__a="<pad>"  ,					__a="[CLS]"  ,					__a="[MASK]"  ,					**__a  ,					):


					"""simple docstring"""

					A__							 =       (
					    AddedToken(__a  ,					lstrip=__a  ,					rstrip=__a  ,					normalized=__a						)
					    if isinstance(__a  ,					__a						)
					    else mask_token
					)

					super().__init__(
					    __a  ,					tokenizer_file=__a  ,					do_lower_case=__a  ,					remove_space=__a  ,					keep_accents=__a  ,					unk_token=__a  ,					sep_token=__a  ,					pad_token=__a  ,					cls_token=__a  ,					mask_token=__a  ,					**__a  ,					)

					A__							 =       do_lower_case
					A__							 =       remove_space
					A__							 =       keep_accents
					A__							 =       vocab_file
					A__							 =       False if not self.vocab_file else True


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None						):


					"""simple docstring"""

					A__							 =       [self.sep_token_id]
					A__							 =       [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       _UpperCAmelCase						(      self  ,					__a  ,					__a = None						):


					"""simple docstring"""

					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						) * [0] + len(token_ids_a + sep						) * [1]






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None						):


					"""simple docstring"""

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

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

					return (out_vocab_file,)










"""simple docstring"""






import torch

from transformers import AutoModel





class       snake_case_       (    torch.nn.Module       ):



	"""simple docstring"""

	def __init__(      self  ,					__a="sayef/fsner-bert-base-uncased"						):


					"""simple docstring"""

					super(__a  ,					self						).__init__()

					A__							 =       AutoModel.from_pretrained(__a  ,					return_dict=__a						)
					A__							 =       torch.nn.CosineSimilarity(3  ,					1E-08						)
					A__							 =       torch.nn.Softmax(dim=1						)


	def       _UpperCAmelCase						(      self  ,					**__a						):


					"""simple docstring"""

					return self.bert(**__a						).last_hidden_state


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return token_embeddings.sum(2  ,					keepdim=__a						)


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a=1						):


					"""simple docstring"""

					return self.softmax(T * self.cos(__a  ,					__a						)						)






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       W_supports['sizes'].tolist()
					A__							 =       W_supports['start_token_id'].item()
					A__							 =       W_supports['end_token_id'].item()

					del W_supports["sizes"]
					del W_supports["start_token_id"]
					del W_supports["end_token_id"]

					A__							 =       self.BERT(**__a						)
					A__							 =       self.BERT(**__a						)

					A__							 =       None
					A__							 =       None

					A__							 =       W_supports['input_ids'] == start_token_id
					A__							 =       W_supports['input_ids'] == end_token_id

					for i, size in enumerate(__a						):
									if i == 0:
													A__							 =       0
									else:
													A__							 =       support_sizes[i - 1]

									A__							 =       S[s : s + size][start_token_masks[s : s + size]]
									A__							 =       S[s : s + size][end_token_masks[s : s + size]]

									A__							 =       torch.matmul(q[i]  ,					s_start.T						).sum(1						).softmax(0						)
									A__							 =       torch.matmul(q[i]  ,					s_end.T						).sum(1						).softmax(0						)

									if p_starts is not None:
													A__							 =       torch.vstack((p_starts, p_start)						)
													A__							 =       torch.vstack((p_ends, p_end)						)
									else:
													A__							 =       p_start
													A__							 =       p_end

					return p_starts, p_ends


