






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

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


if is_sentencepiece_available():
    from .tokenization_ta import TaTokenizer
else:
    _SCREAMING_SNAKE_CASE   =  None


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =  {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_SCREAMING_SNAKE_CASE   =  {
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
_SCREAMING_SNAKE_CASE   =  {
    "t5-small": 5_12,
    "t5-base": 5_12,
    "t5-large": 5_12,
    "t5-3b": 5_12,
    "t5-11b": 5_12,
}






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Optional[Any]					=VOCAB_FILES_NAMES
       __lowerCAmelCase    :		List[Any]					=PRETRAINED_VOCAB_FILES_MAP
       __lowerCAmelCase    :		Optional[int]					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowerCAmelCase    :		Any					=['''input_ids''', '''attention_mask''']
       __lowerCAmelCase    :		Dict					=TaTokenizer

       __lowerCAmelCase    :		List[int]					=[]





       def __init__(				self	:Optional[Any],      snake_case	:str=None,      snake_case	:List[Any]=None,      snake_case	:Optional[Any]="</s>",      snake_case	:int="<unk>",      snake_case	:str="<pad>",      snake_case	:int=100,      snake_case	:Optional[Any]=None,      **snake_case	:str,      ):



          """simple docstring"""

          if extra_ids > 0 and additional_special_tokens is None:
             _lowercase              =[f'''<extra_id_{i}>''' for i in range(snake_case)]
          elif extra_ids > 0 and additional_special_tokens is not None:
             # Check that we have the right number of extra special tokens
             _lowercase              =len(set(filter(lambda snake_case: bool('extra_id_' in str(snake_case)),      snake_case)))
             if extra_tokens != extra_ids:
                raise ValueError(
                    f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
                    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
                    ' tokens')

          super().__init__(
              snake_case,      tokenizer_file=snake_case,      eos_token=snake_case,      unk_token=snake_case,      pad_token=snake_case,      extra_ids=snake_case,      additional_special_tokens=snake_case,      **snake_case,      )

          _lowercase              =vocab_file
          _lowercase              =False if not self.vocab_file else True
          _lowercase              =extra_ids





       @staticmethod
       def 				UpperCamelCase__						(				snake_case	:int,      snake_case	:Optional[Any],      snake_case	:Optional[Any]):



          """simple docstring"""

          if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
             _lowercase              =TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
             if init_max_model_length is not None and init_max_model_length != max_model_length:
                return init_max_model_length
             elif init_max_model_length is None:
                warnings.warn(
                    'This tokenizer was incorrectly instantiated with a model max length of'
                    f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
                    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
                    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
                    f''' {pretrained_model_name_or_path} automatically truncating your input to'''
                    f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
                    f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
                    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
                    ' instantiate this tokenizer with `model_max_length` set to your preferred value.',      snake_case,      )

          return max_model_length





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:str,      snake_case	:Optional[str] = None):



          """simple docstring"""

          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(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'])

          if os.path.abspath(self.vocab_file) != os.path.abspath(snake_case):
             copyfile(self.vocab_file,      snake_case)
             logger.info(f'''Copy vocab file to {out_vocab_file}''')

          return (out_vocab_file,)





       def 				UpperCamelCase__						(				self	:Optional[int],      snake_case	:List[int],      snake_case	:Optional[List[int]] = None):



          """simple docstring"""

          _lowercase              =token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
             return self.prefix_tokens + token_ids_a
          else:
             _lowercase              =token_ids_a + [self.eos_token_id]
             return self.prefix_tokens + token_ids_a + token_ids_a





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:List[int],      snake_case	:Optional[List[int]] = None):



          """simple docstring"""

          _lowercase              =[self.eos_token_id]

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





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          return list(
              set(filter(lambda snake_case: bool(re.search(r'<extra_id_\d+>',      snake_case)) is not None,      self.additional_special_tokens)))





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          return [self.convert_tokens_to_ids(snake_case) for token in self.get_sentinel_tokens()]










import torch

from transformers import AutoModel






class 					SCREAMING_SNAKE_CASE_  (       torch.nn.Module ):
       """simple docstring"""


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



          """simple docstring"""

          super(snake_case,      self).__init__()

          _lowercase              =AutoModel.from_pretrained(snake_case,      return_dict=snake_case)
          _lowercase              =torch.nn.CosineSimilarity(3,      1e-0_8)
          _lowercase              =torch.nn.Softmax(dim=1)





       def 				UpperCamelCase__						(				self	:str,      **snake_case	:int):



          """simple docstring"""

          return self.bert(**snake_case).last_hidden_state





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:Optional[Any]):



          """simple docstring"""

          return token_embeddings.sum(2,      keepdim=snake_case)





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:int,      snake_case	:Dict,      snake_case	:Dict=1):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:int,      snake_case	:List[str]):



          """simple docstring"""

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

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

          _lowercase              =self.BERT(**snake_case)
          _lowercase              =self.BERT(**snake_case)

          _lowercase              =None
          _lowercase              =None

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

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

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

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

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

          return p_starts, p_ends


