import torch

from transformers import AutoTokenizer






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   def __init__(    self							,     lowerCAmelCase_    )	->Tuple:
        a  :    Dict				=						AutoTokenizer.from_pretrained(lowerCAmelCase_    )
   def _lowercase(    self							,     lowerCAmelCase_    )	->Optional[Any]:







        if isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ) and all(isinstance(_x							,     lowerCAmelCase_    ) for _x in x    ):
             a  :    Tuple				=						None
             for l in x:
                  a  :    Dict				=						self.tokenizer(
                      lowerCAmelCase_							,     padding='''max_length'''							,     max_length=3_84							,     truncation=lowerCAmelCase_							,     return_tensors='''pt'''							,     )
                  a  :    Tuple				=						torch.tensor([len(lowerCAmelCase_    )]    )
                  if d is not None:
                       for k in d.keys():
                            a  :    Tuple				=						torch.cat((d[k], t[k])							,     0    )
                  else:
                       a  :    Union[str, Any]				=						t

             a  :    List[str]				=						torch.tensor(self.tokenizer.convert_tokens_to_ids('''[E]'''    )    )
             a  :    List[str]				=						torch.tensor(self.tokenizer.convert_tokens_to_ids('''[/E]'''    )    )

        elif isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ) and all(isinstance(_x							,     lowerCAmelCase_    ) for _x in x    ):
             a  :    Union[str, Any]				=						self.tokenizer(
                 lowerCAmelCase_							,     padding='''max_length'''							,     max_length=3_84							,     truncation=lowerCAmelCase_							,     return_tensors='''pt'''							,     )

        else:
             raise Exception(
                 '''Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'''
                 ''' strings` for supports are supported.'''    )

        return d


   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_=0.70    )	->List[str]:
        a  :    List[str]				=						[]
        for idx in range(len(W_query['''input_ids''']    )    ):
             a  :    Dict				=						range(p_start.shape[1]    )

             a  :    int				=						[]
             for start_id in start_indexes:
                  for end_id in end_indexes:
                       if start_id < end_id:
                            output.append(
                                (
                                    start_id,
                                    end_id,
                                    p_start[idx][start_id].item(),
                                    p_end[idx][end_id].item(),
                                )    )

             output.sort(key=lambda lowerCAmelCase_    : (tup[2] * tup[3])							,     reverse=lowerCAmelCase_    )
             a  :    int				=						[]
             for k in range(len(lowerCAmelCase_    )    ):
                  if output[k][2] * output[k][3] >= thresh:
                       a			,							a  :    Tuple				=						output[k][0], output[k][1]
                       a  :    Optional[Any]				=						self.tokenizer.decode(W_query['''input_ids'''][idx][c_start_pos:c_end_pos]    )
                       temp.append((decoded, output[k][2] * output[k][3])    )

             final_outputs.append(lowerCAmelCase_    )

        return final_outputs
# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    """--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"""
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/