




'''simple docstring'''




import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save





def      a_							(      __snake_case							:			Optional[int]   ,       __snake_case							:			Dict   ,       __snake_case							:			Any=1024   ,       __snake_case							:			List[Any]=1024   ,       __snake_case							:			Optional[Any]=False   ,       **__snake_case							:			Tuple   )							->						Any:



       """simple docstring"""
       lowerCamelCase_							=AutoTokenizer.from_pretrained(snake_case_   )
       lowerCamelCase_							=SeqaSeqDataset(snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_   ,       type_path='''train'''   ,       **snake_case_   )
       lowerCamelCase_							=tok.pad_token_id

       def get_lens(__snake_case							:			int   ):
              lowerCamelCase_							=tqdm(
                  DataLoader(snake_case_   ,       batch_size=512   ,       num_workers=8   ,       shuffle=snake_case_   ,       collate_fn=ds.collate_fn   )   ,       desc=str(ds.len_file   )   ,       )
              lowerCamelCase_							=[]
              for batch in dl:
                     lowerCamelCase_							=batch['''input_ids'''].ne(snake_case_   ).sum(1   ).tolist()
                     lowerCamelCase_							=batch['''labels'''].ne(snake_case_   ).sum(1   ).tolist()
                     if consider_target:
                            for src, tgt in zip(snake_case_   ,       snake_case_   ):
                                   max_lens.append(max(snake_case_   ,       snake_case_   )   )
                     else:
                            max_lens.extend(snake_case_   )
              return max_lens

       lowerCamelCase_							=get_lens(snake_case_   )
       lowerCamelCase_							=SeqaSeqDataset(snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_   ,       type_path='''val'''   ,       **snake_case_   )
       lowerCamelCase_							=get_lens(snake_case_   )
       pickle_save(snake_case_   ,       train_ds.len_file   )
       pickle_save(snake_case_   ,       val_ds.len_file   )


if __name__ == "__main__":
     fire.Fire(save_len_file)











"""simple docstring"""



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """facebook/bart-large-mnli"""
 _lowerCamelCase        =     (
     """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
     """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
     """It returns the most likely label in the list of provided `labels` for the input text."""
 )
 _lowerCamelCase        =     """text_classifier"""
 _lowerCamelCase        =     AutoTokenizer
 _lowerCamelCase        =     AutoModelForSequenceClassification

 _lowerCamelCase        =     ["""text""", ["""text"""]]
 _lowerCamelCase        =     ["""text"""]

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     super().setup()
     __A    :						List[str]						 =     self.model.config
     __A    :						int						 =     -1
     for idx, label in config.idalabel.items():
         if label.lower().startswith('''entail'''  ):
             __A    :						List[str]						 =     int(__lowerCamelCase  )
     if self.entailment_id == -1:
         raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.'''  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     labels
     return self.pre_processor(
         [text] * len(__lowerCamelCase  )					,						[F"""This example is {label}""" for label in labels]					,						return_tensors='''pt'''					,						padding='''max_length'''					,						)







 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     outputs.logits
     __A    :						List[str]						 =     torch.argmax(logits[:, 2]  ).item()
     return self._labels[label_id]




