






'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_					=	logging.get_logger(__name__)

a_					=	{
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/config.json',
    'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/config.json',
    'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/config.json',
    'bert-base-multilingual-uncased': 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json',
    'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json',
    'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/config.json',
    'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/config.json',
    'bert-large-uncased-whole-word-masking': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking': (
        'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-base-cased-finetuned-mrpc': 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json',
    'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json',
    'bert-base-german-dbmdz-uncased': 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese': 'https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-cased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-uncased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'
    ),
    'wietsedv/bert-base-dutch-cased': 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json',
    # See all BERT models at https://huggingface.co/models?filter=bert
}






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							"""bert"""




 def __init__( self		:							Dict   , __lowercase		:							str=3_05_22   , __lowercase		:							Dict=7_68   , __lowercase		:							Tuple=12   , __lowercase		:							Tuple=12   , __lowercase		:							Any=30_72   , __lowercase		:							List[Any]="gelu"   , __lowercase		:							Optional[Any]=0.1   , __lowercase		:							Optional[Any]=0.1   , __lowercase		:							int=5_12   , __lowercase		:							Optional[int]=2   , __lowercase		:							List[str]=0.02   , __lowercase		:							Tuple=1e-12   , __lowercase		:							str=0   , __lowercase		:							Optional[Any]="absolute"   , __lowercase		:							str=True   , __lowercase		:							List[str]=None   , **__lowercase		:							List[Any]   , )		->  str:
       super().__init__(pad_token_id=__lowercase   , **__lowercase )

       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=vocab_size
       SCREAMING_SNAKE_CASE__       :			str													=hidden_size
       SCREAMING_SNAKE_CASE__       :			Tuple													=num_hidden_layers
       SCREAMING_SNAKE_CASE__       :			Dict													=num_attention_heads
       SCREAMING_SNAKE_CASE__       :			str													=hidden_act
       SCREAMING_SNAKE_CASE__       :			List[str]													=intermediate_size
       SCREAMING_SNAKE_CASE__       :			Dict													=hidden_dropout_prob
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__       :			Tuple													=max_position_embeddings
       SCREAMING_SNAKE_CASE__       :			List[Any]													=type_vocab_size
       SCREAMING_SNAKE_CASE__       :			int													=initializer_range
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=layer_norm_eps
       SCREAMING_SNAKE_CASE__       :			Any													=position_embedding_type
       SCREAMING_SNAKE_CASE__       :			Any													=use_cache
       SCREAMING_SNAKE_CASE__       :			Any													=classifier_dropout






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 @property
 def        __magic_name__		( self		:							Tuple )		->  Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
             SCREAMING_SNAKE_CASE__       :			Any													={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
             SCREAMING_SNAKE_CASE__       :			Optional[int]													={0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
               ('''token_type_ids''', dynamic_axis),
           ] )







'''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        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							"""facebook/bart-large-mnli"""
 snake_case_												=							(
     """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."""
 )
 snake_case_												=							"""text_classifier"""
 snake_case_												=							AutoTokenizer
 snake_case_												=							AutoModelForSequenceClassification

 snake_case_												=							["""text""", ["""text"""]]
 snake_case_												=							["""text"""]




 def        __magic_name__		( self		:							Any )		->  Any:
       super().setup()
       SCREAMING_SNAKE_CASE__       :			int													=self.model.config
       SCREAMING_SNAKE_CASE__       :			int													=-1
       for idx, label in config.idalabel.items():
             if label.lower().startswith('''entail''' ):
                   SCREAMING_SNAKE_CASE__       :			Any													=int(__lowercase )
       if self.entailment_id == -1:
             raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.''' )




 def        __magic_name__		( self		:							str   , __lowercase		:							Any   , __lowercase		:							Optional[Any] )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Dict													=labels
       return self.pre_processor(
           [text] * len(__lowercase )   , [F"This example is {label}" for label in labels]   , return_tensors='''pt'''   , padding='''max_length'''   , )




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							str )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			int													=outputs.logits
       SCREAMING_SNAKE_CASE__       :			Any													=torch.argmax(logits[:, 2] ).item()
       return self._labels[label_id]