

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase      				=     logging.get_logger(__name__)

lowerCamelCase      				=     {
    '''google/realm-cc-news-pretrained-embedder''': (
        '''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'''
    ),
    '''google/realm-cc-news-pretrained-encoder''': (
        '''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'''
    ),
    '''google/realm-cc-news-pretrained-scorer''': (
        '''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'''
    ),
    '''google/realm-cc-news-pretrained-openqa''': (
        '''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'''
    ),
    '''google/realm-orqa-nq-openqa''': '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json''',
    '''google/realm-orqa-nq-reader''': '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json''',
    '''google/realm-orqa-wq-openqa''': '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json''',
    '''google/realm-orqa-wq-reader''': '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json''',
    # See all REALM models at https://huggingface.co/models?filter=realm
}





class     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       ="""realm"""




							def __init__( self					,						_A=3_0_5_2_2					,						_A=7_6_8					,						_A=1_2_8					,						_A=1_2					,						_A=1_2					,						_A=8					,						_A=3_0_7_2					,						_A="gelu_new"					,						_A=0.1					,						_A=0.1					,						_A=5_1_2					,						_A=2					,						_A=0.02					,						_A=1e-1_2					,						_A=2_5_6					,						_A=1_0					,						_A=1e-3					,						_A=5					,						_A=3_2_0					,						_A=1_3_3_5_3_7_1_8					,						_A=5_0_0_0					,						_A=1					,						_A=0					,						_A=2					,						**_A					,						):
												super().__init__(pad_token_id=_A					,						bos_token_id=_A					,						eos_token_id=_A					,						**_A							)

												# Common config
												__lowerCAmelCase					=							vocab_size
												__lowerCAmelCase					=							max_position_embeddings
												__lowerCAmelCase					=							hidden_size
												__lowerCAmelCase					=							retriever_proj_size
												__lowerCAmelCase					=							num_hidden_layers
												__lowerCAmelCase					=							num_attention_heads
												__lowerCAmelCase					=							num_candidates
												__lowerCAmelCase					=							intermediate_size
												__lowerCAmelCase					=							hidden_act
												__lowerCAmelCase					=							hidden_dropout_prob
												__lowerCAmelCase					=							attention_probs_dropout_prob
												__lowerCAmelCase					=							initializer_range
												__lowerCAmelCase					=							type_vocab_size
												__lowerCAmelCase					=							layer_norm_eps

												# Reader config
												__lowerCAmelCase					=							span_hidden_size
												__lowerCAmelCase					=							max_span_width
												__lowerCAmelCase					=							reader_layer_norm_eps
												__lowerCAmelCase					=							reader_beam_size
												__lowerCAmelCase					=							reader_seq_len

												# Retrieval config
												__lowerCAmelCase					=							num_block_records
												__lowerCAmelCase					=							searcher_beam_size









# 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_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       ="""facebook/bart-large-mnli"""
							__UpperCAmelCase       =(
							    """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."""
							)
							__UpperCAmelCase       ="""text_classifier"""
							__UpperCAmelCase       =AutoTokenizer
							__UpperCAmelCase       =AutoModelForSequenceClassification

							__UpperCAmelCase       =["""text""", ["""text"""]]
							__UpperCAmelCase       =["""text"""]




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




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							labels
												return self.pre_processor(
												    [text] * len(_A							)					,						[F"""This example is {label}""" for label in labels]					,						return_tensors='pt'					,						padding='max_length'					,						)




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							outputs.logits
												__lowerCAmelCase					=							torch.argmax(logits[:, 2]							).item()
												return self._labels[label_id]






