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


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{
    '''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 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Any						=      """realm"""







    def __init__(	self , __lowercase=30_522 , __lowercase=768 , __lowercase=128 , __lowercase=12 , __lowercase=12 , __lowercase=8 , __lowercase=3_072 , __lowercase="gelu_new" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=512 , __lowercase=2 , __lowercase=0.02 , __lowercase=1E-1_2 , __lowercase=256 , __lowercase=10 , __lowercase=1E-3 , __lowercase=5 , __lowercase=320 , __lowercase=13_353_718 , __lowercase=5_000 , __lowercase=1 , __lowercase=0 , __lowercase=2 , **__lowercase , )       ->	Tuple:
        super().__init__(pad_token_id=__lowercase , bos_token_id=__lowercase , eos_token_id=__lowercase , **__lowercase)

        # Common config
        __UpperCamelCase     :Union[str, Any]			  =		vocab_size
        __UpperCamelCase     :int			  =		max_position_embeddings
        __UpperCamelCase     :List[Any]			  =		hidden_size
        __UpperCamelCase     :List[str]			  =		retriever_proj_size
        __UpperCamelCase     :int			  =		num_hidden_layers
        __UpperCamelCase     :str			  =		num_attention_heads
        __UpperCamelCase     :List[str]			  =		num_candidates
        __UpperCamelCase     :List[Any]			  =		intermediate_size
        __UpperCamelCase     :List[str]			  =		hidden_act
        __UpperCamelCase     :List[Any]			  =		hidden_dropout_prob
        __UpperCamelCase     :int			  =		attention_probs_dropout_prob
        __UpperCamelCase     :List[Any]			  =		initializer_range
        __UpperCamelCase     :Dict			  =		type_vocab_size
        __UpperCamelCase     :List[str]			  =		layer_norm_eps

        # Reader config
        __UpperCamelCase     :List[Any]			  =		span_hidden_size
        __UpperCamelCase     :int			  =		max_span_width
        __UpperCamelCase     :List[str]			  =		reader_layer_norm_eps
        __UpperCamelCase     :Optional[int]			  =		reader_beam_size
        __UpperCamelCase     :Optional[Any]			  =		reader_seq_len

        # Retrieval config
        __UpperCamelCase     :str			  =		num_block_records
        __UpperCamelCase     :Any			  =		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 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Optional[Any]						=      """facebook/bart-large-mnli"""
    a__	:						int						=      (
        """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."""
    )
    a__	:						Optional[Any]						=      """text_classifier"""
    a__	:						Any						=      AutoTokenizer
    a__	:						str						=      AutoModelForSequenceClassification

    a__	:						str						=      ["""text""", ["""text"""]]
    a__	:						Optional[int]						=      ["""text"""]







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        super().setup()
        __UpperCamelCase     :int			  =		self.model.config
        __UpperCamelCase     :Optional[Any]			  =		-1
        for idx, label in config.idalabel.items():
            if label.lower().startswith('''entail'''):
                __UpperCamelCase     :List[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 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	Union[str, Any]:
        __UpperCamelCase     :Any			  =		labels
        return self.pre_processor(
            [text] * len(__lowercase) , [f"""This example is {label}""" for label in labels] , return_tensors='''pt''' , padding='''max_length''' , )







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Optional[Any]:
        __UpperCamelCase     :List[Any]			  =		outputs.logits
        __UpperCamelCase     :Any			  =		torch.argmax(logits[:, 2]).item()
        return self._labels[label_id]





