
import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel





class     __UpperCamelCase :






     """simple docstring"""







     def __init__(				self							:							Optional[Any]     ,						_A							:							Optional[Any]     ,						_A							:							str=13     ,						_A							:							Optional[int]=7     ,						_A							:							Any=True     ,						_A							:							Tuple=True     ,						_A							:							Any=False     ,						_A							:							Optional[Any]=True     ,						_A							:							Optional[int]=99     ,						_A							:							Dict=32     ,						_A							:							Any=5     ,						_A							:							int=4     ,						_A							:							Tuple=37     ,						_A							:							Optional[Any]="gelu"     ,						_A							:							Tuple=0.1     ,						_A							:							str=0.1     ,						_A							:							str=512     ,						_A							:							Tuple=16     ,						_A							:							Any=2     ,						_A							:							Dict=0.02     ,						_A							:							Union[str, Any]=3     ,						_A							:							Optional[Any]=4     ,						_A							:							str=None     ,						):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[str]					=		parent
       __SCREAMING_SNAKE_CASE      :	str					=		batch_size
       __SCREAMING_SNAKE_CASE      :	str					=		seq_length
       __SCREAMING_SNAKE_CASE      :	str					=		is_training
       __SCREAMING_SNAKE_CASE      :	Tuple					=		use_input_mask
       __SCREAMING_SNAKE_CASE      :	List[str]					=		use_token_type_ids
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		use_labels
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		vocab_size
       __SCREAMING_SNAKE_CASE      :	Any					=		hidden_size
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		num_hidden_layers
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		num_attention_heads
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		intermediate_size
       __SCREAMING_SNAKE_CASE      :	str					=		hidden_act
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		hidden_dropout_prob
       __SCREAMING_SNAKE_CASE      :	List[str]					=		attention_probs_dropout_prob
       __SCREAMING_SNAKE_CASE      :	str					=		max_position_embeddings
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		type_vocab_size
       __SCREAMING_SNAKE_CASE      :	int					=		type_sequence_label_size
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		initializer_range
       __SCREAMING_SNAKE_CASE      :	List[str]					=		num_labels
       __SCREAMING_SNAKE_CASE      :	Tuple					=		num_choices
       __SCREAMING_SNAKE_CASE      :	str					=		scope



     def  UpperCAmelCase__							(				self							:							Union[str, Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	int					=		ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size )

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		None
       if self.use_input_mask:
         __SCREAMING_SNAKE_CASE      :	Tuple					=		random_attention_mask([self.batch_size, self.seq_length] )

       __SCREAMING_SNAKE_CASE      :	str					=		None
       if self.use_token_type_ids:
         __SCREAMING_SNAKE_CASE      :	List[str]					=		ids_tensor([self.batch_size, self.seq_length]     ,						self.type_vocab_size )

       __SCREAMING_SNAKE_CASE      :	str					=		None
       __SCREAMING_SNAKE_CASE      :	Dict					=		None
       __SCREAMING_SNAKE_CASE      :	List[str]					=		None
       if self.use_labels:
         __SCREAMING_SNAKE_CASE      :	List[str]					=		ids_tensor([self.batch_size]     ,						self.type_sequence_label_size )
         __SCREAMING_SNAKE_CASE      :	List[str]					=		ids_tensor([self.batch_size, self.seq_length]     ,						self.num_labels )
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		ids_tensor([self.batch_size]     ,						self.num_choices )

       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



     def  UpperCAmelCase__							(				self							:							str ):







       """simple docstring"""




       return OpenLlamaConfig(
           vocab_size=self.vocab_size     ,						hidden_size=self.hidden_size     ,						num_hidden_layers=self.num_hidden_layers     ,						num_attention_heads=self.num_attention_heads     ,						intermediate_size=self.intermediate_size     ,						hidden_act=self.hidden_act     ,						hidden_dropout_prob=self.hidden_dropout_prob     ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,						max_position_embeddings=self.max_position_embeddings     ,						type_vocab_size=self.type_vocab_size     ,						is_decoder=_A     ,						initializer_range=self.initializer_range     ,						use_stable_embedding=_A     ,						)



     def  UpperCAmelCase__							(				self							:							str     ,						_A							:							str     ,						_A							:							Optional[int]     ,						_A							:							Dict     ,						_A							:							Optional[int]     ,						_A							:							Dict     ,						_A							:							List[Any]     ,						_A							:							Optional[int] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		OpenLlamaModel(config=_A )
       model.to(_A )
       model.eval()
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		model(_A     ,						attention_mask=_A )
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		model(_A )
       self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size) )



     def  UpperCAmelCase__							(				self							:							List[str]     ,						_A							:							Optional[Any]     ,						_A							:							Any     ,						_A							:							Dict     ,						_A							:							Union[str, Any]     ,						_A							:							Dict     ,						_A							:							str     ,						_A							:							List[str]     ,						_A							:							Optional[Any]     ,						_A							:							int     ,						):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	int					=		True
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		OpenLlamaModel(_A )
       model.to(_A )
       model.eval()
       __SCREAMING_SNAKE_CASE      :	Tuple					=		model(
           _A     ,						attention_mask=_A     ,						encoder_hidden_states=_A     ,						encoder_attention_mask=_A     ,						)
       __SCREAMING_SNAKE_CASE      :	Tuple					=		model(
           _A     ,						attention_mask=_A     ,						encoder_hidden_states=_A     ,						)
       __SCREAMING_SNAKE_CASE      :	Tuple					=		model(_A     ,						attention_mask=_A )
       self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size) )



     def  UpperCAmelCase__							(				self							:							Union[str, Any]     ,						_A							:							Dict     ,						_A							:							Any     ,						_A							:							Optional[int]     ,						_A							:							Optional[Any]     ,						_A							:							str     ,						_A							:							List[str]     ,						_A							:							int     ,						_A							:							Union[str, Any]     ,						_A							:							Tuple     ,						):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	int					=		OpenLlamaForCausalLM(config=_A )
       model.to(_A )
       model.eval()
       __SCREAMING_SNAKE_CASE      :	Dict					=		model(_A     ,						attention_mask=_A     ,						labels=_A )
       self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.vocab_size) )



     def  UpperCAmelCase__							(				self							:							List[str]     ,						_A							:							List[Any]     ,						_A							:							Tuple     ,						_A							:							Tuple     ,						_A							:							Optional[Any]     ,						_A							:							List[str]     ,						_A							:							Dict     ,						_A							:							List[str]     ,						_A							:							Tuple     ,						_A							:							Any     ,						):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	int					=		True
       __SCREAMING_SNAKE_CASE      :	List[str]					=		True
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		OpenLlamaForCausalLM(config=_A )
       model.to(_A )
       model.eval()

       # first forward pass
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		model(
           _A     ,						attention_mask=_A     ,						encoder_hidden_states=_A     ,						encoder_attention_mask=_A     ,						use_cache=_A     ,						)
       __SCREAMING_SNAKE_CASE      :	int					=		outputs.past_key_values

       # create hypothetical multiple next token and extent to next_input_ids
       __SCREAMING_SNAKE_CASE      :	Tuple					=		ids_tensor((self.batch_size, 3)     ,						config.vocab_size )
       __SCREAMING_SNAKE_CASE      :	Tuple					=		ids_tensor((self.batch_size, 3)     ,						vocab_size=2 )

       # append to next input_ids and
       __SCREAMING_SNAKE_CASE      :	int					=		torch.cat([input_ids, next_tokens]     ,						dim=-1 )
       __SCREAMING_SNAKE_CASE      :	Any					=		torch.cat([input_mask, next_mask]     ,						dim=-1 )

       __SCREAMING_SNAKE_CASE      :	List[str]					=		model(
           _A     ,						attention_mask=_A     ,						encoder_hidden_states=_A     ,						encoder_attention_mask=_A     ,						output_hidden_states=_A     ,						)['''hidden_states'''][0]
       __SCREAMING_SNAKE_CASE      :	str					=		model(
           _A     ,						attention_mask=_A     ,						encoder_hidden_states=_A     ,						encoder_attention_mask=_A     ,						past_key_values=_A     ,						output_hidden_states=_A     ,						)['''hidden_states'''][0]

       # select random slice
       __SCREAMING_SNAKE_CASE      :	Any					=		ids_tensor((1,)     ,						output_from_past.shape[-1] ).item()
       __SCREAMING_SNAKE_CASE      :	Any					=		output_from_no_past[:, -3:, random_slice_idx].detach()
       __SCREAMING_SNAKE_CASE      :	Dict					=		output_from_past[:, :, random_slice_idx].detach()

       self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(_A     ,						_A     ,						atol=1e-3 ) )




     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Any					=		self.prepare_config_and_inputs()
       (
           (
           __SCREAMING_SNAKE_CASE
       ),		(
           __SCREAMING_SNAKE_CASE
       ),		(
           __SCREAMING_SNAKE_CASE
       ),		(
           __SCREAMING_SNAKE_CASE
       ),		(
           __SCREAMING_SNAKE_CASE
       ),		(
           __SCREAMING_SNAKE_CASE
       ),		(
           __SCREAMING_SNAKE_CASE
       ),		
       )      :	int					=		config_and_inputs
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
       return config, inputs_dict






@require_torch
class     __UpperCamelCase (							lowerCAmelCase__ ,						lowerCAmelCase__ ,						lowerCAmelCase__ ,						unittest.TestCase       ):






     """simple docstring"""







     lowerCAmelCase_							   =			(
         (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
     )
     lowerCAmelCase_							   =			(OpenLlamaForCausalLM,) if is_torch_available() else ()
     lowerCAmelCase_							   =			(
         {
             '''feature-extraction''': OpenLlamaModel,
             '''text-classification''': OpenLlamaForSequenceClassification,
             '''text-generation''': OpenLlamaForCausalLM,
             '''zero-shot''': OpenLlamaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     lowerCAmelCase_							   =			False
     lowerCAmelCase_							   =			False



     def  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Any					=		OpenLlamaModelTester(self )
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		ConfigTester(self     ,						config_class=_A     ,						hidden_size=37 )



     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       self.config_tester.run_common_tests()



     def  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_A )



     def  UpperCAmelCase__							(				self							:							str ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		type
         self.model_tester.create_and_check_model(*_A )



     def  UpperCAmelCase__							(				self							:							Dict ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	str					=		self.model_tester.prepare_config_and_inputs_for_common()
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		3
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		input_dict['''input_ids''']
       __SCREAMING_SNAKE_CASE      :	Dict					=		input_ids.ne(1 ).to(_A )
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		ids_tensor([self.model_tester.batch_size]     ,						self.model_tester.type_sequence_label_size )
       __SCREAMING_SNAKE_CASE      :	int					=		OpenLlamaForSequenceClassification(_A )
       model.to(_A )
       model.eval()
       __SCREAMING_SNAKE_CASE      :	Any					=		model(_A     ,						attention_mask=_A     ,						labels=_A )
       self.assertEqual(result.logits.shape     ,						(self.model_tester.batch_size, self.model_tester.num_labels) )



     def  UpperCAmelCase__							(				self							:							Optional[int] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	List[str]					=		self.model_tester.prepare_config_and_inputs_for_common()
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		3
       __SCREAMING_SNAKE_CASE      :	int					=		'''single_label_classification'''
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		input_dict['''input_ids''']
       __SCREAMING_SNAKE_CASE      :	Tuple					=		input_ids.ne(1 ).to(_A )
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		ids_tensor([self.model_tester.batch_size]     ,						self.model_tester.type_sequence_label_size )
       __SCREAMING_SNAKE_CASE      :	int					=		OpenLlamaForSequenceClassification(_A )
       model.to(_A )
       model.eval()
       __SCREAMING_SNAKE_CASE      :	Dict					=		model(_A     ,						attention_mask=_A     ,						labels=_A )
       self.assertEqual(result.logits.shape     ,						(self.model_tester.batch_size, self.model_tester.num_labels) )



     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self.model_tester.prepare_config_and_inputs_for_common()
       __SCREAMING_SNAKE_CASE      :	List[str]					=		3
       __SCREAMING_SNAKE_CASE      :	Dict					=		'''multi_label_classification'''
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		input_dict['''input_ids''']
       __SCREAMING_SNAKE_CASE      :	Any					=		input_ids.ne(1 ).to(_A )
       __SCREAMING_SNAKE_CASE      :	Any					=		ids_tensor(
           [self.model_tester.batch_size, config.num_labels]     ,						self.model_tester.type_sequence_label_size ).to(torch.float )
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		OpenLlamaForSequenceClassification(_A )
       model.to(_A )
       model.eval()
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		model(_A     ,						attention_mask=_A     ,						labels=_A )
       self.assertEqual(result.logits.shape     ,						(self.model_tester.batch_size, self.model_tester.num_labels) )



     @unittest.skip('''Open-Llama buffers include complex numbers, which breaks this test''' )
     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




       pass




     @parameterized.expand([('''linear''',), ('''dynamic''',)] )
     def  UpperCAmelCase__							(				self							:							int     ,						_A							:							int ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	List[str]					=		self.model_tester.prepare_config_and_inputs_for_common()
       __SCREAMING_SNAKE_CASE      :	str					=		ids_tensor([1, 10]     ,						config.vocab_size )
       __SCREAMING_SNAKE_CASE      :	List[str]					=		ids_tensor([1, int(config.max_position_embeddings * 1.5 )]     ,						config.vocab_size )

       set_seed(42 )  # Fixed seed at init time so the two models get the same random weights
       __SCREAMING_SNAKE_CASE      :	Any					=		OpenLlamaModel(_A )
       original_model.to(_A )
       original_model.eval()
       __SCREAMING_SNAKE_CASE      :	int					=		original_model(_A ).last_hidden_state
       __SCREAMING_SNAKE_CASE      :	Dict					=		original_model(_A ).last_hidden_state

       set_seed(42 )  # Fixed seed at init time so the two models get the same random weights
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		{'''type''': scaling_type, '''factor''': 10.0}
       __SCREAMING_SNAKE_CASE      :	Any					=		OpenLlamaModel(_A )
       scaled_model.to(_A )
       scaled_model.eval()
       __SCREAMING_SNAKE_CASE      :	Any					=		scaled_model(_A ).last_hidden_state
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		scaled_model(_A ).last_hidden_state

       # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
       # maximum sequence length, so the outputs for the short input should match.
       if scaling_type == "dynamic":
         self.assertTrue(torch.allclose(_A     ,						_A     ,						atol=1e-5 ) )
       else:
         self.assertFalse(torch.allclose(_A     ,						_A     ,						atol=1e-5 ) )

       # The output should be different for long inputs
       self.assertFalse(torch.allclose(_A     ,						_A     ,						atol=1e-5 ) )


import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
    embed_questions_for_retrieval,
    make_qa_sas_model,
    qa_sas_generate,
    query_es_index,
    query_qa_dense_index,
)

import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer


lowerCamelCase__						=				"bart"
lowerCamelCase__						=				True


@st.cache(allow_output_mutation=lowerCAmelCase			)
def 				__A()		->					Dict:

		"""simple docstring"""






		if LOAD_DENSE_INDEX:
				_UpperCamelCase         =						AutoTokenizer.from_pretrained("""yjernite/retribert-base-uncased"""			)
				_UpperCamelCase         =						AutoModel.from_pretrained("""yjernite/retribert-base-uncased"""			).to("""cuda:0"""			)
				_UpperCamelCase         =						qar_model.eval()
		else:
				_UpperCamelCase     ,						_UpperCamelCase         =						(None, None)
		if MODEL_TYPE == "bart":
				_UpperCamelCase         =						AutoTokenizer.from_pretrained("""yjernite/bart_eli5"""			)
				_UpperCamelCase         =						AutoModelForSeqaSeqLM.from_pretrained("""yjernite/bart_eli5"""			).to("""cuda:0"""			)
				_UpperCamelCase         =						torch.load("""seq2seq_models/eli5_bart_model_blm_2.pth"""			)
				sas_model.load_state_dict(save_dict["""model"""]			)
				_UpperCamelCase         =						sas_model.eval()
		else:
				_UpperCamelCase     ,						_UpperCamelCase         =						make_qa_sas_model(
				    model_name="""t5-small"""    ,	from_file="""seq2seq_models/eli5_t5_model_1024_4.pth"""    ,	device="""cuda:0"""			)
		return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)


@st.cache(allow_output_mutation=lowerCAmelCase			)
def 				__A()		->					str:

		"""simple docstring"""






		if LOAD_DENSE_INDEX:
				_UpperCamelCase         =						faiss.StandardGpuResources()
				_UpperCamelCase         =						datasets.load_dataset(path="""wiki_snippets"""    ,	name="""wiki40b_en_100_0"""			)["""train"""]
				_UpperCamelCase         =						np.memmap(
				    """wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat"""    ,	dtype="""float32"""    ,	mode="""r"""    ,	shape=(wikiaab_passages.num_rows, 1_2_8)    ,	)
				_UpperCamelCase         =						faiss.IndexFlatIP(1_2_8			)
				_UpperCamelCase         =						faiss.index_cpu_to_gpu(lowerCAmelCase    ,	1    ,	lowerCAmelCase			)
				wikiaab_gpu_index_flat.add(lowerCAmelCase			)  # TODO fix for larger GPU
		else:
				_UpperCamelCase     ,						_UpperCamelCase         =						(None, None)
		_UpperCamelCase         =						Elasticsearch([{"""host""": """localhost""", """port""": """9200"""}]			)
		return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)


@st.cache(allow_output_mutation=lowerCAmelCase			)
def 				__A()		->					str:

		"""simple docstring"""






		_UpperCamelCase         =						datasets.load_dataset("""eli5"""    ,	name="""LFQA_reddit"""			)
		_UpperCamelCase         =						elia["""train_eli5"""]
		_UpperCamelCase         =						np.memmap(
		    """eli5_questions_reps.dat"""    ,	dtype="""float32"""    ,	mode="""r"""    ,	shape=(elia_train.num_rows, 1_2_8)			)
		_UpperCamelCase         =						faiss.IndexFlatIP(1_2_8			)
		eli5_train_q_index.add(lowerCAmelCase			)
		return (elia_train, eli5_train_q_index)


lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__						=				load_indexes()
lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__						=				load_models()
lowerCamelCase__,						lowerCamelCase__						=				load_train_data()


def 				__A(lowerCAmelCase    ,	lowerCAmelCase=1_0			)		->					int:

		"""simple docstring"""






		_UpperCamelCase         =						embed_questions_for_retrieval([question]    ,	lowerCAmelCase    ,	lowerCAmelCase			)
		_UpperCamelCase     ,						_UpperCamelCase         =						eli5_train_q_index.search(lowerCAmelCase    ,	lowerCAmelCase			)
		_UpperCamelCase         =						[elia_train[int(lowerCAmelCase			)] for i in I[0]]
		return nn_examples


def 				__A(lowerCAmelCase    ,	lowerCAmelCase="wiki40b"    ,	lowerCAmelCase="dense"    ,	lowerCAmelCase=1_0			)		->					Tuple:

		"""simple docstring"""






		if source == "none":
				_UpperCamelCase     ,						_UpperCamelCase         =						(""" <P> """.join(["""""" for _ in range(1_1			)]			).strip(), [])
		else:
				if method == "dense":
						_UpperCamelCase     ,						_UpperCamelCase         =						query_qa_dense_index(
						    lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase			)
				else:
						_UpperCamelCase     ,						_UpperCamelCase         =						query_es_index(
						    lowerCAmelCase    ,	lowerCAmelCase    ,	index_name="""english_wiki40b_snippets_100w"""    ,	n_results=lowerCAmelCase    ,	)
		_UpperCamelCase         =						[
		    (res["""article_title"""], res["""section_title"""].strip(), res["""score"""], res["""passage_text"""]) for res in hit_lst
		]
		_UpperCamelCase         =						"""question: {} context: {}""".format(lowerCAmelCase    ,	lowerCAmelCase			)
		return question_doc, support_list


@st.cache(
    hash_funcs={
        torch.Tensor: (lambda lowerCAmelCase			: None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda lowerCAmelCase			: None),
    }			)
def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase=6_4    ,	lowerCAmelCase=2_5_6    ,	lowerCAmelCase=False    ,	lowerCAmelCase=2    ,	lowerCAmelCase=0.95    ,	lowerCAmelCase=0.8			)		->					List[Any]:

		"""simple docstring"""






		with torch.no_grad():
				_UpperCamelCase         =						qa_sas_generate(
				    lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	num_answers=1    ,	num_beams=lowerCAmelCase    ,	min_len=lowerCAmelCase    ,	max_len=lowerCAmelCase    ,	do_sample=lowerCAmelCase    ,	temp=lowerCAmelCase    ,	top_p=lowerCAmelCase    ,	top_k=lowerCAmelCase    ,	max_input_length=1_0_2_4    ,	device="""cuda:0"""    ,	)[0]
		return (answer, support_list)


st.title("Long Form Question Answering with ELI5")

# Start sidebar
lowerCamelCase__						=				"<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"
lowerCamelCase__						=				"\n<html>\n  <head>\n    <style>\n      .img-container {\n        padding-left: 90px;\n        padding-right: 90px;\n        padding-top: 50px;\n        padding-bottom: 50px;\n        background-color: #f0f3f9;\n      }\n    </style>\n  </head>\n  <body>\n    <span class=\"img-container\"> <!-- Inline parent element -->\n      %s\n    </span>\n  </body>\n</html>\n" % (
    header_html,
)
st.sidebar.markdown(
    header_full,
    unsafe_allow_html=True,
)

# Long Form QA with ELI5 and Wikipedia
lowerCamelCase__						=				"\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n"
st.sidebar.markdown(description, unsafe_allow_html=True)

lowerCamelCase__						=				[
    "Answer the question",
    "View the retrieved document only",
    "View the most similar ELI5 question and answer",
    "Show me everything, please!",
]
lowerCamelCase__						=				st.sidebar.checkbox("Demo options")
if demo_options:
		lowerCamelCase__						=				st.sidebar.selectbox(
		    "",
		    action_list,
		    index=3,
		)
		lowerCamelCase__						=				action_list.index(action_st)
		lowerCamelCase__						=				st.sidebar.selectbox(
		    "",
		    ["Show full text of passages", "Show passage section titles"],
		    index=0,
		)
		lowerCamelCase__						=				show_type == "Show full text of passages"
else:
		lowerCamelCase__						=				3
		lowerCamelCase__						=				True

lowerCamelCase__						=				st.sidebar.checkbox("Retrieval options")
if retrieval_options:
		lowerCamelCase__						=				"\n    ### Information retriever options\n\n    The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n    trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n    The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n    "
		st.sidebar.markdown(retriever_info)
		lowerCamelCase__						=				st.sidebar.selectbox("Which Wikipedia format should the model use?", ["wiki40b", "none"])
		lowerCamelCase__						=				st.sidebar.selectbox("Which Wikipedia indexer should the model use?", ["dense", "sparse", "mixed"])
else:
		lowerCamelCase__						=				"wiki40b"
		lowerCamelCase__						=				"dense"

lowerCamelCase__						=				"beam"
lowerCamelCase__						=				2
lowerCamelCase__						=				64
lowerCamelCase__						=				256
lowerCamelCase__						=				None
lowerCamelCase__						=				None
lowerCamelCase__						=				st.sidebar.checkbox("Generation options")
if generate_options:
		lowerCamelCase__						=				"\n    ### Answer generation options\n\n    The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n    weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n    **beam** search, or **sample** from the decoder's output probabilities.\n    "
		st.sidebar.markdown(generate_info)
		lowerCamelCase__						=				st.sidebar.selectbox("Would you like to use beam search or sample an answer?", ["beam", "sampled"])
		lowerCamelCase__						=				st.sidebar.slider(
		    "Minimum generation length", min_value=8, max_value=256, value=64, step=8, format=None, key=None
		)
		lowerCamelCase__						=				st.sidebar.slider(
		    "Maximum generation length", min_value=64, max_value=512, value=256, step=16, format=None, key=None
		)
		if sampled == "beam":
				lowerCamelCase__						=				st.sidebar.slider("Beam size", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
		else:
				lowerCamelCase__						=				st.sidebar.slider(
				    "Nucleus sampling p", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
				)
				lowerCamelCase__						=				st.sidebar.slider(
				    "Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
				)
				lowerCamelCase__						=				None

# start main text
lowerCamelCase__						=				[
    "<MY QUESTION>",
    "How do people make chocolate?",
    "Why do we get a fever when we are sick?",
    "How can different animals perceive different colors?",
    "What is natural language processing?",
    "What's the best way to treat a sunburn?",
    "What exactly are vitamins ?",
    "How does nuclear energy provide electricity?",
    "What's the difference between viruses and bacteria?",
    "Why are flutes classified as woodwinds when most of them are made out of metal ?",
    "Why do people like drinking coffee even though it tastes so bad?",
    "What happens when wine ages? How does it make the wine taste better?",
    "If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?",
    "How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?",
    "How does New Zealand have so many large bird predators?",
]
lowerCamelCase__						=				st.selectbox(
    "What would you like to ask? ---- select <MY QUESTION> to enter a new query",
    questions_list,
    index=1,
)
if question_s == "<MY QUESTION>":
		lowerCamelCase__						=				st.text_input("Enter your question here:", "")
else:
		lowerCamelCase__						=				question_s

if st.button("Show me!"):
		if action in [0, 1, 3]:
				if index_type == "mixed":
						lowerCamelCase__,						lowerCamelCase__						=				make_support(question, source=wiki_source, method="dense", n_results=10)
						lowerCamelCase__,						lowerCamelCase__						=				make_support(question, source=wiki_source, method="sparse", n_results=10)
						lowerCamelCase__						=				[]
						for res_d, res_s in zip(support_list_dense, support_list_sparse):
								if tuple(res_d) not in support_list:
										support_list += [tuple(res_d)]
								if tuple(res_s) not in support_list:
										support_list += [tuple(res_s)]
						lowerCamelCase__						=				support_list[:10]
						lowerCamelCase__						=				"<P> " + " <P> ".join([res[-1] for res in support_list])
				else:
						lowerCamelCase__,						lowerCamelCase__						=				make_support(question, source=wiki_source, method=index_type, n_results=10)
		if action in [0, 3]:
				lowerCamelCase__,						lowerCamelCase__						=				answer_question(
				    question_doc,
				    sas_model,
				    sas_tokenizer,
				    min_len=min_len,
				    max_len=int(max_len),
				    sampling=(sampled == "sampled"),
				    n_beams=n_beams,
				    top_p=top_p,
				    temp=temp,
				)
				st.markdown("### The model generated answer is:")
				st.write(answer)
		if action in [0, 1, 3] and wiki_source != "none":
				st.markdown("--- \n ### The model is drawing information from the following Wikipedia passages:")
				for i, res in enumerate(support_list):
						lowerCamelCase__						=				"https://en.wikipedia.org/wiki/{}".format(res[0].replace(" ", "_"))
						lowerCamelCase__						=				res[1].strip()
						if sec_titles == "":
								lowerCamelCase__						=				"[{}]({})".format(res[0], wiki_url)
						else:
								lowerCamelCase__						=				sec_titles.split(" & ")
								lowerCamelCase__						=				" & ".join(
								    ["[{}]({}#{})".format(sec.strip(), wiki_url, sec.strip().replace(" ", "_")) for sec in sec_list]
								)
						st.markdown(
						    "{0:02d} - **Article**: {1:<18} <br>  _Section_: {2}".format(i + 1, res[0], sections),
						    unsafe_allow_html=True,
						)
						if show_passages:
								st.write(
								    "> <span style=\"font-family:arial; font-size:10pt;\">" + res[-1] + "</span>", unsafe_allow_html=True
								)
		if action in [2, 3]:
				lowerCamelCase__						=				find_nearest_training(question)
				lowerCamelCase__						=				nn_train_list[0]
				st.markdown(
				    "--- \n ### The most similar question in the ELI5 training set was: \n\n {}".format(train_exple["title"])
				)
				lowerCamelCase__						=				[
				    "{}. {}".format(i + 1, "  \n".join([line.strip() for line in ans.split("\n") if line.strip() != ""]))
				    for i, (ans, sc) in enumerate(zip(train_exple["answers"]["text"], train_exple["answers"]["score"]))
				    if i == 0 or sc > 2
				]
				st.markdown("##### Its answers were: \n\n {}".format("\n".join(answers_st)))


lowerCamelCase__						=				"\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n"







st.sidebar.markdown(disclaimer, unsafe_allow_html=True)



