



import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

__snake_case						:List[str]      		=     [
    # (stable-diffusion, HF Diffusers)
    ('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
    ('''time_embed.0.bias''', '''time_embedding.linear_1.bias'''),
    ('''time_embed.2.weight''', '''time_embedding.linear_2.weight'''),
    ('''time_embed.2.bias''', '''time_embedding.linear_2.bias'''),
    ('''input_blocks.0.0.weight''', '''conv_in.weight'''),
    ('''input_blocks.0.0.bias''', '''conv_in.bias'''),
    ('''out.0.weight''', '''conv_norm_out.weight'''),
    ('''out.0.bias''', '''conv_norm_out.bias'''),
    ('''out.2.weight''', '''conv_out.weight'''),
    ('''out.2.bias''', '''conv_out.bias'''),
]

__snake_case						:Tuple      		=     [
    # (stable-diffusion, HF Diffusers)
    ('''in_layers.0''', '''norm1'''),
    ('''in_layers.2''', '''conv1'''),
    ('''out_layers.0''', '''norm2'''),
    ('''out_layers.3''', '''conv2'''),
    ('''emb_layers.1''', '''time_emb_proj'''),
    ('''skip_connection''', '''conv_shortcut'''),
]

__snake_case						:List[Any]      		=     []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
 # loop over downblocks/upblocks

 for j in range(2):
  # loop over resnets/attentions for downblocks
  __snake_case						:Tuple      		=     f'down_blocks.{i}.resnets.{j}.'
  __snake_case						:Tuple      		=     f'input_blocks.{3*i + j + 1}.0.'
  unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

  if i < 3:
   # no attention layers in down_blocks.3
   __snake_case						:Union[str, Any]      		=     f'down_blocks.{i}.attentions.{j}.'
   __snake_case						:Dict      		=     f'input_blocks.{3*i + j + 1}.1.'
   unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

 for j in range(3):
  # loop over resnets/attentions for upblocks
  __snake_case						:int      		=     f'up_blocks.{i}.resnets.{j}.'
  __snake_case						:Dict      		=     f'output_blocks.{3*i + j}.0.'
  unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

  if i > 0:
   # no attention layers in up_blocks.0
   __snake_case						:int      		=     f'up_blocks.{i}.attentions.{j}.'
   __snake_case						:Tuple      		=     f'output_blocks.{3*i + j}.1.'
   unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

 if i < 3:
  # no downsample in down_blocks.3
  __snake_case						:Any      		=     f'down_blocks.{i}.downsamplers.0.conv.'
  __snake_case						:Optional[Any]      		=     f'input_blocks.{3*(i+1)}.0.op.'
  unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

  # no upsample in up_blocks.3
  __snake_case						:int      		=     f'up_blocks.{i}.upsamplers.0.'
  __snake_case						:Any      		=     f'output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'
  unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

__snake_case						:Tuple      		=     '''mid_block.attentions.0.'''
__snake_case						:int      		=     '''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
 __snake_case						:Union[str, Any]      		=     f'mid_block.resnets.{j}.'
 __snake_case						:Optional[Any]      		=     f'middle_block.{2*j}.'
 unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))





def      __snake_case	(			_UpperCAmelCase				):
    # buyer beware: this is a *brittle* function,
    # and correct output requires that all of these pieces interact in
    # the exact order in which I have arranged them.
    __a						       =				{k: k for k in unet_state_dict.keys()}
    for sd_name, hf_name in unet_conversion_map:
        __a						       =				sd_name
    for k, v in mapping.items():
        if "resnets" in k:
            for sd_part, hf_part in unet_conversion_map_resnet:
                __a						       =				v.replace(_UpperCAmelCase   , _UpperCAmelCase				)
            __a						       =				v
    for k, v in mapping.items():
        for sd_part, hf_part in unet_conversion_map_layer:
            __a						       =				v.replace(_UpperCAmelCase   , _UpperCAmelCase				)
        __a						       =				v
    __a						       =				{v: unet_state_dict[k] for k, v in mapping.items()}
    return new_state_dict


# ================#
# VAE Conversion #
# ================#

__snake_case						:int      		=     [
    # (stable-diffusion, HF Diffusers)
    ('''nin_shortcut''', '''conv_shortcut'''),
    ('''norm_out''', '''conv_norm_out'''),
    ('''mid.attn_1.''', '''mid_block.attentions.0.'''),
]

for i in range(4):
 # down_blocks have two resnets
 for j in range(2):
  __snake_case						:Tuple      		=     f'encoder.down_blocks.{i}.resnets.{j}.'
  __snake_case						:Union[str, Any]      		=     f'encoder.down.{i}.block.{j}.'
  vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

 if i < 3:
  __snake_case						:str      		=     f'down_blocks.{i}.downsamplers.0.'
  __snake_case						:Tuple      		=     f'down.{i}.downsample.'
  vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

  __snake_case						:Union[str, Any]      		=     f'up_blocks.{i}.upsamplers.0.'
  __snake_case						:List[str]      		=     f'up.{3-i}.upsample.'
  vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

 # up_blocks have three resnets
 # also, up blocks in hf are numbered in reverse from sd
 for j in range(3):
  __snake_case						:Optional[Any]      		=     f'decoder.up_blocks.{i}.resnets.{j}.'
  __snake_case						:Optional[Any]      		=     f'decoder.up.{3-i}.block.{j}.'
  vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
 __snake_case						:int      		=     f'mid_block.resnets.{i}.'
 __snake_case						:str      		=     f'mid.block_{i+1}.'
 vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


__snake_case						:Optional[int]      		=     [
    # (stable-diffusion, HF Diffusers)
    ('''norm.''', '''group_norm.'''),
    ('''q.''', '''query.'''),
    ('''k.''', '''key.'''),
    ('''v.''', '''value.'''),
    ('''proj_out.''', '''proj_attn.'''),
]





def      __snake_case	(			_UpperCAmelCase				):
    # convert HF linear weights to SD conv2d weights
    return w.reshape(*w.shape   , 1   , 1				)





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				{k: k for k in vae_state_dict.keys()}
    for k, v in mapping.items():
        for sd_part, hf_part in vae_conversion_map:
            __a						       =				v.replace(_UpperCAmelCase   , _UpperCAmelCase				)
        __a						       =				v
    for k, v in mapping.items():
        if "attentions" in k:
            for sd_part, hf_part in vae_conversion_map_attn:
                __a						       =				v.replace(_UpperCAmelCase   , _UpperCAmelCase				)
            __a						       =				v
    __a						       =				{v: vae_state_dict[k] for k, v in mapping.items()}
    __a						       =				['''q''', '''k''', '''v''', '''proj_out''']
    for k, v in new_state_dict.items():
        for weight_name in weights_to_convert:
            if f'mid.attn_1.{weight_name}.weight' in k:
                print(f'Reshaping {k} for SD format'				)
                __a						       =				reshape_weight_for_sd(_UpperCAmelCase				)
    return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


__snake_case						:Any      		=     [
    # (stable-diffusion, HF Diffusers)
    ('''resblocks.''', '''text_model.encoder.layers.'''),
    ('''ln_1''', '''layer_norm1'''),
    ('''ln_2''', '''layer_norm2'''),
    ('''.c_fc.''', '''.fc1.'''),
    ('''.c_proj.''', '''.fc2.'''),
    ('''.attn''', '''.self_attn'''),
    ('''ln_final.''', '''transformer.text_model.final_layer_norm.'''),
    ('''token_embedding.weight''', '''transformer.text_model.embeddings.token_embedding.weight'''),
    ('''positional_embedding''', '''transformer.text_model.embeddings.position_embedding.weight'''),
]
__snake_case						:Dict      		=     {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
__snake_case						:Dict      		=     re.compile('''|'''.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
__snake_case						:Any      		=     {'''q''': 0, '''k''': 1, '''v''': 2}





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				{}
    __a						       =				{}
    __a						       =				{}
    for k, v in text_enc_dict.items():
        if (
            k.endswith('''.self_attn.q_proj.weight'''				)
            or k.endswith('''.self_attn.k_proj.weight'''				)
            or k.endswith('''.self_attn.v_proj.weight'''				)
        ):
            __a						       =				k[: -len('''.q_proj.weight'''				)]
            __a						       =				k[-len('''q_proj.weight'''				)]
            if k_pre not in capture_qkv_weight:
                __a						       =				[None, None, None]
            __a						       =				v
            continue

        if (
            k.endswith('''.self_attn.q_proj.bias'''				)
            or k.endswith('''.self_attn.k_proj.bias'''				)
            or k.endswith('''.self_attn.v_proj.bias'''				)
        ):
            __a						       =				k[: -len('''.q_proj.bias'''				)]
            __a						       =				k[-len('''q_proj.bias'''				)]
            if k_pre not in capture_qkv_bias:
                __a						       =				[None, None, None]
            __a						       =				v
            continue

        __a						       =				textenc_pattern.sub(lambda _UpperCAmelCase				: protected[re.escape(m.group(0				)				)]   , _UpperCAmelCase				)
        __a						       =				v

    for k_pre, tensors in capture_qkv_weight.items():
        if None in tensors:
            raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''				)
        __a						       =				textenc_pattern.sub(lambda _UpperCAmelCase				: protected[re.escape(m.group(0				)				)]   , _UpperCAmelCase				)
        __a						       =				torch.cat(_UpperCAmelCase				)

    for k_pre, tensors in capture_qkv_bias.items():
        if None in tensors:
            raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''				)
        __a						       =				textenc_pattern.sub(lambda _UpperCAmelCase				: protected[re.escape(m.group(0				)				)]   , _UpperCAmelCase				)
        __a						       =				torch.cat(_UpperCAmelCase				)

    return new_state_dict





def      __snake_case	(			_UpperCAmelCase				):
    return text_enc_dict


if __name__ == "__main__":
 __snake_case						:List[str]      		=     argparse.ArgumentParser()

 parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
 parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
 parser.add_argument(
     '''--use_safetensors''', action='''store_true''', help='''Save weights use safetensors, default is ckpt.'''
 )

 __snake_case						:Tuple      		=     parser.parse_args()

 assert args.model_path is not None, "Must provide a model path!"

 assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

 # Path for safetensors
 __snake_case						:Optional[int]      		=     osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
 __snake_case						:int      		=     osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
 __snake_case						:List[Any]      		=     osp.join(args.model_path, '''text_encoder''', '''model.safetensors''')

 # Load models from safetensors if it exists, if it doesn't pytorch
 if osp.exists(unet_path):
  __snake_case						:Any      		=     load_file(unet_path, device='''cpu''')
 else:
  __snake_case						:List[str]      		=     osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
  __snake_case						:Tuple      		=     torch.load(unet_path, map_location='''cpu''')

 if osp.exists(vae_path):
  __snake_case						:Tuple      		=     load_file(vae_path, device='''cpu''')
 else:
  __snake_case						:str      		=     osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
  __snake_case						:Dict      		=     torch.load(vae_path, map_location='''cpu''')

 if osp.exists(text_enc_path):
  __snake_case						:List[str]      		=     load_file(text_enc_path, device='''cpu''')
 else:
  __snake_case						:Optional[Any]      		=     osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
  __snake_case						:Dict      		=     torch.load(text_enc_path, map_location='''cpu''')

 # Convert the UNet model
 __snake_case						:Optional[int]      		=     convert_unet_state_dict(unet_state_dict)
 __snake_case						:Dict      		=     {'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

 # Convert the VAE model
 __snake_case						:Tuple      		=     convert_vae_state_dict(vae_state_dict)
 __snake_case						:Tuple      		=     {'''first_stage_model.''' + k: v for k, v in vae_state_dict.items()}

 # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
 __snake_case						:List[str]      		=     '''text_model.encoder.layers.22.layer_norm2.bias''' in text_enc_dict

 if is_vaa_model:
  # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
  __snake_case						:Optional[int]      		=     {'''transformer.''' + k: v for k, v in text_enc_dict.items()}
  __snake_case						:Optional[Any]      		=     convert_text_enc_state_dict_vaa(text_enc_dict)
  __snake_case						:Optional[Any]      		=     {'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
 else:
  __snake_case						:List[str]      		=     convert_text_enc_state_dict(text_enc_dict)
  __snake_case						:List[str]      		=     {'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

 # Put together new checkpoint
 __snake_case						:Union[str, Any]      		=     {**unet_state_dict, **vae_state_dict, **text_enc_dict}
 if args.half:
  __snake_case						:Any      		=     {k: v.half() for k, v in state_dict.items()}

 if args.use_safetensors:
  save_file(state_dict, args.checkpoint_path)
 else:
  __snake_case						:Optional[Any]      		=     {'''state_dict''': state_dict}
  torch.save(state_dict, args.checkpoint_path)










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


__snake_case						:Dict      		=     '''bart'''
__snake_case						:Tuple      		=     True





@st.cache(allow_output_mutation=_UpperCAmelCase				)
def      __snake_case	(			):
    if LOAD_DENSE_INDEX:
        __a						       =				AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased'''				)
        __a						       =				AutoModel.from_pretrained('''yjernite/retribert-base-uncased'''				).to('''cuda:0'''				)
        __a						       =				qar_model.eval()
    else:
        __a       ,   __a						       =				(None, None)
    if MODEL_TYPE == "bart":
        __a						       =				AutoTokenizer.from_pretrained('''yjernite/bart_eli5'''				)
        __a						       =				AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5'''				).to('''cuda:0'''				)
        __a						       =				torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth'''				)
        sas_model.load_state_dict(save_dict['''model''']				)
        __a						       =				sas_model.eval()
    else:
        __a       ,   __a						       =				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=_UpperCAmelCase				)
def      __snake_case	(			):
    if LOAD_DENSE_INDEX:
        __a						       =				faiss.StandardGpuResources()
        __a						       =				datasets.load_dataset(path='''wiki_snippets'''   , name='''wiki40b_en_100_0'''				)['''train''']
        __a						       =				np.memmap(
            '''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat'''   , dtype='''float32'''   , mode='''r'''   , shape=(wikiaab_passages.num_rows, 128)   , )
        __a						       =				faiss.IndexFlatIP(128				)
        __a						       =				faiss.index_cpu_to_gpu(_UpperCAmelCase   , 1   , _UpperCAmelCase				)
        wikiaab_gpu_index_flat.add(_UpperCAmelCase				)  # TODO fix for larger GPU
    else:
        __a       ,   __a						       =				(None, None)
    __a						       =				Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}]				)
    return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)





@st.cache(allow_output_mutation=_UpperCAmelCase				)
def      __snake_case	(			):
    __a						       =				datasets.load_dataset('''eli5'''   , name='''LFQA_reddit'''				)
    __a						       =				elia['''train_eli5''']
    __a						       =				np.memmap(
        '''eli5_questions_reps.dat'''   , dtype='''float32'''   , mode='''r'''   , shape=(elia_train.num_rows, 128)				)
    __a						       =				faiss.IndexFlatIP(128				)
    eli5_train_q_index.add(_UpperCAmelCase				)
    return (elia_train, eli5_train_q_index)


__snake_case		,__snake_case		,__snake_case						:List[str]      		=     load_indexes()
__snake_case		,__snake_case		,__snake_case		,__snake_case						:Dict      		=     load_models()
__snake_case		,__snake_case						:Tuple      		=     load_train_data()





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase=10				):
    __a						       =				embed_questions_for_retrieval([question]   , _UpperCAmelCase   , _UpperCAmelCase				)
    __a       ,   __a						       =				eli5_train_q_index.search(_UpperCAmelCase   , _UpperCAmelCase				)
    __a						       =				[elia_train[int(_UpperCAmelCase				)] for i in I[0]]
    return nn_examples





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase="wiki40b"   , _UpperCAmelCase="dense"   , _UpperCAmelCase=10				):
    if source == "none":
        __a       ,   __a						       =				(''' <P> '''.join(['''''' for _ in range(11				)]				).strip(), [])
    else:
        if method == "dense":
            __a       ,   __a						       =				query_qa_dense_index(
                _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				)
        else:
            __a       ,   __a						       =				query_es_index(
                _UpperCAmelCase   , _UpperCAmelCase   , index_name='''english_wiki40b_snippets_100w'''   , n_results=_UpperCAmelCase   , )
    __a						       =				[
        (res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
    ]
    __a						       =				'''question: {} context: {}'''.format(_UpperCAmelCase   , _UpperCAmelCase				)
    return question_doc, support_list





@st.cache(
    hash_funcs={
        torch.Tensor: (lambda _UpperCAmelCase				: None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda _UpperCAmelCase				: None),
    }				)
def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase=64   , _UpperCAmelCase=256   , _UpperCAmelCase=False   , _UpperCAmelCase=2   , _UpperCAmelCase=0.95   , _UpperCAmelCase=0.8				):
    with torch.no_grad():
        __a						       =				qa_sas_generate(
            _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , num_answers=1   , num_beams=_UpperCAmelCase   , min_len=_UpperCAmelCase   , max_len=_UpperCAmelCase   , do_sample=_UpperCAmelCase   , temp=_UpperCAmelCase   , top_p=_UpperCAmelCase   , top_k=_UpperCAmelCase   , max_input_length=1024   , device='''cuda:0'''   , )[0]
    return (answer, support_list)


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

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

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

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

__snake_case						:Optional[Any]      		=     st.sidebar.checkbox('''Retrieval options''')
if retrieval_options:
 __snake_case						:List[str]      		=     '''
    ### Information retriever options

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

__snake_case						:Dict      		=     '''beam'''
__snake_case						:int      		=     2
__snake_case						:str      		=     64
__snake_case						:Tuple      		=     256
__snake_case						:int      		=     None
__snake_case						:List[Any]      		=     None
__snake_case						:int      		=     st.sidebar.checkbox('''Generation options''')
if generate_options:
 __snake_case						:Tuple      		=     '''
    ### Answer generation options

    The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
    weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
    **beam** search, or **sample** from the decoder\'s output probabilities.
    '''
 st.sidebar.markdown(generate_info)
 __snake_case						:Tuple      		=     st.sidebar.selectbox('''Would you like to use beam search or sample an answer?''', ['''beam''', '''sampled'''])
 __snake_case						:Dict      		=     st.sidebar.slider(
     '''Minimum generation length''', min_value=8, max_value=256, value=64, step=8, format=None, key=None
 )
 __snake_case						:Dict      		=     st.sidebar.slider(
     '''Maximum generation length''', min_value=64, max_value=512, value=256, step=16, format=None, key=None
 )
 if sampled == "beam":
  __snake_case						:List[str]      		=     st.sidebar.slider('''Beam size''', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
 else:
  __snake_case						:Tuple      		=     st.sidebar.slider(
      '''Nucleus sampling p''', min_value=0.1, max_value=1.0, value=0.9_5, step=0.0_1, format=None, key=None
  )
  __snake_case						:Any      		=     st.sidebar.slider(
      '''Temperature''', min_value=0.1, max_value=1.0, value=0.7, step=0.0_1, format=None, key=None
  )
  __snake_case						:Any      		=     None

# start main text
__snake_case						:Dict      		=     [
    '''<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?''',
]
__snake_case						:int      		=     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>":
 __snake_case						:Optional[int]      		=     st.text_input('''Enter your question here:''', '''''')
else:
 __snake_case						:Optional[int]      		=     question_s

if st.button('''Show me!'''):
 if action in [0, 1, 3]:
  if index_type == "mixed":
   __snake_case		,__snake_case						:int      		=     make_support(question, source=wiki_source, method='''dense''', n_results=10)
   __snake_case		,__snake_case						:Optional[int]      		=     make_support(question, source=wiki_source, method='''sparse''', n_results=10)
   __snake_case						:Optional[Any]      		=     []
   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)]
   __snake_case						:Union[str, Any]      		=     support_list[:10]
   __snake_case						:Optional[int]      		=     '''<P> ''' + ''' <P> '''.join([res[-1] for res in support_list])
  else:
   __snake_case		,__snake_case						:Tuple      		=     make_support(question, source=wiki_source, method=index_type, n_results=10)
 if action in [0, 3]:
  __snake_case		,__snake_case						:Optional[int]      		=     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):
   __snake_case						:Dict      		=     '''https://en.wikipedia.org/wiki/{}'''.format(res[0].replace(''' ''', '''_'''))
   __snake_case						:int      		=     res[1].strip()
   if sec_titles == "":
    __snake_case						:List[Any]      		=     '''[{}]({})'''.format(res[0], wiki_url)
   else:
    __snake_case						:Optional[int]      		=     sec_titles.split(''' & ''')
    __snake_case						:str      		=     ''' & '''.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]:
  __snake_case						:str      		=     find_nearest_training(question)
  __snake_case						:str      		=     nn_train_list[0]
  st.markdown(
      '''--- \n ### The most similar question in the ELI5 training set was: \n\n {}'''.format(train_exple['''title'''])
  )
  __snake_case						:Optional[Any]      		=     [
      '''{}. {}'''.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)))


__snake_case						:Tuple      		=     '''
---

**Disclaimer**

*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
'''
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)





