






"""simple docstring"""





import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase							=  logging.get_logger(__name__)



def   a    (     __UpperCAmelCase      :    Dict  )    ->							Dict:
						__magic_name__:	List[Any]							=       torch.load(__UpperCAmelCase      ,   map_location="""cpu"""  )
						if "model" in sd.keys():
												__magic_name__:	str							=       torch.load(__UpperCAmelCase      ,   map_location="""cpu"""  )["""model"""]

						# pop unnecessary weights
						__magic_name__:	Any							=       [
						    """decoder.version""",
						    """decoder.output_projection.weight""",
						]
						for key in keys_to_delete:
												if key in sd:
																		sd.pop(__UpperCAmelCase  )

						__magic_name__:	List[Any]							=       {
						    """decoder.project_in_dim.weight""": """decoder.project_in.weight""",
						    """decoder.project_out_dim.weight""": """decoder.project_out.weight""",
						    """decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
						    """decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
						}
						for old_key, new_key in keys_to_rename.items():
												if old_key in sd:
																		__magic_name__:	Tuple							=       sd.pop(__UpperCAmelCase  )

						__magic_name__:	str							=       list(sd.keys()  )
						for key in keys:
												if ".qkv_proj." in key:
																		__magic_name__:	List[str]							=       sd[key]
																		# We split QKV in separate Q,K,V

																		__magic_name__:	Union[str, Any]							=       key.replace(""".qkv_proj."""      ,   """.q_proj."""  )
																		__magic_name__:	int							=       key.replace(""".qkv_proj."""      ,   """.k_proj."""  )
																		__magic_name__:	List[str]							=       key.replace(""".qkv_proj."""      ,   """.v_proj."""  )

																		__magic_name__:	str							=       value.shape[0]
																		assert depth % 3 == 0
																		# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
																		# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
																		__magic_name__,			__magic_name__,			__magic_name__:	int							=       torch.split(__UpperCAmelCase      ,   depth // 3      ,   dim=0  )

																		__magic_name__:	int							=       q
																		__magic_name__:	Optional[Any]							=       k
																		__magic_name__:	str							=       v
																		del sd[key]

						return sd



@torch.no_grad()
def   a    (     __UpperCAmelCase      :    List[str]      ,   __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    int=None  )    ->							Any:
						__magic_name__:	str							=       load_checkpoint(__UpperCAmelCase  )

						if config is not None:
												__magic_name__:	Tuple							=       OPTConfig.from_pretrained(__UpperCAmelCase  )
						else:
												__magic_name__:	List[Any]							=       OPTConfig()

						__magic_name__:	Tuple							=       OPTModel(__UpperCAmelCase  ).half().eval()
						model.load_state_dict(__UpperCAmelCase  )

						# Check results
						Path(__UpperCAmelCase  ).mkdir(exist_ok=__UpperCAmelCase  )
						model.save_pretrained(__UpperCAmelCase  )


if __name__ == "__main__":
							__lowerCamelCase							=  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--fairseq_path',
							    type=str,
							    help=(
							        'path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'
							        ' https://huggingface.co/models?other=opt_metasq'
							    ),
							)
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							parser.add_argument('--hf_config', default=None, type=str, help='Define HF config.')
							__lowerCamelCase							=  parser.parse_args()
							convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)





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)



