




'''simple docstring'''


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


_lowercase  =			"""bart"""
_lowercase  =			True
@st.cache(allow_output_mutation=_A							)
def 						lowerCamelCase__   (				):


						if LOAD_DENSE_INDEX:
												__snake_case						 =   AutoTokenizer.from_pretrained('yjernite/retribert-base-uncased'							)
												__snake_case						 =   AutoModel.from_pretrained('yjernite/retribert-base-uncased'							).to('cuda:0'							)
												__snake_case						 =   qar_model.eval()
						else:
												__snake_case ,       __snake_case						 =   (None, None)
						if MODEL_TYPE == "bart":
												__snake_case						 =   AutoTokenizer.from_pretrained('yjernite/bart_eli5'							)
												__snake_case						 =   AutoModelForSeqaSeqLM.from_pretrained('yjernite/bart_eli5'							).to('cuda:0'							)
												__snake_case						 =   torch.load('seq2seq_models/eli5_bart_model_blm_2.pth'							)
												sas_model.load_state_dict(save_dict['model']							)
												__snake_case						 =   sas_model.eval()
						else:
												__snake_case ,       __snake_case						 =   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=_A							)
def 						lowerCamelCase__   (				):


						if LOAD_DENSE_INDEX:
												__snake_case						 =   faiss.StandardGpuResources()
												__snake_case						 =   datasets.load_dataset(path='wiki_snippets'    ,	name='wiki40b_en_100_0'							)['train']
												__snake_case						 =   np.memmap(
												    'wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat'    ,	dtype='float32'    ,	mode='r'    ,	shape=(wikiaab_passages.num_rows, 128)    ,	)
												__snake_case						 =   faiss.IndexFlatIP(128							)
												__snake_case						 =   faiss.index_cpu_to_gpu(_A    ,	1    ,	_A							)
												wikiaab_gpu_index_flat.add(_A							)  # TODO fix for larger GPU
						else:
												__snake_case ,       __snake_case						 =   (None, None)
						__snake_case						 =   Elasticsearch([{'host': 'localhost', 'port': '9200'}]							)
						return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=_A							)
def 						lowerCamelCase__   (				):
						__snake_case						 =   datasets.load_dataset('eli5'    ,	name='LFQA_reddit'							)
						__snake_case						 =   elia['train_eli5']
						__snake_case						 =   np.memmap(
						    'eli5_questions_reps.dat'    ,	dtype='float32'    ,	mode='r'    ,	shape=(elia_train.num_rows, 128)							)
						__snake_case						 =   faiss.IndexFlatIP(128							)
						eli5_train_q_index.add(_A							)
						return (elia_train, eli5_train_q_index)


_lowercase  =			load_indexes()
_lowercase  =			load_models()
_lowercase  =			load_train_data()
def 						lowerCamelCase__   (				a    ,	a=10							):
						__snake_case						 =   embed_questions_for_retrieval([question]    ,	_A    ,	_A							)
						__snake_case ,       __snake_case						 =   eli5_train_q_index.search(_A    ,	_A							)
						__snake_case						 =   [elia_train[int(_A							)] for i in I[0]]
						return nn_examples
def 						lowerCamelCase__   (				a    ,	a="wiki40b"    ,	a="dense"    ,	a=10							):


						if source == "none":
												__snake_case ,       __snake_case						 =   (' <P> '.join(['' for _ in range(11							)]							).strip(), [])
						else:
												if method == "dense":
																		__snake_case ,       __snake_case						 =   query_qa_dense_index(
																		    _A    ,	_A    ,	_A    ,	_A    ,	_A    ,	_A							)
												else:
																		__snake_case ,       __snake_case						 =   query_es_index(
																		    _A    ,	_A    ,	index_name='english_wiki40b_snippets_100w'    ,	n_results=_A    ,	)
						__snake_case						 =   [
						    (res['article_title'], res['section_title'].strip(), res['score'], res['passage_text']) for res in hit_lst
						]
						__snake_case						 =   'question: {} context: {}'.format(_A    ,	_A							)
						return question_doc, support_list
@st.cache(
    hash_funcs={
        torch.Tensor: (lambda a							: None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda a							: None),
    }							)
def 						lowerCamelCase__   (				a    ,	a    ,	a    ,	a=64    ,	a=256    ,	a=False    ,	a=2    ,	a=0.95    ,	a=0.8							):


						with torch.no_grad():
												__snake_case						 =   qa_sas_generate(
												    _A    ,	_A    ,	_A    ,	num_answers=1    ,	num_beams=_A    ,	min_len=_A    ,	max_len=_A    ,	do_sample=_A    ,	temp=_A    ,	top_p=_A    ,	top_k=_A    ,	max_input_length=1024    ,	device='cuda:0'    ,	)[0]
						return (answer, support_list)


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

# Start sidebar
_lowercase  =			"""<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"""
_lowercase  =			"""
<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
_lowercase  =			"""
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)

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

_lowercase  =			st.sidebar.checkbox("""Retrieval options""")
if retrieval_options:
	_lowercase  =			"""
    ### 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)
	_lowercase  =			st.sidebar.selectbox("""Which Wikipedia format should the model use?""", ["""wiki40b""", """none"""])
	_lowercase  =			st.sidebar.selectbox("""Which Wikipedia indexer should the model use?""", ["""dense""", """sparse""", """mixed"""])
else:
	_lowercase  =			"""wiki40b"""
	_lowercase  =			"""dense"""

_lowercase  =			"""beam"""
_lowercase  =			2
_lowercase  =			64
_lowercase  =			2_56
_lowercase  =			None
_lowercase  =			None
_lowercase  =			st.sidebar.checkbox("""Generation options""")
if generate_options:
	_lowercase  =			"""
    ### 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)
	_lowercase  =			st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""])
	_lowercase  =			st.sidebar.slider(
	    """Minimum generation length""", min_value=8, max_value=2_56, value=64, step=8, format=None, key=None
	)
	_lowercase  =			st.sidebar.slider(
	    """Maximum generation length""", min_value=64, max_value=5_12, value=2_56, step=16, format=None, key=None
	)
	if sampled == "beam":
		_lowercase  =			st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
	else:
		_lowercase  =			st.sidebar.slider(
		    """Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
		)
		_lowercase  =			st.sidebar.slider(
		    """Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
		)
		_lowercase  =			None

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

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


_lowercase  =			"""
---

**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)


import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE			:						Optional[int]				  =		logging.get_logger(__name__)


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		"""https://openaipublic.azureedge.net/jukebox/models/"""
SCREAMING_SNAKE_CASE			:						int				  =		{
    """jukebox-1b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """1b_lyrics/prior_level_2.pth.tar""",
    ],
    """jukebox-5b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """5b_lyrics/prior_level_2.pth.tar""",
    ],
}



def 	__A		(      _A       ):



 """simple docstring"""


 if key.endswith(".model.1.bias"       ) and len(key.split("."       )       ) > 10:
  __a							=	key.replace(".model.1.bias"				,					".conv1d_1.bias"       )
 elif key.endswith(".model.1.weight"       ) and len(key.split("."       )       ) > 10:
  __a							=	key.replace(".model.1.weight"				,					".conv1d_1.weight"       )
 elif key.endswith(".model.3.bias"       ) and len(key.split("."       )       ) > 10:
  __a							=	key.replace(".model.3.bias"				,					".conv1d_2.bias"       )
 elif key.endswith(".model.3.weight"       ) and len(key.split("."       )       ) > 10:
  __a							=	key.replace(".model.3.weight"				,					".conv1d_2.weight"       )

 if "conditioner_blocks.0." in key:
  __a							=	key.replace("conditioner_blocks.0"				,					"conditioner_blocks"       )

 if "prime_prior" in key:
  __a							=	key.replace("prime_prior"				,					"encoder"       )

 if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
  __a							=	key.replace(".emb."				,					"."       )

 if key.endswith("k"       ):  # replace vqvae.X.k with vqvae.X.codebook
  return key.replace(".k"				,					".codebook"       )
 if "y_emb." in key:
  return key.replace("y_emb."				,					"metadata_embedding."       )

 if "x_emb.emb." in key:
  __a							=	key.replace("0.x_emb.emb"				,					"embed_tokens"       )

 if "prime_state_ln" in key:
  return key.replace("prime_state_ln"				,					"encoder.final_layer_norm"       )
 if ".ln" in key:
  return key.replace(".ln"				,					".layer_norm"       )
 if "_ln" in key:
  return key.replace("_ln"				,					"_layer_norm"       )

 if "prime_state_proj" in key:
  return key.replace("prime_state_proj"				,					"encoder.proj_in"       )
 if "prime_x_out" in key:
  return key.replace("prime_x_out"				,					"encoder.lm_head"       )
 if "prior.x_out" in key:
  return key.replace("x_out"				,					"fc_proj_out"       )
 if "x_emb" in key:
  return key.replace("x_emb"				,					"embed_tokens"       )

 return key



def 	__A		(      _A				,					_A				,					_A				,					_A       ):



 """simple docstring"""


 __a							=	{}
 import re

 __a							=	re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"       )
 __a							=	re.compile(
     r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"       )
 __a							=	re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"       )

 __a							=	re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"       )
 __a							=	re.compile(
     r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"       )
 __a							=	re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"       )

 __a							=	re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"       )
 __a							=	re.compile(
     r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"       )
 __a							=	re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)"       )

 for original_key, value in state_dict.items():
  # rename vqvae.encoder keys
  if re_encoder_block_conv_in.fullmatch(_A       ):
   __a							=	re_encoder_block_conv_in.match(_A       )
   __a							=	regex_match.groups()
   __a							=	int(groups[2]       ) * 2 + int(groups[3]       )
   __a							=	f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
   __a							=	re_encoder_block_conv_in.sub(_A				,					_A       )

  elif re_encoder_block_resnet.fullmatch(_A       ):
   __a							=	re_encoder_block_resnet.match(_A       )
   __a							=	regex_match.groups()
   __a							=	int(groups[2]       ) * 2 + int(groups[3]       )
   __a							=	{"1": 1, "3": 2}[groups[-2]]
   __a							=	f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
   __a							=	f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
   __a							=	prefix + resnet_block
   __a							=	re_encoder_block_resnet.sub(_A				,					_A       )

  elif re_encoder_block_proj_out.fullmatch(_A       ):
   __a							=	re_encoder_block_proj_out.match(_A       )
   __a							=	regex_match.groups()
   __a							=	f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
   __a							=	re_encoder_block_proj_out.sub(_A				,					_A       )

  # rename vqvae.decoder keys
  elif re_decoder_block_conv_out.fullmatch(_A       ):
   __a							=	re_decoder_block_conv_out.match(_A       )
   __a							=	regex_match.groups()
   __a							=	int(groups[2]       ) * 2 + int(groups[3]       ) - 2
   __a							=	f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
   __a							=	re_decoder_block_conv_out.sub(_A				,					_A       )

  elif re_decoder_block_resnet.fullmatch(_A       ):
   __a							=	re_decoder_block_resnet.match(_A       )
   __a							=	regex_match.groups()
   __a							=	int(groups[2]       ) * 2 + int(groups[3]       ) - 2
   __a							=	{"1": 1, "3": 2}[groups[-2]]
   __a							=	f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
   __a							=	f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
   __a							=	prefix + resnet_block
   __a							=	re_decoder_block_resnet.sub(_A				,					_A       )

  elif re_decoder_block_proj_in.fullmatch(_A       ):
   __a							=	re_decoder_block_proj_in.match(_A       )
   __a							=	regex_match.groups()
   __a							=	f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
   __a							=	re_decoder_block_proj_in.sub(_A				,					_A       )

  # rename prior cond.model to upsampler.upsample_block and resnet
  elif re_prior_cond_conv_out.fullmatch(_A       ):
   __a							=	re_prior_cond_conv_out.match(_A       )
   __a							=	regex_match.groups()
   __a							=	int(groups[1]       ) * 2 + int(groups[2]       ) - 2
   __a							=	f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
   __a							=	re_prior_cond_conv_out.sub(_A				,					_A       )

  elif re_prior_cond_resnet.fullmatch(_A       ):
   __a							=	re_prior_cond_resnet.match(_A       )
   __a							=	regex_match.groups()
   __a							=	int(groups[1]       ) * 2 + int(groups[2]       ) - 2
   __a							=	{"1": 1, "3": 2}[groups[-2]]
   __a							=	f"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
   __a							=	f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
   __a							=	prefix + resnet_block
   __a							=	re_prior_cond_resnet.sub(_A				,					_A       )

  elif re_prior_cond_proj_in.fullmatch(_A       ):
   __a							=	re_prior_cond_proj_in.match(_A       )
   __a							=	regex_match.groups()
   __a							=	f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
   __a							=	re_prior_cond_proj_in.sub(_A				,					_A       )

  # keep original key
  else:
   __a							=	original_key

  __a							=	replace_key(_A       )

  if f"""{key_prefix}.{key}""" not in model_state_dict or key is None:
   print(f"""failed converting {original_key} to {key}, does not match"""       )

  # handle missmatched shape
  elif value.shape != model_state_dict[f"""{key_prefix}.{key}"""].shape:
   __a							=	model_state_dict[f"""{key_prefix}.{key}"""]
   print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""       )
   __a							=	original_key

  __a							=	original_key
  __a							=	value

 return new_dict



@torch.no_grad()
def 	__A		(      _A=None				,					_A=None       ):



 """simple docstring"""


 for file in MODEL_MAPPING[model_name]:
  if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split("/"       )[-1]}"""       ):
   __a							=	requests.get(f"""{PREFIX}{file}"""				,					allow_redirects=_A       )
   os.makedirs(f"""{pytorch_dump_folder_path}/"""				,					exist_ok=_A       )
   open(f"""{pytorch_dump_folder_path}/{file.split("/"       )[-1]}"""				,					"wb"       ).write(r.content       )

 __a							=	MODEL_MAPPING[model_name.split("/"       )[-1]]

 __a							=	JukeboxConfig.from_pretrained(_A       )
 __a							=	JukeboxModel(_A       )

 __a							=	[]
 __a							=	{}
 for i, dict_name in enumerate(_A       ):
  __a							=	torch.load(f"""{pytorch_dump_folder_path}/{dict_name.split("/"       )[-1]}"""       )["model"]

  __a							=	{}
  for k in old_dic.keys():
   if k.endswith(".b"       ):
    __a							=	old_dic[k]
   elif k.endswith(".w"       ):
    __a							=	old_dic[k]
   elif "level_2" not in dict_name and "cond.model." in k:
    __a							=	old_dic[k]
   else:
    __a							=	old_dic[k]

  __a							=	"vqvae" if i == 0 else f"""priors.{3 - i}"""
  __a							=	fix_jukebox_keys(_A				,					model.state_dict()				,					_A				,					_A       )
  weight_dict.append(_A       )

 __a							=	weight_dict.pop(0       )
 model.vqvae.load_state_dict(_A       )
 for i in range(len(_A       )       ):
  model.priors[i].load_state_dict(weight_dict[2 - i]       )

 Path(_A       ).mkdir(exist_ok=_A       )
 with open(f"""{pytorch_dump_folder_path}/mapping.json"""				,					"w"       ) as txtfile:
  json.dump(_A				,					_A       )

 print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""       )
 model.save_pretrained(_A       )

 return weight_dict


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default="""jukebox-5b-lyrics""",
      type=str,
      help="""Name of the model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default="""jukebox-5b-lyrics-converted""",
      type=str,
      help="""Path to the output PyTorch model directory.""",
  )
  SCREAMING_SNAKE_CASE			:						Optional[int]				  =		parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)


