



'''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


a     :    Any      						=					"""bart"""
a     :    int      						=					True



@st.cache(allow_output_mutation=a_	)
def    __lowerCamelCase						(      )   ->    List[Any]:
							if LOAD_DENSE_INDEX:
														UpperCAmelCase  :	Any							  =	AutoTokenizer.from_pretrained("""yjernite/retribert-base-uncased"""	)
														UpperCAmelCase  :	Dict							  =	AutoModel.from_pretrained("""yjernite/retribert-base-uncased"""	).to("""cuda:0"""	)
														UpperCAmelCase  :	Any							  =	qar_model.eval()
							else:
														UpperCAmelCase  :	Tuple							  =	(None, None)
							if MODEL_TYPE == "bart":
														UpperCAmelCase  :	List[str]							  =	AutoTokenizer.from_pretrained("""yjernite/bart_eli5"""	)
														UpperCAmelCase  :	Tuple							  =	AutoModelForSeqaSeqLM.from_pretrained("""yjernite/bart_eli5"""	).to("""cuda:0"""	)
														UpperCAmelCase  :	Optional[int]							  =	torch.load("""seq2seq_models/eli5_bart_model_blm_2.pth"""	)
														sas_model.load_state_dict(save_dict["""model"""]	)
														UpperCAmelCase  :	str							  =	sas_model.eval()
							else:
														UpperCAmelCase  :	int							  =	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						(      )   ->    Optional[Any]:
							if LOAD_DENSE_INDEX:
														UpperCAmelCase  :	Union[str, Any]							  =	faiss.StandardGpuResources()
														UpperCAmelCase  :	Optional[int]							  =	datasets.load_dataset(path="""wiki_snippets"""			,       name="""wiki40b_en_100_0"""	)['''train''']
														UpperCAmelCase  :	Dict							  =	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  :	List[str]							  =	faiss.IndexFlatIP(1_2_8	)
														UpperCAmelCase  :	List[str]							  =	faiss.index_cpu_to_gpu(a_			,       1			,       a_	)
														wikiaab_gpu_index_flat.add(a_	)  # TODO fix for larger GPU
							else:
														UpperCAmelCase  :	Any							  =	(None, None)
							UpperCAmelCase  :	Union[str, Any]							  =	Elasticsearch([{"""host""": """localhost""", """port""": """9200"""}]	)
							return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)



@st.cache(allow_output_mutation=a_	)
def    __lowerCamelCase						(      )   ->    Any:
							UpperCAmelCase  :	Optional[Any]							  =	datasets.load_dataset("""eli5"""			,       name="""LFQA_reddit"""	)
							UpperCAmelCase  :	List[Any]							  =	elia['''train_eli5''']
							UpperCAmelCase  :	Optional[int]							  =	np.memmap(
							    """eli5_questions_reps.dat"""			,       dtype="""float32"""			,       mode="""r"""			,       shape=(elia_train.num_rows, 1_2_8)	)
							UpperCAmelCase  :	str							  =	faiss.IndexFlatIP(1_2_8	)
							eli5_train_q_index.add(a_	)
							return (elia_train, eli5_train_q_index)


a						,   a						,   a     :    Any      						=					load_indexes()
a						,   a						,   a						,   a     :    Union[str, Any]      						=					load_models()
a						,   a     :    Optional[int]      						=					load_train_data()



def    __lowerCamelCase						(      _lowercase			,       _lowercase=1_0	)   ->    Dict:
							UpperCAmelCase  :	Any							  =	embed_questions_for_retrieval([question]			,       a_			,       a_	)
							UpperCAmelCase  :	Any							  =	eli5_train_q_index.search(a_			,       a_	)
							UpperCAmelCase  :	Dict							  =	[elia_train[int(a_	)] for i in I[0]]
							return nn_examples



def    __lowerCamelCase						(      _lowercase			,       _lowercase="wiki40b"			,       _lowercase="dense"			,       _lowercase=1_0	)   ->    List[Any]:
							if source == "none":
														UpperCAmelCase  :	int							  =	(''' <P> '''.join(["""""" for _ in range(1_1	)]	).strip(), [])
							else:
														if method == "dense":
																					UpperCAmelCase  :	List[str]							  =	query_qa_dense_index(
																					    a_			,       a_			,       a_			,       a_			,       a_			,       a_	)
														else:
																					UpperCAmelCase  :	Dict							  =	query_es_index(
																					    a_			,       a_			,       index_name="""english_wiki40b_snippets_100w"""			,       n_results=a_			,       )
							UpperCAmelCase  :	Any							  =	[
							    (res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
							]
							UpperCAmelCase  :	List[Any]							  =	'''question: {} context: {}'''.format(a_			,       a_	)
							return question_doc, support_list



@st.cache(
    hash_funcs={
        torch.Tensor: (lambda _lowercase	: None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda _lowercase	: None),
    }	)
def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase=6_4			,       _lowercase=2_5_6			,       _lowercase=False			,       _lowercase=2			,       _lowercase=0.95			,       _lowercase=0.8	)   ->    Dict:
							with torch.no_grad():
														UpperCAmelCase  :	Optional[Any]							  =	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=1_0_2_4			,       device="""cuda:0"""			,       )[0]
							return (answer, support_list)


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

# Start sidebar
a     :    Any      						=					"""<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"""
a     :    Optional[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
a     :    Dict      						=					"""
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)

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

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

a     :    Dict      						=					"""beam"""
a     :    Union[str, Any]      						=					2
a     :    Optional[int]      						=					6_4
a     :    Tuple      						=					2_5_6
a     :    List[str]      						=					None
a     :    List[Any]      						=					None
a     :    Any      						=					st.sidebar.checkbox("""Generation options""")
if generate_options:
							a     :    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)
							a     :    Optional[int]      						=					st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""])
							a     :    int      						=					st.sidebar.slider(
							    """Minimum generation length""", min_value=8, max_value=2_5_6, value=6_4, step=8, format=None, key=None
							)
							a     :    int      						=					st.sidebar.slider(
							    """Maximum generation length""", min_value=6_4, max_value=5_1_2, value=2_5_6, step=1_6, format=None, key=None
							)
							if sampled == "beam":
														a     :    Tuple      						=					st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
							else:
														a     :    Optional[Any]      						=					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
														)
														a     :    Union[str, Any]      						=					st.sidebar.slider(
														    """Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.0_1, format=None, key=None
														)
														a     :    Optional[Any]      						=					None

# start main text
a     :    int      						=					[
    """<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?""",
]
a     :    Optional[Any]      						=					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>":
							a     :    str      						=					st.text_input("""Enter your question here:""", """""")
else:
							a     :    List[Any]      						=					question_s

if st.button("""Show me!"""):
							if action in [0, 1, 3]:
														if index_type == "mixed":
																					a						,   a     :    Tuple      						=					make_support(question, source=wiki_source, method="""dense""", n_results=1_0)
																					a						,   a     :    List[str]      						=					make_support(question, source=wiki_source, method="""sparse""", n_results=1_0)
																					a     :    Dict      						=					[]
																					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)]
																					a     :    int      						=					support_list[:1_0]
																					a     :    Optional[Any]      						=					"""<P> """ + """ <P> """.join([res[-1] for res in support_list])
														else:
																					a						,   a     :    Union[str, Any]      						=					make_support(question, source=wiki_source, method=index_type, n_results=1_0)
							if action in [0, 3]:
														a						,   a     :    List[str]      						=					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):
																					a     :    Tuple      						=					"""https://en.wikipedia.org/wiki/{}""".format(res[0].replace(""" """, """_"""))
																					a     :    Tuple      						=					res[1].strip()
																					if sec_titles == "":
																												a     :    int      						=					"""[{}]({})""".format(res[0], wiki_url)
																					else:
																												a     :    Dict      						=					sec_titles.split(""" & """)
																												a     :    List[Any]      						=					""" & """.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]:
														a     :    int      						=					find_nearest_training(question)
														a     :    str      						=					nn_train_list[0]
														st.markdown(
														    """--- \n ### The most similar question in the ELI5 training set was: \n\n {}""".format(train_exple["""title"""])
														)
														a     :    Union[str, 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)))


a     :    int      						=					"""
---

**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)






'''simple docstring'''




import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
a     :    List[Any]      						=					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a     :    List[str]      						=					[]
for i in range(6):
							# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
							rename_keys.append(
							    (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
							)
							rename_keys.append(
							    (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
							)
							rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
							rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
							rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
							rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
							rename_keys.append(
							    (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
							)
							rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
							rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
							rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
							# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
							)
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
							)
							rename_keys.append(
							    (
							        F'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
							        F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
							    )
							)
							rename_keys.append(
							    (
							        F'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
							        F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
							    )
							)
							rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
							rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
							rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
							rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
							)
							rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
							)
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
							)
							rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
							rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

							# q, k, v projections in self/cross-attention in decoder for conditional DETR
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
							)
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
							)
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
							)
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
							)
							rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
							)
							# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
							)
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
							)
							rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
							)

							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
							)
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
							)
							rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
							rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
							rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
							)
							# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
							)
							rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
							rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
							)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Optional[Any]:
							UpperCAmelCase  :	List[str]							  =	state_dict.pop(_lowercase	)
							UpperCAmelCase  :	List[str]							  =	val



def    __lowerCamelCase						(      _lowercase	)   ->    Any:
							UpperCAmelCase  :	Union[str, Any]							  =	OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					UpperCAmelCase  :	List[str]							  =	key.replace("""backbone.0.body"""			,       """backbone.conv_encoder.model"""	)
																					UpperCAmelCase  :	Dict							  =	value
														else:
																					UpperCAmelCase  :	List[Any]							  =	value

							return new_state_dict



def    __lowerCamelCase						(      _lowercase			,       _lowercase=False	)   ->    Optional[int]:
							UpperCAmelCase  :	Dict							  =	""""""
							if is_panoptic:
														UpperCAmelCase  :	Tuple							  =	"""conditional_detr."""

							# first: transformer encoder
							for i in range(6	):
														# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
														UpperCAmelCase  :	List[Any]							  =	state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''	)
														UpperCAmelCase  :	List[Any]							  =	state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''	)
														# next, add query, keys and values (in that order) to the state dict
														UpperCAmelCase  :	Dict							  =	in_proj_weight[:2_5_6, :]
														UpperCAmelCase  :	Optional[Any]							  =	in_proj_bias[:2_5_6]
														UpperCAmelCase  :	List[Any]							  =	in_proj_weight[2_5_6:5_1_2, :]
														UpperCAmelCase  :	Tuple							  =	in_proj_bias[2_5_6:5_1_2]
														UpperCAmelCase  :	List[str]							  =	in_proj_weight[-2_5_6:, :]
														UpperCAmelCase  :	List[str]							  =	in_proj_bias[-2_5_6:]



def    __lowerCamelCase						(      )   ->    Dict:
							UpperCAmelCase  :	List[Any]							  =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase  :	Tuple							  =	Image.open(requests.get(_lowercase			,       stream=_lowercase	).raw	)

							return im



@torch.no_grad()
def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    str:
							UpperCAmelCase  :	str							  =	ConditionalDetrConfig()
							# set backbone and dilation attributes
							if "resnet101" in model_name:
														UpperCAmelCase  :	List[Any]							  =	"""resnet101"""
							if "dc5" in model_name:
														UpperCAmelCase  :	Optional[int]							  =	True
							UpperCAmelCase  :	List[Any]							  =	"""panoptic""" in model_name
							if is_panoptic:
														UpperCAmelCase  :	Union[str, Any]							  =	2_5_0
							else:
														UpperCAmelCase  :	int							  =	9_1
														UpperCAmelCase  :	Tuple							  =	"""huggingface/label-files"""
														UpperCAmelCase  :	List[Any]							  =	"""coco-detection-id2label.json"""
														UpperCAmelCase  :	Optional[int]							  =	json.load(open(hf_hub_download(_lowercase			,       _lowercase			,       repo_type="""dataset"""	)			,       """r"""	)	)
														UpperCAmelCase  :	Dict							  =	{int(_lowercase	): v for k, v in idalabel.items()}
														UpperCAmelCase  :	Optional[Any]							  =	idalabel
														UpperCAmelCase  :	List[Any]							  =	{v: k for k, v in idalabel.items()}

							# load image processor
							UpperCAmelCase  :	List[str]							  =	"""coco_panoptic""" if is_panoptic else """coco_detection"""
							UpperCAmelCase  :	List[Any]							  =	ConditionalDetrImageProcessor(format=_lowercase	)

							# prepare image
							UpperCAmelCase  :	Union[str, Any]							  =	prepare_img()
							UpperCAmelCase  :	Dict							  =	image_processor(images=_lowercase			,       return_tensors="""pt"""	)
							UpperCAmelCase  :	List[Any]							  =	encoding["""pixel_values"""]

							logger.info(F'''Converting model {model_name}...'''	)

							# load original model from torch hub
							UpperCAmelCase  :	int							  =	torch.hub.load("""DeppMeng/ConditionalDETR"""			,       _lowercase			,       pretrained=_lowercase	).eval()
							UpperCAmelCase  :	List[Any]							  =	conditional_detr.state_dict()
							# rename keys
							for src, dest in rename_keys:
														if is_panoptic:
																					UpperCAmelCase  :	List[Any]							  =	"""conditional_detr.""" + src
														rename_key(_lowercase			,       _lowercase			,       _lowercase	)
							UpperCAmelCase  :	List[Any]							  =	rename_backbone_keys(_lowercase	)
							# query, key and value matrices need special treatment
							read_in_q_k_v(_lowercase			,       is_panoptic=_lowercase	)
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							UpperCAmelCase  :	int							  =	"""conditional_detr.model.""" if is_panoptic else """model."""
							for key in state_dict.copy().keys():
														if is_panoptic:
																					if (
																					    key.startswith("""conditional_detr"""	)
																					    and not key.startswith("""class_labels_classifier"""	)
																					    and not key.startswith("""bbox_predictor"""	)
																					):
																												UpperCAmelCase  :	Union[str, Any]							  =	state_dict.pop(_lowercase	)
																												UpperCAmelCase  :	int							  =	val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												UpperCAmelCase  :	Any							  =	state_dict.pop(_lowercase	)
																												UpperCAmelCase  :	Optional[Any]							  =	val
																					elif key.startswith("""bbox_attention"""	) or key.startswith("""mask_head"""	):
																												continue
																					else:
																												UpperCAmelCase  :	List[Any]							  =	state_dict.pop(_lowercase	)
																												UpperCAmelCase  :	str							  =	val
														else:
																					if not key.startswith("""class_labels_classifier"""	) and not key.startswith("""bbox_predictor"""	):
																												UpperCAmelCase  :	Optional[int]							  =	state_dict.pop(_lowercase	)
																												UpperCAmelCase  :	Union[str, Any]							  =	val
    # finally, create HuggingFace model and load state dict
							UpperCAmelCase  :	List[Any]							  =	ConditionalDetrForSegmentation(_lowercase	) if is_panoptic else ConditionalDetrForObjectDetection(_lowercase	)
							model.load_state_dict(_lowercase	)
							model.eval()
							model.push_to_hub(repo_id=_lowercase			,       organization="""DepuMeng"""			,       commit_message="""Add model"""	)
							# verify our conversion
							UpperCAmelCase  :	Union[str, Any]							  =	conditional_detr(_lowercase	)
							UpperCAmelCase  :	int							  =	model(_lowercase	)
							assert torch.allclose(outputs.logits			,       original_outputs["""pred_logits"""]			,       atol=1e-4	)
							assert torch.allclose(outputs.pred_boxes			,       original_outputs["""pred_boxes"""]			,       atol=1e-4	)
							if is_panoptic:
														assert torch.allclose(outputs.pred_masks			,       original_outputs["""pred_masks"""]			,       atol=1e-4	)

							# Save model and image processor
							logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''	)
							Path(_lowercase	).mkdir(exist_ok=_lowercase	)
							model.save_pretrained(_lowercase	)
							image_processor.save_pretrained(_lowercase	)


if __name__ == "__main__":
							a     :    Tuple      						=					argparse.ArgumentParser()

							parser.add_argument(
							    """--model_name""",
							    default="""conditional_detr_resnet50""",
							    type=str,
							    help="""Name of the CONDITIONAL_DETR model you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
							)
							a     :    Optional[Any]      						=					parser.parse_args()
							convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

