


'''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      :      Union[str, Any]            =					"""bart"""
_lowercase      :      int            =					True


@st.cache(allow_output_mutation=A					)
def 				lowerCamelCase__				(	):




						'''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=A					)
def 				lowerCamelCase__				(	):




						'''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_28)				,       )
												UpperCAmelCase					    =     faiss.IndexFlatIP(1_28					)
												UpperCAmelCase					    =     faiss.index_cpu_to_gpu(A				,       1				,       A					)
												wikiaab_gpu_index_flat.add(A					)  # 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=A					)
def 				lowerCamelCase__				(	):




						'''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_28)					)
						UpperCAmelCase					    =     faiss.IndexFlatIP(1_28					)
						eli5_train_q_index.add(A					)
						return (elia_train, eli5_train_q_index)


_lowercase       ,	_lowercase       ,	_lowercase      :      Any            =					load_indexes()
_lowercase       ,	_lowercase       ,	_lowercase       ,	_lowercase      :      Any            =					load_models()
_lowercase       ,	_lowercase      :      Any            =					load_train_data()


def 				lowerCamelCase__				(	A       :      Any				,       A       :      List[str]=10					):




						'''simple docstring'''






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


def 				lowerCamelCase__				(	A       :      Tuple				,       A       :      Optional[Any]="wiki40b"				,       A       :      str="dense"				,       A       :      Dict=10					):




						'''simple docstring'''






						if source == "none":
												UpperCAmelCase    ,    UpperCAmelCase					    =     (''' <P> '''.join(['''''' for _ in range(11					)]					).strip(), [])
						else:
												if method == "dense":
																		UpperCAmelCase    ,    UpperCAmelCase					    =     query_qa_dense_index(
																		    A				,       A				,       A				,       A				,       A				,       A					)
												else:
																		UpperCAmelCase    ,    UpperCAmelCase					    =     query_es_index(
																		    A				,       A				,       index_name='''english_wiki40b_snippets_100w'''				,       n_results=A				,       )
						UpperCAmelCase					    =     [
						    (res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
						]
						UpperCAmelCase					    =     '''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       :      Optional[int]				,       A       :      Any				,       A       :      Tuple				,       A       :      List[str]=64				,       A       :      Any=2_56				,       A       :      Optional[int]=False				,       A       :      Dict=2				,       A       :      int=0.95				,       A       :      Dict=0.8					):




						'''simple docstring'''






						with torch.no_grad():
												UpperCAmelCase					    =     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=10_24				,       device='''cuda:0'''				,       )[0]
						return (answer, support_list)


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

# Start sidebar
_lowercase      :      Tuple            =					"""<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"""
_lowercase      :      List[Any]            =					"""
<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      :      List[Any]            =					"""
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      :      str            =					[
    """Answer the question""",
    """View the retrieved document only""",
    """View the most similar ELI5 question and answer""",
    """Show me everything, please!""",
]
_lowercase      :      str            =					st.sidebar.checkbox("""Demo options""")
if demo_options:
	_lowercase      :      Dict            =					st.sidebar.selectbox(
	    """""",
	    action_list,
	    index=3,
	)
	_lowercase      :      str            =					action_list.index(action_st)
	_lowercase      :      Optional[int]            =					st.sidebar.selectbox(
	    """""",
	    ["""Show full text of passages""", """Show passage section titles"""],
	    index=0,
	)
	_lowercase      :      int            =					show_type == """Show full text of passages"""
else:
	_lowercase      :      str            =					3
	_lowercase      :      List[Any]            =					True

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

_lowercase      :      Optional[int]            =					"""beam"""
_lowercase      :      Dict            =					2
_lowercase      :      Tuple            =					64
_lowercase      :      Optional[Any]            =					256
_lowercase      :      str            =					None
_lowercase      :      List[str]            =					None
_lowercase      :      Optional[int]            =					st.sidebar.checkbox("""Generation options""")
if generate_options:
	_lowercase      :      List[Any]            =					"""
    ### 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      :      List[str]            =					st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""])
	_lowercase      :      List[Any]            =					st.sidebar.slider(
	    """Minimum generation length""", min_value=8, max_value=256, value=64, step=8, format=None, key=None
	)
	_lowercase      :      Tuple            =					st.sidebar.slider(
	    """Maximum generation length""", min_value=64, max_value=512, value=256, step=16, format=None, key=None
	)
	if sampled == "beam":
		_lowercase      :      str            =					st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
	else:
		_lowercase      :      str            =					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      :      Optional[int]            =					st.sidebar.slider(
		    """Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
		)
		_lowercase      :      int            =					None

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

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

**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 flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD






class 	UpperCamelCase__(       nn.Module	):
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				float  		=	0.0
				__magic_name__    :				int  		=	1
				__magic_name__    :				int  		=	1
				__magic_name__    :				bool  		=	True
				__magic_name__    :				bool  		=	False
				__magic_name__    :				bool  		=	False
				__magic_name__    :				bool  		=	False
				__magic_name__    :				jnp.dtype  		=	jnp.floataa




				def 					a__(		self      :			str				)->					Dict:

										"""simple docstring"""




										UpperCAmelCase					    =     []
										UpperCAmelCase					    =     []

										for i in range(self.num_layers				):
																UpperCAmelCase					    =     self.in_channels if i == 0 else self.out_channels

																UpperCAmelCase					    =     FlaxResnetBlockaD(
																    in_channels=lowerCAmelCase				,   out_channels=self.out_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
																resnets.append(lowerCAmelCase				)

																UpperCAmelCase					    =     FlaxTransformeraDModel(
																    in_channels=self.out_channels				,   n_heads=self.num_attention_heads				,   d_head=self.out_channels // self.num_attention_heads				,   depth=1				,   use_linear_projection=self.use_linear_projection				,   only_cross_attention=self.only_cross_attention				,   use_memory_efficient_attention=self.use_memory_efficient_attention				,   dtype=self.dtype				,   )
																attentions.append(lowerCAmelCase				)

										UpperCAmelCase					    =     resnets
										UpperCAmelCase					    =     attentions

										if self.add_downsample:
																UpperCAmelCase					    =     FlaxDownsampleaD(self.out_channels				,   dtype=self.dtype				)





				def __call__(		self      :			List[Any]				,   lowerCAmelCase      :			Any				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			List[str]=True				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     ()

										for resnet, attn in zip(self.resnets				,   self.attentions				):
																UpperCAmelCase					    =     resnet(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)
																UpperCAmelCase					    =     attn(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)
																output_states += (hidden_states,)

										if self.add_downsample:
																UpperCAmelCase					    =     self.downsamplers_a(lowerCAmelCase				)
																output_states += (hidden_states,)

										return hidden_states, output_states







class 	UpperCamelCase__(       nn.Module	):
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				float  		=	0.0
				__magic_name__    :				int  		=	1
				__magic_name__    :				bool  		=	True
				__magic_name__    :				jnp.dtype  		=	jnp.floataa




				def 					a__(		self      :			List[str]				)->					Any:

										"""simple docstring"""




										UpperCAmelCase					    =     []

										for i in range(self.num_layers				):
																UpperCAmelCase					    =     self.in_channels if i == 0 else self.out_channels

																UpperCAmelCase					    =     FlaxResnetBlockaD(
																    in_channels=lowerCAmelCase				,   out_channels=self.out_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
																resnets.append(lowerCAmelCase				)
										UpperCAmelCase					    =     resnets

										if self.add_downsample:
																UpperCAmelCase					    =     FlaxDownsampleaD(self.out_channels				,   dtype=self.dtype				)





				def __call__(		self      :			List[str]				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			List[str]=True				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     ()

										for resnet in self.resnets:
																UpperCAmelCase					    =     resnet(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)
																output_states += (hidden_states,)

										if self.add_downsample:
																UpperCAmelCase					    =     self.downsamplers_a(lowerCAmelCase				)
																output_states += (hidden_states,)

										return hidden_states, output_states







class 	UpperCamelCase__(       nn.Module	):
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				float  		=	0.0
				__magic_name__    :				int  		=	1
				__magic_name__    :				int  		=	1
				__magic_name__    :				bool  		=	True
				__magic_name__    :				bool  		=	False
				__magic_name__    :				bool  		=	False
				__magic_name__    :				bool  		=	False
				__magic_name__    :				jnp.dtype  		=	jnp.floataa




				def 					a__(		self      :			List[str]				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     []
										UpperCAmelCase					    =     []

										for i in range(self.num_layers				):
																UpperCAmelCase					    =     self.in_channels if (i == self.num_layers - 1) else self.out_channels
																UpperCAmelCase					    =     self.prev_output_channel if i == 0 else self.out_channels

																UpperCAmelCase					    =     FlaxResnetBlockaD(
																    in_channels=resnet_in_channels + res_skip_channels				,   out_channels=self.out_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
																resnets.append(lowerCAmelCase				)

																UpperCAmelCase					    =     FlaxTransformeraDModel(
																    in_channels=self.out_channels				,   n_heads=self.num_attention_heads				,   d_head=self.out_channels // self.num_attention_heads				,   depth=1				,   use_linear_projection=self.use_linear_projection				,   only_cross_attention=self.only_cross_attention				,   use_memory_efficient_attention=self.use_memory_efficient_attention				,   dtype=self.dtype				,   )
																attentions.append(lowerCAmelCase				)

										UpperCAmelCase					    =     resnets
										UpperCAmelCase					    =     attentions

										if self.add_upsample:
																UpperCAmelCase					    =     FlaxUpsampleaD(self.out_channels				,   dtype=self.dtype				)





				def __call__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			Optional[int]				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			Any				,   lowerCAmelCase      :			Union[str, Any]=True				)->					Optional[int]:

										"""simple docstring"""




										for resnet, attn in zip(self.resnets				,   self.attentions				):
																# pop res hidden states
																UpperCAmelCase					    =     res_hidden_states_tuple[-1]
																UpperCAmelCase					    =     res_hidden_states_tuple[:-1]
																UpperCAmelCase					    =     jnp.concatenate((hidden_states, res_hidden_states)				,   axis=-1				)

																UpperCAmelCase					    =     resnet(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)
																UpperCAmelCase					    =     attn(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)

										if self.add_upsample:
																UpperCAmelCase					    =     self.upsamplers_a(lowerCAmelCase				)

										return hidden_states







class 	UpperCamelCase__(       nn.Module	):
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				float  		=	0.0
				__magic_name__    :				int  		=	1
				__magic_name__    :				bool  		=	True
				__magic_name__    :				jnp.dtype  		=	jnp.floataa




				def 					a__(		self      :			Optional[int]				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     []

										for i in range(self.num_layers				):
																UpperCAmelCase					    =     self.in_channels if (i == self.num_layers - 1) else self.out_channels
																UpperCAmelCase					    =     self.prev_output_channel if i == 0 else self.out_channels

																UpperCAmelCase					    =     FlaxResnetBlockaD(
																    in_channels=resnet_in_channels + res_skip_channels				,   out_channels=self.out_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
																resnets.append(lowerCAmelCase				)

										UpperCAmelCase					    =     resnets

										if self.add_upsample:
																UpperCAmelCase					    =     FlaxUpsampleaD(self.out_channels				,   dtype=self.dtype				)





				def __call__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			Union[str, Any]				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			Dict=True				)->					Tuple:

										"""simple docstring"""




										for resnet in self.resnets:
																# pop res hidden states
																UpperCAmelCase					    =     res_hidden_states_tuple[-1]
																UpperCAmelCase					    =     res_hidden_states_tuple[:-1]
																UpperCAmelCase					    =     jnp.concatenate((hidden_states, res_hidden_states)				,   axis=-1				)

																UpperCAmelCase					    =     resnet(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)

										if self.add_upsample:
																UpperCAmelCase					    =     self.upsamplers_a(lowerCAmelCase				)

										return hidden_states







class 	UpperCamelCase__(       nn.Module	):
				__magic_name__    :				int
				__magic_name__    :				float  		=	0.0
				__magic_name__    :				int  		=	1
				__magic_name__    :				int  		=	1
				__magic_name__    :				bool  		=	False
				__magic_name__    :				bool  		=	False
				__magic_name__    :				jnp.dtype  		=	jnp.floataa




				def 					a__(		self      :			int				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     [
										    FlaxResnetBlockaD(
										        in_channels=self.in_channels				,   out_channels=self.in_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
										]

										UpperCAmelCase					    =     []

										for _ in range(self.num_layers				):
																UpperCAmelCase					    =     FlaxTransformeraDModel(
																    in_channels=self.in_channels				,   n_heads=self.num_attention_heads				,   d_head=self.in_channels // self.num_attention_heads				,   depth=1				,   use_linear_projection=self.use_linear_projection				,   use_memory_efficient_attention=self.use_memory_efficient_attention				,   dtype=self.dtype				,   )
																attentions.append(lowerCAmelCase				)

																UpperCAmelCase					    =     FlaxResnetBlockaD(
																    in_channels=self.in_channels				,   out_channels=self.in_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
																resnets.append(lowerCAmelCase				)

										UpperCAmelCase					    =     resnets
										UpperCAmelCase					    =     attentions





				def __call__(		self      :			Dict				,   lowerCAmelCase      :			Optional[Any]				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			Any=True				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.resnets[0](lowerCAmelCase				,   lowerCAmelCase				)
										for attn, resnet in zip(self.attentions				,   self.resnets[1:]				):
																UpperCAmelCase					    =     attn(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)
																UpperCAmelCase					    =     resnet(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)

										return hidden_states





