
'''simple docstring'''







import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase   =       logging.get_logger(__name__)

__lowerCAmelCase   =       {
    """microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}







class UpperCAmelCase__      (					lowercase__      ):





  """simple docstring"""

  __UpperCAmelCase							:							str    					=   '''wavlm'''


  def __init__(						self :	Dict				,_a :	str=32				,_a :	List[str]=768				,_a :	Optional[Any]=12				,_a :	int=12				,_a :	str=3072				,_a :	List[Any]="gelu"				,_a :	Any=0.1				,_a :	Optional[int]=0.1				,_a :	Optional[Any]=0.1				,_a :	List[Any]=0.0				,_a :	Dict=0.1				,_a :	Any=0.1				,_a :	Optional[Any]=0.02				,_a :	Union[str, Any]=1E-5				,_a :	str="group"				,_a :	Optional[int]="gelu"				,_a :	List[Any]=(512, 512, 512, 512, 512, 512, 512)				,_a :	Tuple=(5, 2, 2, 2, 2, 2, 2)				,_a :	List[str]=(10, 3, 3, 3, 3, 2, 2)				,_a :	Tuple=False				,_a :	Tuple=128				,_a :	List[str]=16				,_a :	Optional[int]=320				,_a :	Optional[int]=800				,_a :	int=False				,_a :	Optional[int]=True				,_a :	Optional[int]=0.05				,_a :	Optional[int]=10				,_a :	Any=2				,_a :	Tuple=0.0				,_a :	str=10				,_a :	str=320				,_a :	Tuple=2				,_a :	List[str]=0.1				,_a :	List[Any]=100				,_a :	Dict=256				,_a :	Union[str, Any]=256				,_a :	List[str]=0.1				,_a :	List[str]="mean"				,_a :	Optional[int]=False				,_a :	str=False				,_a :	Any=256				,_a :	List[Any]=(512, 512, 512, 512, 1500)				,_a :	List[Any]=(5, 3, 3, 1, 1)				,_a :	Union[str, Any]=(1, 2, 3, 1, 1)				,_a :	List[str]=512				,_a :	List[Any]=80				,_a :	Tuple=0				,_a :	Optional[int]=1				,_a :	List[Any]=2				,_a :	Union[str, Any]=False				,_a :	str=3				,_a :	Tuple=2				,_a :	int=3				,_a :	Union[str, Any]=None				,**_a :	Optional[int]				,):


     '''simple docstring'''






     super().__init__(**__a				,pad_token_id=__a				,bos_token_id=__a				,eos_token_id=__a			)
     _a							:      int     =  hidden_size
     _a							:      List[Any]     =  feat_extract_norm
     _a							:      Optional[Any]     =  feat_extract_activation
     _a							:      Optional[Any]     =  list(__a			)
     _a							:      List[str]     =  list(__a			)
     _a							:      Optional[int]     =  list(__a			)
     _a							:      str     =  conv_bias
     _a							:      int     =  num_buckets
     _a							:      Union[str, Any]     =  max_bucket_distance
     _a							:      Union[str, Any]     =  num_conv_pos_embeddings
     _a							:      Optional[Any]     =  num_conv_pos_embedding_groups
     _a							:      int     =  len(self.conv_dim			)
     _a							:      str     =  num_hidden_layers
     _a							:      List[str]     =  intermediate_size
     _a							:      Tuple     =  hidden_act
     _a							:      str     =  num_attention_heads
     _a							:      Tuple     =  hidden_dropout
     _a							:      int     =  attention_dropout
     _a							:      Optional[int]     =  activation_dropout
     _a							:      int     =  feat_proj_dropout
     _a							:      Tuple     =  final_dropout
     _a							:      Optional[int]     =  layerdrop
     _a							:      Union[str, Any]     =  layer_norm_eps
     _a							:      Optional[int]     =  initializer_range
     _a							:      Union[str, Any]     =  num_ctc_classes
     _a							:      Tuple     =  vocab_size
     _a							:      Dict     =  do_stable_layer_norm
     _a							:      int     =  use_weighted_layer_sum
     _a							:      List[Any]     =  classifier_proj_size

     if (
         (len(self.conv_stride			) != self.num_feat_extract_layers)
         or (len(self.conv_kernel			) != self.num_feat_extract_layers)
         or (len(self.conv_dim			) != self.num_feat_extract_layers)
     ):
        raise ValueError(
            'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
            ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
            F""" {len(self.conv_dim			)}`, `len(config.conv_stride) = {len(self.conv_stride			)}`,"""
            F""" `len(config.conv_kernel) = {len(self.conv_kernel			)}`."""			)

     # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
     _a							:      List[Any]     =  apply_spec_augment
     _a							:      Tuple     =  mask_time_prob
     _a							:      Any     =  mask_time_length
     _a							:      Optional[Any]     =  mask_time_min_masks
     _a							:      Tuple     =  mask_feature_prob
     _a							:      int     =  mask_feature_length

     # parameters for pretraining with codevector quantized representations
     _a							:      Optional[Any]     =  num_codevectors_per_group
     _a							:      Optional[Any]     =  num_codevector_groups
     _a							:      str     =  contrastive_logits_temperature
     _a							:      List[Any]     =  num_negatives
     _a							:      Union[str, Any]     =  codevector_dim
     _a							:      Any     =  proj_codevector_dim
     _a							:      int     =  diversity_loss_weight

     # ctc loss
     _a							:      Optional[int]     =  ctc_loss_reduction
     _a							:      Tuple     =  ctc_zero_infinity

     # adapter
     _a							:      int     =  add_adapter
     _a							:      List[str]     =  adapter_kernel_size
     _a							:      Dict     =  adapter_stride
     _a							:      Optional[Any]     =  num_adapter_layers
     _a							:      Union[str, Any]     =  output_hidden_size or hidden_size

     # SequenceClassification-specific parameter. Feel free to ignore for other classes.
     _a							:      Optional[int]     =  classifier_proj_size

     # XVector-specific parameters. Feel free to ignore for other classes.
     _a							:      Union[str, Any]     =  list(__a			)
     _a							:      int     =  list(__a			)
     _a							:      List[Any]     =  list(__a			)
     _a							:      Any     =  xvector_output_dim


  @property
  def     __lowercase						(						self :	Tuple			):


     '''simple docstring'''






     return functools.reduce(operator.mul				,self.conv_stride				,1			)





"""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								=						"""bart"""
_a								=						True



@st.cache(allow_output_mutation=__snake_case      )
def  lowerCamelCase__						(     )				->						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=__snake_case      )
def  lowerCamelCase__						(     )				->						Tuple:


					"""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(__snake_case,	1,	__snake_case      )
										wikiaab_gpu_index_flat.add(__snake_case      )  # 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=__snake_case      )
def  lowerCamelCase__						(     )				->						int:


					"""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(__snake_case      )
					return (elia_train, eli5_train_q_index)


_a				,				_a				,				_a								=						load_indexes()
_a				,				_a				,				_a				,				_a								=						load_models()
_a				,				_a								=						load_train_data()



def  lowerCamelCase__						(     __snake_case,	__snake_case=10      )				->						List[Any]:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case,	__snake_case="wiki40b",	__snake_case="dense",	__snake_case=10      )				->						List[str]:


					"""simple docstring"""





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



@st.cache(
    hash_funcs={
        torch.Tensor: (lambda __snake_case      : None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda __snake_case      : None),
    }      )
def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case=64,	__snake_case=2_56,	__snake_case=False,	__snake_case=2,	__snake_case=0.95,	__snake_case=0.8      )				->						Dict:


					"""simple docstring"""





					with torch.no_grad():
										_UpperCamelCase									=    qa_sas_generate(
										    __snake_case,	__snake_case,	__snake_case,	num_answers=1,	num_beams=__snake_case,	min_len=__snake_case,	max_len=__snake_case,	do_sample=__snake_case,	temp=__snake_case,	top_p=__snake_case,	top_k=__snake_case,	max_input_length=10_24,	device='''cuda:0''',	)[0]
					return (answer, support_list)


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

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

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

_a								=						"""beam"""
_a								=						2
_a								=						64
_a								=						256
_a								=						None
_a								=						None
_a								=						st.sidebar.checkbox("""Generation options""")
if generate_options:
				_a								=						"""
    ### 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								=						st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""])
				_a								=						st.sidebar.slider(
				    """Minimum generation length""", min_value=8, max_value=256, value=64, step=8, format=None, key=None
				)
				_a								=						st.sidebar.slider(
				    """Maximum generation length""", min_value=64, max_value=512, value=256, step=16, format=None, key=None
				)
				if sampled == "beam":
								_a								=						st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
				else:
								_a								=						st.sidebar.slider(
								    """Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
								)
								_a								=						st.sidebar.slider(
								    """Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
								)
								_a								=						None

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

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

**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)






