
"""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					:	int							=		'''bart'''
a					:	str							=		True


@st.cache(allow_output_mutation=_A      )
def  _UpperCamelCase	(						)    ->  str:





    """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  _UpperCamelCase	(						)    ->  Dict:





    """simple docstring"""

    if LOAD_DENSE_INDEX:
        _UpperCAmelCase          =   faiss.StandardGpuResources()
        _UpperCAmelCase          =   datasets.load_dataset(path="""wiki_snippets"""					,      name="""wiki40b_en_100_0"""      )["""train"""]
        _UpperCAmelCase          =   np.memmap(
            """wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat"""					,      dtype="""float32"""					,      mode="""r"""					,      shape=(wikiaab_passages.num_rows, 1_2_8)					,      )
        _UpperCAmelCase          =   faiss.IndexFlatIP(1_2_8      )
        _UpperCAmelCase          =   faiss.index_cpu_to_gpu(_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  _UpperCamelCase	(						)    ->  List[str]:





    """simple docstring"""

    _UpperCAmelCase          =   datasets.load_dataset("""eli5"""					,      name="""LFQA_reddit"""      )
    _UpperCAmelCase          =   elia["""train_eli5"""]
    _UpperCAmelCase          =   np.memmap(
        """eli5_questions_reps.dat"""					,      dtype="""float32"""					,      mode="""r"""					,      shape=(elia_train.num_rows, 1_2_8)      )
    _UpperCAmelCase          =   faiss.IndexFlatIP(1_2_8      )
    eli5_train_q_index.add(_A      )
    return (elia_train, eli5_train_q_index)


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


def  _UpperCamelCase	(						_A					,      _A=1_0      )    ->  List[Any]:





    """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  _UpperCamelCase	(						_A					,      _A="wiki40b"					,      _A="dense"					,      _A=1_0      )    ->  List[Any]:





    """simple docstring"""

    if source == "none":
        _UpperCAmelCase    ,_UpperCAmelCase          =   (""" <P> """.join(["""""" for _ in range(1_1      )]      ).strip(), [])
    else:
        if method == "dense":
            _UpperCAmelCase    ,_UpperCAmelCase          =   query_qa_dense_index(
                _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  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A=6_4					,      _A=2_5_6					,      _A=False					,      _A=2					,      _A=0.95					,      _A=0.8      )    ->  Any:





    """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=1_0_2_4					,      device="""cuda:0"""					,      )[0]
    return (answer, support_list)


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

# Start sidebar
a					:	Optional[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					:	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)

a					:	Dict							=		[
    '''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					:	List[str]							=		st.sidebar.selectbox(
     '''''',
     action_list,
     index=3,
 )
 a					:	Any							=		action_list.index(action_st)
 a					:	List[Any]							=		st.sidebar.selectbox(
     '''''',
     ['''Show full text of passages''', '''Show passage section titles'''],
     index=0,
 )
 a					:	Optional[Any]							=		show_type == '''Show full text of passages'''
else:
 a					:	Optional[int]							=		3
 a					:	Optional[Any]							=		True

a					:	Optional[Any]							=		st.sidebar.checkbox('''Retrieval options''')
if retrieval_options:
 a					:	Union[str, 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					:	Dict							=		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					:	int							=		'''wiki40b'''
 a					:	Dict							=		'''dense'''

a					:	Any							=		'''beam'''
a					:	str							=		2
a					:	Tuple							=		6_4
a					:	Union[str, Any]							=		2_5_6
a					:	Optional[int]							=		None
a					:	Tuple							=		None
a					:	str							=		st.sidebar.checkbox('''Generation options''')
if generate_options:
 a					:	str							=		'''
    ### 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					:	Dict							=		st.sidebar.selectbox('''Would you like to use beam search or sample an answer?''', ['''beam''', '''sampled'''])
 a					:	List[Any]							=		st.sidebar.slider(
     '''Minimum generation length''', min_value=8, max_value=2_5_6, value=6_4, step=8, format=None, key=None
 )
 a					:	Optional[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					:	List[Any]							=		st.sidebar.slider('''Beam size''', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
 else:
  a					:	int							=		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					:	Optional[Any]							=		st.sidebar.slider(
      '''Temperature''', min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
  )
  a					:	Optional[Any]							=		None

# start main text
a					:	Union[str, Any]							=		[
    '''<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					:	Dict							=		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					:	Union[str, Any]							=		st.text_input('''Enter your question here:''', '''''')
else:
 a					:	Tuple							=		question_s

if st.button('''Show me!'''):
 if action in [0, 1, 3]:
  if index_type == "mixed":
   a		, a					:	List[Any]							=		make_support(question, source=wiki_source, method='''dense''', n_results=1_0)
   a		, a					:	Any							=		make_support(question, source=wiki_source, method='''sparse''', n_results=1_0)
   a					:	Any							=		[]
   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					:	List[Any]							=		support_list[:1_0]
   a					:	List[Any]							=		'''<P> ''' + ''' <P> '''.join([res[-1] for res in support_list])
  else:
   a		, a					:	Any							=		make_support(question, source=wiki_source, method=index_type, n_results=1_0)
 if action in [0, 3]:
  a		, a					:	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):
   a					:	str							=		'''https://en.wikipedia.org/wiki/{}'''.format(res[0].replace(''' ''', '''_'''))
   a					:	Tuple							=		res[1].strip()
   if sec_titles == "":
    a					:	List[Any]							=		'''[{}]({})'''.format(res[0], wiki_url)
   else:
    a					:	Union[str, Any]							=		sec_titles.split(''' & ''')
    a					:	Optional[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					:	int							=		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					:	Any							=		'''
---

**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 tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel




class 		a_						:







    def __init__(			self			:       Tuple     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       List[Any]=99     ,					__UpperCamelCase			:       Optional[int]=13     ,					__UpperCamelCase			:       List[str]=7     ,					__UpperCamelCase			:       List[Any]=9     ,					__UpperCamelCase			:       List[Any]=True     ,					__UpperCamelCase			:       str=True     ,					__UpperCamelCase			:       int=False     ,					__UpperCamelCase			:       int=32     ,					__UpperCamelCase			:       Dict=5     ,					__UpperCamelCase			:       Optional[int]=4     ,					__UpperCamelCase			:       Any=37     ,					__UpperCamelCase			:       List[Any]=8     ,					__UpperCamelCase			:       Optional[int]=0.1     ,					__UpperCamelCase			:       str=0.0_0_2     ,					__UpperCamelCase			:       Union[str, Any]=1     ,					__UpperCamelCase			:       List[Any]=0     ,					__UpperCamelCase			:       Tuple=0     ,					__UpperCamelCase			:       Optional[int]=None     ,					__UpperCamelCase			:       Any=None     ,					)		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   parent
        _UpperCAmelCase          =   batch_size
        _UpperCAmelCase          =   encoder_seq_length
        _UpperCAmelCase          =   decoder_seq_length
        # For common tests
        _UpperCAmelCase          =   self.decoder_seq_length
        _UpperCAmelCase          =   is_training
        _UpperCAmelCase          =   use_attention_mask
        _UpperCAmelCase          =   use_labels
        _UpperCAmelCase          =   vocab_size
        _UpperCAmelCase          =   hidden_size
        _UpperCAmelCase          =   num_hidden_layers
        _UpperCAmelCase          =   num_attention_heads
        _UpperCAmelCase          =   d_ff
        _UpperCAmelCase          =   relative_attention_num_buckets
        _UpperCAmelCase          =   dropout_rate
        _UpperCAmelCase          =   initializer_factor
        _UpperCAmelCase          =   eos_token_id
        _UpperCAmelCase          =   pad_token_id
        _UpperCAmelCase          =   decoder_start_token_id
        _UpperCAmelCase          =   None
        _UpperCAmelCase          =   decoder_layers







    def      _snake_case					(			self			:       Union[str, Any]     )		->Union[str, Any]:






        '''simple docstring'''






        return TaConfig.from_pretrained("""google/umt5-base"""     )







    def      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Tuple=None     ,					__UpperCamelCase			:       Optional[Any]=None     ,					__UpperCamelCase			:       Optional[Any]=None     ,					__UpperCamelCase			:       Any=None     ,					__UpperCamelCase			:       str=None     ,					)		->int:






        '''simple docstring'''






        if attention_mask is None:
            _UpperCAmelCase          =   input_ids.ne(config.pad_token_id     )
        if decoder_attention_mask is None:
            _UpperCAmelCase          =   decoder_input_ids.ne(config.pad_token_id     )
        if head_mask is None:
            _UpperCAmelCase          =   torch.ones(config.num_hidden_layers     ,					config.num_attention_heads     ,					device=__UpperCamelCase     )
        if decoder_head_mask is None:
            _UpperCAmelCase          =   torch.ones(config.num_decoder_layers     ,					config.num_attention_heads     ,					device=__UpperCamelCase     )
        if cross_attn_head_mask is None:
            _UpperCAmelCase          =   torch.ones(
                config.num_decoder_layers     ,					config.num_attention_heads     ,					device=__UpperCamelCase     )
        return {
            "input_ids": input_ids,
            "decoder_input_ids": decoder_input_ids,
            "attention_mask": attention_mask,
            "decoder_attention_mask": decoder_attention_mask,
            "head_mask": head_mask,
            "decoder_head_mask": decoder_head_mask,
            "cross_attn_head_mask": cross_attn_head_mask,
        }







    def      _snake_case					(			self			:       List[Any]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   ids_tensor([self.batch_size, self.encoder_seq_length]     ,					self.vocab_size     )
        _UpperCAmelCase          =   ids_tensor([self.batch_size, self.decoder_seq_length]     ,					self.vocab_size     )

        # we need to clamp the input ids here to avoid having pad token in between
        # this is because for NllbMoe the position_ids are prepared such that
        # all pad tokens have pos id = 2 and rest are between 2..seq_length
        # and the seq_length here is seq_length - num_pad_tokens
        # but when using past, there is no way of knowing if the past input ids had
        # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
        # position_ids being off by num_pad_tokens in past input
        _UpperCAmelCase          =   input_ids.clamp(self.pad_token_id + 1     )
        _UpperCAmelCase          =   decoder_input_ids.clamp(self.pad_token_id + 1     )

        _UpperCAmelCase          =   self.get_config()
        _UpperCAmelCase          =   config.num_attention_heads
        _UpperCAmelCase          =   self.prepare_inputs_dict(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     )
        return config, input_dict







    def      _snake_case					(			self			:       Union[str, Any]     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.prepare_config_and_inputs()
        return config, inputs_dict







    def      _snake_case					(			self			:       Dict     )		->List[str]:






        '''simple docstring'''






        return TaConfig(
            vocab_size=1_66     ,					d_model=self.hidden_size     ,					d_ff=self.d_ff     ,					d_kv=self.hidden_size // self.num_attention_heads     ,					num_layers=self.num_hidden_layers     ,					num_decoder_layers=self.decoder_layers     ,					num_heads=self.num_attention_heads     ,					relative_attention_num_buckets=self.relative_attention_num_buckets     ,					dropout_rate=self.dropout_rate     ,					initializer_factor=self.initializer_factor     ,					eos_token_id=self.eos_token_id     ,					bos_token_id=self.pad_token_id     ,					pad_token_id=self.pad_token_id     ,					decoder_start_token_id=self.decoder_start_token_id     ,					)







    def      _snake_case					(			self			:       Tuple     )		->Dict:






        '''simple docstring'''






        return TaConfig(
            vocab_size=self.vocab_size     ,					d_model=self.hidden_size     ,					d_ff=self.d_ff     ,					d_kv=self.hidden_size // self.num_attention_heads     ,					num_layers=self.num_hidden_layers     ,					num_decoder_layers=self.decoder_layers     ,					num_heads=self.num_attention_heads     ,					relative_attention_num_buckets=self.relative_attention_num_buckets     ,					dropout_rate=self.dropout_rate     ,					initializer_factor=self.initializer_factor     ,					eos_token_id=self.eos_token_id     ,					bos_token_id=self.pad_token_id     ,					pad_token_id=self.pad_token_id     ,					decoder_start_token_id=self.decoder_start_token_id     ,					)







    def      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       List[str]     ,					)		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaModel(config=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(
            input_ids=__UpperCamelCase     ,					decoder_input_ids=__UpperCamelCase     ,					attention_mask=__UpperCamelCase     ,					decoder_attention_mask=__UpperCamelCase     ,					)
        _UpperCAmelCase          =   model(input_ids=__UpperCamelCase     ,					decoder_input_ids=__UpperCamelCase     )
        _UpperCAmelCase          =   result.last_hidden_state
        _UpperCAmelCase          =   result.past_key_values
        _UpperCAmelCase          =   result.encoder_last_hidden_state

        self.parent.assertEqual(encoder_output.size()     ,					(self.batch_size, self.encoder_seq_length, self.hidden_size)     )
        self.parent.assertEqual(decoder_output.size()     ,					(self.batch_size, self.decoder_seq_length, self.hidden_size)     )
        # There should be `num_layers` key value embeddings stored in decoder_past
        self.parent.assertEqual(len(__UpperCamelCase     )     ,					config.num_layers     )
        # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
        self.parent.assertEqual(len(decoder_past[0]     )     ,					4     )







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       List[str]     ,					)		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaModel(config=__UpperCamelCase     ).get_decoder().to(__UpperCamelCase     ).eval()
        # first forward pass
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					use_cache=__UpperCamelCase     )
        _UpperCAmelCase          =   model(__UpperCamelCase     )
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					use_cache=__UpperCamelCase     )

        self.parent.assertTrue(len(__UpperCamelCase     ) == len(__UpperCamelCase     )     )
        self.parent.assertTrue(len(__UpperCamelCase     ) == len(__UpperCamelCase     ) + 1     )

        _UpperCAmelCase    ,_UpperCAmelCase          =   outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        _UpperCAmelCase          =   ids_tensor((self.batch_size, 1)     ,					config.vocab_size     )

        # append to next input_ids and
        _UpperCAmelCase          =   torch.cat([input_ids, next_tokens]     ,					dim=-1     )

        _UpperCAmelCase          =   model(__UpperCamelCase     )["""last_hidden_state"""]
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					past_key_values=__UpperCamelCase     )["""last_hidden_state"""]

        # select random slice
        _UpperCAmelCase          =   ids_tensor((1,)     ,					output_from_past.shape[-1]     ).item()
        _UpperCAmelCase          =   output_from_no_past[:, -1, random_slice_idx].detach()
        _UpperCAmelCase          =   output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(__UpperCamelCase     ,					__UpperCamelCase     ,					atol=1e-3     )     )







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       Dict     ,					)		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaModel(config=__UpperCamelCase     ).to(__UpperCamelCase     ).half().eval()
        _UpperCAmelCase          =   model(**__UpperCamelCase     )["""last_hidden_state"""]
        self.parent.assertFalse(torch.isnan(__UpperCamelCase     ).any().item()     )







@require_torch
class 		a_						( _UpperCAmelCase     ,						_UpperCAmelCase     ,						_UpperCAmelCase     ,						unittest.TestCase	):
    a      :				List[str]     =				(
        (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
    )
    a      :				Tuple     =				(UMTaForConditionalGeneration,) if is_torch_available() else ()
    a      :				Optional[Any]     =				(
        {
            'conversational': UMTaForConditionalGeneration,
            'feature-extraction': UMTaModel,
            'summarization': UMTaForConditionalGeneration,
            'text2text-generation': UMTaForConditionalGeneration,
            'translation': UMTaForConditionalGeneration,
            'question-answering': UMTaForQuestionAnswering,
        }
        if is_torch_available()
        else {}
    )
    a      :				Any     =				True
    a      :				Optional[int]     =				False
    a      :				Any     =				False
    a      :				Optional[int]     =				True
    a      :				Optional[Any]     =				True
    # The small UMT5 model needs higher percentages for CPU/MP tests
    a      :				int     =				[0.8, 0.9]







    def      _snake_case					(			self			:       Optional[Any]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaModelTester(self     )







    @unittest.skip("""Test has a segmentation fault on torch 1.8.0"""     )
    def      _snake_case					(			self			:       Union[str, Any]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        _UpperCAmelCase          =   UMTaModel(config_and_inputs[0]     ).to(__UpperCamelCase     )
        with tempfile.TemporaryDirectory() as tmpdirname:
            torch.onnx.export(
                __UpperCamelCase     ,					(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])     ,					f"""{tmpdirname}/t5_test.onnx"""     ,					export_params=__UpperCamelCase     ,					opset_version=9     ,					input_names=["""input_ids""", """decoder_input_ids"""]     ,					)







    @unittest.skipIf(torch_device == """cpu"""     ,					"""Cant do half precision"""     )
    def      _snake_case					(			self			:       Tuple     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_fpaa_forward(*__UpperCamelCase     )







    def      _snake_case					(			self			:       Any     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        _UpperCAmelCase          =   config_and_inputs[0]
        _UpperCAmelCase          =   UMTaForConditionalGeneration(__UpperCamelCase     ).eval()
        model.to(__UpperCamelCase     )

        _UpperCAmelCase          =   {
            """head_mask""": torch.zeros(config.num_layers     ,					config.num_heads     ,					device=__UpperCamelCase     ),
            """decoder_head_mask""": torch.zeros(config.num_decoder_layers     ,					config.num_heads     ,					device=__UpperCamelCase     ),
            """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers     ,					config.num_heads     ,					device=__UpperCamelCase     ),
        }

        for attn_name, (name, mask) in zip(__UpperCamelCase     ,					head_masking.items()     ):
            _UpperCAmelCase          =   {name: mask}
            # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
            if name == "head_mask":
                _UpperCAmelCase          =   torch.ones(
                    config.num_decoder_layers     ,					config.num_heads     ,					device=__UpperCamelCase     )

            _UpperCAmelCase          =   model.generate(
                config_and_inputs[1]["""input_ids"""]     ,					num_beams=1     ,					max_length=3     ,					output_attentions=__UpperCamelCase     ,					return_dict_in_generate=__UpperCamelCase     ,					**__UpperCamelCase     ,					)
            # We check the state of decoder_attentions and cross_attentions just from the last step
            _UpperCAmelCase          =   out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
            self.assertEqual(sum([w.sum().item() for w in attn_weights]     )     ,					0.0     )







    @unittest.skip("""Does not work on the tiny model as we keep hitting edge cases."""     )
    def      _snake_case					(			self			:       Tuple     )		->List[Any]:






        '''simple docstring'''






        pass







@require_torch
@require_sentencepiece
@require_tokenizers
class 		a_						( unittest.TestCase	):







    @slow
    @unittest.skip(
        """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"""     )
    def      _snake_case					(			self			:       Optional[Any]     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""     ,					return_dict=__UpperCamelCase     ).to(__UpperCamelCase     )
        _UpperCAmelCase          =   AutoTokenizer.from_pretrained("""google/umt5-small"""     ,					use_fast=__UpperCamelCase     ,					legacy=__UpperCamelCase     )
        _UpperCAmelCase          =   [
            """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
            """No se como puedo <extra_id_0>.""",
            """This is the reason why we <extra_id_0> them.""",
            """The <extra_id_0> walks in <extra_id_1>, seats""",
            """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
        ]
        _UpperCAmelCase          =   tokenizer(__UpperCamelCase     ,					return_tensors="""pt"""     ,					padding=__UpperCamelCase     ).input_ids
        # fmt: off
        _UpperCAmelCase          =   torch.tensor(
            [
                [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
                [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
            ]     )
        # fmt: on
        torch.testing.assert_allclose(__UpperCamelCase     ,					__UpperCamelCase     )

        _UpperCAmelCase          =   model.generate(input_ids.to(__UpperCamelCase     )     )
        _UpperCAmelCase          =   [
            """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
            """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
            """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
            """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
            """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
        ]
        _UpperCAmelCase          =   tokenizer.batch_decode(__UpperCamelCase     )
        self.assertEqual(__UpperCamelCase     ,					__UpperCamelCase     )