





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


snake_case__					:							Union[str, Any]													=     'bart'
snake_case__					:							Dict													=     True



@st.cache(allow_output_mutation=lowerCamelCase )
def _a				(	)	->						Optional[int]:







      '''simple docstring'''






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







      '''simple docstring'''






      if LOAD_DENSE_INDEX:
            __A          =				faiss.StandardGpuResources()
            __A          =				datasets.load_dataset(path='''wiki_snippets'''					,				name='''wiki40b_en_100_0''' )['''train''']
            __A          =				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)					,				)
            __A          =				faiss.IndexFlatIP(1_28 )
            __A          =				faiss.index_cpu_to_gpu(lowerCamelCase					,				1					,				lowerCamelCase )
            wikiaab_gpu_index_flat.add(lowerCamelCase )  # TODO fix for larger GPU
      else:
            __A   ,						__A          =				(None, None)
      __A          =				Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}] )
      return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)



@st.cache(allow_output_mutation=lowerCamelCase )
def _a				(	)	->						Optional[Any]:







      '''simple docstring'''






      __A          =				datasets.load_dataset('''eli5'''					,				name='''LFQA_reddit''' )
      __A          =				elia['''train_eli5''']
      __A          =				np.memmap(
          '''eli5_questions_reps.dat'''					,				dtype='''float32'''					,				mode='''r'''					,				shape=(elia_train.num_rows, 1_28) )
      __A          =				faiss.IndexFlatIP(1_28 )
      eli5_train_q_index.add(lowerCamelCase )
      return (elia_train, eli5_train_q_index)


snake_case__						,    snake_case__						,    snake_case__					:							str													=     load_indexes()
snake_case__						,    snake_case__						,    snake_case__						,    snake_case__					:							Any													=     load_models()
snake_case__						,    snake_case__					:							List[Any]													=     load_train_data()



def _a				(	lowerCamelCase:   int					,				lowerCamelCase:   Optional[int]=10 )	->						Union[str, Any]:







      '''simple docstring'''






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



def _a				(	lowerCamelCase:   Optional[Any]					,				lowerCamelCase:   Dict="wiki40b"					,				lowerCamelCase:   Optional[int]="dense"					,				lowerCamelCase:   str=10 )	->						str:







      '''simple docstring'''






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



@st.cache(
    hash_funcs={
        torch.Tensor: (lambda lowerCamelCase : None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda lowerCamelCase : None),
    } )
def _a				(	lowerCamelCase:   List[Any]					,				lowerCamelCase:   List[Any]					,				lowerCamelCase:   str					,				lowerCamelCase:   Dict=64					,				lowerCamelCase:   Optional[int]=2_56					,				lowerCamelCase:   Dict=False					,				lowerCamelCase:   Union[str, Any]=2					,				lowerCamelCase:   Any=0.95					,				lowerCamelCase:   List[str]=0.8 )	->						List[str]:







      '''simple docstring'''






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


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

# Start sidebar
snake_case__					:							List[str]													=     '<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'
snake_case__					:							Optional[int]													=     '\n<html>\n  <head>\n    <style>\n      .img-container {\n        padding-left: 90px;\n        padding-right: 90px;\n        padding-top: 50px;\n        padding-bottom: 50px;\n        background-color: #f0f3f9;\n      }\n    </style>\n  </head>\n  <body>\n    <span class="img-container"> <!-- Inline parent element -->\n      %s\n    </span>\n  </body>\n</html>\n' % (
    header_html,
)
st.sidebar.markdown(
    header_full,
    unsafe_allow_html=True,
)

# Long Form QA with ELI5 and Wikipedia
snake_case__					:							List[str]													=     '\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n'
st.sidebar.markdown(description, unsafe_allow_html=True)

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

snake_case__					:							Union[str, Any]													=     st.sidebar.checkbox('Retrieval options')
if retrieval_options:
       snake_case__					:							List[Any]													=     '\n    ### Information retriever options\n\n    The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n    trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n    The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n    '
       st.sidebar.markdown(retriever_info)
       snake_case__					:							str													=     st.sidebar.selectbox('Which Wikipedia format should the model use?', ['wiki40b', 'none'])
       snake_case__					:							Union[str, Any]													=     st.sidebar.selectbox('Which Wikipedia indexer should the model use?', ['dense', 'sparse', 'mixed'])
else:
       snake_case__					:							Union[str, Any]													=     'wiki40b'
       snake_case__					:							Optional[Any]													=     'dense'

snake_case__					:							int													=     'beam'
snake_case__					:							Tuple													=     2
snake_case__					:							List[Any]													=     64
snake_case__					:							List[str]													=     256
snake_case__					:							Optional[int]													=     None
snake_case__					:							str													=     None
snake_case__					:							Union[str, Any]													=     st.sidebar.checkbox('Generation options')
if generate_options:
       snake_case__					:							Optional[Any]													=     '\n    ### Answer generation options\n\n    The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n    weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n    **beam** search, or **sample** from the decoder\'s output probabilities.\n    '
       st.sidebar.markdown(generate_info)
       snake_case__					:							Union[str, Any]													=     st.sidebar.selectbox('Would you like to use beam search or sample an answer?', ['beam', 'sampled'])
       snake_case__					:							List[Any]													=     st.sidebar.slider(
           'Minimum generation length', min_value=8, max_value=256, value=64, step=8, format=None, key=None
       )
       snake_case__					:							List[str]													=     st.sidebar.slider(
           'Maximum generation length', min_value=64, max_value=512, value=256, step=16, format=None, key=None
       )
       if sampled == "beam":
              snake_case__					:							int													=     st.sidebar.slider('Beam size', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
       else:
              snake_case__					:							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
              )
              snake_case__					:							int													=     st.sidebar.slider(
                  'Temperature', min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
              )
              snake_case__					:							Optional[int]													=     None

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

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


snake_case__					:							Optional[Any]													=     '\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n'







st.sidebar.markdown(disclaimer, unsafe_allow_html=True)












import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
       import os

       # The slow tests are often failing with OOM error on GPU
       # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
       # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
       snake_case__					:							Tuple													=     'platform'

       import jax
       import jax.numpy as jnp

       from transformers import BlenderbotTokenizer
       from transformers.models.blenderbot.modeling_flax_blenderbot import (
           FlaxBlenderbotForConditionalGeneration,
           FlaxBlenderbotModel,
           shift_tokens_right,
       )



def _a				(	lowerCamelCase:   Any					,				lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   int=None					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Tuple=None					,				lowerCamelCase:   Union[str, Any]=None					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Optional[Any]=None					,				)	->						Union[str, Any]:







      '''simple docstring'''






      if attention_mask is None:
            __A          =				np.where(input_ids != config.pad_token_id					,				1					,				0 )
      if decoder_attention_mask is None:
            __A          =				np.where(decoder_input_ids != config.pad_token_id					,				1					,				0 )
      if head_mask is None:
            __A          =				np.ones((config.encoder_layers, config.encoder_attention_heads) )
      if decoder_head_mask is None:
            __A          =				np.ones((config.decoder_layers, config.decoder_attention_heads) )
      if cross_attn_head_mask is None:
            __A          =				np.ones((config.decoder_layers, config.decoder_attention_heads) )
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": attention_mask,
      }





class A_					:




   def __init__(self	:int    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:Dict=13    ,   _UpperCamelCase	:Optional[Any]=7    ,   _UpperCamelCase	:str=True    ,   _UpperCamelCase	:Tuple=False    ,   _UpperCamelCase	:int=99    ,   _UpperCamelCase	:int=16    ,   _UpperCamelCase	:int=2    ,   _UpperCamelCase	:int=4    ,   _UpperCamelCase	:str=4    ,   _UpperCamelCase	:Dict="gelu"    ,   _UpperCamelCase	:int=0.1    ,   _UpperCamelCase	:Tuple=0.1    ,   _UpperCamelCase	:Union[str, Any]=32    ,   _UpperCamelCase	:Any=2    ,   _UpperCamelCase	:Union[str, Any]=1    ,   _UpperCamelCase	:Tuple=0    ,   _UpperCamelCase	:List[str]=0.0_2    ,   )->					str:
         __A          =				parent
         __A          =				batch_size
         __A          =				seq_length
         __A          =				is_training
         __A          =				use_labels
         __A          =				vocab_size
         __A          =				hidden_size
         __A          =				num_hidden_layers
         __A          =				num_attention_heads
         __A          =				intermediate_size
         __A          =				hidden_act
         __A          =				hidden_dropout_prob
         __A          =				attention_probs_dropout_prob
         __A          =				max_position_embeddings
         __A          =				eos_token_id
         __A          =				pad_token_id
         __A          =				bos_token_id
         __A          =				initializer_range




   def _lowerCAmelCase      (self	:Optional[int] )->					Union[str, Any]:
         __A          =				np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,   self.vocab_size )    ,   3    ,   self.vocab_size )
         __A          =				np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,   dtype=np.intaa ))    ,   -1 )

         __A          =				shift_tokens_right(_UpperCamelCase    ,   1    ,   2 )

         __A          =				BlenderbotConfig(
             vocab_size=self.vocab_size    ,   d_model=self.hidden_size    ,   encoder_layers=self.num_hidden_layers    ,   decoder_layers=self.num_hidden_layers    ,   encoder_attention_heads=self.num_attention_heads    ,   decoder_attention_heads=self.num_attention_heads    ,   encoder_ffn_dim=self.intermediate_size    ,   decoder_ffn_dim=self.intermediate_size    ,   dropout=self.hidden_dropout_prob    ,   attention_dropout=self.attention_probs_dropout_prob    ,   max_position_embeddings=self.max_position_embeddings    ,   eos_token_id=self.eos_token_id    ,   bos_token_id=self.bos_token_id    ,   pad_token_id=self.pad_token_id    ,   initializer_range=self.initializer_range    ,   use_cache=_UpperCamelCase    ,   )
         __A          =				prepare_blenderbot_inputs_dict(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )
         return config, inputs_dict




   def _lowerCAmelCase      (self	:Union[str, Any] )->					Tuple:
         __A   ,						__A          =				self.prepare_config_and_inputs()
         return config, inputs_dict




   def _lowerCAmelCase      (self	:Dict    ,   _UpperCamelCase	:Union[str, Any]    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:Optional[int] )->					str:
         __A          =				20
         __A          =				model_class_name(_UpperCamelCase )

         __A          =				model.encode(inputs_dict['''input_ids'''] )

         __A   ,						__A          =				(
             inputs_dict['''decoder_input_ids'''],
             inputs_dict['''decoder_attention_mask'''],
         )

         __A          =				model.init_cache(decoder_input_ids.shape[0]    ,   _UpperCamelCase    ,   _UpperCamelCase )
         __A          =				jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,   dtype='''i4''' )

         __A          =				jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]    ,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,   )
         __A          =				model.decode(
             decoder_input_ids[:, :-1]    ,   _UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase    ,   past_key_values=_UpperCamelCase    ,   decoder_position_ids=_UpperCamelCase    ,   )

         __A          =				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,   dtype='''i4''' )
         __A          =				model.decode(
             decoder_input_ids[:, -1:]    ,   _UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase    ,   past_key_values=outputs_cache.past_key_values    ,   decoder_position_ids=_UpperCamelCase    ,   )

         __A          =				model.decode(_UpperCamelCase    ,   _UpperCamelCase )

         __A          =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
         self.parent.assertTrue(diff < 1e-3    ,   msg=f"""Max diff is {diff}""" )






   def _lowerCAmelCase      (self	:Optional[int]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:Any )->					Dict:
         __A          =				20
         __A          =				model_class_name(_UpperCamelCase )

         __A          =				model.encode(inputs_dict['''input_ids'''] )

         __A   ,						__A          =				(
             inputs_dict['''decoder_input_ids'''],
             inputs_dict['''decoder_attention_mask'''],
         )

         __A          =				jnp.concatenate(
             [
                 decoder_attention_mask,
                 jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
             ]    ,   axis=-1    ,   )

         __A          =				model.init_cache(decoder_input_ids.shape[0]    ,   _UpperCamelCase    ,   _UpperCamelCase )
         __A          =				jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]    ,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,   )

         __A          =				model.decode(
             decoder_input_ids[:, :-1]    ,   _UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase    ,   past_key_values=_UpperCamelCase    ,   decoder_position_ids=_UpperCamelCase    ,   )
         __A          =				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,   dtype='''i4''' )
         __A          =				model.decode(
             decoder_input_ids[:, -1:]    ,   _UpperCamelCase    ,   past_key_values=outputs_cache.past_key_values    ,   decoder_attention_mask=_UpperCamelCase    ,   decoder_position_ids=_UpperCamelCase    ,   )

         __A          =				model.decode(_UpperCamelCase    ,   _UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase )

         __A          =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
         self.parent.assertTrue(diff < 1e-3    ,   msg=f"""Max diff is {diff}""" )





@require_flax
class A_					(			unittest.TestCase   ):
   lowerCAmelCase__        = 99




   def _lowerCAmelCase      (self	:Dict )->					int:
         __A          =				np.array(
             [
                 [71, 82, 18, 33, 46, 91, 2],
                 [68, 34, 26, 58, 30, 82, 2],
                 [5, 97, 17, 39, 94, 40, 2],
                 [76, 83, 94, 25, 70, 78, 2],
                 [87, 59, 41, 35, 48, 66, 2],
                 [55, 13, 16, 58, 5, 2, 1],  # note padding
                 [64, 27, 31, 51, 12, 75, 2],
                 [52, 64, 86, 17, 83, 39, 2],
                 [48, 61, 9, 24, 71, 82, 2],
                 [26, 1, 60, 48, 22, 13, 2],
                 [21, 5, 62, 28, 14, 76, 2],
                 [45, 98, 37, 86, 59, 48, 2],
                 [70, 70, 50, 9, 28, 0, 2],
             ]    ,   dtype=np.intaa    ,   )

         __A          =				input_ids.shape[0]
         __A          =				BlenderbotConfig(
             vocab_size=self.vocab_size    ,   d_model=24    ,   encoder_layers=2    ,   decoder_layers=2    ,   encoder_attention_heads=2    ,   decoder_attention_heads=2    ,   encoder_ffn_dim=32    ,   decoder_ffn_dim=32    ,   max_position_embeddings=48    ,   eos_token_id=2    ,   pad_token_id=1    ,   bos_token_id=0    ,   )
         return config, input_ids, batch_size




   def _lowerCAmelCase      (self	:Any )->					List[Any]:
         __A   ,						__A   ,						__A          =				self._get_config_and_data()
         __A          =				FlaxBlenderbotForConditionalGeneration(_UpperCamelCase )
         __A          =				lm_model(input_ids=_UpperCamelCase )
         __A          =				(batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:int )->					Dict:
         __A          =				BlenderbotConfig(
             vocab_size=self.vocab_size    ,   d_model=14    ,   encoder_layers=2    ,   decoder_layers=2    ,   encoder_attention_heads=2    ,   decoder_attention_heads=2    ,   encoder_ffn_dim=8    ,   decoder_ffn_dim=8    ,   max_position_embeddings=48    ,   )
         __A          =				FlaxBlenderbotForConditionalGeneration(_UpperCamelCase )
         __A          =				np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,   dtype=np.intaa )
         __A          =				np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,   dtype=np.intaa )
         __A          =				lm_model(input_ids=_UpperCamelCase    ,   decoder_input_ids=_UpperCamelCase )
         __A          =				(*summary.shape, config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape    ,   _UpperCamelCase )






   def _lowerCAmelCase      (self	:Tuple )->					Tuple:
         __A          =				np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,   dtype=np.intaa )
         __A          =				shift_tokens_right(_UpperCamelCase    ,   1    ,   2 )
         __A          =				np.equal(_UpperCamelCase    ,   1 ).astype(np.floataa ).sum()
         __A          =				np.equal(_UpperCamelCase    ,   1 ).astype(np.floataa ).sum()
         self.assertEqual(shifted.shape    ,   input_ids.shape )
         self.assertEqual(_UpperCamelCase    ,   n_pad_before - 1 )
         self.assertTrue(np.equal(shifted[:, 0]    ,   2 ).all() )





@require_flax
class A_					(			_lowerCamelCase ,      unittest.TestCase ,      _lowerCamelCase   ):
   lowerCAmelCase__        = True
   lowerCAmelCase__        = (
       (
           FlaxBlenderbotModel,
           FlaxBlenderbotForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   lowerCAmelCase__        = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




   def _lowerCAmelCase      (self	:List[str] )->					Optional[int]:
         __A          =				FlaxBlenderbotModelTester(self )




   def _lowerCAmelCase      (self	:List[str] )->					List[str]:
         __A   ,						__A          =				self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:Dict )->					List[str]:
         __A   ,						__A          =				self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward_with_attn_mask(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:Union[str, Any] )->					Union[str, Any]:
         __A   ,						__A          =				self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__ ):
                     __A          =				self._prepare_for_class(_UpperCamelCase    ,   _UpperCamelCase )
                     __A          =				model_class(_UpperCamelCase )

                     @jax.jit
                     def encode_jitted(_UpperCamelCase	:int    ,   _UpperCamelCase	:int=None    ,   **_UpperCamelCase	:Dict ):
                           return model.encode(input_ids=_UpperCamelCase    ,   attention_mask=_UpperCamelCase )

                     with self.subTest('''JIT Enabled''' ):
                           __A          =				encode_jitted(**_UpperCamelCase ).to_tuple()

                     with self.subTest('''JIT Disabled''' ):
                           with jax.disable_jit():
                                 __A          =				encode_jitted(**_UpperCamelCase ).to_tuple()

                     self.assertEqual(len(_UpperCamelCase )    ,   len(_UpperCamelCase ) )
                     for jitted_output, output in zip(_UpperCamelCase    ,   _UpperCamelCase ):
                           self.assertEqual(jitted_output.shape    ,   output.shape )




   def _lowerCAmelCase      (self	:List[str] )->					List[Any]:
         __A   ,						__A          =				self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__ ):
                     __A          =				model_class(_UpperCamelCase )
                     __A          =				model.encode(inputs_dict['''input_ids''']    ,   inputs_dict['''attention_mask'''] )

                     __A          =				{
                         '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                         '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                         '''encoder_outputs''': encoder_outputs,
                     }

                     @jax.jit
                     def decode_jitted(_UpperCamelCase	:str    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:Dict ):
                           return model.decode(
                               decoder_input_ids=_UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase    ,   encoder_outputs=_UpperCamelCase    ,   )

                     with self.subTest('''JIT Enabled''' ):
                           __A          =				decode_jitted(**_UpperCamelCase ).to_tuple()

                     with self.subTest('''JIT Disabled''' ):
                           with jax.disable_jit():
                                 __A          =				decode_jitted(**_UpperCamelCase ).to_tuple()

                     self.assertEqual(len(_UpperCamelCase )    ,   len(_UpperCamelCase ) )
                     for jitted_output, output in zip(_UpperCamelCase    ,   _UpperCamelCase ):
                           self.assertEqual(jitted_output.shape    ,   output.shape )




   @slow
   def _lowerCAmelCase      (self	:int )->					Optional[int]:
         for model_class_name in self.all_model_classes:
               __A          =				model_class_name.from_pretrained('''facebook/blenderbot-400M-distill''' )
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               __A          =				np.ones((1, 1) ) * model.config.eos_token_id
               __A          =				model(_UpperCamelCase )
               self.assertIsNotNone(_UpperCamelCase )






   @unittest.skipUnless(jax_device != '''cpu'''    ,   '''3B test too slow on CPU.''' )
   @slow
   def _lowerCAmelCase      (self	:Dict )->					List[str]:
         __A          =				{'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
         __A          =				{'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

         __A          =				FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''    ,   from_pt=_UpperCamelCase )
         __A          =				BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B''' )

         __A          =				['''Sam''']
         __A          =				tokenizer(_UpperCamelCase    ,   return_tensors='''jax''' )

         __A          =				model.generate(**_UpperCamelCase    ,   **_UpperCamelCase )
         __A          =				'''Sam is a great name. It means "sun" in Gaelic.'''

         __A          =				tokenizer.batch_decode(_UpperCamelCase    ,   **_UpperCamelCase )
         assert generated_txt[0].strip() == tgt_text





