






"""simple docstring"""






import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.generation import (
      FlaxForcedBOSTokenLogitsProcessor,
      FlaxForcedEOSTokenLogitsProcessor,
      FlaxLogitsProcessorList,
      FlaxMinLengthLogitsProcessor,
      FlaxTemperatureLogitsWarper,
      FlaxTopKLogitsWarper,
      FlaxTopPLogitsWarper,
  )




@require_flax
class 				lowerCAmelCase__							(     unittest.TestCase					):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE					(		self	:    str		,      lowercase_	:    int		,      lowercase_	:    int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_ :    Dict       		=						jnp.ones((batch_size, length)) / length
          return scores
      def _SCREAMING_SNAKE_CASE					(		self	:    int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_ :    List[str]       		=						None
          SCREAMING_SNAKE_CASE_ :    Tuple       		=						20

          SCREAMING_SNAKE_CASE_ :    Dict       		=						self._get_uniform_logits(batch_size=2		,      length=lowercase_)

          # tweak scores to not be uniform anymore
          SCREAMING_SNAKE_CASE_ :    int       		=						scores.at[1, 5].set((1 / length) + 0.1)  # peak, 1st batch
          SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						scores.at[1, 10].set((1 / length) - 0.4)  # valley, 1st batch

          # compute softmax
          SCREAMING_SNAKE_CASE_ :    str       		=						jax.nn.softmax(lowercase_		,      axis=-1)

          SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						FlaxTemperatureLogitsWarper(temperature=0.5)
          SCREAMING_SNAKE_CASE_ :    Tuple       		=						FlaxTemperatureLogitsWarper(temperature=1.3)

          SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						jax.nn.softmax(temp_dist_warper_sharper(lowercase_		,      scores.copy()		,      cur_len=lowercase_)		,      axis=-1)
          SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						jax.nn.softmax(temp_dist_warper_smoother(lowercase_		,      scores.copy()		,      cur_len=lowercase_)		,      axis=-1)

          # uniform distribution stays uniform
          self.assertTrue(jnp.allclose(probs[0, :]		,      warped_prob_sharp[0, :]		,      atol=1e-3))
          self.assertTrue(jnp.allclose(probs[0, :]		,      warped_prob_smooth[0, :]		,      atol=1e-3))

          # sharp peaks get higher, valleys get lower
          self.assertLess(probs[1, :].max()		,      warped_prob_sharp[1, :].max())
          self.assertGreater(probs[1, :].min()		,      warped_prob_sharp[1, :].min())

          # smooth peaks get lower, valleys get higher
          self.assertGreater(probs[1, :].max()		,      warped_prob_smooth[1, :].max())
          self.assertLess(probs[1, :].min()		,      warped_prob_smooth[1, :].min())
      def _SCREAMING_SNAKE_CASE					(		self	:    int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_ :    int       		=						None
          SCREAMING_SNAKE_CASE_ :    List[str]       		=						10
          SCREAMING_SNAKE_CASE_ :    List[Any]       		=						2

          # create ramp distribution
          SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						np.broadcast_to(np.arange(lowercase_)[None, :]		,      (batch_size, vocab_size)).copy()
          SCREAMING_SNAKE_CASE_ :    Dict       		=						ramp_logits[1:, : vocab_size // 2] + vocab_size

          SCREAMING_SNAKE_CASE_ :    List[str]       		=						FlaxTopKLogitsWarper(3)

          SCREAMING_SNAKE_CASE_ :    Dict       		=						top_k_warp(lowercase_		,      lowercase_		,      cur_len=lowercase_)

          # check that correct tokens are filtered
          self.assertListEqual(jnp.isinf(scores[0]).tolist()		,      7 * [True] + 3 * [False])
          self.assertListEqual(jnp.isinf(scores[1]).tolist()		,      2 * [True] + 3 * [False] + 5 * [True])

          # check special case
          SCREAMING_SNAKE_CASE_ :    Any       		=						5
          SCREAMING_SNAKE_CASE_ :    Dict       		=						FlaxTopKLogitsWarper(top_k=1		,      filter_value=0.0		,      min_tokens_to_keep=3)

          SCREAMING_SNAKE_CASE_ :    Any       		=						np.broadcast_to(np.arange(lowercase_)[None, :]		,      (batch_size, length)).copy()
          SCREAMING_SNAKE_CASE_ :    List[str]       		=						top_k_warp_safety_check(lowercase_		,      lowercase_		,      cur_len=lowercase_)

          # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
          self.assertListEqual((scores == 0.0).sum(axis=-1).tolist()		,      [2, 2])
      def _SCREAMING_SNAKE_CASE					(		self	:    List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_ :    List[Any]       		=						None
          SCREAMING_SNAKE_CASE_ :    Any       		=						10
          SCREAMING_SNAKE_CASE_ :    List[str]       		=						2

          # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
          SCREAMING_SNAKE_CASE_ :    Any       		=						np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]))

          SCREAMING_SNAKE_CASE_ :    Any       		=						FlaxTopPLogitsWarper(0.8)
          SCREAMING_SNAKE_CASE_ :    Tuple       		=						np.exp(top_p_warp(lowercase_		,      lowercase_		,      cur_len=lowercase_))

          # dist should be filtered to keep min num values so that sum is >= top_p
          # exp (-inf) => 0
          SCREAMING_SNAKE_CASE_ :    Any       		=						np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]])
          self.assertTrue(np.allclose(lowercase_		,      lowercase_		,      atol=1e-3))

          # check edge cases with negative and extreme logits
          SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						np.broadcast_to(np.arange(lowercase_)[None, :]		,      (batch_size, vocab_size)).copy() - (
              vocab_size // 2
          )

          # make ramp_logits more extreme
          SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						ramp_logits[1] * 1_00.0

          # make sure at least 2 tokens are kept
          SCREAMING_SNAKE_CASE_ :    Union[str, Any]       		=						FlaxTopPLogitsWarper(0.9		,      min_tokens_to_keep=2		,      filter_value=0.0)
          SCREAMING_SNAKE_CASE_ :    Union[str, Any]       		=						top_p_warp(lowercase_		,      lowercase_		,      cur_len=lowercase_)

          # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
          self.assertListEqual((filtered_dist != 0.0).sum(axis=-1).tolist()		,      [3, 2])
      def _SCREAMING_SNAKE_CASE					(		self	:    Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_ :    Any       		=						20
          SCREAMING_SNAKE_CASE_ :    Tuple       		=						4
          SCREAMING_SNAKE_CASE_ :    List[Any]       		=						0

          SCREAMING_SNAKE_CASE_ :    Tuple       		=						FlaxMinLengthLogitsProcessor(min_length=10		,      eos_token_id=lowercase_)

          # check that min length is applied at length 5
          SCREAMING_SNAKE_CASE_ :    Union[str, Any]       		=						ids_tensor((batch_size, 20)		,      vocab_size=20)
          SCREAMING_SNAKE_CASE_ :    int       		=						5
          SCREAMING_SNAKE_CASE_ :    Dict       		=						self._get_uniform_logits(lowercase_		,      lowercase_)
          SCREAMING_SNAKE_CASE_ :    Any       		=						min_dist_processor(lowercase_		,      lowercase_		,      cur_len=lowercase_)
          self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()		,      4 * [-float('''inf''')])

          # check that min length is not applied anymore at length 15
          SCREAMING_SNAKE_CASE_ :    str       		=						self._get_uniform_logits(lowercase_		,      lowercase_)
          SCREAMING_SNAKE_CASE_ :    List[Any]       		=						15
          SCREAMING_SNAKE_CASE_ :    str       		=						min_dist_processor(lowercase_		,      lowercase_		,      cur_len=lowercase_)
          self.assertFalse(jnp.isinf(lowercase_).any())
      def _SCREAMING_SNAKE_CASE					(		self	:    Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_ :    Dict       		=						20
          SCREAMING_SNAKE_CASE_ :    List[str]       		=						4
          SCREAMING_SNAKE_CASE_ :    Tuple       		=						0

          SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowercase_)

          # check that all scores are -inf except the bos_token_id score
          SCREAMING_SNAKE_CASE_ :    List[Any]       		=						ids_tensor((batch_size, 1)		,      vocab_size=20)
          SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						1
          SCREAMING_SNAKE_CASE_ :    Tuple       		=						self._get_uniform_logits(lowercase_		,      lowercase_)
          SCREAMING_SNAKE_CASE_ :    List[str]       		=						logits_processor(lowercase_		,      lowercase_		,      cur_len=lowercase_)
          self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]).all())
          self.assertListEqual(scores[:, bos_token_id].tolist()		,      4 * [0])  # score for bos_token_id shold be zero

          # check that bos_token_id is not forced if current length is greater than 1
          SCREAMING_SNAKE_CASE_ :    Any       		=						3
          SCREAMING_SNAKE_CASE_ :    Union[str, Any]       		=						self._get_uniform_logits(lowercase_		,      lowercase_)
          SCREAMING_SNAKE_CASE_ :    Any       		=						logits_processor(lowercase_		,      lowercase_		,      cur_len=lowercase_)
          self.assertFalse(jnp.isinf(lowercase_).any())
      def _SCREAMING_SNAKE_CASE					(		self	:    Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_ :    List[Any]       		=						20
          SCREAMING_SNAKE_CASE_ :    Union[str, Any]       		=						4
          SCREAMING_SNAKE_CASE_ :    Dict       		=						0
          SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						5

          SCREAMING_SNAKE_CASE_ :    List[Any]       		=						FlaxForcedEOSTokenLogitsProcessor(max_length=lowercase_		,      eos_token_id=lowercase_)

          # check that all scores are -inf except the eos_token_id when max_length is reached
          SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						ids_tensor((batch_size, 4)		,      vocab_size=20)
          SCREAMING_SNAKE_CASE_ :    int       		=						4
          SCREAMING_SNAKE_CASE_ :    Any       		=						self._get_uniform_logits(lowercase_		,      lowercase_)
          SCREAMING_SNAKE_CASE_ :    Dict       		=						logits_processor(lowercase_		,      lowercase_		,      cur_len=lowercase_)
          self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]).all())
          self.assertListEqual(scores[:, eos_token_id].tolist()		,      4 * [0])  # score for eos_token_id should be zero

          # check that eos_token_id is not forced if max_length is not reached
          SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						3
          SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						self._get_uniform_logits(lowercase_		,      lowercase_)
          SCREAMING_SNAKE_CASE_ :    Any       		=						logits_processor(lowercase_		,      lowercase_		,      cur_len=lowercase_)
          self.assertFalse(jnp.isinf(lowercase_).any())
      def _SCREAMING_SNAKE_CASE					(		self	:    Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						4
          SCREAMING_SNAKE_CASE_ :    int       		=						10
          SCREAMING_SNAKE_CASE_ :    List[Any]       		=						15
          SCREAMING_SNAKE_CASE_ :    Union[str, Any]       		=						2
          SCREAMING_SNAKE_CASE_ :    List[str]       		=						1
          SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						15

          # dummy input_ids and scores
          SCREAMING_SNAKE_CASE_ :    Dict       		=						ids_tensor((batch_size, sequence_length)		,      lowercase_)
          SCREAMING_SNAKE_CASE_ :    Any       		=						input_ids.copy()

          SCREAMING_SNAKE_CASE_ :    List[Any]       		=						self._get_uniform_logits(lowercase_		,      lowercase_)
          SCREAMING_SNAKE_CASE_ :    str       		=						scores.copy()

          # instantiate all dist processors
          SCREAMING_SNAKE_CASE_ :    Tuple       		=						FlaxTemperatureLogitsWarper(temperature=0.5)
          SCREAMING_SNAKE_CASE_ :    Union[str, Any]       		=						FlaxTopKLogitsWarper(3)
          SCREAMING_SNAKE_CASE_ :    List[str]       		=						FlaxTopPLogitsWarper(0.8)

          # instantiate all logits processors
          SCREAMING_SNAKE_CASE_ :    Union[str, Any]       		=						FlaxMinLengthLogitsProcessor(min_length=10		,      eos_token_id=lowercase_)
          SCREAMING_SNAKE_CASE_ :    Union[str, Any]       		=						FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowercase_)
          SCREAMING_SNAKE_CASE_ :    str       		=						FlaxForcedEOSTokenLogitsProcessor(max_length=lowercase_		,      eos_token_id=lowercase_)

          SCREAMING_SNAKE_CASE_ :    Tuple       		=						10

          # no processor list
          SCREAMING_SNAKE_CASE_ :    Dict       		=						temp_dist_warp(lowercase_		,      lowercase_		,      cur_len=lowercase_)
          SCREAMING_SNAKE_CASE_ :    Any       		=						top_k_warp(lowercase_		,      lowercase_		,      cur_len=lowercase_)
          SCREAMING_SNAKE_CASE_ :    str       		=						top_p_warp(lowercase_		,      lowercase_		,      cur_len=lowercase_)
          SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						min_dist_proc(lowercase_		,      lowercase_		,      cur_len=lowercase_)
          SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						bos_dist_proc(lowercase_		,      lowercase_		,      cur_len=lowercase_)
          SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						eos_dist_proc(lowercase_		,      lowercase_		,      cur_len=lowercase_)

          # with processor list
          SCREAMING_SNAKE_CASE_ :    List[Any]       		=						FlaxLogitsProcessorList(
              [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
          SCREAMING_SNAKE_CASE_ :    int       		=						processor(lowercase_		,      lowercase_		,      cur_len=lowercase_)

          # scores should be equal
          self.assertTrue(jnp.allclose(lowercase_		,      lowercase_		,      atol=1e-3))

          # input_ids should never be changed
          self.assertListEqual(input_ids.tolist()		,      input_ids_comp.tolist())



      def _SCREAMING_SNAKE_CASE					(		self	:    Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_ :    Union[str, Any]       		=						4
          SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						10
          SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						15
          SCREAMING_SNAKE_CASE_ :    Dict       		=						2
          SCREAMING_SNAKE_CASE_ :    Tuple       		=						1
          SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						15

          # dummy input_ids and scores
          SCREAMING_SNAKE_CASE_ :    List[Any]       		=						ids_tensor((batch_size, sequence_length)		,      lowercase_)
          SCREAMING_SNAKE_CASE_ :    str       		=						input_ids.copy()

          SCREAMING_SNAKE_CASE_ :    str       		=						self._get_uniform_logits(lowercase_		,      lowercase_)
          SCREAMING_SNAKE_CASE_ :    int       		=						scores.copy()

          # instantiate all dist processors
          SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						FlaxTemperatureLogitsWarper(temperature=0.5)
          SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						FlaxTopKLogitsWarper(3)
          SCREAMING_SNAKE_CASE_ :    str       		=						FlaxTopPLogitsWarper(0.8)

          # instantiate all logits processors
          SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						FlaxMinLengthLogitsProcessor(min_length=10		,      eos_token_id=lowercase_)
          SCREAMING_SNAKE_CASE_ :    Union[str, Any]       		=						FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowercase_)
          SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						FlaxForcedEOSTokenLogitsProcessor(max_length=lowercase_		,      eos_token_id=lowercase_)

          SCREAMING_SNAKE_CASE_ :    Dict       		=						10

          # no processor list
          def run_no_processor_list(lowercase_	:    str		,      lowercase_	:    List[str]		,      lowercase_	:    int):
              SCREAMING_SNAKE_CASE_ :    Union[str, Any]       		=						temp_dist_warp(lowercase_		,      lowercase_		,      cur_len=lowercase_)
              SCREAMING_SNAKE_CASE_ :    Tuple       		=						top_k_warp(lowercase_		,      lowercase_		,      cur_len=lowercase_)
              SCREAMING_SNAKE_CASE_ :    List[Any]       		=						top_p_warp(lowercase_		,      lowercase_		,      cur_len=lowercase_)
              SCREAMING_SNAKE_CASE_ :    Union[str, Any]       		=						min_dist_proc(lowercase_		,      lowercase_		,      cur_len=lowercase_)
              SCREAMING_SNAKE_CASE_ :    List[Any]       		=						bos_dist_proc(lowercase_		,      lowercase_		,      cur_len=lowercase_)
              SCREAMING_SNAKE_CASE_ :    List[str]       		=						eos_dist_proc(lowercase_		,      lowercase_		,      cur_len=lowercase_)
              return scores

          # with processor list
          def run_processor_list(lowercase_	:    int		,      lowercase_	:    Any		,      lowercase_	:    Dict):
              SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						FlaxLogitsProcessorList(
                  [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
              SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						processor(lowercase_		,      lowercase_		,      cur_len=lowercase_)
              return scores

          SCREAMING_SNAKE_CASE_ :    int       		=						jax.jit(lowercase_)
          SCREAMING_SNAKE_CASE_ :    List[Any]       		=						jax.jit(lowercase_)

          SCREAMING_SNAKE_CASE_ :    List[str]       		=						jitted_run_no_processor_list(lowercase_		,      lowercase_		,      lowercase_)
          SCREAMING_SNAKE_CASE_ :    Tuple       		=						jitted_run_processor_list(lowercase_		,      lowercase_		,      lowercase_)

          # scores should be equal
          self.assertTrue(jnp.allclose(lowercase_		,      lowercase_		,      atol=1e-3))

          # input_ids should never be changed
          self.assertListEqual(input_ids.tolist()		,      input_ids_comp.tolist())









'''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


__UpperCamelCase					    =	"bart"
__UpperCamelCase					    =	True







@st.cache(allow_output_mutation=_lowerCamelCase )
def 					_a						(       ) ->				Union[str, Any]:



			"""simple docstring"""


			if LOAD_DENSE_INDEX:
						__snake_case					:      int							      =     AutoTokenizer.from_pretrained("""yjernite/retribert-base-uncased""" )
						__snake_case					:      Tuple							      =     AutoModel.from_pretrained("""yjernite/retribert-base-uncased""" ).to("""cuda:0""" )
						__snake_case					:      List[Any]							      =     qar_model.eval()
			else:
						__snake_case      ,     __snake_case					:      Optional[Any]							      =     (None, None)
			if MODEL_TYPE == "bart":
						__snake_case					:      List[str]							      =     AutoTokenizer.from_pretrained("""yjernite/bart_eli5""" )
						__snake_case					:      Any							      =     AutoModelForSeqaSeqLM.from_pretrained("""yjernite/bart_eli5""" ).to("""cuda:0""" )
						__snake_case					:      int							      =     torch.load("""seq2seq_models/eli5_bart_model_blm_2.pth""" )
						sas_model.load_state_dict(save_dict["""model"""] )
						__snake_case					:      int							      =     sas_model.eval()
			else:
						__snake_case      ,     __snake_case					:      Dict							      =     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						(       ) ->				Tuple:



			"""simple docstring"""


			if LOAD_DENSE_INDEX:
						__snake_case					:      Tuple							      =     faiss.StandardGpuResources()
						__snake_case					:      Optional[Any]							      =     datasets.load_dataset(path="""wiki_snippets"""    ,	name="""wiki40b_en_100_0""" )["""train"""]
						__snake_case					:      str							      =     np.memmap(
						    """wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat"""    ,	dtype="""float32"""    ,	mode="""r"""    ,	shape=(wikiaab_passages.num_rows, 128)    ,	)
						__snake_case					:      Optional[int]							      =     faiss.IndexFlatIP(128 )
						__snake_case					:      Any							      =     faiss.index_cpu_to_gpu(_lowerCamelCase    ,	1    ,	_lowerCamelCase )
						wikiaab_gpu_index_flat.add(_lowerCamelCase )  # TODO fix for larger GPU
			else:
						__snake_case      ,     __snake_case					:      Tuple							      =     (None, None)
			__snake_case					:      List[str]							      =     Elasticsearch([{"""host""": """localhost""", """port""": """9200"""}] )
			return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)







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



			"""simple docstring"""


			__snake_case					:      Tuple							      =     datasets.load_dataset("""eli5"""    ,	name="""LFQA_reddit""" )
			__snake_case					:      Dict							      =     elia["""train_eli5"""]
			__snake_case					:      int							      =     np.memmap(
			    """eli5_questions_reps.dat"""    ,	dtype="""float32"""    ,	mode="""r"""    ,	shape=(elia_train.num_rows, 128) )
			__snake_case					:      Dict							      =     faiss.IndexFlatIP(128 )
			eli5_train_q_index.add(_lowerCamelCase )
			return (elia_train, eli5_train_q_index)


__UpperCamelCase    ,		__UpperCamelCase    ,		__UpperCamelCase					    =	load_indexes()
__UpperCamelCase    ,		__UpperCamelCase    ,		__UpperCamelCase    ,		__UpperCamelCase					    =	load_models()
__UpperCamelCase    ,		__UpperCamelCase					    =	load_train_data()







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase=10 ) ->				int:



			"""simple docstring"""


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







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase="wiki40b"    ,	_lowerCamelCase="dense"    ,	_lowerCamelCase=10 ) ->				Optional[Any]:



			"""simple docstring"""


			if source == "none":
						__snake_case      ,     __snake_case					:      Dict							      =     (""" <P> """.join(["""""" for _ in range(11 )] ).strip(), [])
			else:
						if method == "dense":
									__snake_case      ,     __snake_case					:      Dict							      =     query_qa_dense_index(
									    _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )
						else:
									__snake_case      ,     __snake_case					:      str							      =     query_es_index(
									    _lowerCamelCase    ,	_lowerCamelCase    ,	index_name="""english_wiki40b_snippets_100w"""    ,	n_results=_lowerCamelCase    ,	)
			__snake_case					:      Optional[int]							      =     [
			    (res["""article_title"""], res["""section_title"""].strip(), res["""score"""], res["""passage_text"""]) for res in hit_lst
			]
			__snake_case					:      Optional[Any]							      =     """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    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase=64    ,	_lowerCamelCase=256    ,	_lowerCamelCase=False    ,	_lowerCamelCase=2    ,	_lowerCamelCase=0.95    ,	_lowerCamelCase=0.8 ) ->				List[str]:



			"""simple docstring"""


			with torch.no_grad():
						__snake_case					:      Union[str, Any]							      =     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=1024    ,	device="""cuda:0"""    ,	)[0]
			return (answer, support_list)


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

# Start sidebar
__UpperCamelCase					    =	"<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"
__UpperCamelCase					    =	"\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
__UpperCamelCase					    =	"\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)

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

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

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

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

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


__UpperCamelCase					    =	"\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)


