
"""simple docstring"""


from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
      from PIL import Image

      from transformers import ViTImageProcessor



class   __a  :






       def __init__(  self :							int       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							List[str]=13       ,      UpperCAmelCase_ :							Any=30       ,      UpperCAmelCase_ :							Dict=2       ,      UpperCAmelCase_ :							Optional[Any]=3       ,      UpperCAmelCase_ :							str=True       ,      UpperCAmelCase_ :							Dict=True       ,      UpperCAmelCase_ :							str=32       ,      UpperCAmelCase_ :							Tuple=2       ,      UpperCAmelCase_ :							Dict=4       ,      UpperCAmelCase_ :							Dict=37       ,      UpperCAmelCase_ :							Union[str, Any]="gelu"       ,      UpperCAmelCase_ :							Any=0.1       ,      UpperCAmelCase_ :							Union[str, Any]=0.1       ,      UpperCAmelCase_ :							List[str]=10       ,      UpperCAmelCase_ :							Dict=0.02       ,      UpperCAmelCase_ :							str=3       ,      UpperCAmelCase_ :							List[str]=0.6       ,      UpperCAmelCase_ :							Optional[Any]=None       ,      )->  List[Any]:







              """simple docstring"""
              UpperCamelCase					 =		parent
              UpperCamelCase					 =		batch_size
              UpperCamelCase					 =		image_size
              UpperCamelCase					 =		patch_size
              UpperCamelCase					 =		num_channels
              UpperCamelCase					 =		is_training
              UpperCamelCase					 =		use_labels
              UpperCamelCase					 =		hidden_size
              UpperCamelCase					 =		num_hidden_layers
              UpperCamelCase					 =		num_attention_heads
              UpperCamelCase					 =		intermediate_size
              UpperCamelCase					 =		hidden_act
              UpperCamelCase					 =		hidden_dropout_prob
              UpperCamelCase					 =		attention_probs_dropout_prob
              UpperCamelCase					 =		type_sequence_label_size
              UpperCamelCase					 =		initializer_range
              UpperCamelCase					 =		mask_ratio
              UpperCamelCase					 =		scope

              # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
              # (we add 1 for the [CLS] token)
              UpperCamelCase					 =		(image_size // patch_size) ** 2
              UpperCamelCase					 =		int(math.ceil((1 - mask_ratio) * (num_patches + 1)			)			)






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







              """simple docstring"""
              UpperCamelCase					 =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

              UpperCamelCase					 =		None
              if self.use_labels:
                     UpperCamelCase					 =		ids_tensor([self.batch_size]       ,      self.type_sequence_label_size			)

              UpperCamelCase					 =		self.get_config()

              return config, pixel_values, labels






       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  List[str]:







              """simple docstring"""
              return ViTMAEConfig(
                  image_size=self.image_size       ,      patch_size=self.patch_size       ,      num_channels=self.num_channels       ,      hidden_size=self.hidden_size       ,      num_hidden_layers=self.num_hidden_layers       ,      num_attention_heads=self.num_attention_heads       ,      intermediate_size=self.intermediate_size       ,      decoder_hidden_size=self.hidden_size       ,      decoder_num_hidden_layers=self.num_hidden_layers       ,      decoder_num_attention_heads=self.num_attention_heads       ,      decoder_intermediate_size=self.intermediate_size       ,      hidden_act=self.hidden_act       ,      hidden_dropout_prob=self.hidden_dropout_prob       ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,      is_decoder=UpperCAmelCase_       ,      initializer_range=self.initializer_range       ,      mask_ratio=self.mask_ratio       ,      )






       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							Union[str, Any]       ,      UpperCAmelCase_ :							Dict			)->  List[Any]:







              """simple docstring"""
              UpperCamelCase					 =		TFViTMAEModel(config=UpperCAmelCase_			)
              UpperCamelCase					 =		model(UpperCAmelCase_       ,      training=UpperCAmelCase_			)
              self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.seq_length, self.hidden_size)			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]       ,      UpperCAmelCase_ :							List[Any]       ,      UpperCAmelCase_ :							Any       ,      UpperCAmelCase_ :							int			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		TFViTMAEForPreTraining(UpperCAmelCase_			)
              UpperCamelCase					 =		model(UpperCAmelCase_       ,      training=UpperCAmelCase_			)
              # expected sequence length = num_patches
              UpperCamelCase					 =		(self.image_size // self.patch_size) ** 2
              UpperCamelCase					 =		self.patch_size**2 * self.num_channels
              self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, num_patches, expected_num_channels)			)

              # test greyscale images
              UpperCamelCase					 =		1
              UpperCamelCase					 =		TFViTMAEForPreTraining(UpperCAmelCase_			)

              UpperCamelCase					 =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
              UpperCamelCase					 =		model(UpperCAmelCase_       ,      training=UpperCAmelCase_			)
              UpperCamelCase					 =		self.patch_size**2
              self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, num_patches, expected_num_channels)			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		self.prepare_config_and_inputs()
              ((UpperCamelCase) ,       (UpperCamelCase) ,       (UpperCamelCase))					 =		config_and_inputs
              UpperCamelCase					 =		{"pixel_values": pixel_values}
              return config, inputs_dict





@require_tf
class   __a  (      _lowerCAmelCase    ,     _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					Any   =			(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
       UpperCamelCase_		:					List[Any]   =			{'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

       UpperCamelCase_		:					Optional[int]   =			False
       UpperCamelCase_		:					Dict   =			False
       UpperCamelCase_		:					int   =			False
       UpperCamelCase_		:					Optional[Any]   =			False






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







              """simple docstring"""
              UpperCamelCase					 =		TFViTMAEModelTester(self			)
              UpperCamelCase					 =		ConfigTester(self       ,      config_class=UpperCAmelCase_       ,      has_text_modality=UpperCAmelCase_       ,      hidden_size=37			)






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







              """simple docstring"""
              self.config_tester.run_common_tests()






       @unittest.skip(reason="ViTMAE does not use inputs_embeds"			)
       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Optional[int]:







              """simple docstring"""
              pass






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







              """simple docstring"""
              UpperCamelCase ,       UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     UpperCamelCase					 =		model_class(UpperCAmelCase_			)
                     self.assertIsInstance(model.get_input_embeddings()       ,      (tf.keras.layers.Layer)			)
                     UpperCamelCase					 =		model.get_output_embeddings()
                     self.assertTrue(x is None or isinstance(UpperCAmelCase_       ,      tf.keras.layers.Layer			)			)






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







              """simple docstring"""
              UpperCamelCase ,       UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     UpperCamelCase					 =		model_class(UpperCAmelCase_			)
                     UpperCamelCase					 =		inspect.signature(model.call			)
                     # signature.parameters is an OrderedDict => so arg_names order is deterministic
                     UpperCamelCase					 =		[*signature.parameters.keys()]

                     UpperCamelCase					 =		["pixel_values"]
                     self.assertListEqual(arg_names[:1]       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  List[str]:







              """simple docstring"""
              UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Tuple:







              """simple docstring"""
              UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase_			)






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







              """simple docstring"""
              # make the mask reproducible
              np.random.seed(2			)

              UpperCamelCase ,       UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs_for_common()

              UpperCamelCase					 =		int((config.image_size // config.patch_size) ** 2			)
              UpperCamelCase					 =		np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)

              for model_class in self.all_model_classes:
                     UpperCamelCase					 =		model_class(UpperCAmelCase_			)
                     UpperCamelCase					 =		self._prepare_for_class(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		model(UpperCAmelCase_       ,      noise=UpperCAmelCase_			)

                     UpperCamelCase					 =		copy.deepcopy(self._prepare_for_class(UpperCAmelCase_       ,      UpperCAmelCase_			)			)
                     UpperCamelCase					 =		model(**UpperCAmelCase_       ,      noise=UpperCAmelCase_			)
                     UpperCamelCase					 =		outputs_dict[0].numpy()
                     UpperCamelCase					 =		outputs_keywords[0].numpy()

                     self.assertLess(np.sum(np.abs(output_dict - output_keywords			)			)       ,      1e-6			)






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







              """simple docstring"""
              # make the mask reproducible
              np.random.seed(2			)

              UpperCamelCase ,       UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs_for_common()

              UpperCamelCase					 =		int((config.image_size // config.patch_size) ** 2			)
              UpperCamelCase					 =		np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)

              def prepare_numpy_arrays(UpperCAmelCase_ :							Union[str, Any]			):
                     UpperCamelCase					 =		{}
                     for k, v in inputs_dict.items():
                            if tf.is_tensor(UpperCAmelCase_			):
                                   UpperCamelCase					 =		v.numpy()
                            else:
                                   UpperCamelCase					 =		np.array(UpperCAmelCase_			)

                     return inputs_np_dict

              for model_class in self.all_model_classes:
                     UpperCamelCase					 =		model_class(UpperCAmelCase_			)

                     UpperCamelCase					 =		self._prepare_for_class(UpperCAmelCase_       ,      UpperCAmelCase_			)
                     UpperCamelCase					 =		prepare_numpy_arrays(UpperCAmelCase_			)

                     UpperCamelCase					 =		model(UpperCAmelCase_       ,      noise=UpperCAmelCase_			)
                     UpperCamelCase					 =		model(**UpperCAmelCase_       ,      noise=UpperCAmelCase_			)
                     self.assert_outputs_same(UpperCAmelCase_       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple       ,      UpperCAmelCase_ :							Any       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str			)->  List[str]:







              """simple docstring"""
              # make masks reproducible
              np.random.seed(2			)

              UpperCamelCase					 =		int((tf_model.config.image_size // tf_model.config.patch_size) ** 2			)
              UpperCamelCase					 =		np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)
              UpperCamelCase					 =		tf.constant(UpperCAmelCase_			)

              # Add `noise` argument.
              # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
              UpperCamelCase					 =		tf_noise

              super().check_pt_tf_models(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Tuple:







              """simple docstring"""
              # make mask reproducible
              np.random.seed(2			)

              UpperCamelCase ,       UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs_for_common()

              UpperCamelCase					 =		{
                  module_member
                  for model_class in self.all_model_classes
                  for module in (import_module(model_class.__module__			),)
                  for module_member_name in dir(UpperCAmelCase_			)
                  if module_member_name.endswith("MainLayer"			)
                  # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
                  and module_member_name[: -len("MainLayer"			)] == model_class.__name__[: -len("Model"			)]
                  for module_member in (getattr(UpperCAmelCase_       ,      UpperCAmelCase_			),)
                  if isinstance(UpperCAmelCase_       ,      UpperCAmelCase_			)
                  and tf.keras.layers.Layer in module_member.__bases__
                  and getattr(UpperCAmelCase_       ,      "_keras_serializable"       ,      UpperCAmelCase_			)
              }

              UpperCamelCase					 =		int((config.image_size // config.patch_size) ** 2			)
              UpperCamelCase					 =		np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)
              UpperCamelCase					 =		tf.convert_to_tensor(UpperCAmelCase_			)
              inputs_dict.update({"noise": noise}			)

              for main_layer_class in tf_main_layer_classes:
                     UpperCamelCase					 =		main_layer_class(UpperCAmelCase_			)

                     UpperCamelCase					 =		{
                         name: tf.keras.Input(tensor.shape[1:]       ,      dtype=tensor.dtype			) for name, tensor in inputs_dict.items()
                     }

                     UpperCamelCase					 =		tf.keras.Model(UpperCAmelCase_       ,      outputs=main_layer(UpperCAmelCase_			)			)
                     UpperCamelCase					 =		model(UpperCAmelCase_			)

                     with tempfile.TemporaryDirectory() as tmpdirname:
                            UpperCamelCase					 =		os.path.join(UpperCAmelCase_       ,      "keras_model.h5"			)
                            model.save(UpperCAmelCase_			)
                            UpperCamelCase					 =		tf.keras.models.load_model(
                                UpperCAmelCase_       ,      custom_objects={main_layer_class.__name__: main_layer_class}			)
                            assert isinstance(UpperCAmelCase_       ,      tf.keras.Model			)
                            UpperCamelCase					 =		model(UpperCAmelCase_			)
                            self.assert_outputs_same(UpperCAmelCase_       ,      UpperCAmelCase_			)






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Dict:







              """simple docstring"""
              # make mask reproducible
              np.random.seed(2			)

              UpperCamelCase ,       UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs_for_common()

              UpperCamelCase					 =		int((config.image_size // config.patch_size) ** 2			)
              UpperCamelCase					 =		np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)

              for model_class in self.all_model_classes:
                     UpperCamelCase					 =		model_class(UpperCAmelCase_			)
                     UpperCamelCase					 =		self._prepare_for_class(UpperCAmelCase_       ,      UpperCAmelCase_			)
                     UpperCamelCase					 =		model(UpperCAmelCase_       ,      noise=UpperCAmelCase_			)

                     if model_class.__name__ == "TFViTMAEModel":
                            UpperCamelCase					 =		outputs.last_hidden_state.numpy()
                            UpperCamelCase					 =		0
                     else:
                            UpperCamelCase					 =		outputs.logits.numpy()
                            UpperCamelCase					 =		0

                     with tempfile.TemporaryDirectory() as tmpdirname:
                            model.save_pretrained(UpperCAmelCase_       ,      saved_model=UpperCAmelCase_			)
                            UpperCamelCase					 =		model_class.from_pretrained(UpperCAmelCase_			)
                            UpperCamelCase					 =		model(UpperCAmelCase_       ,      noise=UpperCAmelCase_			)

                            if model_class.__name__ == "TFViTMAEModel":
                                   UpperCamelCase					 =		after_outputs["last_hidden_state"].numpy()
                                   UpperCamelCase					 =		0
                            else:
                                   UpperCamelCase					 =		after_outputs["logits"].numpy()
                                   UpperCamelCase					 =		0

                            UpperCamelCase					 =		np.amax(np.abs(out_a - out_a			)			)
                            self.assertLessEqual(UpperCAmelCase_       ,      1e-5			)






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







              """simple docstring"""
              # make mask reproducible
              np.random.seed(2			)

              UpperCamelCase ,       UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs_for_common()

              UpperCamelCase					 =		int((config.image_size // config.patch_size) ** 2			)
              UpperCamelCase					 =		np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)

              for model_class in self.all_model_classes:
                     UpperCamelCase					 =		model_class(UpperCAmelCase_			)
                     UpperCamelCase					 =		self._prepare_for_class(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		model(UpperCAmelCase_       ,      noise=UpperCAmelCase_			)
                     UpperCamelCase					 =		model.get_config()
                     # make sure that returned config is jsonifiable, which is required by keras
                     json.dumps(UpperCAmelCase_			)
                     UpperCamelCase					 =		model_class.from_config(model.get_config()			)
                     # make sure it also accepts a normal config
                     UpperCamelCase					 =		model_class.from_config(model.config			)
                     UpperCamelCase					 =		new_model(UpperCAmelCase_			)  # Build model
                     new_model.set_weights(model.get_weights()			)
                     UpperCamelCase					 =		new_model(UpperCAmelCase_       ,      noise=UpperCAmelCase_			)

                     self.assert_outputs_same(UpperCAmelCase_       ,      UpperCAmelCase_			)






       @unittest.skip(
           reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."			)
       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  str:







              """simple docstring"""
              pass






       @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"			)
       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  int:







              """simple docstring"""
              pass






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  Optional[Any]:







              """simple docstring"""
              UpperCamelCase					 =		TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"			)
              self.assertIsNotNone(UpperCAmelCase_			)





def 	lowerCamelCase__			(							)->							Tuple:







       """simple docstring"""
       UpperCamelCase					 =		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
       return image



@require_tf
@require_vision
class   __a  (      unittest.TestCase	):






       @cached_property
       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  Dict:







              """simple docstring"""
              return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"			) if is_vision_available() else None






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  Tuple:







              """simple docstring"""
              # make random mask reproducible across the PT and TF model
              np.random.seed(2			)

              UpperCamelCase					 =		TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"			)

              UpperCamelCase					 =		self.default_image_processor
              UpperCamelCase					 =		prepare_img()
              UpperCamelCase					 =		image_processor(images=UpperCAmelCase_       ,      return_tensors="tf"			)

              # prepare a noise vector that will be also used for testing the TF model
              # (this way we can ensure that the PT and TF models operate on the same inputs)
              UpperCamelCase					 =		ViTMAEConfig()
              UpperCamelCase					 =		int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2			)
              UpperCamelCase					 =		np.random.uniform(size=(1, num_patches)			)

              # forward pass
              UpperCamelCase					 =		model(**UpperCAmelCase_       ,      noise=UpperCAmelCase_			)

              # verify the logits
              UpperCamelCase					 =		tf.convert_to_tensor([1, 196, 768]			)
              self.assertEqual(outputs.logits.shape       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		tf.convert_to_tensor(
                  [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]			)

              tf.debugging.assert_near(outputs.logits[0, :3, :3]       ,      UpperCAmelCase_       ,      atol=1e-4			)







"""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


SCREAMING_SNAKE_CASE      =     """bart"""
SCREAMING_SNAKE_CASE      =     True



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







       """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=UpperCAmelCase_				)
def 	lowerCamelCase__			(							)->							Optional[int]:







       """simple docstring"""
       if LOAD_DENSE_INDEX:
              UpperCamelCase					 =		faiss.StandardGpuResources()
              UpperCamelCase					 =		datasets.load_dataset(path="wiki_snippets"		,      name="wiki40b_en_100_0"				)["train"]
              UpperCamelCase					 =		np.memmap(
                  "wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat"		,      dtype="float32"		,      mode="r"		,      shape=(wikiaab_passages.num_rows, 1_28)		,      )
              UpperCamelCase					 =		faiss.IndexFlatIP(1_28				)
              UpperCamelCase					 =		faiss.index_cpu_to_gpu(UpperCAmelCase_		,      1		,      UpperCAmelCase_				)
              wikiaab_gpu_index_flat.add(UpperCAmelCase_				)  # 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=UpperCAmelCase_				)
def 	lowerCamelCase__			(							)->							Optional[Any]:







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


SCREAMING_SNAKE_CASE     ,      SCREAMING_SNAKE_CASE     ,      SCREAMING_SNAKE_CASE      =     load_indexes()
SCREAMING_SNAKE_CASE     ,      SCREAMING_SNAKE_CASE     ,      SCREAMING_SNAKE_CASE     ,      SCREAMING_SNAKE_CASE      =     load_models()
SCREAMING_SNAKE_CASE     ,      SCREAMING_SNAKE_CASE      =     load_train_data()



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_=10				)->							str:







       """simple docstring"""
       UpperCamelCase					 =		embed_questions_for_retrieval([question]		,      UpperCAmelCase_		,      UpperCAmelCase_				)
       UpperCamelCase ,       UpperCamelCase					 =		eli5_train_q_index.search(UpperCAmelCase_		,      UpperCAmelCase_				)
       UpperCamelCase					 =		[elia_train[int(UpperCAmelCase_				)] for i in I[0]]
       return nn_examples



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







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



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







       """simple docstring"""
       with torch.no_grad():
              UpperCamelCase					 =		qa_sas_generate(
                  UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      num_answers=1		,      num_beams=UpperCAmelCase_		,      min_len=UpperCAmelCase_		,      max_len=UpperCAmelCase_		,      do_sample=UpperCAmelCase_		,      temp=UpperCAmelCase_		,      top_p=UpperCAmelCase_		,      top_k=UpperCAmelCase_		,      max_input_length=10_24		,      device="cuda:0"		,      )[0]
       return (answer, support_list)


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

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

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

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

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

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

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


SCREAMING_SNAKE_CASE      =     """
---

**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)





