






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__      :	Tuple       	=      '''bart'''
snake_case__      :	List[Any]       	=      True






@st.cache(allow_output_mutation=lowerCAmelCase__)
def     _snake_case    ():
   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=lowerCAmelCase__)
def     _snake_case    ():
   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, 128)    ,      )
      UpperCamelCase_									=     faiss.IndexFlatIP(128)
      UpperCamelCase_									=     faiss.index_cpu_to_gpu(lowerCAmelCase__    ,      1    ,      lowerCAmelCase__)
      wikiaab_gpu_index_flat.add(lowerCAmelCase__)  # 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=lowerCAmelCase__)
def     _snake_case    ():
   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, 128))
   UpperCamelCase_									=     faiss.IndexFlatIP(128)
   eli5_train_q_index.add(lowerCAmelCase__)
   return (elia_train, eli5_train_q_index)


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






def     _snake_case    (__lowercase    ,      __lowercase=10):
   UpperCamelCase_									=     embed_questions_for_retrieval([question]    ,      lowerCAmelCase__    ,      lowerCAmelCase__)
   UpperCamelCase_		,      UpperCamelCase_									=     eli5_train_q_index.search(lowerCAmelCase__    ,      lowerCAmelCase__)
   UpperCamelCase_									=     [elia_train[int(lowerCAmelCase__)] for i in I[0]]
   return nn_examples






def     _snake_case    (__lowercase    ,      __lowercase="wiki40b"    ,      __lowercase="dense"    ,      __lowercase=10):
   if source == "none":
      UpperCamelCase_		,      UpperCamelCase_									=     (' <P> '.join(['' for _ in range(11)]).strip(), [])
   else:
      if method == "dense":
         UpperCamelCase_		,      UpperCamelCase_									=     query_qa_dense_index(
             lowerCAmelCase__    ,      lowerCAmelCase__    ,      lowerCAmelCase__    ,      lowerCAmelCase__    ,      lowerCAmelCase__    ,      lowerCAmelCase__)
      else:
         UpperCamelCase_		,      UpperCamelCase_									=     query_es_index(
             lowerCAmelCase__    ,      lowerCAmelCase__    ,      index_name='english_wiki40b_snippets_100w'    ,      n_results=lowerCAmelCase__    ,      )
   UpperCamelCase_									=     [
       (res['article_title'], res['section_title'].strip(), res['score'], res['passage_text']) for res in hit_lst
   ]
   UpperCamelCase_									=     'question: {} context: {}'.format(lowerCAmelCase__    ,      lowerCAmelCase__)
   return question_doc, support_list






@st.cache(
    hash_funcs={
        torch.Tensor: (lambda __lowercase: None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda __lowercase: None),
    })
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase=64    ,      __lowercase=256    ,      __lowercase=False    ,      __lowercase=2    ,      __lowercase=0.95    ,      __lowercase=0.8):
   with torch.no_grad():
      UpperCamelCase_									=     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
snake_case__      :	Union[str, Any]       	=      '''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'''
snake_case__      :	Tuple       	=      '''
<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
snake_case__      :	str       	=      '''
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)

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

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

snake_case__      :	str       	=      '''beam'''
snake_case__      :	List[Any]       	=      2
snake_case__      :	Union[str, Any]       	=      6_4
snake_case__      :	List[str]       	=      2_5_6
snake_case__      :	Optional[int]       	=      None
snake_case__      :	Tuple       	=      None
snake_case__      :	str       	=      st.sidebar.checkbox("""Generation options""")
if generate_options:
 snake_case__      :	Dict       	=      '''
    ### 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)
 snake_case__      :	Optional[Any]       	=      st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""])
 snake_case__      :	Optional[Any]       	=      st.sidebar.slider(
     """Minimum generation length""", min_value=8, max_value=2_5_6, value=6_4, step=8, format=None, key=None
 )
 snake_case__      :	Any       	=      st.sidebar.slider(
     """Maximum generation length""", min_value=6_4, max_value=5_1_2, value=2_5_6, step=1_6, format=None, key=None
 )
 if sampled == "beam":
  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__      :	Optional[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__      :	Optional[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__      :	Any       	=      None

# start main text
snake_case__      :	Dict       	=      [
    '''<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__      :	str       	=      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__      :	Optional[Any]       	=      st.text_input("""Enter your question here:""", """""")
else:
 snake_case__      :	Dict       	=      question_s

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

**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)










import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ViTMSNForImageClassification, ViTMSNModel
 from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor


class 						_a       :






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase=13		,		_UpperCAmelCase=30		,		_UpperCAmelCase=2		,		_UpperCAmelCase=3		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=32		,		_UpperCAmelCase=5		,		_UpperCAmelCase=4		,		_UpperCAmelCase=37		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=10		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=None		,		)			-> Optional[Any]:
     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_									=     scope

     # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
     UpperCamelCase_									=     (image_size // patch_size) ** 2
     UpperCamelCase_									=     num_patches + 1




  def    _UpperCAmelCase     (     self )			-> Tuple:
     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    _UpperCAmelCase     (     self )			-> int:
     return ViTMSNConfig(
         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		,		hidden_act=self.hidden_act		,		hidden_dropout_prob=self.hidden_dropout_prob		,		attention_probs_dropout_prob=self.attention_probs_dropout_prob		,		initializer_range=self.initializer_range		,		)




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> Any:
     UpperCamelCase_									=     ViTMSNModel(config=_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()
     UpperCamelCase_									=     model(_UpperCAmelCase )
     self.parent.assertEqual(result.last_hidden_state.shape		,		(self.batch_size, self.seq_length, self.hidden_size) )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> str:
     UpperCamelCase_									=     self.type_sequence_label_size
     UpperCamelCase_									=     ViTMSNForImageClassification(_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()
     UpperCamelCase_									=     model(_UpperCAmelCase		,		labels=_UpperCAmelCase )
     print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}' )
     print('Labels: {labels}' )
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.type_sequence_label_size) )

     # test greyscale images
     UpperCamelCase_									=     1
     UpperCamelCase_									=     ViTMSNForImageClassification(_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()

     UpperCamelCase_									=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
     UpperCamelCase_									=     model(_UpperCAmelCase )
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.type_sequence_label_size) )




  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     self.prepare_config_and_inputs()
     UpperCamelCase_		,      UpperCamelCase_		,      UpperCamelCase_									=     config_and_inputs
     UpperCamelCase_									=     {'pixel_values': pixel_values}
     return config, inputs_dict




@require_torch
class 						_a       (  UpperCAmelCase__						,       UpperCAmelCase__						,       unittest.TestCase    ):






  """simple docstring"""





  A_						=   (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
  A_						=   (
      {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
      if is_torch_available()
      else {}
  )

  A_						=   False
  A_						=   False
  A_						=   False
  A_						=   False




  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     ViTMSNModelTester(self )
     UpperCamelCase_									=     ConfigTester(self		,		config_class=_UpperCAmelCase		,		has_text_modality=_UpperCAmelCase		,		hidden_size=37 )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     self.config_tester.run_common_tests()




  @unittest.skip(reason='ViTMSN does not use inputs_embeds' )
  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     pass




  def    _UpperCAmelCase     (     self )			-> List[str]:
     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()		,		(nn.Module) )
        UpperCamelCase_									=     model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(_UpperCAmelCase		,		nn.Linear ) )




  def    _UpperCAmelCase     (     self )			-> List[str]:
     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.forward )
        # 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    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase )




  @slow
  def    _UpperCAmelCase     (     self )			-> Dict:
     for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase_									=     ViTMSNModel.from_pretrained(_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )






def     _snake_case    ():
   UpperCamelCase_									=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
   return image


@require_torch
@require_vision
class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  @cached_property
  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     return ViTImageProcessor.from_pretrained('facebook/vit-msn-small' ) if is_vision_available() else None




  @slow
  def    _UpperCAmelCase     (     self )			-> List[str]:
     torch.manual_seed(2 )
     UpperCamelCase_									=     ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small' ).to(_UpperCAmelCase )

     UpperCamelCase_									=     self.default_image_processor
     UpperCamelCase_									=     prepare_img()
     UpperCamelCase_									=     image_processor(images=_UpperCAmelCase		,		return_tensors='pt' ).to(_UpperCAmelCase )

     # forward pass
     with torch.no_grad():
        UpperCamelCase_									=     model(**_UpperCAmelCase )

     # verify the logits
     UpperCamelCase_									=     torch.Size((1, 1000) )
     self.assertEqual(outputs.logits.shape		,		_UpperCAmelCase )

     UpperCamelCase_									=     torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5] ).to(_UpperCAmelCase )

     self.assertTrue(torch.allclose(outputs.logits[0, :3]		,		_UpperCAmelCase		,		atol=1e-4 ) )





