

'''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  :		Dict  =		'''bart'''
_UpperCAmelCase  :		Tuple  =		True




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




  '''simple docstring'''



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




  '''simple docstring'''



  if LOAD_DENSE_INDEX:
    lowercase									=faiss.StandardGpuResources()
    lowercase									=datasets.load_dataset(path='''wiki_snippets'''     ,					name='''wiki40b_en_100_0'''	)['''train''']
    lowercase									=np.memmap(
        '''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat'''     ,					dtype='''float32'''     ,					mode='''r'''     ,					shape=(wikiaab_passages.num_rows, 1_2_8)     ,					)
    lowercase									=faiss.IndexFlatIP(1_2_8	)
    lowercase									=faiss.index_cpu_to_gpu(lowercase_     ,					1     ,					lowercase_	)
    wikiaab_gpu_index_flat.add(lowercase_	)  # TODO fix for larger GPU
  else:
    lowercase							, lowercase									=(None, None)
  lowercase									=Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}]	)
  return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)




@st.cache(allow_output_mutation=lowercase_	)
def 	UpperCamelCase   (       )		->	List[str]:




  '''simple docstring'''



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


_UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase  :		Any  =		load_indexes()
_UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase  :		str  =		load_models()
_UpperCAmelCase  , _UpperCAmelCase  :		Optional[int]  =		load_train_data()




def 	UpperCamelCase   (       lowercase_						:    int     ,					lowercase_						:    str=1_0	)		->	Dict:




  '''simple docstring'''



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




def 	UpperCamelCase   (       lowercase_						:    Tuple     ,					lowercase_						:    Tuple="wiki40b"     ,					lowercase_						:    Any="dense"     ,					lowercase_						:    int=1_0	)		->	int:




  '''simple docstring'''



  if source == "none":
    lowercase							, lowercase									=(''' <P> '''.join(['''''' for _ in range(1_1	)]	).strip(), [])
  else:
    if method == "dense":
      lowercase							, lowercase									=query_qa_dense_index(
          lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_	)
    else:
      lowercase							, lowercase									=query_es_index(
          lowercase_     ,					lowercase_     ,					index_name='''english_wiki40b_snippets_100w'''     ,					n_results=lowercase_     ,					)
  lowercase									=[
      (res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
  ]
  lowercase									='''question: {} context: {}'''.format(lowercase_     ,					lowercase_	)
  return question_doc, support_list




@st.cache(
    hash_funcs={
        torch.Tensor: (lambda lowercase_	: None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda lowercase_	: None),
    }	)
def 	UpperCamelCase   (       lowercase_						:    Dict     ,					lowercase_						:    str     ,					lowercase_						:    Union[str, Any]     ,					lowercase_						:    int=6_4     ,					lowercase_						:    str=2_5_6     ,					lowercase_						:    List[Any]=False     ,					lowercase_						:    str=2     ,					lowercase_						:    List[Any]=0.9_5     ,					lowercase_						:    Union[str, Any]=0.8	)		->	Optional[Any]:




  '''simple docstring'''



  with torch.no_grad():
    lowercase									=qa_sas_generate(
        lowercase_     ,					lowercase_     ,					lowercase_     ,					num_answers=1     ,					num_beams=lowercase_     ,					min_len=lowercase_     ,					max_len=lowercase_     ,					do_sample=lowercase_     ,					temp=lowercase_     ,					top_p=lowercase_     ,					top_k=lowercase_     ,					max_input_length=1_0_2_4     ,					device='''cuda:0'''     ,					)[0]
  return (answer, support_list)


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

# Start sidebar
_UpperCAmelCase  :		List[str]  =		'''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'''
_UpperCAmelCase  :		List[str]  =		'''
<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
_UpperCAmelCase  :		List[Any]  =		'''
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
'''
st.sidebar.markdown(description, unsafe_allow_html=True)

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

_UpperCAmelCase  :		str  =		st.sidebar.checkbox('''Retrieval options''')
if retrieval_options:
     _UpperCAmelCase  :		Union[str, Any]  =		'''
    ### Information retriever options

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

_UpperCAmelCase  :		int  =		'''beam'''
_UpperCAmelCase  :		Optional[Any]  =		2
_UpperCAmelCase  :		Optional[Any]  =		64
_UpperCAmelCase  :		Dict  =		2_56
_UpperCAmelCase  :		Optional[int]  =		None
_UpperCAmelCase  :		Any  =		None
_UpperCAmelCase  :		Dict  =		st.sidebar.checkbox('''Generation options''')
if generate_options:
     _UpperCAmelCase  :		Union[str, Any]  =		'''
    ### 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)
     _UpperCAmelCase  :		Tuple  =		st.sidebar.selectbox('''Would you like to use beam search or sample an answer?''', ['''beam''', '''sampled'''])
     _UpperCAmelCase  :		List[Any]  =		st.sidebar.slider(
         '''Minimum generation length''', min_value=8, max_value=2_56, value=64, step=8, format=None, key=None
     )
     _UpperCAmelCase  :		Tuple  =		st.sidebar.slider(
         '''Maximum generation length''', min_value=64, max_value=5_12, value=2_56, step=16, format=None, key=None
     )
     if sampled == "beam":
          _UpperCAmelCase  :		Optional[Any]  =		st.sidebar.slider('''Beam size''', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
     else:
          _UpperCAmelCase  :		Any  =		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  :		Union[str, Any]  =		st.sidebar.slider(
              '''Temperature''', min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
          )
          _UpperCAmelCase  :		Any  =		None

# start main text
_UpperCAmelCase  :		int  =		[
    '''<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  :		Union[str, Any]  =		st.selectbox(
    '''What would you like to ask? ---- select <MY QUESTION> to enter a new query''',
    questions_list,
    index=1,
)
if question_s == "<MY QUESTION>":
     _UpperCAmelCase  :		Optional[int]  =		st.text_input('''Enter your question here:''', '''''')
else:
     _UpperCAmelCase  :		List[Any]  =		question_s

if st.button('''Show me!'''):
     if action in [0, 1, 3]:
          if index_type == "mixed":
               _UpperCAmelCase  , _UpperCAmelCase  :		Any  =		make_support(question, source=wiki_source, method='''dense''', n_results=10)
               _UpperCAmelCase  , _UpperCAmelCase  :		List[Any]  =		make_support(question, source=wiki_source, method='''sparse''', n_results=10)
               _UpperCAmelCase  :		Optional[Any]  =		[]
               for res_d, res_s in zip(support_list_dense, support_list_sparse):
                    if tuple(res_d) not in support_list:
                         support_list += [tuple(res_d)]
                    if tuple(res_s) not in support_list:
                         support_list += [tuple(res_s)]
               _UpperCAmelCase  :		Tuple  =		support_list[:10]
               _UpperCAmelCase  :		int  =		'''<P> ''' + ''' <P> '''.join([res[-1] for res in support_list])
          else:
               _UpperCAmelCase  , _UpperCAmelCase  :		Dict  =		make_support(question, source=wiki_source, method=index_type, n_results=10)
     if action in [0, 3]:
          _UpperCAmelCase  , _UpperCAmelCase  :		Tuple  =		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  :		List[Any]  =		'''https://en.wikipedia.org/wiki/{}'''.format(res[0].replace(''' ''', '''_'''))
               _UpperCAmelCase  :		Optional[int]  =		res[1].strip()
               if sec_titles == "":
                    _UpperCAmelCase  :		Tuple  =		'''[{}]({})'''.format(res[0], wiki_url)
               else:
                    _UpperCAmelCase  :		Dict  =		sec_titles.split(''' & ''')
                    _UpperCAmelCase  :		str  =		''' & '''.join(
                        ['''[{}]({}#{})'''.format(sec.strip(), wiki_url, sec.strip().replace(''' ''', '''_''')) for sec in sec_list]
                    )
               st.markdown(
                   '''{0:02d} - **Article**: {1:<18} <br>  _Section_: {2}'''.format(i + 1, res[0], sections),
                   unsafe_allow_html=True,
               )
               if show_passages:
                    st.write(
                        '''> <span style="font-family:arial; font-size:10pt;">''' + res[-1] + '''</span>''', unsafe_allow_html=True
                    )
     if action in [2, 3]:
          _UpperCAmelCase  :		Union[str, Any]  =		find_nearest_training(question)
          _UpperCAmelCase  :		str  =		nn_train_list[0]
          st.markdown(
              '''--- \n ### The most similar question in the ELI5 training set was: \n\n {}'''.format(train_exple['''title'''])
          )
          _UpperCAmelCase  :		List[Any]  =		[
              '''{}. {}'''.format(i + 1, '''  \n'''.join([line.strip() for line in ans.split('''\n''') if line.strip() != '''''']))
              for i, (ans, sc) in enumerate(zip(train_exple['''answers''']['''text'''], train_exple['''answers''']['''score''']))
              if i == 0 or sc > 2
          ]
          st.markdown('''##### Its answers were: \n\n {}'''.format('''\n'''.join(answers_st)))


_UpperCAmelCase  :		List[str]  =		'''
---

**Disclaimer**

*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
'''




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





"""simple docstring"""







import 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				,   A				,   A=13				,   A=30				,   A=2				,   A=3				,   A=True				,   A=True				,   A=32				,   A=5				,   A=4				,   A=37				,   A="gelu"				,   A=0.1				,   A=0.1				,   A=10				,   A=0.0_2				,   A=None				,   ):
						_lowerCamelCase					:					Dict	       =    parent
						_lowerCamelCase					:					List[Any]	       =    batch_size
						_lowerCamelCase					:					int	       =    image_size
						_lowerCamelCase					:					List[Any]	       =    patch_size
						_lowerCamelCase					:					Optional[int]	       =    num_channels
						_lowerCamelCase					:					Union[str, Any]	       =    is_training
						_lowerCamelCase					:					Optional[Any]	       =    use_labels
						_lowerCamelCase					:					Dict	       =    hidden_size
						_lowerCamelCase					:					Tuple	       =    num_hidden_layers
						_lowerCamelCase					:					List[Any]	       =    num_attention_heads
						_lowerCamelCase					:					str	       =    intermediate_size
						_lowerCamelCase					:					str	       =    hidden_act
						_lowerCamelCase					:					str	       =    hidden_dropout_prob
						_lowerCamelCase					:					int	       =    attention_probs_dropout_prob
						_lowerCamelCase					:					Any	       =    type_sequence_label_size
						_lowerCamelCase					:					int	       =    initializer_range
						_lowerCamelCase					:					Union[str, Any]	       =    scope

						# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						_lowerCamelCase					:					Dict	       =    (image_size // patch_size) ** 2
						_lowerCamelCase					:					Any	       =    num_patches + 1
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Any	       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

						_lowerCamelCase					:					Union[str, Any]	       =    None
						if self.use_labels:
									_lowerCamelCase					:					List[str]	       =    ids_tensor([self.batch_size]				,   self.type_sequence_label_size							)

						_lowerCamelCase					:					Dict	       =    self.get_config()

						return config, pixel_values, labels
			def    _lowerCAmelCase		(	self							):
						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    _lowerCAmelCase		(	self				,   A				,   A				,   A							):
						_lowerCamelCase					:					int	       =    ViTMSNModel(config=A							)
						model.to(A							)
						model.eval()
						_lowerCamelCase					:					List[Any]	       =    model(A							)
						self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, self.seq_length, self.hidden_size)							)
			def    _lowerCAmelCase		(	self				,   A				,   A				,   A							):
						_lowerCamelCase					:					List[Any]	       =    self.type_sequence_label_size
						_lowerCamelCase					:					List[str]	       =    ViTMSNForImageClassification(A							)
						model.to(A							)
						model.eval()
						_lowerCamelCase					:					Optional[Any]	       =    model(A				,   labels=A							)
						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
						_lowerCamelCase					:					Optional[Any]	       =    1
						_lowerCamelCase					:					List[Any]	       =    ViTMSNForImageClassification(A							)
						model.to(A							)
						model.eval()

						_lowerCamelCase					:					Any	       =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
						_lowerCamelCase					:					Union[str, Any]	       =    model(A							)
						self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.type_sequence_label_size)							)

			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					int	       =    self.prepare_config_and_inputs()
						_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase					:					Tuple	       =    config_and_inputs
						_lowerCamelCase					:					List[str]	       =    {'pixel_values': pixel_values}
						return config, inputs_dict







@require_torch
class 					A_(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   unittest.TestCase      ):







			"""simple docstring"""



			a_       :			Union[str, Any]												=			(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
			a_       :			Union[str, Any]												=			(
			    {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
			    if is_torch_available()
			    else {}
			)

			a_       :			Tuple												=			False
			a_       :			Union[str, Any]												=			False
			a_       :			Tuple												=			False
			a_       :			List[str]												=			False
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					List[str]	       =    ViTMSNModelTester(self							)
						_lowerCamelCase					:					List[str]	       =    ConfigTester(self				,   config_class=A				,   has_text_modality=A				,   hidden_size=37							)
			def    _lowerCAmelCase		(	self							):
						self.config_tester.run_common_tests()
			@unittest.skip(reason='ViTMSN does not use inputs_embeds'							)
			def    _lowerCAmelCase		(	self							):
						pass
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase       ,							_lowerCamelCase					:					Optional[Any]	       =    self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									_lowerCamelCase					:					List[str]	       =    model_class(A							)
									self.assertIsInstance(model.get_input_embeddings()				,   (nn.Module)							)
									_lowerCamelCase					:					Dict	       =    model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(A				,   nn.Linear							)							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase       ,							_lowerCamelCase					:					List[str]	       =    self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									_lowerCamelCase					:					int	       =    model_class(A							)
									_lowerCamelCase					:					Tuple	       =    inspect.signature(model.forward							)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									_lowerCamelCase					:					Optional[int]	       =    [*signature.parameters.keys()]

									_lowerCamelCase					:					Union[str, Any]	       =    ['pixel_values']
									self.assertListEqual(arg_names[:1]				,   A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					List[Any]	       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					List[Any]	       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A							)

			@slow
			def    _lowerCAmelCase		(	self							):
						for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_lowerCamelCase					:					Any	       =    ViTMSNModel.from_pretrained(A							)
									self.assertIsNotNone(A							)






def     UpperCAmelCase_					(				):




			'''simple docstring'''



			_lowerCamelCase					:					Optional[Any]	       =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
			return image







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







			"""simple docstring"""



			@cached_property
			def    _lowerCAmelCase		(	self							):
						return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'							) if is_vision_available() else None

			@slow
			def    _lowerCAmelCase		(	self							):
						torch.manual_seed(2							)
						_lowerCamelCase					:					Tuple	       =    ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'							).to(A							)

						_lowerCamelCase					:					int	       =    self.default_image_processor
						_lowerCamelCase					:					Optional[int]	       =    prepare_img()
						_lowerCamelCase					:					int	       =    image_processor(images=A				,   return_tensors='pt'							).to(A							)

						# forward pass
						with torch.no_grad():
									_lowerCamelCase					:					str	       =    model(**A							)

						# verify the logits
						_lowerCamelCase					:					Optional[Any]	       =    torch.Size((1, 1000)							)
						self.assertEqual(outputs.logits.shape				,   A							)

						_lowerCamelCase					:					Any	       =    torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]							).to(A							)

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





