



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 A						(       )						->      List[str]:
			if LOAD_DENSE_INDEX:
						A__       =    AutoTokenizer.from_pretrained('yjernite/retribert-base-uncased'					)
						A__       =    AutoModel.from_pretrained('yjernite/retribert-base-uncased'					).to('cuda:0'					)
						A__       =    qar_model.eval()
			else:
						A__     , A__       =    (None, None)
			if MODEL_TYPE == "bart":
						A__       =    AutoTokenizer.from_pretrained('yjernite/bart_eli5'					)
						A__       =    AutoModelForSeqaSeqLM.from_pretrained('yjernite/bart_eli5'					).to('cuda:0'					)
						A__       =    torch.load('seq2seq_models/eli5_bart_model_blm_2.pth'					)
						sas_model.load_state_dict(save_dict['model']					)
						A__       =    sas_model.eval()
			else:
						A__     , A__       =    make_qa_sas_model(
						    model_name='t5-small' ,	from_file='seq2seq_models/eli5_t5_model_1024_4.pth' ,	device='cuda:0'					)
			return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)


@st.cache(allow_output_mutation=__UpperCamelCase					)
def A						(       )						->      Any:
			if LOAD_DENSE_INDEX:
						A__       =    faiss.StandardGpuResources()
						A__       =    datasets.load_dataset(path='wiki_snippets' ,	name='wiki40b_en_100_0'					)['train']
						A__       =    np.memmap(
						    'wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat' ,	dtype='float32' ,	mode='r' ,	shape=(wikiaab_passages.num_rows, 128) ,	)
						A__       =    faiss.IndexFlatIP(128					)
						A__       =    faiss.index_cpu_to_gpu(__UpperCamelCase ,	1 ,	__UpperCamelCase					)
						wikiaab_gpu_index_flat.add(__UpperCamelCase					)  # TODO fix for larger GPU
			else:
						A__     , A__       =    (None, None)
			A__       =    Elasticsearch([{'host': 'localhost', 'port': '9200'}]					)
			return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)


@st.cache(allow_output_mutation=__UpperCamelCase					)
def A						(       )						->      List[str]:
			A__       =    datasets.load_dataset('eli5' ,	name='LFQA_reddit'					)
			A__       =    elia['train_eli5']
			A__       =    np.memmap(
			    'eli5_questions_reps.dat' ,	dtype='float32' ,	mode='r' ,	shape=(elia_train.num_rows, 128)					)
			A__       =    faiss.IndexFlatIP(128					)
			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 A						(       __UpperCamelCase ,	__UpperCamelCase=10					)						->      int:
			A__       =    embed_questions_for_retrieval([question] ,	__UpperCamelCase ,	__UpperCamelCase					)
			A__     , A__       =    eli5_train_q_index.search(__UpperCamelCase ,	__UpperCamelCase					)
			A__       =    [elia_train[int(__UpperCamelCase					)] for i in I[0]]
			return nn_examples


def A						(       __UpperCamelCase ,	__UpperCamelCase="wiki40b" ,	__UpperCamelCase="dense" ,	__UpperCamelCase=10					)						->      int:
			if source == "none":
						A__     , A__       =    (' <P> '.join(['' for _ in range(11					)]					).strip(), [])
			else:
						if method == "dense":
									A__     , A__       =    query_qa_dense_index(
									    __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)
						else:
									A__     , A__       =    query_es_index(
									    __UpperCamelCase ,	__UpperCamelCase ,	index_name='english_wiki40b_snippets_100w' ,	n_results=__UpperCamelCase ,	)
			A__       =    [
			    (res['article_title'], res['section_title'].strip(), res['score'], res['passage_text']) for res in hit_lst
			]
			A__       =    '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 A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase=64 ,	__UpperCamelCase=256 ,	__UpperCamelCase=False ,	__UpperCamelCase=2 ,	__UpperCamelCase=0.95 ,	__UpperCamelCase=0.8					)						->      Tuple:
			with torch.no_grad():
						A__       =    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=1_024 ,	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__       =      6_4
SCREAMING_SNAKE_CASE__       =      2_5_6
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=2_5_6, value=6_4, step=8, format=None, key=None
					)
					SCREAMING_SNAKE_CASE__       =      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":
										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=1_0)
															SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__       =      make_support(question, source=wiki_source, method='''sparse''', n_results=1_0)
															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[:1_0]
															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=1_0)
					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)









import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
							from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image


class      __lowerCAmelCase     :
					"""simple docstring"""






					def __init__(     self					:       List[Any]				,					_snake_case					:       Any				,					_snake_case					:       Optional[int]=13				,					_snake_case					:       Optional[Any]=64				,					_snake_case					:       List[str]=2				,					_snake_case					:       Any=3				,					_snake_case					:       Union[str, Any]=True				,					_snake_case					:       Dict=True				,					_snake_case					:       int=32				,					_snake_case					:       int=5				,					_snake_case					:       Union[str, Any]=4				,					_snake_case					:       int=37				,					_snake_case					:       Tuple="gelu"				,					_snake_case					:       Optional[int]=0.1				,					_snake_case					:       Dict=0.1				,					_snake_case					:       List[str]=10				,					_snake_case					:       Union[str, Any]=0.02				,					_snake_case					:       Dict=[1, 16, 4, 4]				,					_snake_case					:       Dict=None				,					):






								"""simple docstring"""


								A__       =    parent
								A__       =    batch_size
								A__       =    image_size
								A__       =    patch_size
								A__       =    num_channels
								A__       =    is_training
								A__       =    use_labels
								A__       =    hidden_size
								A__       =    num_hidden_layers
								A__       =    num_attention_heads
								A__       =    intermediate_size
								A__       =    hidden_act
								A__       =    hidden_dropout_prob
								A__       =    attention_probs_dropout_prob
								A__       =    type_sequence_label_size
								A__       =    initializer_range
								A__       =    scope
								A__       =    backbone_featmap_shape

								# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								# the number of patches is based on the feature map of the backbone, which by default uses an output stride
								# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
								A__       =    (self.image_size // 32) ** 2
								A__       =    num_patches + 1



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

								A__       =    self.get_config()

								return config, pixel_values, labels



					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    {
								    'global_padding': 'same',
								    'layer_type': 'bottleneck',
								    'depths': [3, 4, 9],
								    'out_features': ['stage1', 'stage2', 'stage3'],
								    'embedding_dynamic_padding': True,
								    'hidden_sizes': [4, 8, 16, 32],
								    'num_groups': 2,
								}

								return ViTHybridConfig(
								    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				,					is_decoder=_snake_case				,					initializer_range=self.initializer_range				,					backbone_featmap_shape=self.backbone_featmap_shape				,					backbone_config=_snake_case				,					)



					def     _a							(     self					:       int				,					_snake_case					:       Optional[int]				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								A__       =    ViTHybridModel(config=_snake_case					)
								model.to(_snake_case					)
								model.eval()
								A__       =    model(_snake_case					)
								self.parent.assertEqual(result.last_hidden_state.shape				,					(self.batch_size, self.seq_length, self.hidden_size)					)



					def     _a							(     self					:       List[str]				,					_snake_case					:       str				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       Any					):






								"""simple docstring"""


								A__       =    self.type_sequence_label_size
								A__       =    ViTHybridForImageClassification(_snake_case					)
								model.to(_snake_case					)
								model.eval()
								A__       =    model(_snake_case				,					labels=_snake_case					)
								self.parent.assertEqual(result.logits.shape				,					(self.batch_size, self.type_sequence_label_size)					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								A__       =    self.prepare_config_and_inputs()
								A__     , A__     , A__       =    config_and_inputs
								A__       =    {'pixel_values': pixel_values}
								return config, inputs_dict







@require_torch
class      __lowerCAmelCase     (		UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Union[str, Any]			       =    (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
					A__				:		str			       =    (
					    {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
					    if is_torch_available()
					    else {}
					)
					A__				:		Union[str, Any]			       =    False
					A__				:		Any			       =    False
					A__				:		Union[str, Any]			       =    False



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								A__       =    ViTHybridModelTester(self					)
								A__       =    ConfigTester(self				,					config_class=_snake_case				,					has_text_modality=_snake_case				,					hidden_size=37					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								self.config_tester.run_common_tests()



					@unittest.skip(reason='ViT does not use inputs_embeds'					)
					def     _a							(     self					:       int					):






								"""simple docstring"""


								pass



					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											A__       =    model_class(_snake_case					)
											self.assertIsInstance(model.get_input_embeddings()				,					(nn.Module)					)
											A__       =    model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_snake_case				,					nn.Linear					)					)



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

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

											A__       =    ['pixel_values']
											self.assertListEqual(arg_names[:1]				,					_snake_case					)



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case					)



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case					)



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

								A__       =    _config_zero_init(_snake_case					)
								for model_class in self.all_model_classes:
											A__       =    model_class(config=_snake_case					)
											# Skip the check for the backbone
											for name, module in model.named_modules():
														if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																	A__       =    [F'''{name}.{key}''' for key in module.state_dict().keys()]
																	break

											for name, param in model.named_parameters():
														if param.requires_grad:
																	if name in backbone_params:
																				continue
																	self.assertIn(
																	    ((param.data.mean() * 1E9).round() / 1E9).item()				,					[0.0, 1.0]				,					msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''				,					)



					@slow
					def     _a							(     self					:       int					):






								"""simple docstring"""


								for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__       =    ViTHybridModel.from_pretrained(_snake_case					)
											self.assertIsNotNone(_snake_case					)







def A						(       )						->      Union[str, Any]:
			A__       =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
			return image






@require_torch
@require_vision
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					@cached_property
					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								return (
								    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
								    if is_vision_available()
								    else None
								)



					@slow
					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(
								    _snake_case					)

								A__       =    self.default_image_processor
								A__       =    prepare_img()
								A__       =    image_processor(images=_snake_case				,					return_tensors='pt'					).to(_snake_case					)

								# forward pass
								with torch.no_grad():
											A__       =    model(**_snake_case					)

								# verify the logits
								A__       =    torch.Size((1, 10_00)					)
								self.assertEqual(outputs.logits.shape				,					_snake_case					)

								A__       =    torch.tensor([-1.9090, -0.4993, -0.2389]					).to(_snake_case					)

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



					@slow
					@require_accelerate
					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384'					)
								A__       =    ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'				,					device_map='auto'					)

								A__       =    prepare_img()

								A__       =    image_processor(images=_snake_case				,					return_tensors='pt'					)
								A__       =    model(**_snake_case					)
								A__       =    outputs.logits
								# model predicts one of the 1000 ImageNet classes
								A__       =    logits.argmax(-1					).item()

								self.assertTrue(model.config.idalabel[predicted_class_idx]				,					'tabby, tabby cat'					)




