






"""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_   : int   			= """bart"""
UpperCAmelCase_   : Dict   			= True



@st.cache(allow_output_mutation=__a							)
def 					_A			() ->       Union[str, Any]:
    """simple docstring"""






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






    if LOAD_DENSE_INDEX:
        SCREAMING_SNAKE_CASE_      :  int    						=		faiss.StandardGpuResources()
        SCREAMING_SNAKE_CASE_      :  int    						=		datasets.load_dataset(path='''wiki_snippets'''    ,				name='''wiki40b_en_100_0'''							)['''train''']
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		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)    ,				)
        SCREAMING_SNAKE_CASE_      :  str    						=		faiss.IndexFlatIP(1_28							)
        SCREAMING_SNAKE_CASE_      :  Dict    						=		faiss.index_cpu_to_gpu(__a    ,				1    ,				__a							)
        wikiaab_gpu_index_flat.add(__a							)  # TODO fix for larger GPU
    else:
        SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		(None, None)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}]							)
    return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)



@st.cache(allow_output_mutation=__a							)
def 					_A			() ->       Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		datasets.load_dataset('''eli5'''    ,				name='''LFQA_reddit'''							)
    SCREAMING_SNAKE_CASE_      :  str    						=		elia['''train_eli5''']
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		np.memmap(
        '''eli5_questions_reps.dat'''    ,				dtype='''float32'''    ,				mode='''r'''    ,				shape=(elia_train.num_rows, 1_28)							)
    SCREAMING_SNAKE_CASE_      :  str    						=		faiss.IndexFlatIP(1_28							)
    eli5_train_q_index.add(__a							)
    return (elia_train, eli5_train_q_index)


UpperCAmelCase_			,  UpperCAmelCase_			,  UpperCAmelCase_   : Union[str, Any]   			= load_indexes()
UpperCAmelCase_			,  UpperCAmelCase_			,  UpperCAmelCase_			,  UpperCAmelCase_   : Any   			= load_models()
UpperCAmelCase_			,  UpperCAmelCase_   : List[Any]   			= load_train_data()



def 					_A			(__a    ,				__a=10							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[str]    						=		embed_questions_for_retrieval([question]    ,				__a    ,				__a							)
    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Tuple    						=		eli5_train_q_index.search(__a    ,				__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		[elia_train[int(__a							)] for i in I[0]]
    return nn_examples



def 					_A			(__a    ,				__a="wiki40b"    ,				__a="dense"    ,				__a=10							) ->       str:
    """simple docstring"""






    if source == "none":
        SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Any    						=		(''' <P> '''.join(['''''' for _ in range(11							)]							).strip(), [])
    else:
        if method == "dense":
            SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		query_qa_dense_index(
                __a    ,				__a    ,				__a    ,				__a    ,				__a    ,				__a							)
        else:
            SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		query_es_index(
                __a    ,				__a    ,				index_name='''english_wiki40b_snippets_100w'''    ,				n_results=__a    ,				)
    SCREAMING_SNAKE_CASE_      :  str    						=		[
        (res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
    ]
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		'''question: {} context: {}'''.format(__a    ,				__a							)
    return question_doc, support_list



@st.cache(
    hash_funcs={
        torch.Tensor: (lambda __a							: None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda __a							: None),
    }							)
def 					_A			(__a    ,				__a    ,				__a    ,				__a=64    ,				__a=2_56    ,				__a=False    ,				__a=2    ,				__a=0.95    ,				__a=0.8							) ->       Union[str, Any]:
    """simple docstring"""






    with torch.no_grad():
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		qa_sas_generate(
            __a    ,				__a    ,				__a    ,				num_answers=1    ,				num_beams=__a    ,				min_len=__a    ,				max_len=__a    ,				do_sample=__a    ,				temp=__a    ,				top_p=__a    ,				top_k=__a    ,				max_input_length=10_24    ,				device='''cuda:0'''    ,				)[0]
    return (answer, support_list)


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

# Start sidebar
UpperCAmelCase_   : Any   			= """<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"""
UpperCAmelCase_   : Optional[int]   			= """
<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_   : 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_   : Optional[Any]   			= [
    """Answer the question""",
    """View the retrieved document only""",
    """View the most similar ELI5 question and answer""",
    """Show me everything, please!""",
]
UpperCAmelCase_   : Optional[Any]   			= st.sidebar.checkbox("""Demo options""")
if demo_options:
  UpperCAmelCase_   : Optional[Any]   			= st.sidebar.selectbox(
      """""",
      action_list,
      index=3,
  )
  UpperCAmelCase_   : Tuple   			= action_list.index(action_st)
  UpperCAmelCase_   : Optional[Any]   			= 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_   : int   			= 3
  UpperCAmelCase_   : Optional[int]   			= True

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

UpperCAmelCase_   : Any   			= """beam"""
UpperCAmelCase_   : Optional[Any]   			= 2
UpperCAmelCase_   : Optional[int]   			= 64
UpperCAmelCase_   : Any   			= 256
UpperCAmelCase_   : Union[str, Any]   			= None
UpperCAmelCase_   : Any   			= None
UpperCAmelCase_   : Any   			= st.sidebar.checkbox("""Generation options""")
if generate_options:
  UpperCAmelCase_   : int   			= """
    ### 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_   : Optional[int]   			= st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""])
  UpperCAmelCase_   : Optional[Any]   			= st.sidebar.slider(
      """Minimum generation length""", min_value=8, max_value=256, value=64, step=8, format=None, key=None
  )
  UpperCAmelCase_   : Optional[int]   			= st.sidebar.slider(
      """Maximum generation length""", min_value=64, max_value=512, value=256, step=16, format=None, key=None
  )
  if sampled == "beam":
    UpperCAmelCase_   : Union[str, Any]   			= st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
  else:
    UpperCAmelCase_   : str   			= st.sidebar.slider(
        """Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.9_5, step=0.0_1, format=None, key=None
    )
    UpperCAmelCase_   : str   			= st.sidebar.slider(
        """Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.0_1, format=None, key=None
    )
    UpperCAmelCase_   : Union[str, Any]   			= None

# start main text
UpperCAmelCase_   : Optional[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_   : int   			= question_s

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


UpperCAmelCase_   : Dict   			= """
---

**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
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 transformers import (
      MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      MODEL_MAPPING,
      LevitForImageClassification,
      LevitForImageClassificationWithTeacher,
      LevitModel,
  )
  from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import LevitImageProcessor




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.config_class(**self.inputs_dict)
          self.parent.assertTrue(hasattr(lowercase_						,	'''hidden_sizes'''))
          self.parent.assertTrue(hasattr(lowercase_						,	'''num_attention_heads'''))




class 							lowerCAmelCase__				:






      '''simple docstring'''

      def __init__(						self     :  str						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  List[Any]=13						,	lowercase_     :  Dict=64						,	lowercase_     :  Dict=3						,	lowercase_     :  Optional[Any]=3						,	lowercase_     :  List[Any]=2						,	lowercase_     :  Any=1						,	lowercase_     :  List[Any]=16						,	lowercase_     :  int=[128, 256, 384]						,	lowercase_     :  str=[4, 6, 8]						,	lowercase_     :  Optional[Any]=[2, 3, 4]						,	lowercase_     :  Union[str, Any]=[16, 16, 16]						,	lowercase_     :  Optional[Any]=0						,	lowercase_     :  Optional[int]=[2, 2, 2]						,	lowercase_     :  Any=[2, 2, 2]						,	lowercase_     :  List[str]=0.02						,	lowercase_     :  Any=True						,	lowercase_     :  Union[str, Any]=True						,	lowercase_     :  Optional[int]=2						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		parent
          SCREAMING_SNAKE_CASE_      :  Any    						=		batch_size
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		image_size
          SCREAMING_SNAKE_CASE_      :  int    						=		num_channels
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		kernel_size
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		stride
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		padding
          SCREAMING_SNAKE_CASE_      :  int    						=		hidden_sizes
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		num_attention_heads
          SCREAMING_SNAKE_CASE_      :  int    						=		depths
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		key_dim
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		drop_path_rate
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		patch_size
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		attention_ratio
          SCREAMING_SNAKE_CASE_      :  str    						=		mlp_ratio
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		initializer_range
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		[
              ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
              ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
          ]
          SCREAMING_SNAKE_CASE_      :  Any    						=		is_training
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		use_labels
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		num_labels
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		initializer_range
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          SCREAMING_SNAKE_CASE_      :  Dict    						=		None
          if self.use_labels:
              SCREAMING_SNAKE_CASE_      :  str    						=		ids_tensor([self.batch_size]						,	self.num_labels)

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.get_config()
          return config, pixel_values, labels
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          return LevitConfig(
              image_size=self.image_size						,	num_channels=self.num_channels						,	kernel_size=self.kernel_size						,	stride=self.stride						,	padding=self.padding						,	patch_size=self.patch_size						,	hidden_sizes=self.hidden_sizes						,	num_attention_heads=self.num_attention_heads						,	depths=self.depths						,	key_dim=self.key_dim						,	drop_path_rate=self.drop_path_rate						,	mlp_ratio=self.mlp_ratio						,	attention_ratio=self.attention_ratio						,	initializer_range=self.initializer_range						,	down_ops=self.down_ops						,	)
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]						,	lowercase_     :  Any						,	lowercase_     :  int						,	lowercase_     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		LevitModel(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		model(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Any    						=		(self.image_size, self.image_size)
          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Tuple    						=		image_size[0], image_size[1]
          for _ in range(4):
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1)
              SCREAMING_SNAKE_CASE_      :  Dict    						=		floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1)
          self.parent.assertEqual(
              result.last_hidden_state.shape						,	(self.batch_size, ceil(height / 4) * ceil(width / 4), self.hidden_sizes[-1])						,	)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  int						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		self.num_labels
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		LevitForImageClassification(lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model(lowercase_						,	labels=lowercase_)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels))



      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.prepare_config_and_inputs()
          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		config_and_inputs
          SCREAMING_SNAKE_CASE_      :  int    						=		{'''pixel_values''': pixel_values}
          return config, inputs_dict




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






      '''simple docstring'''

      __UpperCamelCase	 =  (
          (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
          if is_torch_available()
          else ()
      )
      __UpperCamelCase	 =  (
          {
              "feature-extraction": LevitModel,
              "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
          }
          if is_torch_available()
          else {}
      )

      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		LevitModelTester(self)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		ConfigTester(self						,	config_class=lowercase_						,	has_text_modality=lowercase_						,	hidden_size=37)
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          return
      @unittest.skip(reason='''Levit does not use inputs_embeds''')
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          pass
      @unittest.skip(reason='''Levit does not support input and output embeddings''')
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          pass
      @unittest.skip(reason='''Levit does not output attentions''')
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_      :  Any    						=		model_class(lowercase_)
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		inspect.signature(model.forward)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE_      :  Dict    						=		[*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		['''pixel_values''']
              self.assertListEqual(arg_names[:1]						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          def check_hidden_states_output(lowercase_     :  List[str]						,	lowercase_     :  Optional[Any]						,	lowercase_     :  str):
              SCREAMING_SNAKE_CASE_      :  str    						=		model_class(lowercase_)
              model.to(lowercase_)
              model.eval()

              with torch.no_grad():
                  SCREAMING_SNAKE_CASE_      :  Tuple    						=		model(**self._prepare_for_class(lowercase_						,	lowercase_))

              SCREAMING_SNAKE_CASE_      :  str    						=		outputs.hidden_states

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		len(self.model_tester.depths) + 1
              self.assertEqual(len(lowercase_)						,	lowercase_)

              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		(self.model_tester.image_size, self.model_tester.image_size)
              SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		image_size[0], image_size[1]
              for _ in range(4):
                  SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		floor(
                      (
                          (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                          / self.model_tester.stride
                      )
                      + 1)
                  SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		floor(
                      (
                          (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                          / self.model_tester.stride
                      )
                      + 1)
              # verify the first hidden states (first block)
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:])						,	[
                      height * width,
                      self.model_tester.hidden_sizes[0],
                  ]						,	)

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		True
              check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		True

              check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_)
      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  Optional[Any]						,	lowercase_     :  Optional[int]						,	lowercase_     :  Tuple=False):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		super()._prepare_for_class(lowercase_						,	lowercase_						,	return_labels=lowercase_)

          if return_labels:
              if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                  del inputs_dict["labels"]

          return inputs_dict
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          if not self.model_tester.is_training:
              return

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		True

          for model_class in self.all_model_classes:
              # LevitForImageClassificationWithTeacher supports inference-only
              if (
                  model_class in get_values(lowercase_)
                  or model_class.__name__ == "LevitForImageClassificationWithTeacher"
              ):
                  continue
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		model_class(lowercase_)
              model.to(lowercase_)
              model.train()
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self._prepare_for_class(lowercase_						,	lowercase_						,	return_labels=lowercase_)
              SCREAMING_SNAKE_CASE_      :  Dict    						=		model(**lowercase_).loss
              loss.backward()
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  int    						=		self.model_tester.prepare_config_and_inputs_for_common()
          if not self.model_tester.is_training:
              return

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		False
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		True

          for model_class in self.all_model_classes:
              if model_class in get_values(lowercase_) or not model_class.supports_gradient_checkpointing:
                  continue
              # LevitForImageClassificationWithTeacher supports inference-only
              if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                  continue
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		model_class(lowercase_)
              model.gradient_checkpointing_enable()
              model.to(lowercase_)
              model.train()
              SCREAMING_SNAKE_CASE_      :  Dict    						=		self._prepare_for_class(lowercase_						,	lowercase_						,	return_labels=lowercase_)
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model(**lowercase_).loss
              loss.backward()
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  str    						=		self.model_tester.prepare_config_and_inputs_for_common()

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		[
              {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
              {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
              {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
          ]

          for model_class in self.all_model_classes:
              if (
                  model_class
                  not in [
                      *get_values(lowercase_),
                  ]
                  or model_class.__name__ == "LevitForImageClassificationWithTeacher"
              ):
                  continue

              for problem_type in problem_types:
                  with self.subTest(msg=F'Testing {model_class} with {problem_type["title"]}'):
                      SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		problem_type['''title''']
                      SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		problem_type['''num_labels''']

                      SCREAMING_SNAKE_CASE_      :  str    						=		model_class(lowercase_)
                      model.to(lowercase_)
                      model.train()

                      SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self._prepare_for_class(lowercase_						,	lowercase_						,	return_labels=lowercase_)

                      if problem_type["num_labels"] > 1:
                          SCREAMING_SNAKE_CASE_      :  str    						=		inputs['''labels'''].unsqueeze(1).repeat(1						,	problem_type['''num_labels'''])

                      SCREAMING_SNAKE_CASE_      :  Any    						=		inputs['''labels'''].to(problem_type['''dtype'''])

                      # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                      # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                      # they have the same size." which is a symptom something in wrong for the regression problem.
                      # See https://github.com/huggingface/transformers/issues/11780
                      with warnings.catch_warnings(record=lowercase_) as warning_list:
                          SCREAMING_SNAKE_CASE_      :  int    						=		model(**lowercase_).loss
                      for w in warning_list:
                          if "Using a target size that is different to the input size" in str(w.message):
                              raise ValueError(
                                  F'Something is going wrong in the regression problem: intercepted {w.message}')

                      loss.backward()



      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		LevitModel.from_pretrained(lowercase_)
              self.assertIsNotNone(lowercase_)



def 					_A			() ->       Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Any    						=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
    return image





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






      '''simple docstring'''

      @cached_property
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0])



      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
              lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.default_image_processor
          SCREAMING_SNAKE_CASE_      :  str    						=		prepare_img()
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		image_processor(images=lowercase_						,	return_tensors='''pt''').to(lowercase_)

          # forward pass
          with torch.no_grad():
              SCREAMING_SNAKE_CASE_      :  Any    						=		model(**lowercase_)

          # verify the logits
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		torch.Size((1, 1000))
          self.assertEqual(outputs.logits.shape						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		torch.tensor([1.04_48, -0.37_45, -1.83_17]).to(lowercase_)

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


