


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


lowerCamelCase__							:						Tuple							     =   """bart"""
lowerCamelCase__							:						Dict							     =   True







@st.cache(allow_output_mutation=__lowerCAmelCase				)
def SCREAMING_SNAKE_CASE	(			)    ->				Optional[Any]:
  if LOAD_DENSE_INDEX:
    snake_case__						= AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased'''				)
    snake_case__						= AutoModel.from_pretrained('''yjernite/retribert-base-uncased'''				).to('''cuda:0'''				)
    snake_case__						= qar_model.eval()
  else:
    snake_case__				,				snake_case__						= (None, None)
  if MODEL_TYPE == "bart":
    snake_case__						= AutoTokenizer.from_pretrained('''yjernite/bart_eli5'''				)
    snake_case__						= AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5'''				).to('''cuda:0'''				)
    snake_case__						= torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth'''				)
    sas_model.load_state_dict(save_dict['''model''']				)
    snake_case__						= sas_model.eval()
  else:
    snake_case__				,				snake_case__						= make_qa_sas_model(
        model_name='''t5-small'''							,   from_file='''seq2seq_models/eli5_t5_model_1024_4.pth'''							,   device='''cuda:0'''				)
  return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)







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







@st.cache(allow_output_mutation=__lowerCAmelCase				)
def SCREAMING_SNAKE_CASE	(			)    ->				List[str]:
  snake_case__						= datasets.load_dataset('''eli5'''							,   name='''LFQA_reddit'''				)
  snake_case__						= elia['''train_eli5''']
  snake_case__						= np.memmap(
      '''eli5_questions_reps.dat'''							,   dtype='''float32'''							,   mode='''r'''							,   shape=(elia_train.num_rows, 128)				)
  snake_case__						= faiss.IndexFlatIP(128				)
  eli5_train_q_index.add(__lowerCAmelCase				)
  return (elia_train, eli5_train_q_index)


lowerCamelCase__     ,							lowerCamelCase__     ,							lowerCamelCase__							:						Optional[int]							     =   load_indexes()
lowerCamelCase__     ,							lowerCamelCase__     ,							lowerCamelCase__     ,							lowerCamelCase__							:						List[str]							     =   load_models()
lowerCamelCase__     ,							lowerCamelCase__							:						Union[str, Any]							     =   load_train_data()







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase=10				)    ->				List[Any]:
  snake_case__						= embed_questions_for_retrieval([question]							,   __lowerCAmelCase							,   __lowerCAmelCase				)
  snake_case__				,				snake_case__						= eli5_train_q_index.search(__lowerCAmelCase							,   __lowerCAmelCase				)
  snake_case__						= [elia_train[int(__lowerCAmelCase				)] for i in I[0]]
  return nn_examples







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







@st.cache(
    hash_funcs={
        torch.Tensor: (lambda __lowerCAmelCase				: None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda __lowerCAmelCase				: None),
    }				)
def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase=64							,   __lowerCAmelCase=256							,   __lowerCAmelCase=False							,   __lowerCAmelCase=2							,   __lowerCAmelCase=0.95							,   __lowerCAmelCase=0.8				)    ->				Dict:
  with torch.no_grad():
    snake_case__						= qa_sas_generate(
        __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   num_answers=1							,   num_beams=__lowerCAmelCase							,   min_len=__lowerCAmelCase							,   max_len=__lowerCAmelCase							,   do_sample=__lowerCAmelCase							,   temp=__lowerCAmelCase							,   top_p=__lowerCAmelCase							,   top_k=__lowerCAmelCase							,   max_input_length=1024							,   device='''cuda:0'''							,   )[0]
  return (answer, support_list)


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

# Start sidebar
lowerCamelCase__							:						int							     =   """<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"""
lowerCamelCase__							:						List[Any]							     =   """
<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
lowerCamelCase__							:						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)

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

lowerCamelCase__							:						Tuple							     =   st.sidebar.checkbox("""Retrieval options""")
if retrieval_options:
   lowerCamelCase__							:						Optional[int]							     =   """
    ### Information retriever options

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

lowerCamelCase__							:						int							     =   """beam"""
lowerCamelCase__							:						Optional[int]							     =   2
lowerCamelCase__							:						int							     =   6_4
lowerCamelCase__							:						List[Any]							     =   2_5_6
lowerCamelCase__							:						Optional[Any]							     =   None
lowerCamelCase__							:						List[str]							     =   None
lowerCamelCase__							:						str							     =   st.sidebar.checkbox("""Generation options""")
if generate_options:
   lowerCamelCase__							:						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)
   lowerCamelCase__							:						List[str]							     =   st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""])
   lowerCamelCase__							:						Optional[Any]							     =   st.sidebar.slider(
       """Minimum generation length""", min_value=8, max_value=2_5_6, value=6_4, step=8, format=None, key=None
   )
   lowerCamelCase__							:						Optional[int]							     =   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":
      lowerCamelCase__							:						Optional[int]							     =   st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
   else:
      lowerCamelCase__							:						Optional[Any]							     =   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
      )
      lowerCamelCase__							:						Union[str, Any]							     =   st.sidebar.slider(
          """Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.0_1, format=None, key=None
      )
      lowerCamelCase__							:						Dict							     =   None

# start main text
lowerCamelCase__							:						Union[str, Any]							     =   [
    """<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?""",
]
lowerCamelCase__							:						Optional[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>":
   lowerCamelCase__							:						Dict							     =   st.text_input("""Enter your question here:""", """""")
else:
   lowerCamelCase__							:						Union[str, Any]							     =   question_s

if st.button("""Show me!"""):
   if action in [0, 1, 3]:
      if index_type == "mixed":
         lowerCamelCase__     ,							lowerCamelCase__							:						List[str]							     =   make_support(question, source=wiki_source, method="""dense""", n_results=1_0)
         lowerCamelCase__     ,							lowerCamelCase__							:						Any							     =   make_support(question, source=wiki_source, method="""sparse""", n_results=1_0)
         lowerCamelCase__							:						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)]
         lowerCamelCase__							:						Union[str, Any]							     =   support_list[:1_0]
         lowerCamelCase__							:						Optional[Any]							     =   """<P> """ + """ <P> """.join([res[-1] for res in support_list])
      else:
         lowerCamelCase__     ,							lowerCamelCase__							:						Optional[Any]							     =   make_support(question, source=wiki_source, method=index_type, n_results=1_0)
   if action in [0, 3]:
      lowerCamelCase__     ,							lowerCamelCase__							:						Any							     =   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):
         lowerCamelCase__							:						Tuple							     =   """https://en.wikipedia.org/wiki/{}""".format(res[0].replace(""" """, """_"""))
         lowerCamelCase__							:						List[Any]							     =   res[1].strip()
         if sec_titles == "":
            lowerCamelCase__							:						int							     =   """[{}]({})""".format(res[0], wiki_url)
         else:
            lowerCamelCase__							:						str							     =   sec_titles.split(""" & """)
            lowerCamelCase__							:						int							     =   """ & """.join(
                ["""[{}]({}#{})""".format(sec.strip(), wiki_url, sec.strip().replace(""" """, """_""")) for sec in sec_list]
            )
         st.markdown(
             """{0:02d} - **Article**: {1:<18} <br>  _Section_: {2}""".format(i + 1, res[0], sections),
             unsafe_allow_html=True,
         )
         if show_passages:
            st.write(
                """> <span style=\"font-family:arial; font-size:10pt;\">""" + res[-1] + """</span>""", unsafe_allow_html=True
            )
   if action in [2, 3]:
      lowerCamelCase__							:						List[Any]							     =   find_nearest_training(question)
      lowerCamelCase__							:						int							     =   nn_train_list[0]
      st.markdown(
          """--- \n ### The most similar question in the ELI5 training set was: \n\n {}""".format(train_exple["""title"""])
      )
      lowerCamelCase__							:						Optional[int]							     =   [
          """{}. {}""".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)))


lowerCamelCase__							:						Union[str, Any]							     =   """
---

**Disclaimer**

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


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





import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= 0

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       snake_case__						= AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''   )
       self.assertIsInstance(_a     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       with tempfile.TemporaryDirectory() as tmpdirname:
         snake_case__						= Path(_a   ) / '''preprocessor_config.json'''
         snake_case__						= Path(_a   ) / '''config.json'''
         json.dump(
             {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}     ,		open(_a     ,		'''w'''   )     ,		)
         json.dump({'''model_type''': '''clip'''}     ,		open(_a     ,		'''w'''   )   )

         snake_case__						= AutoImageProcessor.from_pretrained(_a   )
         self.assertIsInstance(_a     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       # Ensure we can load the image processor from the feature extractor config
       with tempfile.TemporaryDirectory() as tmpdirname:
         snake_case__						= Path(_a   ) / '''preprocessor_config.json'''
         snake_case__						= Path(_a   ) / '''config.json'''
         json.dump(
             {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}     ,		open(_a     ,		'''w'''   )     ,		)
         json.dump({'''model_type''': '''clip'''}     ,		open(_a     ,		'''w'''   )   )

         snake_case__						= AutoImageProcessor.from_pretrained(_a   )
         self.assertIsInstance(_a     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       with tempfile.TemporaryDirectory() as tmpdirname:
         snake_case__						= CLIPConfig()

         # Create a dummy config file with image_proceesor_type
         snake_case__						= Path(_a   ) / '''preprocessor_config.json'''
         snake_case__						= Path(_a   ) / '''config.json'''
         json.dump(
             {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}     ,		open(_a     ,		'''w'''   )     ,		)
         json.dump({'''model_type''': '''clip'''}     ,		open(_a     ,		'''w'''   )   )

         # remove image_processor_type to make sure config.json alone is enough to load image processor locally
         snake_case__						= AutoImageProcessor.from_pretrained(_a   ).to_dict()

         config_dict.pop('''image_processor_type'''   )
         snake_case__						= CLIPImageProcessor(**_a   )

         # save in new folder
         model_config.save_pretrained(_a   )
         config.save_pretrained(_a   )

         snake_case__						= AutoImageProcessor.from_pretrained(_a   )

         # make sure private variable is not incorrectly saved
         snake_case__						= json.loads(config.to_json_string()   )
         self.assertTrue('''_processor_class''' not in dict_as_saved   )

       self.assertIsInstance(_a     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       with tempfile.TemporaryDirectory() as tmpdirname:
         snake_case__						= Path(_a   ) / '''preprocessor_config.json'''
         json.dump(
             {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}     ,		open(_a     ,		'''w'''   )     ,		)

         snake_case__						= AutoImageProcessor.from_pretrained(_a   )
         self.assertIsInstance(_a     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       with self.assertRaisesRegex(
           _a     ,		'''clip-base is not a local folder and is not a valid model identifier'''   ):
         snake_case__						= AutoImageProcessor.from_pretrained('''clip-base'''   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       with self.assertRaisesRegex(
           _a     ,		r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''   ):
         snake_case__						= AutoImageProcessor.from_pretrained(_a     ,		revision='''aaaaaa'''   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       with self.assertRaisesRegex(
           _a     ,		'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''     ,		):
         snake_case__						= AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model'''   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       # If remote code is not set, we will time out when asking whether to load the model.
       with self.assertRaises(_a   ):
         snake_case__						= AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''   )
       # If remote code is disabled, we can't load this config.
       with self.assertRaises(_a   ):
         snake_case__						= AutoImageProcessor.from_pretrained(
             '''hf-internal-testing/test_dynamic_image_processor'''     ,		trust_remote_code=_a   )

       snake_case__						= AutoImageProcessor.from_pretrained(
           '''hf-internal-testing/test_dynamic_image_processor'''     ,		trust_remote_code=_a   )
       self.assertEqual(image_processor.__class__.__name__     ,		'''NewImageProcessor'''   )

       # Test image processor can be reloaded.
       with tempfile.TemporaryDirectory() as tmp_dir:
         image_processor.save_pretrained(_a   )
         snake_case__						= AutoImageProcessor.from_pretrained(_a     ,		trust_remote_code=_a   )
       self.assertEqual(reloaded_image_processor.__class__.__name__     ,		'''NewImageProcessor'''   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       try:
         AutoConfig.register('''custom'''     ,		_a   )
         AutoImageProcessor.register(_a     ,		_a   )
         # Trying to register something existing in the Transformers library will raise an error
         with self.assertRaises(_a   ):
           AutoImageProcessor.register(_a     ,		_a   )

         with tempfile.TemporaryDirectory() as tmpdirname:
           snake_case__						= Path(_a   ) / '''preprocessor_config.json'''
           snake_case__						= Path(_a   ) / '''config.json'''
           json.dump(
               {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}     ,		open(_a     ,		'''w'''   )     ,		)
           json.dump({'''model_type''': '''clip'''}     ,		open(_a     ,		'''w'''   )   )

           snake_case__						= CustomImageProcessor.from_pretrained(_a   )

         # Now that the config is registered, it can be used as any other config with the auto-API
         with tempfile.TemporaryDirectory() as tmp_dir:
           image_processor.save_pretrained(_a   )
           snake_case__						= AutoImageProcessor.from_pretrained(_a   )
           self.assertIsInstance(_a     ,		_a   )

       finally:
         if "custom" in CONFIG_MAPPING._extra_content:
           del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
           del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]







     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       class 		__magic_name__						(snake_case_				):





            '''simple docstring'''



            __lowercase				:							List[str]	   =       True

       try:
         AutoConfig.register('''custom'''     ,		_a   )
         AutoImageProcessor.register(_a     ,		_a   )
         # If remote code is not set, the default is to use local
         snake_case__						= AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''   )
         self.assertEqual(image_processor.__class__.__name__     ,		'''NewImageProcessor'''   )
         self.assertTrue(image_processor.is_local   )

         # If remote code is disabled, we load the local one.
         snake_case__						= AutoImageProcessor.from_pretrained(
             '''hf-internal-testing/test_dynamic_image_processor'''     ,		trust_remote_code=_a   )
         self.assertEqual(image_processor.__class__.__name__     ,		'''NewImageProcessor'''   )
         self.assertTrue(image_processor.is_local   )

         # If remote is enabled, we load from the Hub
         snake_case__						= AutoImageProcessor.from_pretrained(
             '''hf-internal-testing/test_dynamic_image_processor'''     ,		trust_remote_code=_a   )
         self.assertEqual(image_processor.__class__.__name__     ,		'''NewImageProcessor'''   )
         self.assertTrue(not hasattr(_a     ,		'''is_local'''   )   )

       finally:
         if "custom" in CONFIG_MAPPING._extra_content:
           del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
           del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]

