





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       : Optional[Any]			     =       """bart"""
_UpperCAmelCase       : List[Any]			     =       True


@st.cache(allow_output_mutation=_UpperCAmelCase				)
def  SCREAMING_SNAKE_CASE   ( )    ->  List[Any]:
    if LOAD_DENSE_INDEX:
        lowerCamelCase__					:   Any      				=	AutoTokenizer.from_pretrained('yjernite/retribert-base-uncased'				)
        lowerCamelCase__					:   Optional[Any]      				=	AutoModel.from_pretrained('yjernite/retribert-base-uncased'				).to('cuda:0'				)
        lowerCamelCase__					:   List[str]      				=	qar_model.eval()
    else:
        lowerCamelCase__					:   Dict      				=	(None, None)
    if MODEL_TYPE == "bart":
        lowerCamelCase__					:   Dict      				=	AutoTokenizer.from_pretrained('yjernite/bart_eli5'				)
        lowerCamelCase__					:   List[str]      				=	AutoModelForSeqaSeqLM.from_pretrained('yjernite/bart_eli5'				).to('cuda:0'				)
        lowerCamelCase__					:   List[str]      				=	torch.load('seq2seq_models/eli5_bart_model_blm_2.pth'				)
        sas_model.load_state_dict(save_dict['model']				)
        lowerCamelCase__					:   str      				=	sas_model.eval()
    else:
        lowerCamelCase__					:   List[Any]      				=	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  SCREAMING_SNAKE_CASE   ( )    ->  Any:
    if LOAD_DENSE_INDEX:
        lowerCamelCase__					:   Tuple      				=	faiss.StandardGpuResources()
        lowerCamelCase__					:   Dict      				=	datasets.load_dataset(path='wiki_snippets'		,			name='wiki40b_en_100_0'				)['train']
        lowerCamelCase__					:   str      				=	np.memmap(
            'wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat'		,			dtype='float32'		,			mode='r'		,			shape=(wikiaab_passages.num_rows, 128)		,			)
        lowerCamelCase__					:   Tuple      				=	faiss.IndexFlatIP(128				)
        lowerCamelCase__					:   Union[str, Any]      				=	faiss.index_cpu_to_gpu(_UpperCAmelCase		,			1		,			_UpperCAmelCase				)
        wikiaab_gpu_index_flat.add(_UpperCAmelCase				)  # TODO fix for larger GPU
    else:
        lowerCamelCase__					:   int      				=	(None, None)
    lowerCamelCase__					:   Union[str, Any]      				=	Elasticsearch([{'host': 'localhost', 'port': '9200'}]				)
    return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)


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


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


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


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase="wiki40b"		,			_UpperCAmelCase="dense"		,			_UpperCAmelCase=10				)    ->  List[str]:
    if source == "none":
        lowerCamelCase__					:   Any      				=	(' <P> '.join(['' for _ in range(11				)]				).strip(), [])
    else:
        if method == "dense":
            lowerCamelCase__					:   List[str]      				=	query_qa_dense_index(
                _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)
        else:
            lowerCamelCase__					:   List[str]      				=	query_es_index(
                _UpperCAmelCase		,			_UpperCAmelCase		,			index_name='english_wiki40b_snippets_100w'		,			n_results=_UpperCAmelCase		,			)
    lowerCamelCase__					:   List[Any]      				=	[
        (res['article_title'], res['section_title'].strip(), res['score'], res['passage_text']) for res in hit_lst
    ]
    lowerCamelCase__					:   Optional[int]      				=	'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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase=64		,			_UpperCAmelCase=256		,			_UpperCAmelCase=False		,			_UpperCAmelCase=2		,			_UpperCAmelCase=0.95		,			_UpperCAmelCase=0.8				)    ->  Tuple:
    with torch.no_grad():
        lowerCamelCase__					:   Dict      				=	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=1024		,			device='cuda:0'		,			)[0]
    return (answer, support_list)


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

# Start sidebar
_UpperCAmelCase       : Union[str, Any]			     =       """<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"""
_UpperCAmelCase       : List[str]			     =       """
<html>
  <head>
    <style>
      .img-container {
        padding-left: 90px;
        padding-right: 90px;
        padding-top: 50px;
        padding-bottom: 50px;
        background-color: #f0f3f9;
      }
    </style>
  </head>
  <body>
    <span class=\"img-container\"> <!-- Inline parent element -->
      %s
    </span>
  </body>
</html>
""" % (
    header_html,
)
st.sidebar.markdown(
    header_full,
    unsafe_allow_html=True,
)

# Long Form QA with ELI5 and Wikipedia
_UpperCAmelCase       : List[str]			     =       """
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
"""
st.sidebar.markdown(description, unsafe_allow_html=True)

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

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

_UpperCAmelCase       : Dict			     =       """beam"""
_UpperCAmelCase       : List[Any]			     =       2
_UpperCAmelCase       : Optional[int]			     =       64
_UpperCAmelCase       : Any			     =       2_56
_UpperCAmelCase       : Optional[Any]			     =       None
_UpperCAmelCase       : int			     =       None
_UpperCAmelCase       : Tuple			     =       st.sidebar.checkbox("""Generation options""")
if generate_options:
   _UpperCAmelCase       : str			     =       """
    ### 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       : int			     =       st.sidebar.slider(
       """Minimum generation length""", min_value=8, max_value=2_56, value=64, step=8, format=None, key=None
   )
   _UpperCAmelCase       : int			     =       st.sidebar.slider(
       """Maximum generation length""", min_value=64, max_value=5_12, value=2_56, step=16, format=None, key=None
   )
   if sampled == "beam":
      _UpperCAmelCase       : Tuple			     =       st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
   else:
      _UpperCAmelCase       : Optional[Any]			     =       st.sidebar.slider(
          """Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
      )
      _UpperCAmelCase       : Union[str, Any]			     =       st.sidebar.slider(
          """Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
      )
      _UpperCAmelCase       : Tuple			     =       None

# start main text
_UpperCAmelCase       : Tuple			     =       [
    """<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       : 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       : Union[str, Any]			     =       st.text_input("""Enter your question here:""", """""")
else:
   _UpperCAmelCase       : List[Any]			     =       question_s

if st.button("""Show me!"""):
   if action in [0, 1, 3]:
      if index_type == "mixed":
         _UpperCAmelCase       : int			     =       make_support(question, source=wiki_source, method="""dense""", n_results=10)
         _UpperCAmelCase       : Optional[Any]			     =       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       : Optional[int]			     =       support_list[:10]
         _UpperCAmelCase       : List[str]			     =       """<P> """ + """ <P> """.join([res[-1] for res in support_list])
      else:
         _UpperCAmelCase       : int			     =       make_support(question, source=wiki_source, method=index_type, n_results=10)
   if action in [0, 3]:
      _UpperCAmelCase       : 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):
         _UpperCAmelCase       : Optional[Any]			     =       """https://en.wikipedia.org/wiki/{}""".format(res[0].replace(""" """, """_"""))
         _UpperCAmelCase       : int			     =       res[1].strip()
         if sec_titles == "":
            _UpperCAmelCase       : List[str]			     =       """[{}]({})""".format(res[0], wiki_url)
         else:
            _UpperCAmelCase       : Any			     =       sec_titles.split(""" & """)
            _UpperCAmelCase       : Any			     =       """ & """.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       : str			     =       find_nearest_training(question)
      _UpperCAmelCase       : str			     =       nn_train_list[0]
      st.markdown(
          """--- \n ### The most similar question in the ELI5 training set was: \n\n {}""".format(train_exple["""title"""])
      )
      _UpperCAmelCase       : Optional[Any]			     =       [
          """{}. {}""".format(i + 1, """  \n""".join([line.strip() for line in ans.split("""\n""") if line.strip() != """"""]))
          for i, (ans, sc) in enumerate(zip(train_exple["""answers"""]["""text"""], train_exple["""answers"""]["""score"""]))
          if i == 0 or sc > 2
      ]
      st.markdown("""##### Its answers were: \n\n {}""".format("""\n""".join(answers_st)))


_UpperCAmelCase       : Optional[int]			     =       """
---

**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 logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_UpperCAmelCase       : List[str]			     =       logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt""")

@dataclass
class      lowerCAmelCase							:
 UpperCAmelCase__									=	field(
     default="""cifar10""",						metadata={"""help""": """Name of a dataset from the datasets package"""}    )
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}    )
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={"""help""": """The column name of the images in the files."""}    )
 UpperCAmelCase__									=	field(default=__UpperCamelCase,						metadata={"""help""": """A folder containing the training data."""}    )
 UpperCAmelCase__									=	field(default=__UpperCamelCase,						metadata={"""help""": """A folder containing the validation data."""}    )
 UpperCAmelCase__									=	field(
     default=0.15,						metadata={"""help""": """Percent to split off of train for validation."""}    )
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={
         """help""": (
             """For debugging purposes or quicker training, truncate the number of training examples to this """
             """value if set."""
         )
     },						)
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={
         """help""": (
             """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
             """value if set."""
         )
     },						)
 def   A_			(   self						:   Tuple			)    ->			Optional[Any]:
     lowerCamelCase__					:   Optional[int]      				=	{}
     if self.train_dir is not None:
         lowerCamelCase__					:   int      				=	self.train_dir
     if self.validation_dir is not None:
         lowerCamelCase__					:   Dict      				=	self.validation_dir
     lowerCamelCase__					:   Union[str, Any]      				=	data_files if data_files else None


@dataclass
class      lowerCAmelCase							:
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={
         """help""": (
             """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
         )
     },						)
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}    )
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={
         """help""": (
             """Override some existing default config settings when a model is trained from scratch. Example: """
             """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
         )
     },						)
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}    )
 UpperCAmelCase__									=	field(
     default="""main""",						metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""},						)
 UpperCAmelCase__									=	field(default=__UpperCamelCase,						metadata={"""help""": """Name or path of preprocessor config."""}    )
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={
         """help""": (
             """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
             """with private models)."""
         )
     },						)
 UpperCAmelCase__									=	field(
     default=0.75,						metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""}    )
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}    )


@dataclass
class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	field(
     default=1E-3,						metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""}    )


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Tuple:
    lowerCamelCase__					:   str      				=	torch.stack([example['pixel_values'] for example in examples]				)
    return {"pixel_values": pixel_values}


def  SCREAMING_SNAKE_CASE   ( )    ->  Union[str, Any]:
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    lowerCamelCase__					:   Tuple      				=	HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)				)
    if len(sys.argv				) == 2 and sys.argv[1].endswith('.json'				):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   List[str]      				=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
    else:
        lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Union[str, Any]      				=	parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry('run_mae'		,			_UpperCAmelCase		,			_UpperCAmelCase				)

    # Setup logging
    logging.basicConfig(
        format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'		,			datefmt='%m/%d/%Y %H:%M:%S'		,			handlers=[logging.StreamHandler(sys.stdout				)]		,			)

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    lowerCamelCase__					:   Optional[Any]      				=	training_args.get_process_log_level()
    logger.setLevel(_UpperCAmelCase				)
    transformers.utils.logging.set_verbosity(_UpperCAmelCase				)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
        + F"""distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}"""				)
    logger.info(F"""Training/evaluation parameters {training_args}"""				)

    # Detecting last checkpoint.
    lowerCamelCase__					:   Optional[Any]      				=	None
    if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
        lowerCamelCase__					:   str      				=	get_last_checkpoint(training_args.output_dir				)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
            raise ValueError(
                F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
                'Use --overwrite_output_dir to overcome.'				)
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
                'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'				)

    # Initialize our dataset.
    lowerCamelCase__					:   List[Any]      				=	load_dataset(
        data_args.dataset_name		,			data_args.dataset_config_name		,			data_files=data_args.data_files		,			cache_dir=model_args.cache_dir		,			use_auth_token=True if model_args.use_auth_token else None		,			)

    # If we don't have a validation split, split off a percentage of train as validation.
    lowerCamelCase__					:   List[Any]      				=	None if 'validation' in ds.keys() else data_args.train_val_split
    if isinstance(data_args.train_val_split		,			_UpperCAmelCase				) and data_args.train_val_split > 0.0:
        lowerCamelCase__					:   Optional[int]      				=	ds['train'].train_test_split(data_args.train_val_split				)
        lowerCamelCase__					:   List[str]      				=	split['train']
        lowerCamelCase__					:   List[str]      				=	split['test']

    # Load pretrained model and image processor
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    lowerCamelCase__					:   Union[str, Any]      				=	{
        'cache_dir': model_args.cache_dir,
        'revision': model_args.model_revision,
        'use_auth_token': True if model_args.use_auth_token else None,
    }
    if model_args.config_name:
        lowerCamelCase__					:   Tuple      				=	ViTMAEConfig.from_pretrained(model_args.config_name		,			**_UpperCAmelCase				)
    elif model_args.model_name_or_path:
        lowerCamelCase__					:   Tuple      				=	ViTMAEConfig.from_pretrained(model_args.model_name_or_path		,			**_UpperCAmelCase				)
    else:
        lowerCamelCase__					:   List[Any]      				=	ViTMAEConfig()
        logger.warning('You are instantiating a new config instance from scratch.'				)
        if model_args.config_overrides is not None:
            logger.info(F"""Overriding config: {model_args.config_overrides}"""				)
            config.update_from_string(model_args.config_overrides				)
            logger.info(F"""New config: {config}"""				)

    # adapt config
    config.update(
        {
            'mask_ratio': model_args.mask_ratio,
            'norm_pix_loss': model_args.norm_pix_loss,
        }				)

    # create image processor
    if model_args.image_processor_name:
        lowerCamelCase__					:   Optional[Any]      				=	ViTImageProcessor.from_pretrained(model_args.image_processor_name		,			**_UpperCAmelCase				)
    elif model_args.model_name_or_path:
        lowerCamelCase__					:   Any      				=	ViTImageProcessor.from_pretrained(model_args.model_name_or_path		,			**_UpperCAmelCase				)
    else:
        lowerCamelCase__					:   List[Any]      				=	ViTImageProcessor()

    # create model
    if model_args.model_name_or_path:
        lowerCamelCase__					:   Dict      				=	ViTMAEForPreTraining.from_pretrained(
            model_args.model_name_or_path		,			from_tf=bool('.ckpt' in model_args.model_name_or_path				)		,			config=_UpperCAmelCase		,			cache_dir=model_args.cache_dir		,			revision=model_args.model_revision		,			use_auth_token=True if model_args.use_auth_token else None		,			)
    else:
        logger.info('Training new model from scratch'				)
        lowerCamelCase__					:   Dict      				=	ViTMAEForPreTraining(_UpperCAmelCase				)

    if training_args.do_train:
        lowerCamelCase__					:   Union[str, Any]      				=	ds['train'].column_names
    else:
        lowerCamelCase__					:   Any      				=	ds['validation'].column_names

    if data_args.image_column_name is not None:
        lowerCamelCase__					:   str      				=	data_args.image_column_name
    elif "image" in column_names:
        lowerCamelCase__					:   Tuple      				=	'image'
    elif "img" in column_names:
        lowerCamelCase__					:   int      				=	'img'
    else:
        lowerCamelCase__					:   str      				=	column_names[0]

    # transformations as done in original MAE paper
    # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
    if "shortest_edge" in image_processor.size:
        lowerCamelCase__					:   List[Any]      				=	image_processor.size['shortest_edge']
    else:
        lowerCamelCase__					:   Optional[int]      				=	(image_processor.size['height'], image_processor.size['width'])
    lowerCamelCase__					:   Optional[Any]      				=	Compose(
        [
            Lambda(lambda _UpperCAmelCase				: img.convert('RGB'				) if img.mode != "RGB" else img				),
            RandomResizedCrop(_UpperCAmelCase		,			scale=(0.2, 1.0)		,			interpolation=InterpolationMode.BICUBIC				),
            RandomHorizontalFlip(),
            ToTensor(),
            Normalize(mean=image_processor.image_mean		,			std=image_processor.image_std				),
        ]				)

    def preprocess_images(_UpperCAmelCase				):
        lowerCamelCase__					:   Tuple      				=	[transforms(_UpperCAmelCase				) for image in examples[image_column_name]]
        return examples

    if training_args.do_train:
        if "train" not in ds:
            raise ValueError('--do_train requires a train dataset'				)
        if data_args.max_train_samples is not None:
            lowerCamelCase__					:   Optional[int]      				=	ds['train'].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
        # Set the training transforms
        ds["train"].set_transform(_UpperCAmelCase				)

    if training_args.do_eval:
        if "validation" not in ds:
            raise ValueError('--do_eval requires a validation dataset'				)
        if data_args.max_eval_samples is not None:
            lowerCamelCase__					:   List[str]      				=	(
                ds['validation'].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
            )
        # Set the validation transforms
        ds["validation"].set_transform(_UpperCAmelCase				)

    # Compute absolute learning rate
    lowerCamelCase__					:   Union[str, Any]      				=	(
        training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
    )
    if training_args.base_learning_rate is not None:
        lowerCamelCase__					:   Tuple      				=	training_args.base_learning_rate * total_train_batch_size / 256

    # Initialize our trainer
    lowerCamelCase__					:   str      				=	Trainer(
        model=_UpperCAmelCase		,			args=_UpperCAmelCase		,			train_dataset=ds['train'] if training_args.do_train else None		,			eval_dataset=ds['validation'] if training_args.do_eval else None		,			tokenizer=_UpperCAmelCase		,			data_collator=_UpperCAmelCase		,			)

    # Training
    if training_args.do_train:
        lowerCamelCase__					:   List[str]      				=	None
        if training_args.resume_from_checkpoint is not None:
            lowerCamelCase__					:   Optional[int]      				=	training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            lowerCamelCase__					:   Any      				=	last_checkpoint
        lowerCamelCase__					:   Optional[int]      				=	trainer.train(resume_from_checkpoint=_UpperCAmelCase				)
        trainer.save_model()
        trainer.log_metrics('train'		,			train_result.metrics				)
        trainer.save_metrics('train'		,			train_result.metrics				)
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        lowerCamelCase__					:   Dict      				=	trainer.evaluate()
        trainer.log_metrics('eval'		,			_UpperCAmelCase				)
        trainer.save_metrics('eval'		,			_UpperCAmelCase				)

    # Write model card and (optionally) push to hub
    lowerCamelCase__					:   Union[str, Any]      				=	{
        'tasks': 'masked-auto-encoding',
        'dataset': data_args.dataset_name,
        'tags': ['masked-auto-encoding'],
    }
    if training_args.push_to_hub:
        trainer.push_to_hub(**_UpperCAmelCase				)
    else:
        trainer.create_model_card(**_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Optional[int]:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
    main()





