

import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


SCREAMING_SNAKE_CASE		:str						=     logging.getLogger(__name__)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						List[Any]:



    """simple docstring"""






    return (preds == labels).mean()






@dataclass
class 						__magic_name__					:
     UpperCamelCase_	:str										=      field(
         metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}				)
     UpperCamelCase_	:Optional[str]										=      field(
         default=snake_case			,      metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}				)
     UpperCamelCase_	:Optional[str]										=      field(
         default=snake_case			,      metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}				)
     UpperCamelCase_	:Optional[str]										=      field(
         default=snake_case			,      metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			,      )






@dataclass
class 						__magic_name__					:
     UpperCamelCase_	:str										=      field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()				)}				)
     UpperCamelCase_	:str										=      field(metadata={"""help""": """Should contain the data files for the task."""}				)
     UpperCamelCase_	:int										=      field(
         default=1_2_8			,      metadata={
             """help""": (
                 """The maximum total input sequence length after tokenization. Sequences longer """
                 """than this will be truncated, sequences shorter will be padded."""
             )
         }			,      )
     UpperCamelCase_	:bool										=      field(
         default=snake_case			,      metadata={"""help""": """Overwrite the cached training and evaluation sets"""}				)




def 			lowerCAmelCase(      )->						str:



    """simple docstring"""






    UpperCamelCase_			  =						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
    UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir			)
        and os.listdir(training_args.output_dir			)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
            " --overwrite_output_dir to overcome."			)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"			,						datefmt="%m/%d/%Y %H:%M:%S"			,						level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN			,						)
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"			,						training_args.local_rank			,						training_args.device			,						training_args.n_gpu			,						bool(training_args.local_rank != -1			)			,						training_args.fpaa			,						)
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank			):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info("Training/evaluation parameters %s"			,						SCREAMING_SNAKE_CASE_			)

    # Set seed
    set_seed(training_args.seed			)

    try:
        UpperCamelCase_			  =						processors[data_args.task_name]()
        UpperCamelCase_			  =						processor.get_labels()
        UpperCamelCase_			  =						len(SCREAMING_SNAKE_CASE_			)
    except KeyError:
        raise ValueError("Task not found: %s" % (data_args.task_name)			)

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    UpperCamelCase_			  =						AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path			,						num_labels=SCREAMING_SNAKE_CASE_			,						finetuning_task=data_args.task_name			,						cache_dir=model_args.cache_dir			,						)
    UpperCamelCase_			  =						AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path			,						cache_dir=model_args.cache_dir			,						)
    UpperCamelCase_			  =						AutoModelForMultipleChoice.from_pretrained(
        model_args.model_name_or_path			,						from_tf=bool(".ckpt" in model_args.model_name_or_path			)			,						config=SCREAMING_SNAKE_CASE_			,						cache_dir=model_args.cache_dir			,						)

    # Get datasets
    UpperCamelCase_			  =						(
        MultipleChoiceDataset(
            data_dir=data_args.data_dir			,						tokenizer=SCREAMING_SNAKE_CASE_			,						task=data_args.task_name			,						max_seq_length=data_args.max_seq_length			,						overwrite_cache=data_args.overwrite_cache			,						mode=Split.train			,						)
        if training_args.do_train
        else None
    )
    UpperCamelCase_			  =						(
        MultipleChoiceDataset(
            data_dir=data_args.data_dir			,						tokenizer=SCREAMING_SNAKE_CASE_			,						task=data_args.task_name			,						max_seq_length=data_args.max_seq_length			,						overwrite_cache=data_args.overwrite_cache			,						mode=Split.dev			,						)
        if training_args.do_eval
        else None
    )

    def compute_metrics(SCREAMING_SNAKE_CASE_			) -> Dict:
        UpperCamelCase_			  =						np.argmax(p.predictions			,						axis=1			)
        return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE_			,						p.label_ids			)}

    # Data collator
    UpperCamelCase_			  =						DataCollatorWithPadding(SCREAMING_SNAKE_CASE_			,						pad_to_multiple_of=8			) if training_args.fpaa else None

    # Initialize our Trainer
    UpperCamelCase_			  =						Trainer(
        model=SCREAMING_SNAKE_CASE_			,						args=SCREAMING_SNAKE_CASE_			,						train_dataset=SCREAMING_SNAKE_CASE_			,						eval_dataset=SCREAMING_SNAKE_CASE_			,						compute_metrics=SCREAMING_SNAKE_CASE_			,						data_collator=SCREAMING_SNAKE_CASE_			,						)

    # Training
    if training_args.do_train:
        trainer.train(
            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir			)

    # Evaluation
    UpperCamelCase_			  =						{}
    if training_args.do_eval:
        logger.info("*** Evaluate ***"			)

        UpperCamelCase_			  =						trainer.evaluate()

        UpperCamelCase_			  =						os.path.join(training_args.output_dir			,						"eval_results.txt"			)
        if trainer.is_world_master():
            with open(SCREAMING_SNAKE_CASE_			,						"w"			) as writer:
                logger.info("***** Eval results *****"			)
                for key, value in result.items():
                    logger.info("  %s = %s"			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
                    writer.write("%s = %s\n" % (key, value)			)

                results.update(SCREAMING_SNAKE_CASE_			)

    return results




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						str:



    """simple docstring"""






    main()


if __name__ == "__main__":
 main()



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE		:Optional[int]						=     logging.get_logger(__name__)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						"huggingface/label-files"
    UpperCamelCase_			  =						"imagenet-1k-id2label.json"
    UpperCamelCase_			  =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						repo_type="dataset"			)			,						"r"			)			)
    UpperCamelCase_			  =						{int(SCREAMING_SNAKE_CASE_			): v for k, v in idalabel.items()}
    UpperCamelCase_			  =						{v: k for k, v in idalabel.items()}

    UpperCamelCase_			  =						"std_conv" if "bit" in model_name else False

    # note that when using BiT as backbone for ViT-hybrid checkpoints,
    # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
    # config.conv_layer = "std_conv_same"
    UpperCamelCase_			  =						BitConfig(
        conv_layer=SCREAMING_SNAKE_CASE_			,						num_labels=1_0_0_0			,						idalabel=SCREAMING_SNAKE_CASE_			,						labelaid=SCREAMING_SNAKE_CASE_			,						)

    return config




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Optional[Any]:



    """simple docstring"""






    if "stem.conv" in name:
        UpperCamelCase_			  =						name.replace("stem.conv"			,						"bit.embedder.convolution"			)
    if "blocks" in name:
        UpperCamelCase_			  =						name.replace("blocks"			,						"layers"			)
    if "head.fc" in name:
        UpperCamelCase_			  =						name.replace("head.fc"			,						"classifier.1"			)
    if name.startswith("norm"			):
        UpperCamelCase_			  =						"bit." + name
    if "bit" not in name and "classifier" not in name:
        UpperCamelCase_			  =						"bit.encoder." + name

    return name




def 			lowerCAmelCase(      )->						int:



    """simple docstring"""






    UpperCamelCase_			  =						"http://images.cocodataset.org/val2017/000000039769.jpg"
    UpperCamelCase_			  =						Image.open(requests.get(SCREAMING_SNAKE_CASE_			,						stream=SCREAMING_SNAKE_CASE_			).raw			)
    return im




@torch.no_grad()
def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=False			)->						Tuple:



    """simple docstring"""






    UpperCamelCase_			  =						get_config(SCREAMING_SNAKE_CASE_			)

    # load original model from timm
    UpperCamelCase_			  =						create_model(SCREAMING_SNAKE_CASE_			,						pretrained=SCREAMING_SNAKE_CASE_			)
    timm_model.eval()

    # load state_dict of original model
    UpperCamelCase_			  =						timm_model.state_dict()
    for key in state_dict.copy().keys():
        UpperCamelCase_			  =						state_dict.pop(SCREAMING_SNAKE_CASE_			)
        UpperCamelCase_			  =						val.squeeze() if "head" in key else val

    # load HuggingFace model
    UpperCamelCase_			  =						BitForImageClassification(SCREAMING_SNAKE_CASE_			)
    model.eval()
    model.load_state_dict(SCREAMING_SNAKE_CASE_			)

    # create image processor
    UpperCamelCase_			  =						create_transform(**resolve_data_config({}			,						model=SCREAMING_SNAKE_CASE_			)			)
    UpperCamelCase_			  =						transform.transforms

    UpperCamelCase_			  =						{
        "bilinear": PILImageResampling.BILINEAR,
        "bicubic": PILImageResampling.BICUBIC,
        "nearest": PILImageResampling.NEAREST,
    }

    UpperCamelCase_			  =						BitImageProcessor(
        do_resize=SCREAMING_SNAKE_CASE_			,						size={"shortest_edge": timm_transforms[0].size}			,						resample=pillow_resamplings[timm_transforms[0].interpolation.value]			,						do_center_crop=SCREAMING_SNAKE_CASE_			,						crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}			,						do_normalize=SCREAMING_SNAKE_CASE_			,						image_mean=timm_transforms[-1].mean.tolist()			,						image_std=timm_transforms[-1].std.tolist()			,						)

    UpperCamelCase_			  =						prepare_img()
    UpperCamelCase_			  =						transform(SCREAMING_SNAKE_CASE_			).unsqueeze(0			)
    UpperCamelCase_			  =						processor(SCREAMING_SNAKE_CASE_			,						return_tensors="pt"			).pixel_values

    # verify pixel values
    assert torch.allclose(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)

    # verify logits
    with torch.no_grad():
        UpperCamelCase_			  =						model(SCREAMING_SNAKE_CASE_			)
        UpperCamelCase_			  =						outputs.logits

    print("Logits:"			,						logits[0, :3]			)
    print("Predicted class:"			,						model.config.idalabel[logits.argmax(-1			).item()]			)
    UpperCamelCase_			  =						timm_model(SCREAMING_SNAKE_CASE_			)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(SCREAMING_SNAKE_CASE_			,						outputs.logits			,						atol=1E-3			)
    print("Looks ok!"			)

    if pytorch_dump_folder_path is not None:
        Path(SCREAMING_SNAKE_CASE_			).mkdir(exist_ok=SCREAMING_SNAKE_CASE_			)
        print(f"Saving model {model_name} and processor to {pytorch_dump_folder_path}"			)
        model.save_pretrained(SCREAMING_SNAKE_CASE_			)
        processor.save_pretrained(SCREAMING_SNAKE_CASE_			)

    if push_to_hub:
        print(f"Pushing model {model_name} and processor to the hub"			)
        model.push_to_hub(f"ybelkada/{model_name}"			)
        processor.push_to_hub(f"ybelkada/{model_name}"			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:Optional[Any]						=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""resnetv2_50x1_bitm""",
     type=str,
     help="""Name of the BiT timm model you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
 )
 parser.add_argument(
     """--push_to_hub""",
     action="""store_true""",
     help="""Whether to push the model to the hub.""",
 )

 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     parser.parse_args()
 convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
