
import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



def 		lowercase_    (     A__	)							->							str:
    """simple docstring"""

    if "cls_token" in name:
        snake_case				  =      name.replace("cls_token"				,	"vit.embeddings.cls_token"	)
    if "mask_token" in name:
        snake_case				  =      name.replace("mask_token"				,	"decoder.mask_token"	)
    if "decoder_pos_embed" in name:
        snake_case				  =      name.replace("decoder_pos_embed"				,	"decoder.decoder_pos_embed"	)
    if "pos_embed" in name and "decoder" not in name:
        snake_case				  =      name.replace("pos_embed"				,	"vit.embeddings.position_embeddings"	)
    if "patch_embed.proj" in name:
        snake_case				  =      name.replace("patch_embed.proj"				,	"vit.embeddings.patch_embeddings.projection"	)
    if "patch_embed.norm" in name:
        snake_case				  =      name.replace("patch_embed.norm"				,	"vit.embeddings.norm"	)
    if "decoder_blocks" in name:
        snake_case				  =      name.replace("decoder_blocks"				,	"decoder.decoder_layers"	)
    if "blocks" in name:
        snake_case				  =      name.replace("blocks"				,	"vit.encoder.layer"	)
    if "attn.proj" in name:
        snake_case				  =      name.replace("attn.proj"				,	"attention.output.dense"	)
    if "attn" in name:
        snake_case				  =      name.replace("attn"				,	"attention.self"	)
    if "norm1" in name:
        snake_case				  =      name.replace("norm1"				,	"layernorm_before"	)
    if "norm2" in name:
        snake_case				  =      name.replace("norm2"				,	"layernorm_after"	)
    if "mlp.fc1" in name:
        snake_case				  =      name.replace("mlp.fc1"				,	"intermediate.dense"	)
    if "mlp.fc2" in name:
        snake_case				  =      name.replace("mlp.fc2"				,	"output.dense"	)
    if "decoder_embed" in name:
        snake_case				  =      name.replace("decoder_embed"				,	"decoder.decoder_embed"	)
    if "decoder_norm" in name:
        snake_case				  =      name.replace("decoder_norm"				,	"decoder.decoder_norm"	)
    if "decoder_pred" in name:
        snake_case				  =      name.replace("decoder_pred"				,	"decoder.decoder_pred"	)
    if "norm.weight" in name and "decoder" not in name:
        snake_case				  =      name.replace("norm.weight"				,	"vit.layernorm.weight"	)
    if "norm.bias" in name and "decoder" not in name:
        snake_case				  =      name.replace("norm.bias"				,	"vit.layernorm.bias"	)

    return name



def 		lowercase_    (     A__				,	A__	)							->							Dict:
    """simple docstring"""

    for key in orig_state_dict.copy().keys():
        snake_case				  =      orig_state_dict.pop(A__	)

        if "qkv" in key:
            snake_case				  =      key.split("."	)
            snake_case				  =      int(key_split[1]	)
            if "decoder_blocks" in key:
                snake_case				  =      config.decoder_hidden_size
                snake_case				  =      "decoder.decoder_layers."
                if "weight" in key:
                    snake_case				  =      val[:dim, :]
                    snake_case				  =      val[dim : dim * 2, :]
                    snake_case				  =      val[-dim:, :]
                elif "bias" in key:
                    snake_case				  =      val[:dim]
                    snake_case				  =      val[dim : dim * 2]
                    snake_case				  =      val[-dim:]
            else:
                snake_case				  =      config.hidden_size
                snake_case				  =      "vit.encoder.layer."
                if "weight" in key:
                    snake_case				  =      val[:dim, :]
                    snake_case				  =      val[dim : dim * 2, :]
                    snake_case				  =      val[-dim:, :]
                elif "bias" in key:
                    snake_case				  =      val[:dim]
                    snake_case				  =      val[dim : dim * 2]
                    snake_case				  =      val[-dim:]

        else:
            snake_case				  =      val

    return orig_state_dict



def 		lowercase_    (     A__				,	A__	)							->							Dict:
    """simple docstring"""

    snake_case				  =      ViTMAEConfig()
    if "large" in checkpoint_url:
        snake_case				  =      1024
        snake_case				  =      4096
        snake_case				  =      24
        snake_case				  =      16
    elif "huge" in checkpoint_url:
        snake_case				  =      14
        snake_case				  =      1280
        snake_case				  =      5120
        snake_case				  =      32
        snake_case				  =      16

    snake_case				  =      ViTMAEForPreTraining(A__	)

    snake_case				  =      torch.hub.load_state_dict_from_url(A__				,	map_location="cpu"	)["model"]

    snake_case				  =      ViTMAEImageProcessor(size=config.image_size	)

    snake_case				  =      convert_state_dict(A__				,	A__	)

    model.load_state_dict(A__	)
    model.eval()

    snake_case				  =      "https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"

    snake_case				  =      Image.open(requests.get(A__				,	stream=A__	).raw	)
    snake_case				  =      ViTMAEImageProcessor(size=config.image_size	)
    snake_case				  =      image_processor(images=A__				,	return_tensors="pt"	)

    # forward pass
    torch.manual_seed(2	)
    snake_case				  =      model(**A__	)
    snake_case				  =      outputs.logits

    if "large" in checkpoint_url:
        snake_case				  =      torch.tensor(
            [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]	)
    elif "huge" in checkpoint_url:
        snake_case				  =      torch.tensor(
            [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]	)
    else:
        snake_case				  =      torch.tensor(
            [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]	)

    # verify logits
    assert torch.allclose(logits[0, :3, :3]				,	A__				,	atol=1e-4	)

    print(F'Saving model to {pytorch_dump_folder_path}'	)
    model.save_pretrained(A__	)

    print(F'Saving image processor to {pytorch_dump_folder_path}'	)
    image_processor.save_pretrained(A__	)


if __name__ == "__main__":
   _A						      =				argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--checkpoint_url",
       default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth",
       type=str,
       help="URL of the checkpoint you'd like to convert.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
   )

   _A						      =				parser.parse_args()
   convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()



def 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__				,	A__ = None				,	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      {}

    if train_file is not None:
        snake_case				  =      [train_file]
    if eval_file is not None:
        snake_case				  =      [eval_file]
    if test_file is not None:
        snake_case				  =      [test_file]

    snake_case				  =      datasets.load_dataset("csv"				,	data_files=A__	)
    snake_case				  =      list(ds[list(files.keys()	)[0]].features.keys()	)
    snake_case				  =      features_name.pop(A__	)
    snake_case				  =      list(set(ds[list(files.keys()	)[0]][label_name]	)	)
    snake_case				  =      {label: i for i, label in enumerate(A__	)}
    snake_case				  =      tokenizer.model_input_names
    snake_case				  =      {}

    if len(A__	) == 1:
        for k in files.keys():
            snake_case				  =      ds[k].map(
                lambda A__	: tokenizer.batch_encode_plus(
                    example[features_name[0]]				,	truncation=A__				,	max_length=A__				,	padding="max_length"	)				,	batched=A__				,	)
    elif len(A__	) == 2:
        for k in files.keys():
            snake_case				  =      ds[k].map(
                lambda A__	: tokenizer.batch_encode_plus(
                    (example[features_name[0]], example[features_name[1]])				,	truncation=A__				,	max_length=A__				,	padding="max_length"				,	)				,	batched=A__				,	)

    def gen_train():
        for ex in transformed_ds[datasets.Split.TRAIN]:
            snake_case				  =      {k: v for k, v in ex.items() if k in input_names}
            snake_case				  =      labelaid[ex[label_name]]
            yield (d, label)

    def gen_val():
        for ex in transformed_ds[datasets.Split.VALIDATION]:
            snake_case				  =      {k: v for k, v in ex.items() if k in input_names}
            snake_case				  =      labelaid[ex[label_name]]
            yield (d, label)

    def gen_test():
        for ex in transformed_ds[datasets.Split.TEST]:
            snake_case				  =      {k: v for k, v in ex.items() if k in input_names}
            snake_case				  =      labelaid[ex[label_name]]
            yield (d, label)

    snake_case				  =      (
        tf.data.Dataset.from_generator(
            A__				,	({k: tf.intaa for k in input_names}, tf.intaa)				,	({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))				,	)
        if datasets.Split.TRAIN in transformed_ds
        else None
    )

    if train_ds is not None:
        snake_case				  =      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]	)	)	)

    snake_case				  =      (
        tf.data.Dataset.from_generator(
            A__				,	({k: tf.intaa for k in input_names}, tf.intaa)				,	({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))				,	)
        if datasets.Split.VALIDATION in transformed_ds
        else None
    )

    if val_ds is not None:
        snake_case				  =      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]	)	)	)

    snake_case				  =      (
        tf.data.Dataset.from_generator(
            A__				,	({k: tf.intaa for k in input_names}, tf.intaa)				,	({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))				,	)
        if datasets.Split.TEST in transformed_ds
        else None
    )

    if test_ds is not None:
        snake_case				  =      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]	)	)	)

    return train_ds, val_ds, test_ds, labelaid


_A						      =				logging.getLogger(__name__)







@dataclass
class   lowerCamelCase	:
     UpperCAmelCase__       :							int     =	field(metadata={"help": "Which column contains the label"}						)
     UpperCAmelCase__       :							str     =	field(default=A_							,				metadata={"help": "The path of the training file"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(default=A_							,				metadata={"help": "The path of the development file"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(default=A_							,				metadata={"help": "The path of the test file"}						)
     UpperCAmelCase__       :							int     =	field(
         default=1_28							,				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=A_							,				metadata={"help": "Overwrite the cached training and evaluation sets"}						)







@dataclass
class   lowerCamelCase	:
     UpperCAmelCase__       :							str     =	field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(
         default=A_							,				metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(
         default=A_							,				metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}						)
     UpperCAmelCase__       :							bool     =	field(default=A_							,				metadata={"help": "Set this flag to use fast tokenization."}						)
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     UpperCAmelCase__       :							Optional[str]     =	field(
         default=A_							,				metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}							,				)



def 		lowercase_    (     )							->							Dict:
    """simple docstring"""

    snake_case				  =      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
    snake_case  ,							snake_case  ,							snake_case				  =      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				,	)
    logger.info(
        F'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1	)}, '
        F'16-bits training: {training_args.fpaa}'	)
    logger.info(F'Training/evaluation parameters {training_args}'	)

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

    snake_case				  =      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				,	)

    snake_case  ,							snake_case  ,							snake_case  ,							snake_case				  =      get_tfds(
        train_file=data_args.train_file				,	eval_file=data_args.dev_file				,	test_file=data_args.test_file				,	tokenizer=A__				,	label_column_id=data_args.label_column_id				,	max_seq_length=data_args.max_seq_length				,	)

    snake_case				  =      AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path				,	num_labels=len(A__	)				,	labelaid=A__				,	idalabel={id: label for label, id in labelaid.items()}				,	finetuning_task="text-classification"				,	cache_dir=model_args.cache_dir				,	)

    with training_args.strategy.scope():
        snake_case				  =      TFAutoModelForSequenceClassification.from_pretrained(
            model_args.model_name_or_path				,	from_pt=bool(".bin" in model_args.model_name_or_path	)				,	config=A__				,	cache_dir=model_args.cache_dir				,	)

    def compute_metrics(A__	) -> Dict:
        snake_case				  =      np.argmax(p.predictions				,	axis=1	)

        return {"acc": (preds == p.label_ids).mean()}

    # Initialize our Trainer
    snake_case				  =      TFTrainer(
        model=A__				,	args=A__				,	train_dataset=A__				,	eval_dataset=A__				,	compute_metrics=A__				,	)

    # Training
    if training_args.do_train:
        trainer.train()
        trainer.save_model()
        tokenizer.save_pretrained(training_args.output_dir	)

    # Evaluation
    snake_case				  =      {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***"	)
        snake_case				  =      trainer.evaluate()
        snake_case				  =      os.path.join(training_args.output_dir				,	"eval_results.txt"	)

        with open(A__				,	"w"	) as writer:
            logger.info("***** Eval results *****"	)

            for key, value in result.items():
                logger.info(F'  {key} = {value}'	)
                writer.write(F'{key} = {value}\n'	)

            results.update(A__	)

    return results


if __name__ == "__main__":
   main()

