
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

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


lowercase  :					Optional[Any]						    =							logging.getLogger(__name__)

@dataclass
class __snake_case		:
    _a		:						str=				field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}     )
    _a		:						Optional[str]=				field(
        default=lowerCAmelCase					,	metadata={"help": "Pretrained config name or path if not the same as model_name"}     )
    _a		:						Optional[str]=				field(
        default="NER"					,	metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}     )
    _a		:						Optional[str]=				field(
        default=lowerCAmelCase					,	metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}     )
    _a		:						bool=				field(default=lowerCAmelCase					,	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.
    _a		:						Optional[str]=				field(
        default=lowerCAmelCase					,	metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}					,	)






@dataclass
class __snake_case		:
    _a		:						str=				field(
        metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}     )
    _a		:						Optional[str]=				field(
        default=lowerCAmelCase					,	metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}					,	)
    _a		:						int=				field(
        default=128					,	metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        }					,	)
    _a		:						bool=				field(
        default=lowerCAmelCase					,	metadata={"help": "Overwrite the cached training and evaluation sets"}     )







def 						_snake_case(  ) ->    Dict:
       # 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.

       lowercase							:     Optional[int]       	=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
       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.
              lowercase	,			lowercase	,			lowercase							:     Optional[int]       	=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
       else:
              lowercase	,			lowercase	,			lowercase							:     Any       	=      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."""				)

       lowercase							:     List[str]       	=      import_module("""tasks"""				)
       try:
              lowercase							:     List[Any]       	=      getattr(SCREAMING_SNAKE_CASE__					,	model_args.task_type				)
              lowercase							:     TokenClassificationTask       	=      token_classification_task_clazz()
       except AttributeError:
              raise ValueError(
                  f"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
                  f"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"				)

       # 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				)

       # Prepare CONLL-2003 task
       lowercase							:     List[Any]       	=      token_classification_task.get_labels(data_args.labels				)
       lowercase							:     Dict[int, str]       	=      dict(enumerate(SCREAMING_SNAKE_CASE__				)				)
       lowercase							:     Tuple       	=      len(SCREAMING_SNAKE_CASE__				)

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

       lowercase							:     Optional[int]       	=      AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path					,	num_labels=SCREAMING_SNAKE_CASE__					,	idalabel=SCREAMING_SNAKE_CASE__					,	labelaid={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE__				)}					,	cache_dir=model_args.cache_dir					,	)
       lowercase							:     Dict       	=      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					,	use_fast=model_args.use_fast					,	)
       lowercase							:     Optional[Any]       	=      AutoModelForTokenClassification.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
       lowercase							:     List[Any]       	=      (
           TokenClassificationDataset(
               token_classification_task=SCREAMING_SNAKE_CASE__					,	data_dir=data_args.data_dir					,	tokenizer=SCREAMING_SNAKE_CASE__					,	labels=SCREAMING_SNAKE_CASE__					,	model_type=config.model_type					,	max_seq_length=data_args.max_seq_length					,	overwrite_cache=data_args.overwrite_cache					,	mode=Split.train					,	)
           if training_args.do_train
           else None
       )
       lowercase							:     Tuple       	=      (
           TokenClassificationDataset(
               token_classification_task=SCREAMING_SNAKE_CASE__					,	data_dir=data_args.data_dir					,	tokenizer=SCREAMING_SNAKE_CASE__					,	labels=SCREAMING_SNAKE_CASE__					,	model_type=config.model_type					,	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 align_predictions(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) -> Tuple[List[int], List[int]]:
              lowercase							:     str       	=      np.argmax(SCREAMING_SNAKE_CASE__					,	axis=2				)

              lowercase	,			lowercase							:     int       	=      preds.shape

              lowercase							:     str       	=      [[] for _ in range(SCREAMING_SNAKE_CASE__				)]
              lowercase							:     Optional[Any]       	=      [[] for _ in range(SCREAMING_SNAKE_CASE__				)]

              for i in range(SCREAMING_SNAKE_CASE__				):
                     for j in range(SCREAMING_SNAKE_CASE__				):
                            if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                                   out_label_list[i].append(label_map[label_ids[i][j]]				)
                                   preds_list[i].append(label_map[preds[i][j]]				)

              return preds_list, out_label_list

       def compute_metrics(SCREAMING_SNAKE_CASE__				) -> Dict:
              lowercase	,			lowercase							:     List[str]       	=      align_predictions(p.predictions					,	p.label_ids				)
              return {
                  "accuracy_score": accuracy_score(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				),
                  "precision": precision_score(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				),
                  "recall": recall_score(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				),
                  "f1": fa_score(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				),
              }

       # Data collator
       lowercase							:     str       	=      DataCollatorWithPadding(SCREAMING_SNAKE_CASE__					,	pad_to_multiple_of=8				) if training_args.fpaa else None

       # Initialize our Trainer
       lowercase							:     Dict       	=      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_process_zero():
                     tokenizer.save_pretrained(training_args.output_dir				)

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

              lowercase							:     List[Any]       	=      trainer.evaluate()

              lowercase							:     Optional[Any]       	=      os.path.join(training_args.output_dir					,	"""eval_results.txt"""				)
              if trainer.is_world_process_zero():
                     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__				)

    # Predict
       if training_args.do_predict:
              lowercase							:     Any       	=      TokenClassificationDataset(
                  token_classification_task=SCREAMING_SNAKE_CASE__					,	data_dir=data_args.data_dir					,	tokenizer=SCREAMING_SNAKE_CASE__					,	labels=SCREAMING_SNAKE_CASE__					,	model_type=config.model_type					,	max_seq_length=data_args.max_seq_length					,	overwrite_cache=data_args.overwrite_cache					,	mode=Split.test					,	)

              lowercase	,			lowercase	,			lowercase							:     List[str]       	=      trainer.predict(SCREAMING_SNAKE_CASE__				)
              lowercase	,			lowercase							:     List[Any]       	=      align_predictions(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

              lowercase							:     int       	=      os.path.join(training_args.output_dir					,	"""test_results.txt"""				)
              if trainer.is_world_process_zero():
                     with open(SCREAMING_SNAKE_CASE__					,	"""w"""				) as writer:
                            for key, value in metrics.items():
                                   logger.info("""  %s = %s"""					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
                                   writer.write("""%s = %s\n""" % (key, value)				)

        # Save predictions
              lowercase							:     List[Any]       	=      os.path.join(training_args.output_dir					,	"""test_predictions.txt"""				)
              if trainer.is_world_process_zero():
                     with open(SCREAMING_SNAKE_CASE__					,	"""w"""				) as writer:
                            with open(os.path.join(data_args.data_dir					,	"""test.txt"""				)					,	"""r"""				) as f:
                                   token_classification_task.write_predictions_to_file(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       return results







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Tuple:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
  main()


import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       if "cls_token" in name:
              lowercase							:     List[Any]       	=      name.replace("""cls_token"""					,	"""vit.embeddings.cls_token"""				)
       if "mask_token" in name:
              lowercase							:     Any       	=      name.replace("""mask_token"""					,	"""decoder.mask_token"""				)
       if "decoder_pos_embed" in name:
              lowercase							:     str       	=      name.replace("""decoder_pos_embed"""					,	"""decoder.decoder_pos_embed"""				)
       if "pos_embed" in name and "decoder" not in name:
              lowercase							:     List[str]       	=      name.replace("""pos_embed"""					,	"""vit.embeddings.position_embeddings"""				)
       if "patch_embed.proj" in name:
              lowercase							:     Tuple       	=      name.replace("""patch_embed.proj"""					,	"""vit.embeddings.patch_embeddings.projection"""				)
       if "patch_embed.norm" in name:
              lowercase							:     int       	=      name.replace("""patch_embed.norm"""					,	"""vit.embeddings.norm"""				)
       if "decoder_blocks" in name:
              lowercase							:     Tuple       	=      name.replace("""decoder_blocks"""					,	"""decoder.decoder_layers"""				)
       if "blocks" in name:
              lowercase							:     List[Any]       	=      name.replace("""blocks"""					,	"""vit.encoder.layer"""				)
       if "attn.proj" in name:
              lowercase							:     List[str]       	=      name.replace("""attn.proj"""					,	"""attention.output.dense"""				)
       if "attn" in name:
              lowercase							:     Union[str, Any]       	=      name.replace("""attn"""					,	"""attention.self"""				)
       if "norm1" in name:
              lowercase							:     Optional[Any]       	=      name.replace("""norm1"""					,	"""layernorm_before"""				)
       if "norm2" in name:
              lowercase							:     Union[str, Any]       	=      name.replace("""norm2"""					,	"""layernorm_after"""				)
       if "mlp.fc1" in name:
              lowercase							:     Dict       	=      name.replace("""mlp.fc1"""					,	"""intermediate.dense"""				)
       if "mlp.fc2" in name:
              lowercase							:     Dict       	=      name.replace("""mlp.fc2"""					,	"""output.dense"""				)
       if "decoder_embed" in name:
              lowercase							:     List[str]       	=      name.replace("""decoder_embed"""					,	"""decoder.decoder_embed"""				)
       if "decoder_norm" in name:
              lowercase							:     Dict       	=      name.replace("""decoder_norm"""					,	"""decoder.decoder_norm"""				)
       if "decoder_pred" in name:
              lowercase							:     List[str]       	=      name.replace("""decoder_pred"""					,	"""decoder.decoder_pred"""				)
       if "norm.weight" in name and "decoder" not in name:
              lowercase							:     Tuple       	=      name.replace("""norm.weight"""					,	"""vit.layernorm.weight"""				)
       if "norm.bias" in name and "decoder" not in name:
              lowercase							:     int       	=      name.replace("""norm.bias"""					,	"""vit.layernorm.bias"""				)

       return name







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       for key in orig_state_dict.copy().keys():
              lowercase							:     List[Any]       	=      orig_state_dict.pop(SCREAMING_SNAKE_CASE__				)

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

              else:
                     lowercase							:     Union[str, Any]       	=      val

       return orig_state_dict







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    int:
       lowercase							:     int       	=      ViTMAEConfig()
       if "large" in checkpoint_url:
              lowercase							:     Dict       	=      1_024
              lowercase							:     str       	=      4_096
              lowercase							:     Optional[Any]       	=      24
              lowercase							:     Optional[Any]       	=      16
       elif "huge" in checkpoint_url:
              lowercase							:     int       	=      14
              lowercase							:     List[Any]       	=      1_280
              lowercase							:     int       	=      5_120
              lowercase							:     List[Any]       	=      32
              lowercase							:     Any       	=      16

       lowercase							:     List[str]       	=      ViTMAEForPreTraining(SCREAMING_SNAKE_CASE__				)

       lowercase							:     Optional[Any]       	=      torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__					,	map_location="""cpu"""				)["""model"""]

       lowercase							:     Tuple       	=      ViTMAEImageProcessor(size=config.image_size				)

       lowercase							:     Optional[int]       	=      convert_state_dict(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       model.load_state_dict(SCREAMING_SNAKE_CASE__				)
       model.eval()

       lowercase							:     Union[str, Any]       	=      """https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

       lowercase							:     Union[str, Any]       	=      Image.open(requests.get(SCREAMING_SNAKE_CASE__					,	stream=SCREAMING_SNAKE_CASE__				).raw				)
       lowercase							:     Optional[Any]       	=      ViTMAEImageProcessor(size=config.image_size				)
       lowercase							:     List[Any]       	=      image_processor(images=SCREAMING_SNAKE_CASE__					,	return_tensors="""pt"""				)

       # forward pass
       torch.manual_seed(2				)
       lowercase							:     int       	=      model(**SCREAMING_SNAKE_CASE__				)
       lowercase							:     str       	=      outputs.logits

       if "large" in checkpoint_url:
              lowercase							:     List[Any]       	=      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:
              lowercase							:     Tuple       	=      torch.tensor(
                  [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]				)
       else:
              lowercase							:     List[str]       	=      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]					,	SCREAMING_SNAKE_CASE__					,	atol=1e-4				)

       print(f"Saving model to {pytorch_dump_folder_path}"				)
       model.save_pretrained(SCREAMING_SNAKE_CASE__				)

       print(f"Saving image processor to {pytorch_dump_folder_path}"				)
       image_processor.save_pretrained(SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
  lowercase  :					Union[str, Any]						    =							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."""
  )

  lowercase  :					List[Any]						    =							parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
