





"""simple docstring"""





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__				:str				   =       logging.getLogger(__name__)




@dataclass
class 							snake_case							:

      '''simple docstring'''



      _A     :       Tuple         =       field(
          metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
      _A     :       Tuple         =       field(
          default=_A      ,  metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
      _A     :       str         =       field(
          default='NER'      ,  metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'} )
      _A     :       List[Any]         =       field(
          default=_A      ,  metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
      _A     :       int         =       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.
      _A     :       List[Any]         =       field(
          default=_A      ,  metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}      ,  )




@dataclass
class 							snake_case							:

      '''simple docstring'''



      _A     :       str         =       field(
          metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'} )
      _A     :       List[str]         =       field(
          default=_A      ,  metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}      ,  )
      _A     :       Dict         =       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.'
              )
          }      ,  )
      _A     :       Optional[int]         =       field(
          default=_A      ,  metadata={'help': 'Overwrite the cached training and evaluation sets'} )


def       lowerCamelCase_       (   )						->Union[str, Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Union[str, Any]       =						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.
    __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						str       =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
  else:
    __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						int       =						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.'''  )

  __UpperCAmelCase    :						Optional[Any]       =						import_module('''tasks'''  )
  try:
    __UpperCAmelCase    :						Union[str, Any]       =						getattr(A_  ,			model_args.task_type  )
    __UpperCAmelCase    :						int       =						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'''  ,			A_  )

  # Set seed
  set_seed(training_args.seed  )

  # Prepare CONLL-2003 task
  __UpperCAmelCase    :						Any       =						token_classification_task.get_labels(data_args.labels  )
  __UpperCAmelCase    :						Dict       =						dict(enumerate(A_  )  )
  __UpperCAmelCase    :						List[Any]       =						len(A_  )

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

  __UpperCAmelCase    :						int       =						AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,			num_labels=A_  ,			idalabel=A_  ,			labelaid={label: i for i, label in enumerate(A_  )}  ,			cache_dir=model_args.cache_dir  ,			)
  __UpperCAmelCase    :						List[str]       =						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  ,			)
  __UpperCAmelCase    :						Any       =						AutoModelForTokenClassification.from_pretrained(
      model_args.model_name_or_path  ,			from_tf=bool('''.ckpt''' in model_args.model_name_or_path  )  ,			config=A_  ,			cache_dir=model_args.cache_dir  ,			)

  # Get datasets
  __UpperCAmelCase    :						Union[str, Any]       =						(
      TokenClassificationDataset(
          token_classification_task=A_  ,			data_dir=data_args.data_dir  ,			tokenizer=A_  ,			labels=A_  ,			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
  )
  __UpperCAmelCase    :						Optional[Any]       =						(
      TokenClassificationDataset(
          token_classification_task=A_  ,			data_dir=data_args.data_dir  ,			tokenizer=A_  ,			labels=A_  ,			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(UpperCAmelCase_  ,			UpperCAmelCase_  ) -> Tuple[List[int], List[int]]:
    __UpperCAmelCase    :						Union[str, Any]       =						np.argmax(A_  ,			axis=2  )

    __UpperCAmelCase       ,     __UpperCAmelCase    :						List[str]       =						preds.shape

    __UpperCAmelCase    :						Union[str, Any]       =						[[] for _ in range(A_  )]
    __UpperCAmelCase    :						Optional[int]       =						[[] for _ in range(A_  )]

    for i in range(A_  ):
      for j in range(A_  ):
        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(UpperCAmelCase_  ) -> Dict:
    __UpperCAmelCase       ,     __UpperCAmelCase    :						int       =						align_predictions(p.predictions  ,			p.label_ids  )
    return {
        "accuracy_score": accuracy_score(A_  ,			A_  ),
        "precision": precision_score(A_  ,			A_  ),
        "recall": recall_score(A_  ,			A_  ),
        "f1": fa_score(A_  ,			A_  ),
    }

  # Data collator
  __UpperCAmelCase    :						List[Any]       =						DataCollatorWithPadding(A_  ,			pad_to_multiple_of=8  ) if training_args.fpaa else None

  # Initialize our Trainer
  __UpperCAmelCase    :						List[str]       =						Trainer(
      model=A_  ,			args=A_  ,			train_dataset=A_  ,			eval_dataset=A_  ,			compute_metrics=A_  ,			data_collator=A_  ,			)

  # 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
  __UpperCAmelCase    :						Dict       =						{}
  if training_args.do_eval:
    logger.info('''*** Evaluate ***'''  )

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

    __UpperCAmelCase    :						Optional[int]       =						os.path.join(training_args.output_dir  ,			'''eval_results.txt'''  )
    if trainer.is_world_process_zero():
      with open(A_  ,			'''w'''  ) as writer:
        logger.info('''***** Eval results *****'''  )
        for key, value in result.items():
          logger.info('''  %s = %s'''  ,			A_  ,			A_  )
          writer.write('''%s = %s\n''' % (key, value)  )

      results.update(A_  )

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

    __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						Union[str, Any]       =						trainer.predict(A_  )
    __UpperCAmelCase       ,     __UpperCAmelCase    :						Optional[int]       =						align_predictions(A_  ,			A_  )

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

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

  return results


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Dict:





  """simple docstring"""


  main()


if __name__ == "__main__":
  main()






"""simple docstring"""





import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Any:





  """simple docstring"""


  __UpperCAmelCase    :						Tuple       =						'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
  __UpperCAmelCase    :						Tuple       =						Image.open(requests.get(UpperCAmelCase_  ,			stream=UpperCAmelCase_  ).raw  ).convert('''RGB'''  )

  __UpperCAmelCase    :						Dict       =						transforms.Compose(
      [
          transforms.Resize((image_size, image_size)  ,			interpolation=InterpolationMode.BICUBIC  ),
          transforms.ToTensor(),
          transforms.Normalize((0.48_145_466, 0.4_578_275, 0.40_821_073)  ,			(0.26_862_954, 0.26_130_258, 0.27_577_711)  ),
      ]  )
  __UpperCAmelCase    :						Optional[Any]       =						transform(UpperCAmelCase_  ).unsqueeze(0  ).to(UpperCAmelCase_  )
  return image


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  if "visual_encoder" in key:
    __UpperCAmelCase    :						Tuple       =						re.sub('''visual_encoder*'''  ,			'''vision_model.encoder'''  ,			UpperCAmelCase_  )
  if "blocks" in key:
    __UpperCAmelCase    :						List[str]       =						re.sub(R'''blocks'''  ,			'''layers'''  ,			UpperCAmelCase_  )
  if "attn" in key:
    __UpperCAmelCase    :						Union[str, Any]       =						re.sub(R'''attn'''  ,			'''self_attn'''  ,			UpperCAmelCase_  )
  if "norm1" in key:
    __UpperCAmelCase    :						Optional[Any]       =						re.sub(R'''norm1'''  ,			'''layer_norm1'''  ,			UpperCAmelCase_  )
  if "norm2" in key:
    __UpperCAmelCase    :						Optional[int]       =						re.sub(R'''norm2'''  ,			'''layer_norm2'''  ,			UpperCAmelCase_  )
  if "encoder.norm" in key:
    __UpperCAmelCase    :						Union[str, Any]       =						re.sub(R'''encoder.norm'''  ,			'''post_layernorm'''  ,			UpperCAmelCase_  )
  if "encoder.patch_embed.proj" in key:
    __UpperCAmelCase    :						Optional[int]       =						re.sub(R'''encoder.patch_embed.proj'''  ,			'''embeddings.patch_embedding'''  ,			UpperCAmelCase_  )

  if "encoder.pos_embed" in key:
    __UpperCAmelCase    :						List[Any]       =						re.sub(R'''encoder.pos_embed'''  ,			'''embeddings.position_embedding'''  ,			UpperCAmelCase_  )
  if "encoder.cls_token" in key:
    __UpperCAmelCase    :						List[str]       =						re.sub(R'''encoder.cls_token'''  ,			'''embeddings.class_embedding'''  ,			UpperCAmelCase_  )

  if "self_attn" in key:
    __UpperCAmelCase    :						Any       =						re.sub(R'''self_attn.proj'''  ,			'''self_attn.projection'''  ,			UpperCAmelCase_  )

  return key


@torch.no_grad()
def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=None  )						->Dict:





  """simple docstring"""


  if config_path is not None:
    __UpperCAmelCase    :						Optional[int]       =						BlipConfig.from_pretrained(UpperCAmelCase_  )
  else:
    __UpperCAmelCase    :						Optional[int]       =						BlipConfig(projection_dim=5_12  ,			text_config={}  ,			vision_config={}  )

  __UpperCAmelCase    :						Optional[Any]       =						BlipForConditionalGeneration(UpperCAmelCase_  ).eval()

  __UpperCAmelCase    :						List[str]       =						'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

  __UpperCAmelCase    :						List[Any]       =						blip_decoder(pretrained=UpperCAmelCase_  ,			image_size=3_84  ,			vit='''base'''  )
  __UpperCAmelCase    :						str       =						pt_model.eval()

  __UpperCAmelCase    :						List[Any]       =						pt_model.state_dict()
  for key in modified_state_dict.copy():
    __UpperCAmelCase    :						List[str]       =						modified_state_dict.pop(UpperCAmelCase_  )
    __UpperCAmelCase    :						Optional[int]       =						rename_key(UpperCAmelCase_  )
    __UpperCAmelCase    :						List[Any]       =						value

  hf_model.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						str       =						3_84
  __UpperCAmelCase    :						Tuple       =						load_demo_image(image_size=UpperCAmelCase_  ,			device='''cpu'''  )
  __UpperCAmelCase    :						Optional[Any]       =						BertTokenizer.from_pretrained('''bert-base-uncased'''  )
  __UpperCAmelCase    :						Optional[int]       =						tokenizer(['''a picture of''']  ).input_ids

  __UpperCAmelCase    :						Union[str, Any]       =						hf_model.generate(UpperCAmelCase_  ,			UpperCAmelCase_  )

  assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

  __UpperCAmelCase    :						Optional[int]       =						hf_model.generate(UpperCAmelCase_  )

  assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

  if pytorch_dump_folder_path is not None:
    hf_model.save_pretrained(UpperCAmelCase_  )

  # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
  __UpperCAmelCase    :						Optional[Any]       =						(
      '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
  )

  __UpperCAmelCase    :						List[str]       =						blip_vqa(pretrained=UpperCAmelCase_  ,			image_size=UpperCAmelCase_  ,			vit='''base'''  )
  vqa_model.eval()

  __UpperCAmelCase    :						Dict       =						vqa_model.state_dict()
  for key in modified_state_dict.copy():
    __UpperCAmelCase    :						List[Any]       =						modified_state_dict.pop(UpperCAmelCase_  )
    __UpperCAmelCase    :						int       =						rename_key(UpperCAmelCase_  )
    __UpperCAmelCase    :						Union[str, Any]       =						value

  __UpperCAmelCase    :						List[str]       =						BlipForQuestionAnswering(UpperCAmelCase_  )

  hf_vqa_model.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						str       =						['''How many dogs are in this image?''']
  __UpperCAmelCase    :						Dict       =						tokenizer(UpperCAmelCase_  ,			return_tensors='''pt'''  ).input_ids

  __UpperCAmelCase    :						Any       =						hf_vqa_model.generate(UpperCAmelCase_  ,			UpperCAmelCase_  )
  print(tokenizer.decode(answer[0]  )  )

  assert tokenizer.decode(answer[0]  ) == "[UNK] 1 [SEP]"
  if pytorch_dump_folder_path is not None:
    hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '''_vqa'''  )

  __UpperCAmelCase    :						List[Any]       =						'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

  __UpperCAmelCase    :						List[str]       =						blip_itm(pretrained=UpperCAmelCase_  ,			image_size=UpperCAmelCase_  ,			vit='''base'''  )
  itm_model.eval()

  __UpperCAmelCase    :						Any       =						itm_model.state_dict()
  for key in modified_state_dict.copy():
    __UpperCAmelCase    :						List[str]       =						modified_state_dict.pop(UpperCAmelCase_  )
    __UpperCAmelCase    :						Dict       =						rename_key(UpperCAmelCase_  )
    __UpperCAmelCase    :						int       =						value

  __UpperCAmelCase    :						Optional[int]       =						BlipForImageTextRetrieval(UpperCAmelCase_  )

  __UpperCAmelCase    :						List[str]       =						['''A picture of a woman with a dog sitting in a beach''']
  __UpperCAmelCase    :						Optional[int]       =						tokenizer(
      UpperCAmelCase_  ,			return_tensors='''pt'''  ,			padding='''max_length'''  ,			truncation=UpperCAmelCase_  ,			max_length=35  ,			).input_ids

  hf_itm_model.load_state_dict(UpperCAmelCase_  )
  hf_itm_model.eval()

  __UpperCAmelCase    :						List[Any]       =						hf_itm_model(UpperCAmelCase_  ,			UpperCAmelCase_  ,			use_itm_head=UpperCAmelCase_  )
  __UpperCAmelCase    :						int       =						hf_itm_model(UpperCAmelCase_  ,			UpperCAmelCase_  ,			use_itm_head=UpperCAmelCase_  )

  assert out[0].item() == 0.2_110_687_494_277_954
  assert torch.nn.functional.softmax(out_itm[0]  ,			dim=1  )[:, 1].item() == 0.45_698_845_386_505_127

  if pytorch_dump_folder_path is not None:
    hf_itm_model.save_pretrained(pytorch_dump_folder_path + '''_itm'''  )


if __name__ == "__main__":
  lowercase__				:str				   =       argparse.ArgumentParser()
  parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
  parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
  lowercase__				:int				   =       parser.parse_args()

  convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)