

import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


lowerCamelCase_					  =   logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

lowerCamelCase_					  =   {
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowerCamelCase_					  =   {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowerCamelCase_					  =   sorted(arg_to_scheduler.keys())
lowerCamelCase_					  =   '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''







class 					__lowerCamelCase							(      pl.LightningModule      ):







      def __init__(	self				,    lowerCamelCase				,    lowerCamelCase=None				,    lowerCamelCase="base"				,    lowerCamelCase=None				,    lowerCamelCase=None				,    lowerCamelCase=None				,    **lowerCamelCase				,    )		->			Any:
        super().__init__()
        # TODO: move to self.save_hyperparameters()
        # self.save_hyperparameters()
        # can also expand arguments into trainer signature for easier reading

        self.save_hyperparameters(lowerCamelCase  )
        snake_case_					= 0
        snake_case_					= Path(self.hparams.output_dir  )
        snake_case_					= self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
          snake_case_					= AutoConfig.from_pretrained(
              self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path				,    **({"""num_labels""": num_labels} if num_labels is not None else {})				,    cache_dir=lowerCamelCase				,    **lowerCamelCase				,    )
        else:
          snake_case_					= config

        snake_case_					= ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
        for p in extra_model_params:
          if getattr(self.hparams				,    lowerCamelCase				,    lowerCamelCase  ):
            assert hasattr(self.config				,    lowerCamelCase  ), f'''model config doesn\'t have a `{p}` attribute'''
            setattr(self.config				,    lowerCamelCase				,    getattr(self.hparams				,    lowerCamelCase  )  )

        if tokenizer is None:
          snake_case_					= AutoTokenizer.from_pretrained(
              self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,    cache_dir=lowerCamelCase				,    )
        else:
          snake_case_					= tokenizer
        snake_case_					= MODEL_MODES[mode]
        if model is None:
          snake_case_					= self.model_type.from_pretrained(
              self.hparams.model_name_or_path				,    from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path  )				,    config=self.config				,    cache_dir=lowerCamelCase				,    )
        else:
          snake_case_					= model







      def       lowerCAmelCase_       (	self				,    *lowerCamelCase				,    **lowerCamelCase  )		->			Tuple:
        snake_case_					= self.model_type.from_pretrained(*lowerCamelCase				,    **lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Tuple:
        snake_case_					= arg_to_scheduler[self.hparams.lr_scheduler]
        snake_case_					= get_schedule_func(
            self.opt				,    num_warmup_steps=self.hparams.warmup_steps				,    num_training_steps=self.total_steps()  )
        snake_case_					= {'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
        return scheduler







      def       lowerCAmelCase_       (	self  )		->			Optional[int]:
        snake_case_					= self.model
        snake_case_					= ['bias', 'LayerNorm.weight']
        snake_case_					= [
            {
                'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay  )],
                'weight_decay': self.hparams.weight_decay,
            },
            {
                'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay  )],
                'weight_decay': 0.0,
            },
        ]
        if self.hparams.adafactor:
          snake_case_					= Adafactor(
              lowerCamelCase				,    lr=self.hparams.learning_rate				,    scale_parameter=lowerCamelCase				,    relative_step=lowerCamelCase  )

        else:
          snake_case_					= AdamW(
              lowerCamelCase				,    lr=self.hparams.learning_rate				,    eps=self.hparams.adam_epsilon  )
        snake_case_					= optimizer

        snake_case_					= self.get_lr_scheduler()

        return [optimizer], [scheduler]







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase  )		->			List[str]:
        return self.validation_step(lowerCamelCase				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase  )		->			str:
        return self.validation_end(lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			int:
        snake_case_					= max(1				,    self.hparams.gpus  )  # TODO: consider num_tpu_cores
        snake_case_					= self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
        return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs







      def       lowerCAmelCase_       (	self				,    lowerCamelCase  )		->			Union[str, Any]:
        if mode == "test":
          snake_case_					= len(self.test_dataloader().dataset  )
        else:
          snake_case_					= self.get_dataloader("""train"""				,    self.hparams.train_batch_size				,    shuffle=lowerCamelCase  )
          snake_case_					= len(self.train_dataloader().dataset  )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase = False  )		->			List[str]:
        raise NotImplementedError("""You must implement this for your task"""  )







      def       lowerCAmelCase_       (	self  )		->			Tuple:
        return self.train_loader







      def       lowerCAmelCase_       (	self  )		->			List[Any]:
        return self.get_dataloader("""dev"""				,    self.hparams.eval_batch_size				,    shuffle=lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			List[str]:
        return self.get_dataloader("""test"""				,    self.hparams.eval_batch_size				,    shuffle=lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase  )		->			Optional[Any]:
        return os.path.join(
            self.hparams.data_dir				,    """cached_{}_{}_{}""".format(
                lowerCamelCase				,    list(filter(lowerCamelCase				,    self.hparams.model_name_or_path.split("""/"""  )  )  ).pop()				,    str(self.hparams.max_seq_length  )				,    )				,    )







      @pl.utilities.rank_zero_only
      def       lowerCAmelCase_       (	self				,    lowerCamelCase  )		->			None:
        snake_case_					= self.output_dir.joinpath("""best_tfmr"""  )
        snake_case_					= self.step_count
        self.model.save_pretrained(lowerCamelCase  )
        self.tokenizer.save_pretrained(lowerCamelCase  )







      @staticmethod
      def       lowerCAmelCase_       (	lowerCamelCase				,    lowerCamelCase  )		->			int:
        parser.add_argument(
            """--model_name_or_path"""				,    default=lowerCamelCase				,    type=lowerCamelCase				,    required=lowerCamelCase				,    help="""Path to pretrained model or model identifier from huggingface.co/models"""				,    )
        parser.add_argument(
            """--config_name"""				,    default=""""""				,    type=lowerCamelCase				,    help="""Pretrained config name or path if not the same as model_name"""  )
        parser.add_argument(
            """--tokenizer_name"""				,    default=lowerCamelCase				,    type=lowerCamelCase				,    help="""Pretrained tokenizer name or path if not the same as model_name"""				,    )
        parser.add_argument(
            """--cache_dir"""				,    default=""""""				,    type=lowerCamelCase				,    help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""				,    )
        parser.add_argument(
            """--encoder_layerdrop"""				,    type=lowerCamelCase				,    help="""Encoder layer dropout probability (Optional). Goes into model.config"""				,    )
        parser.add_argument(
            """--decoder_layerdrop"""				,    type=lowerCamelCase				,    help="""Decoder layer dropout probability (Optional). Goes into model.config"""				,    )
        parser.add_argument(
            """--dropout"""				,    type=lowerCamelCase				,    help="""Dropout probability (Optional). Goes into model.config"""				,    )
        parser.add_argument(
            """--attention_dropout"""				,    type=lowerCamelCase				,    help="""Attention dropout probability (Optional). Goes into model.config"""				,    )
        parser.add_argument("""--learning_rate"""				,    default=5e-5				,    type=lowerCamelCase				,    help="""The initial learning rate for Adam."""  )
        parser.add_argument(
            """--lr_scheduler"""				,    default="""linear"""				,    choices=lowerCamelCase				,    metavar=lowerCamelCase				,    type=lowerCamelCase				,    help="""Learning rate scheduler"""				,    )
        parser.add_argument("""--weight_decay"""				,    default=0.0				,    type=lowerCamelCase				,    help="""Weight decay if we apply some."""  )
        parser.add_argument("""--adam_epsilon"""				,    default=1e-8				,    type=lowerCamelCase				,    help="""Epsilon for Adam optimizer."""  )
        parser.add_argument("""--warmup_steps"""				,    default=0				,    type=lowerCamelCase				,    help="""Linear warmup over warmup_steps."""  )
        parser.add_argument("""--num_workers"""				,    default=4				,    type=lowerCamelCase				,    help="""kwarg passed to DataLoader"""  )
        parser.add_argument("""--num_train_epochs"""				,    dest="""max_epochs"""				,    default=3				,    type=lowerCamelCase  )
        parser.add_argument("""--train_batch_size"""				,    default=32				,    type=lowerCamelCase  )
        parser.add_argument("""--eval_batch_size"""				,    default=32				,    type=lowerCamelCase  )
        parser.add_argument("""--adafactor"""				,    action="""store_true"""  )







class 					__lowerCamelCase							(      pl.Callback      ):







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase  )		->			List[Any]:
        snake_case_					= trainer.lr_schedulers[0]['scheduler']
        snake_case_					= {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()  )}
        pl_module.logger.log_metrics(lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase  )		->			List[str]:
        rank_zero_info("""***** Validation results *****"""  )
        snake_case_					= trainer.callback_metrics
        # Log results
        for key in sorted(lowerCamelCase  ):
          if key not in ["log", "progress_bar"]:
            rank_zero_info("""{} = {}\n""".format(lowerCamelCase				,    str(metrics[key]  )  )  )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase  )		->			Tuple:
        rank_zero_info("""***** Test results *****"""  )
        snake_case_					= trainer.callback_metrics
        # Log and save results to file
        snake_case_					= os.path.join(pl_module.hparams.output_dir				,    """test_results.txt"""  )
        with open(lowerCamelCase				,    """w"""  ) as writer:
          for key in sorted(lowerCamelCase  ):
            if key not in ["log", "progress_bar"]:
              rank_zero_info("""{} = {}\n""".format(lowerCamelCase				,    str(metrics[key]  )  )  )
              writer.write("""{} = {}\n""".format(lowerCamelCase				,    str(metrics[key]  )  )  )



def   UpperCamelCase(   lowercase_     ,   lowercase_				)      ->							Optional[Any]:





  '''simple docstring'''





  parser.add_argument(
      """--output_dir"""     ,   default=lowercase_     ,   type=lowercase_     ,   required=lowercase_     ,   help="""The output directory where the model predictions and checkpoints will be written."""     ,   )
  parser.add_argument(
      """--fp16"""     ,   action="""store_true"""     ,   help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""     ,   )

  parser.add_argument(
      """--fp16_opt_level"""     ,   type=lowercase_     ,   default="""O2"""     ,   help=(
          """For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']."""
          """See details at https://nvidia.github.io/apex/amp.html"""
      )     ,   )
  parser.add_argument("""--n_tpu_cores"""     ,   dest="""tpu_cores"""     ,   type=lowercase_				)
  parser.add_argument("""--max_grad_norm"""     ,   dest="""gradient_clip_val"""     ,   default=1.0     ,   type=lowercase_     ,   help="""Max gradient norm"""				)
  parser.add_argument("""--do_train"""     ,   action="""store_true"""     ,   help="""Whether to run training."""				)
  parser.add_argument("""--do_predict"""     ,   action="""store_true"""     ,   help="""Whether to run predictions on the test set."""				)
  parser.add_argument(
      """--gradient_accumulation_steps"""     ,   dest="""accumulate_grad_batches"""     ,   type=lowercase_     ,   default=1     ,   help="""Number of updates steps to accumulate before performing a backward/update pass."""     ,   )
  parser.add_argument("""--seed"""     ,   type=lowercase_     ,   default=42     ,   help="""random seed for initialization"""				)
  parser.add_argument(
      """--data_dir"""     ,   default=lowercase_     ,   type=lowercase_     ,   required=lowercase_     ,   help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""     ,   )



def   UpperCamelCase(   lowercase_     ,   lowercase_     ,   lowercase_=None     ,   lowercase_=True     ,   lowercase_=[]     ,   lowercase_=None     ,   lowercase_=None     ,   **lowercase_     ,   )      ->							List[str]:





  '''simple docstring'''





  pl.seed_everything(args.seed				)

  # init model
  snake_case_					= Path(model.hparams.output_dir				)
  odir.mkdir(exist_ok=lowercase_				)

  # add custom checkpoints
  if checkpoint_callback is None:
    snake_case_					= pl.callbacks.ModelCheckpoint(
        filepath=args.output_dir     ,   prefix="""checkpoint"""     ,   monitor="""val_loss"""     ,   mode="""min"""     ,   save_top_k=1				)
  if early_stopping_callback:
    extra_callbacks.append(lowercase_				)
  if logging_callback is None:
    snake_case_					= LoggingCallback()

  snake_case_					= {}

  # TODO: remove with PyTorch 1.6 since pl uses native amp
  if args.fpaa:
    snake_case_					= 16
    snake_case_					= args.fpaa_opt_level

  if args.gpus > 1:
    snake_case_					= 'ddp'

  snake_case_					= args.accumulate_grad_batches
  snake_case_					= extra_train_kwargs.get("""accelerator"""     ,   lowercase_				)
  snake_case_					= extra_train_kwargs.get("""profiler"""     ,   lowercase_				)

  snake_case_					= pl.Trainer.from_argparse_args(
      lowercase_     ,   weights_summary=lowercase_     ,   callbacks=[logging_callback] + extra_callbacks     ,   logger=lowercase_     ,   checkpoint_callback=lowercase_     ,   **lowercase_     ,   )

  if args.do_train:
    trainer.fit(lowercase_				)

  return trainer
import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel


def SCREAMING_SNAKE_CASE					(  lowercase_:						List[str]		,    lowercase_:						Dict		,    lowercase_:						List[Any]					):
       _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							List[Any]	    =	pt_weights[F"{prefix}.in_proj_weight"].chunk(3		,    dim=0					)
       _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Dict	    =	pt_weights[F"{prefix}.in_proj_bias"].chunk(3		,    dim=0					)

       _UpperCamelCase  :							List[str]	    =	pt_weights[F"{prefix}.out_proj.weight"]
       _UpperCamelCase  :							Any	    =	pt_weights[F"{prefix}.out_proj.bias"]

       _UpperCamelCase  :							List[Any]	    =	q_proj
       _UpperCamelCase  :							Union[str, Any]	    =	q_proj_bias

       _UpperCamelCase  :							str	    =	k_proj
       _UpperCamelCase  :							List[str]	    =	k_proj_bias

       _UpperCamelCase  :							Dict	    =	v_proj
       _UpperCamelCase  :							str	    =	v_proj_bias

       _UpperCamelCase  :							List[str]	    =	out_proj_weights
       _UpperCamelCase  :							Any	    =	out_proj_bias


def SCREAMING_SNAKE_CASE					(  lowercase_:						List[str]		,    lowercase_:						List[Any]		,    lowercase_:						Any					):
       copy_linear(hf_mlp.fca		,    lowercase_		,    F"{prefix}.c_fc"					)
       copy_linear(hf_mlp.fca		,    lowercase_		,    F"{prefix}.c_proj"					)


def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[Any]		,    lowercase_:						List[str]		,    lowercase_:						Any					):
       _UpperCamelCase  :							Dict	    =	pt_weights[F"{prefix}.weight"].data
       _UpperCamelCase  :							List[Any]	    =	pt_weights[F"{prefix}.bias"].data


def SCREAMING_SNAKE_CASE					(  lowercase_:						Any		,    lowercase_:						Optional[int]		,    lowercase_:						Union[str, Any]					):
       # copy layer norms
       copy_linear(hf_layer.layer_norma		,    lowercase_		,    F"{prefix}.ln_1"					)
       copy_linear(hf_layer.layer_norma		,    lowercase_		,    F"{prefix}.ln_2"					)

       # copy MLP
       copy_mlp(hf_layer.mlp		,    lowercase_		,    F"{prefix}.mlp"					)

       # copy attn
       copy_attn_layer(hf_layer.self_attn		,    lowercase_		,    F"{prefix}.attn"					)


def SCREAMING_SNAKE_CASE					(  lowercase_:						str		,    lowercase_:						str		,    lowercase_:						int					):
       for layer_id, hf_layer in enumerate(lowercase_					):
              copy_layer(lowercase_		,    lowercase_		,    F"{prefix}.{layer_id}"					)


def SCREAMING_SNAKE_CASE					(  lowercase_:						Union[str, Any]		,    lowercase_:						List[Any]					):
       # copy projection
       _UpperCamelCase  :							List[str]	    =	pt_weights['text_projection'].data.T

       # copy text encoder
       for name, param in hf_model.text_model.named_parameters():
              _UpperCamelCase  :							Tuple	    =	pt_weights[F"bert.{name}"].data


def SCREAMING_SNAKE_CASE					(  lowercase_:						str		,    lowercase_:						Any					):
       # copy projection
       _UpperCamelCase  :							List[Any]	    =	pt_weights['visual.proj'].data.T

       # copy layer norms
       copy_linear(hf_model.vision_model.pre_layrnorm		,    lowercase_		,    'visual.ln_pre'					)
       copy_linear(hf_model.vision_model.post_layernorm		,    lowercase_		,    'visual.ln_post'					)

       # copy embeddings
       _UpperCamelCase  :							List[Any]	    =	pt_weights['visual.conv1.weight'].data
       _UpperCamelCase  :							List[Any]	    =	pt_weights['visual.class_embedding'].data
       _UpperCamelCase  :							int	    =	pt_weights['visual.positional_embedding'].data

       # copy encoder
       copy_layers(hf_model.vision_model.encoder.layers		,    lowercase_		,    'visual.transformer.resblocks'					)


@torch.no_grad()
def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[Any]		,    lowercase_:						Optional[int]		,    lowercase_:						List[Any]=None					):
       assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
       _UpperCamelCase  :							Optional[Any]	    =	ChineseCLIPConfig.from_pretrained(lowercase_					)

       _UpperCamelCase  :							List[Any]	    =	ChineseCLIPModel(lowercase_					).eval()

       _UpperCamelCase  :							List[Any]	    =	torch.load(lowercase_		,    map_location='cpu'					)['state_dict']
       _UpperCamelCase  :							Union[str, Any]	    =	{(name[7:] if name.startswith('module.'					) else name): value for name, value in pt_weights.items()}

       copy_text_model_and_projection(lowercase_		,    lowercase_					)
       copy_vision_model_and_projection(lowercase_		,    lowercase_					)
       _UpperCamelCase  :							List[Any]	    =	pt_weights['logit_scale'].data

       hf_model.save_pretrained(lowercase_					)


if __name__ == "__main__":
 UpperCAmelCase      		=					argparse.ArgumentParser()
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default=None,
     type=str,
     help='''Path to the output folder storing converted hf PyTorch model.''',
 )
 parser.add_argument(
     '''--checkpoint_path''', default=None, type=str, help='''Path to original github format ChineseCLIP checkpoint.'''
 )
 parser.add_argument(
     '''--config_path''', default=None, required=True, type=str, help='''Path to hf config.json of model to convert.'''
 )
 UpperCAmelCase      		=					parser.parse_args()

 convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
 print('''The conversion is finished!''')



