





'''simple docstring'''






import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


A__				:   Dict      =logging.getLogger(__name__)




@dataclass(frozen=snake_case_   )
class  UpperCAmelCase     :
      _lowercase:			str
      _lowercase:			str
      _lowercase:			Optional[str]						     =   None
      _lowercase:			Optional[str]						     =   None
      _lowercase:			Optional[str]						     =   None




@dataclass(frozen=snake_case_   )
class  UpperCAmelCase     :
      _lowercase:			List[int]
      _lowercase:			Optional[List[int]]						     =   None
      _lowercase:			Optional[List[int]]						     =   None
      _lowercase:			Optional[Union[int, float]]						     =   None
      _lowercase:			Optional[int]						     =   None


if is_torch_available():
      import torch
      from torch.utils.data import Dataset




      class  UpperCAmelCase     (			snake_case_   ):
            _lowercase:			List[InputFeatures]




            def __init__(			self			: str			,			__snake_case			: str			,			__snake_case			: PreTrainedTokenizer			,			__snake_case			: str			,			__snake_case			: Optional[int] = None			,			__snake_case			: int=False			,			__snake_case			: bool = False			,			)				->      Optional[int]:
                  _lowerCAmelCase							     =							hans_processors[task]()

                  _lowerCAmelCase							     =							os.path.join(
                      __snake_case			,			"""cached_{}_{}_{}_{}""".format(
                          """dev""" if evaluate else """train"""			,			tokenizer.__class__.__name__			,			str(__snake_case  )			,			__snake_case			,			)			,			)
                  _lowerCAmelCase							     =							processor.get_labels()
                  if tokenizer.__class__ in (
                      RobertaTokenizer,
                      RobertaTokenizerFast,
                      XLMRobertaTokenizer,
                      BartTokenizer,
                      BartTokenizerFast,
                  ):
                        # HACK(label indices are swapped in RoBERTa pretrained model)
                        _lowerCAmelCase     ,							_lowerCAmelCase							     =							label_list[2], label_list[1]
                  _lowerCAmelCase							     =							label_list

                  # Make sure only the first process in distributed training processes the dataset,
                  # and the others will use the cache.
                  _lowerCAmelCase							     =							cached_features_file + """.lock"""
                  with FileLock(__snake_case  ):
                        if os.path.exists(__snake_case  ) and not overwrite_cache:
                              logger.info(f"Loading features from cached file {cached_features_file}"  )
                              _lowerCAmelCase							     =							torch.load(__snake_case  )
                        else:
                              logger.info(f"Creating features from dataset file at {data_dir}"  )

                              _lowerCAmelCase							     =							(
                                  processor.get_dev_examples(__snake_case  ) if evaluate else processor.get_train_examples(__snake_case  )
                              )

                              logger.info("""Training examples: %s"""			,			len(__snake_case  )  )
                              _lowerCAmelCase							     =							hans_convert_examples_to_features(__snake_case			,			__snake_case			,			__snake_case			,			__snake_case  )
                              logger.info("""Saving features into cached file %s"""			,			__snake_case  )
                              torch.save(self.features			,			__snake_case  )




            def __len__(			self			: List[str]  )				->      List[Any]:
                  return len(self.features  )




            def __getitem__(			self			: Union[str, Any]			,			__snake_case			: Union[str, Any]  )				->      InputFeatures:
                  return self.features[i]




            def 				lowercase__    (			self			: List[Any]  )				->      int:
                  return self.label_list


if is_tf_available():
      import tensorflow as tf




      class  UpperCAmelCase     :
            _lowercase:			List[InputFeatures]




            def __init__(			self			: List[Any]			,			__snake_case			: str			,			__snake_case			: PreTrainedTokenizer			,			__snake_case			: str			,			__snake_case			: Optional[int] = 1_28			,			__snake_case			: Dict=False			,			__snake_case			: bool = False			,			)				->      Union[str, Any]:
                  _lowerCAmelCase							     =							hans_processors[task]()
                  _lowerCAmelCase							     =							processor.get_labels()
                  if tokenizer.__class__ in (
                      RobertaTokenizer,
                      RobertaTokenizerFast,
                      XLMRobertaTokenizer,
                      BartTokenizer,
                      BartTokenizerFast,
                  ):
                        # HACK(label indices are swapped in RoBERTa pretrained model)
                        _lowerCAmelCase     ,							_lowerCAmelCase							     =							label_list[2], label_list[1]
                  _lowerCAmelCase							     =							label_list

                  _lowerCAmelCase							     =							processor.get_dev_examples(__snake_case  ) if evaluate else processor.get_train_examples(__snake_case  )
                  _lowerCAmelCase							     =							hans_convert_examples_to_features(__snake_case			,			__snake_case			,			__snake_case			,			__snake_case  )

                  def gen():
                        for ex_index, ex in tqdm.tqdm(enumerate(self.features  )			,			desc="""convert examples to features"""  ):
                              if ex_index % 1_00_00 == 0:
                                    logger.info("""Writing example %d of %d""" % (ex_index, len(__snake_case  ))  )

                              yield (
                                  {
                                      "example_id": 0,
                                      "input_ids": ex.input_ids,
                                      "attention_mask": ex.attention_mask,
                                      "token_type_ids": ex.token_type_ids,
                                  },
                                  ex.label,
                              )

                  _lowerCAmelCase							     =							tf.data.Dataset.from_generator(
                      __snake_case			,			(
                          {
                              """example_id""": tf.intaa,
                              """input_ids""": tf.intaa,
                              """attention_mask""": tf.intaa,
                              """token_type_ids""": tf.intaa,
                          },
                          tf.intaa,
                      )			,			(
                          {
                              """example_id""": tf.TensorShape([]  ),
                              """input_ids""": tf.TensorShape([None, None]  ),
                              """attention_mask""": tf.TensorShape([None, None]  ),
                              """token_type_ids""": tf.TensorShape([None, None]  ),
                          },
                          tf.TensorShape([]  ),
                      )			,			)




            def 				lowercase__    (			self			: Optional[Any]  )				->      Optional[int]:
                  return self.dataset




            def __len__(			self			: Any  )				->      List[Any]:
                  return len(self.features  )




            def __getitem__(			self			: Any			,			__snake_case			: Optional[Any]  )				->      InputFeatures:
                  return self.features[i]




            def 				lowercase__    (			self			: Dict  )				->      str:
                  return self.label_list




class  UpperCAmelCase     (			snake_case_   ):




      def 				lowercase__    (			self			: Optional[Any]			,			__snake_case			: str  )				->      int:
            return self._create_examples(self._read_tsv(os.path.join(__snake_case			,			"""heuristics_train_set.txt"""  )  )			,			"""train"""  )




      def 				lowercase__    (			self			: str			,			__snake_case			: List[Any]  )				->      Optional[int]:
            return self._create_examples(self._read_tsv(os.path.join(__snake_case			,			"""heuristics_evaluation_set.txt"""  )  )			,			"""dev"""  )




      def 				lowercase__    (			self			: int  )				->      Optional[int]:
            return ["contradiction", "entailment", "neutral"]




      def 				lowercase__    (			self			: List[Any]			,			__snake_case			: Any			,			__snake_case			: Optional[Any]  )				->      int:
            _lowerCAmelCase							     =							[]
            for i, line in enumerate(__snake_case  ):
                  if i == 0:
                        continue
                  _lowerCAmelCase							     =							"""%s-%s""" % (set_type, line[0])
                  _lowerCAmelCase							     =							line[5]
                  _lowerCAmelCase							     =							line[6]
                  _lowerCAmelCase							     =							line[7][2:] if line[7].startswith("""ex"""  ) else line[7]
                  _lowerCAmelCase							     =							line[0]
                  examples.append(InputExample(guid=__snake_case			,			text_a=__snake_case			,			text_b=__snake_case			,			label=__snake_case			,			pairID=__snake_case  )  )
            return examples




def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	):





      """simple docstring"""


      _lowerCAmelCase							     =							{label: i for i, label in enumerate(lowerCAmelCase  )}

      _lowerCAmelCase							     =							[]
      for ex_index, example in tqdm.tqdm(enumerate(lowerCAmelCase  )							,	desc="""convert examples to features"""  ):
            if ex_index % 1_00_00 == 0:
                  logger.info("""Writing example %d""" % (ex_index)  )

            _lowerCAmelCase							     =							tokenizer(
                example.text_a							,	example.text_b							,	add_special_tokens=lowerCAmelCase							,	max_length=lowerCAmelCase							,	padding="""max_length"""							,	truncation=lowerCAmelCase							,	return_overflowing_tokens=lowerCAmelCase							,	)

            _lowerCAmelCase							     =							label_map[example.label] if example.label in label_map else 0

            _lowerCAmelCase							     =							int(example.pairID  )

            features.append(InputFeatures(**lowerCAmelCase							,	label=lowerCAmelCase							,	pairID=lowerCAmelCase  )  )

      for i, example in enumerate(examples[:5]  ):
            logger.info("""*** Example ***"""  )
            logger.info(f"guid: {example}"  )
            logger.info(f"features: {features[i]}"  )

      return features


A__				:   List[Any]      ={
    '''hans''': 3,
}





A__				:   Any      ={
    '''hans''': HansProcessor,
}








import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint


def 						UpperCAmelCase			(	a_						)		->	List[str]:







				"""simple docstring"""



				return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()						)


def 						UpperCAmelCase			(	a_		,   a_						)		->	Tuple:







				"""simple docstring"""



				__A   			= {}

				for key, value in state_dict.items():
								if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
												continue

								__A   			= key.replace("heads.cmd.mim_head.cls.predictions"		,   "mmm_image_head"						)
								__A   			= key.replace("heads.cmd.mlm_head.cls.predictions"		,   "mmm_text_head"						)
								__A   			= key.replace("heads.cmd.itm_head.cls"		,   "itm_head"						)
								__A   			= key.replace("heads.cmd.itm_head.pooler"		,   "itm_head.pooler"						)
								__A   			= key.replace("heads.cmd.clip_head.logit_scale"		,   "flava.logit_scale"						)
								__A   			= key.replace("heads.fairseq_mlm.cls.predictions"		,   "mlm_head"						)
								__A   			= key.replace("heads.imagenet.mim_head.cls.predictions"		,   "mim_head"						)
								__A   			= key.replace("mm_text_projection"		,   "flava.text_to_mm_projection"						)
								__A   			= key.replace("mm_image_projection"		,   "flava.image_to_mm_projection"						)
								__A   			= key.replace("image_encoder.module"		,   "flava.image_model"						)
								__A   			= key.replace("text_encoder.module"		,   "flava.text_model"						)
								__A   			= key.replace("mm_encoder.module.encoder.cls_token"		,   "flava.multimodal_model.cls_token"						)
								__A   			= key.replace("mm_encoder.module"		,   "flava.multimodal_model"						)
								__A   			= key.replace("text_projection"		,   "flava.text_projection"						)
								__A   			= key.replace("image_projection"		,   "flava.image_projection"						)

								__A   			= value.float()

				for key, value in codebook_state_dict.items():
								__A   			= value

				return upgrade


@torch.no_grad()
def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_=None						)		->	Tuple:







				"""simple docstring"""



				if config_path is not None:
								__A   			= FlavaConfig.from_pretrained(a_						)
				else:
								__A   			= FlavaConfig()

				__A   			= FlavaForPreTraining(a_						).eval()

				__A   			= convert_dalle_checkpoint(a_		,   a_		,   save_checkpoint=a_						)

				if os.path.exists(a_						):
								__A   			= torch.load(a_		,   map_location="cpu"						)
				else:
								__A   			= torch.hub.load_state_dict_from_url(a_		,   map_location="cpu"						)

				__A   			= upgrade_state_dict(a_		,   a_						)
				hf_model.load_state_dict(a_						)
				__A   			= hf_model.state_dict()
				__A   			= count_parameters(a_						)
				__A   			= count_parameters(a_						) + count_parameters(a_						)

				assert torch.allclose(a_		,   a_		,   atol=1E-3						)

				hf_model.save_pretrained(a_						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Any							 =				argparse.ArgumentParser()
					parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
					parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
					parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				parser.parse_args()

					convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
