
"""simple docstring"""






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      lowerCAmelCase__			(		__magic_name__      )						->List[str]:
			# encoder.embeddings are double copied in original FLAVA
			return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Dict:
			__lowercase       =       {}

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

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

						__lowercase       =       value.float()

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

			return upgrade



@torch.no_grad()
def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__=None      )						->List[str]:
			if config_path is not None:
						__lowercase       =       FlavaConfig.from_pretrained(__magic_name__      )
			else:
						__lowercase       =       FlavaConfig()

			__lowercase       =       FlavaForPreTraining(__magic_name__      ).eval()

			__lowercase       =       convert_dalle_checkpoint(__magic_name__			,   __magic_name__			,   save_checkpoint=__magic_name__      )

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

			__lowercase       =       upgrade_state_dict(__magic_name__			,   __magic_name__      )
			hf_model.load_state_dict(__magic_name__      )
			__lowercase       =       hf_model.state_dict()
			__lowercase       =       count_parameters(__magic_name__      )
			__lowercase       =       count_parameters(__magic_name__      ) + count_parameters(__magic_name__      )

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

			hf_model.save_pretrained(__magic_name__      )


if __name__ == "__main__":
					_lowercase							=				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''')
					_lowercase							=				parser.parse_args()

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







"""simple docstring"""






import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


_lowercase							=				[
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.de'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.en'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.fr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.frr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.it'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.simple'''},
    {'''dataset''': '''snli''', '''config_name''': '''plain_text'''},
    {'''dataset''': '''eli5''', '''config_name''': '''LFQA_reddit'''},
    {'''dataset''': '''wiki40b''', '''config_name''': '''en'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.compressed'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.no_index'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.multiset.no_index'''},
    {'''dataset''': '''natural_questions''', '''config_name''': '''default'''},
]



def      lowerCAmelCase__			(		__magic_name__=True      )						->int:
			if with_config:
						return [
						    {
						        "testcase_name": d["dataset"] + "/" + d["config_name"],
						        "dataset": d["dataset"],
						        "config_name": d["config_name"],
						    }
						    for d in DATASETS_ON_HF_GCP
						]
			else:
						return [
						    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
						]

@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__a       )       )
class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			int          =   None
							_lowerCamelCase					:			int          =   None
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							Optional[Any]:




										'''simple docstring'''
										with TemporaryDirectory() as tmp_dir:
													__lowercase       =       dataset_module_factory(_lowerCamelCase  ,    cache_dir=_lowerCamelCase       )

													__lowercase       =       import_main_class(dataset_module.module_path  ,    dataset=_lowerCamelCase       )

													__lowercase       =       builder_cls(
													    cache_dir=_lowerCamelCase  ,    config_name=_lowerCamelCase  ,    hash=dataset_module.hash  ,    )

													__lowercase       =       "/".join(
													    [
													        HF_GCP_BASE_URL,
													        builder_instance._relative_data_dir(with_hash=_lowerCamelCase       ).replace(os.sep  ,    "/"       ),
													        config.DATASET_INFO_FILENAME,
													    ]       )
													__lowercase       =       cached_path(_lowerCamelCase  ,    cache_dir=_lowerCamelCase       )
													self.assertTrue(os.path.exists(_lowerCamelCase       )       )





@pytest.mark.integration
def      lowerCAmelCase__			(		__magic_name__      )						->str:
			__lowercase       =       tmp_path_factory.mktemp("test_hf_gcp"      ) / "test_wikipedia_simple"
			__lowercase       =       dataset_module_factory("wikipedia"			,   cache_dir=__magic_name__      )
			__lowercase       =       import_main_class(dataset_module.module_path      )
			__lowercase       =       builder_cls(
			    cache_dir=__magic_name__			,   config_name="20220301.frr"			,   hash=dataset_module.hash			,   )
			# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
			__lowercase       =       None
			builder_instance.download_and_prepare()
			__lowercase       =       builder_instance.as_dataset()
			assert ds







@pytest.mark.integration
def      lowerCAmelCase__			(		__magic_name__      )						->Any:
			__lowercase       =       dataset_module_factory("wikipedia"			,   cache_dir=__magic_name__      )
			__lowercase       =       import_main_class(dataset_module.module_path			,   dataset=__magic_name__      )
			__lowercase       =       builder_cls(
			    cache_dir=__magic_name__			,   config_name="20220301.frr"			,   hash=dataset_module.hash			,   )
			__lowercase       =       builder_instance.as_streaming_dataset()
			assert ds
			assert isinstance(__magic_name__			,   __magic_name__      )
			assert "train" in ds
			assert isinstance(ds["train"]			,   __magic_name__      )
			assert next(iter(ds["train"]      )      )





