
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_(					__magic_name__      :	Tuple      ):


							"""simple docstring"""





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







def   UpperCamelCase_(					__magic_name__      :	Any	,    __magic_name__      :	Union[str, Any]      ):


							"""simple docstring"""





							_lowerCAmelCase :List[str]      	=						{}

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

														_lowerCAmelCase :Optional[Any]      	=						key.replace('heads.cmd.mim_head.cls.predictions'	,    'mmm_image_head'      )
														_lowerCAmelCase :Any      	=						key.replace('heads.cmd.mlm_head.cls.predictions'	,    'mmm_text_head'      )
														_lowerCAmelCase :Dict      	=						key.replace('heads.cmd.itm_head.cls'	,    'itm_head'      )
														_lowerCAmelCase :Union[str, Any]      	=						key.replace('heads.cmd.itm_head.pooler'	,    'itm_head.pooler'      )
														_lowerCAmelCase :Dict      	=						key.replace('heads.cmd.clip_head.logit_scale'	,    'flava.logit_scale'      )
														_lowerCAmelCase :Dict      	=						key.replace('heads.fairseq_mlm.cls.predictions'	,    'mlm_head'      )
														_lowerCAmelCase :Union[str, Any]      	=						key.replace('heads.imagenet.mim_head.cls.predictions'	,    'mim_head'      )
														_lowerCAmelCase :str      	=						key.replace('mm_text_projection'	,    'flava.text_to_mm_projection'      )
														_lowerCAmelCase :Dict      	=						key.replace('mm_image_projection'	,    'flava.image_to_mm_projection'      )
														_lowerCAmelCase :List[Any]      	=						key.replace('image_encoder.module'	,    'flava.image_model'      )
														_lowerCAmelCase :Dict      	=						key.replace('text_encoder.module'	,    'flava.text_model'      )
														_lowerCAmelCase :Optional[int]      	=						key.replace('mm_encoder.module.encoder.cls_token'	,    'flava.multimodal_model.cls_token'      )
														_lowerCAmelCase :Any      	=						key.replace('mm_encoder.module'	,    'flava.multimodal_model'      )
														_lowerCAmelCase :int      	=						key.replace('text_projection'	,    'flava.text_projection'      )
														_lowerCAmelCase :str      	=						key.replace('image_projection'	,    'flava.image_projection'      )

														_lowerCAmelCase :Optional[int]      	=						value.float()

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

							return upgrade







@torch.no_grad()
def   UpperCamelCase_(					__magic_name__      :	Tuple	,    __magic_name__      :	Any	,    __magic_name__      :	List[str]	,    __magic_name__      :	int=None      ):


							"""simple docstring"""





							if config_path is not None:
														_lowerCAmelCase :List[Any]      	=						FlavaConfig.from_pretrained(__lowerCamelCase      )
							else:
														_lowerCAmelCase :Dict      	=						FlavaConfig()

							_lowerCAmelCase :Any      	=						FlavaForPreTraining(__lowerCamelCase      ).eval()

							_lowerCAmelCase :Union[str, Any]      	=						convert_dalle_checkpoint(__lowerCamelCase	,    __lowerCamelCase	,    save_checkpoint=__lowerCamelCase      )

							if os.path.exists(__lowerCamelCase      ):
														_lowerCAmelCase :List[str]      	=						torch.load(__lowerCamelCase	,    map_location='cpu'      )
							else:
														_lowerCAmelCase :List[Any]      	=						torch.hub.load_state_dict_from_url(__lowerCamelCase	,    map_location='cpu'      )

							_lowerCAmelCase :List[Any]      	=						upgrade_state_dict(__lowerCamelCase	,    __lowerCamelCase      )
							hf_model.load_state_dict(__lowerCamelCase      )
							_lowerCAmelCase :Optional[int]      	=						hf_model.state_dict()
							_lowerCAmelCase :List[str]      	=						count_parameters(__lowerCamelCase      )
							_lowerCAmelCase :Union[str, Any]      	=						count_parameters(__lowerCamelCase      ) + count_parameters(__lowerCamelCase      )

							assert torch.allclose(__lowerCamelCase	,    __lowerCamelCase	,    atol=1e-3      )

							hf_model.save_pretrained(__lowerCamelCase      )


if __name__ == "__main__":
						a											=							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""")
						a											=							parser.parse_args()

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




import pytest


lowercase_													=      '''__dummy_dataset1__'''

lowercase_													=      '''
import json
import os

import datasets


REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}


class __DummyDataset1__(datasets.GeneratorBasedBuilder):

    def _info(self):
        features = datasets.Features(
            {
                "tokens": datasets.Sequence(datasets.Value("string")),
                "ner_tags": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            "O",
                            "B-PER",
                            "I-PER",
                            "B-ORG",
                            "I-ORG",
                            "B-LOC",
                            "I-LOC",
                        ]
                    )
                ),
                "langs": datasets.Sequence(datasets.Value("string")),
                "spans": datasets.Sequence(datasets.Value("string")),
            }
        )
        return datasets.DatasetInfo(features=features)

    def _split_generators(self, dl_manager):
        dl_path = dl_manager.download(URLS)
        return [
            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, "r", encoding="utf-8") as f:
            for i, line in enumerate(f):
                yield i, json.loads(line)
'''






@pytest.fixture
def        __lowerCAmelCase		(	)	->    Any:
					return DATASET_LOADING_SCRIPT_NAME






@pytest.fixture
def        __lowerCAmelCase		(	)	->    Union[str, Any]:
					return DATASET_LOADING_SCRIPT_CODE






@pytest.fixture
def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Tuple					, __lowerCamelCase						:     str						)	->    List[str]:
					__lowerCAmelCase						 =dataset_loading_script_name
					__lowerCAmelCase						 =tmp_path / """datasets""" / script_name
					script_dir.mkdir(parents=__lowerCamelCase						)
					__lowerCAmelCase						 =script_dir / f"""{script_name}.py"""
					with open(__lowerCamelCase					, """w"""						) as f:
										f.write(__lowerCamelCase						)
					return str(__lowerCamelCase						)




