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						(	__lowercase						:			Union[str, Any]     ):
		'''simple docstring'''




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




def     UpperCamelCase						(	__lowercase						:			Optional[int]						,__lowercase						:			Tuple     ):
		'''simple docstring'''




		A_		:    Union[str, Any]         =				{}

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

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

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

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

		return upgrade




@torch.no_grad()
def     UpperCamelCase						(	__lowercase						:			int						,__lowercase						:			Dict						,__lowercase						:			int						,__lowercase						:			Dict=None     ):
		'''simple docstring'''




		if config_path is not None:
				A_		:    Optional[Any]         =				FlavaConfig.from_pretrained(__lowercase     )
		else:
				A_		:    Tuple         =				FlavaConfig()

		A_		:    Tuple         =				FlavaForPreTraining(__lowercase     ).eval()

		A_		:    Union[str, Any]         =				convert_dalle_checkpoint(__lowercase						,__lowercase						,save_checkpoint=__lowercase     )

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

		A_		:    Optional[int]         =				upgrade_state_dict(__lowercase						,__lowercase     )
		hf_model.load_state_dict(__lowercase     )
		A_		:    Dict         =				hf_model.state_dict()
		A_		:    Dict         =				count_parameters(__lowercase     )
		A_		:    Tuple         =				count_parameters(__lowercase     ) + count_parameters(__lowercase     )

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

		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 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""")
	_UpperCAmelCase            =		parser.parse_args()

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







import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_UpperCAmelCase            =		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def     UpperCamelCase						(	__lowercase						:			Any						,__lowercase						:			str     ):
		'''simple docstring'''




		return (abs(source - target     ) / target) < 0.01




@pytest.mark.integration
def     UpperCamelCase						(	__lowercase						:			Dict     ):
		'''simple docstring'''




		A_		:    Optional[Any]         =				_TestCommandArgs(dataset=__lowercase						,all_configs=__lowercase						,save_infos=__lowercase     )
		A_		:    List[Any]         =				TestCommand(*__lowercase     )
		test_command.run()
		A_		:    Any         =				os.path.join(__lowercase						,'README.md'     )
		assert os.path.exists(__lowercase     )
		A_		:    Tuple         =				DatasetInfosDict.from_directory(__lowercase     )
		A_		:    Any         =				DatasetInfosDict(
		    {
		        'default': DatasetInfo(
		            features=Features(
		                {
		                    'tokens': Sequence(Value('string'     )     ),
		                    'ner_tags': Sequence(
		                        ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']     )     ),
		                    'langs': Sequence(Value('string'     )     ),
		                    'spans': Sequence(Value('string'     )     ),
		                }     )						,splits=[
		                {
		                    'name': 'train',
		                    'num_bytes': 2_35_15_63,
		                    'num_examples': 1_00_00,
		                },
		                {
		                    'name': 'validation',
		                    'num_bytes': 23_84_18,
		                    'num_examples': 10_00,
		                },
		            ]						,download_size=3_94_06_80						,dataset_size=2_58_99_81						,)
		    }     )
		assert dataset_infos.keys() == expected_dataset_infos.keys()
		for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
				A_       ,      A_		:    Union[str, Any]         =				getattr(dataset_infos['default']						,__lowercase     ), getattr(expected_dataset_infos['default']						,__lowercase     )
				if key == "num_bytes":
						assert is_apercent_close(__lowercase						,__lowercase     )
				elif key == "splits":
						assert list(__lowercase     ) == list(__lowercase     )
						for split in result:
								assert result[split].name == expected[split].name
								assert result[split].num_examples == expected[split].num_examples
								assert is_apercent_close(result[split].num_bytes						,expected[split].num_bytes     )
				else:
						result == expected






