'''simple docstring'''




import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig




def 							lowerCamelCase			(   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      Union[str, Any]   ,   UpperCAmelCase__ :      Tuple   ,   UpperCAmelCase__ :      Optional[Any]				)    ->   List[Any]:
							lowercase_      :    List[Any]       		= s.rsplit(UpperCAmelCase__   ,   UpperCAmelCase__				)
							return new.join(UpperCAmelCase__				)




def 							lowerCamelCase			(   UpperCAmelCase__ :      Dict				)    ->   int:
							# 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			(   UpperCAmelCase__ :      Optional[Any]				)    ->   Union[str, Any]:
							lowercase_      :    int       		= {}

							lowercase_      :    Any       		= ["""group_1""", """group_2""", """group_3""", """group_4"""]
							for key, value in state_dict.items():
														for group_key in group_keys:
																					if group_key in key:
																												lowercase_      :    Tuple       		= key.replace(F'''{group_key}.'''   ,   F'''{group_key}.group.'''				)

														if "res_path" in key:
																					lowercase_      :    Any       		= key.replace("""res_path."""   ,   """res_path.path."""				)

														if key.endswith(""".w"""				):
																					lowercase_      :    int       		= rreplace(UpperCAmelCase__   ,   """.w"""   ,   """.weight"""   ,   1				)
														if key.endswith(""".b"""				):
																					lowercase_      :    int       		= rreplace(UpperCAmelCase__   ,   """.b"""   ,   """.bias"""   ,   1				)

														lowercase_      :    int       		= value.float()

							return upgrade




@torch.no_grad()
def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      List[Any]=None   ,   UpperCAmelCase__ :      Optional[Any]=True				)    ->   Tuple:
							from dall_e import Encoder

							lowercase_      :    Tuple       		= Encoder()
							if os.path.exists(UpperCAmelCase__				):
														lowercase_      :    Tuple       		= torch.load(UpperCAmelCase__				)
							else:
														lowercase_      :    Dict       		= torch.hub.load_state_dict_from_url(UpperCAmelCase__				)

							if isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				):
														lowercase_      :    Dict       		= ckpt.state_dict()
							encoder.load_state_dict(UpperCAmelCase__				)

							if config_path is not None:
														lowercase_      :    Dict       		= FlavaImageCodebookConfig.from_pretrained(UpperCAmelCase__				)
							else:
														lowercase_      :    Any       		= FlavaImageCodebookConfig()

							lowercase_      :    Optional[int]       		= FlavaImageCodebook(UpperCAmelCase__				).eval()
							lowercase_      :    int       		= encoder.state_dict()

							lowercase_      :    Optional[Any]       		= upgrade_state_dict(UpperCAmelCase__				)
							hf_model.load_state_dict(UpperCAmelCase__				)
							lowercase_      :    int       		= hf_model.state_dict()
							lowercase_      :    Dict       		= count_parameters(UpperCAmelCase__				)
							lowercase_      :    List[str]       		= count_parameters(UpperCAmelCase__				)

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

							if save_checkpoint:
														hf_model.save_pretrained(UpperCAmelCase__				)
							else:
														return hf_state_dict


if __name__ == "__main__":
						_lowercase		:				Optional[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("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
						_lowercase		:				Optional[int]	       =		parser.parse_args()

						convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)






'''simple docstring'''




import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset




@pytest.mark.parametrize("""dataset_size"""   ,   [None, 400 * 2**20, 600 * 2**20]				)
@pytest.mark.parametrize("""input_in_memory_max_size"""   ,   ["""default""", 0, 100 * 2**20, 900 * 2**20]				)
def 							lowerCamelCase			(   UpperCAmelCase__ :      Union[str, Any]   ,   UpperCAmelCase__ :      List[Any]   ,   UpperCAmelCase__ :      str				)    ->   Any:
							if input_in_memory_max_size != "default":
														monkeypatch.setattr(datasets.config   ,   """IN_MEMORY_MAX_SIZE"""   ,   UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= datasets.config.IN_MEMORY_MAX_SIZE
							if input_in_memory_max_size == "default":
														assert in_memory_max_size == 0
							else:
														assert in_memory_max_size == input_in_memory_max_size
							if dataset_size and in_memory_max_size:
														lowercase_      :    str       		= dataset_size < in_memory_max_size
							else:
														lowercase_      :    List[Any]       		= False
							lowercase_      :    Any       		= is_small_dataset(UpperCAmelCase__				)
							assert result == expected





