



import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		Any:







						"""simple docstring"""

						A__	:							str						    = s.rsplit(__lowercase							,     __lowercase       )
						return new.join(__lowercase       )
def 	snake_case__       (					__lowercase       )		->		Optional[int]:







						"""simple docstring"""

						return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()       )
def 	snake_case__       (					__lowercase       )		->		Dict:







						"""simple docstring"""

						A__	:							Tuple						    = {}

						A__	:							List[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:
																								A__	:							Tuple						    = key.replace(F'{group_key}.'							,     F'{group_key}.group.'       )

												if "res_path" in key:
																		A__	:							List[Any]						    = key.replace("res_path."							,     "res_path.path."       )

												if key.endswith(".w"       ):
																		A__	:							Optional[Any]						    = rreplace(__lowercase							,     ".w"							,     ".weight"							,     1       )
												if key.endswith(".b"       ):
																		A__	:							Any						    = rreplace(__lowercase							,     ".b"							,     ".bias"							,     1       )

												A__	:							List[str]						    = value.float()

						return upgrade
@torch.no_grad()
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase=None							,     __lowercase=True       )		->		Optional[int]:







						"""simple docstring"""

						from dall_e import Encoder

						A__	:							Optional[int]						    = Encoder()
						if os.path.exists(__lowercase       ):
												A__	:							Tuple						    = torch.load(__lowercase       )
						else:
												A__	:							int						    = torch.hub.load_state_dict_from_url(__lowercase       )

						if isinstance(__lowercase							,     __lowercase       ):
												A__	:							Tuple						    = ckpt.state_dict()
						encoder.load_state_dict(__lowercase       )

						if config_path is not None:
												A__	:							List[Any]						    = FlavaImageCodebookConfig.from_pretrained(__lowercase       )
						else:
												A__	:							str						    = FlavaImageCodebookConfig()

						A__	:							List[Any]						    = FlavaImageCodebook(__lowercase       ).eval()
						A__	:							Tuple						    = encoder.state_dict()

						A__	:							Union[str, Any]						    = upgrade_state_dict(__lowercase       )
						hf_model.load_state_dict(__lowercase       )
						A__	:							int						    = hf_model.state_dict()
						A__	:							Union[str, Any]						    = count_parameters(__lowercase       )
						A__	:							Optional[int]						    = count_parameters(__lowercase       )

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

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


if __name__ == "__main__":
		snake_case    :						List[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')
		snake_case    :						List[str]  =parser.parse_args()

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




import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin




class 							lowerCAmelCase__  (	unittest.TestCase					,							UpperCamelCase						):







					def  _lowercase			(       self      :     List[Any]):
											A__	:							Optional[Any]						    = load_tool("text-classification")
											self.tool.setup()
											A__	:							Any						    = load_tool("text-classification"			,		remote=_A)







					def  _lowercase			(       self      :     List[Any]):
											A__	:							Optional[Any]						    = self.tool("That's quite cool"			,		["positive", "negative"])
											self.assertEqual(_A			,		"positive")







					def  _lowercase			(       self      :     List[str]):
											A__	:							Any						    = self.remote_tool("That's quite cool"			,		["positive", "negative"])
											self.assertEqual(_A			,		"positive")







					def  _lowercase			(       self      :     Optional[Any]):
											A__	:							List[str]						    = self.tool(text="That's quite cool"			,		labels=["positive", "negative"])
											self.assertEqual(_A			,		"positive")







					def  _lowercase			(       self      :     Tuple):
											A__	:							Union[str, Any]						    = self.remote_tool(text="That's quite cool"			,		labels=["positive", "negative"])
											self.assertEqual(_A			,		"positive")