

"""simple docstring"""




import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Any  		=	s.rsplit(A_    ,     A_   )
						return new.join(A_   )







def       A_				(   _UpperCAmelCase   ):
						# 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       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	{}

						SCREAMING_SNAKE_CASE_:			Tuple  		=	['''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:
																								SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	key.replace(f"{group_key}."    ,     f"{group_key}.group."   )

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

												if key.endswith(".w"   ):
																		SCREAMING_SNAKE_CASE_:			Any  		=	rreplace(A_    ,     ".w"    ,     ".weight"    ,     1   )
												if key.endswith(".b"   ):
																		SCREAMING_SNAKE_CASE_:			int  		=	rreplace(A_    ,     ".b"    ,     ".bias"    ,     1   )

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

						return upgrade







@torch.no_grad()
def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase=None    ,     _UpperCAmelCase=True   ):
						from dall_e import Encoder

						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	Encoder()
						if os.path.exists(A_   ):
												SCREAMING_SNAKE_CASE_:			List[Any]  		=	torch.load(A_   )
						else:
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.hub.load_state_dict_from_url(A_   )

						if isinstance(A_    ,     A_   ):
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	ckpt.state_dict()
						encoder.load_state_dict(A_   )

						if config_path is not None:
												SCREAMING_SNAKE_CASE_:			Dict  		=	FlavaImageCodebookConfig.from_pretrained(A_   )
						else:
												SCREAMING_SNAKE_CASE_:			Any  		=	FlavaImageCodebookConfig()

						SCREAMING_SNAKE_CASE_:			str  		=	FlavaImageCodebook(A_   ).eval()
						SCREAMING_SNAKE_CASE_:			Dict  		=	encoder.state_dict()

						SCREAMING_SNAKE_CASE_:			List[str]  		=	upgrade_state_dict(A_   )
						hf_model.load_state_dict(A_   )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	hf_model.state_dict()
						SCREAMING_SNAKE_CASE_:			str  		=	count_parameters(A_   )
						SCREAMING_SNAKE_CASE_:			str  		=	count_parameters(A_   )

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

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


if __name__ == "__main__":
						lowerCAmelCase							:  str            =						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""")
						lowerCAmelCase							:  Dict            =						parser.parse_args()

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









import doctest
from collections import deque

import numpy as np



class   __lowercase    :




			"""simple docstring"""







			def __init__(			self  :					Union[str, Any]):
									SCREAMING_SNAKE_CASE_:			int  		=	[2, 1, 2, -1]
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[1, 2, 3, 4]




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Any  		=	len(self.first_signal)
									SCREAMING_SNAKE_CASE_:			Dict  		=	len(self.second_signal)

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	max(lowerCAmelCase__       ,						lowerCAmelCase__)

									# create a zero matrix of max_length x max_length
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	[[0] * max_length for i in range(lowerCAmelCase__)]

									# fills the smaller signal with zeros to make both signals of same length
									if length_first_signal < length_second_signal:
															self.first_signal += [0] * (max_length - length_first_signal)
									elif length_first_signal > length_second_signal:
															self.second_signal += [0] * (max_length - length_second_signal)
									for i in range(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Tuple  		=	deque(self.second_signal)
															rotated_signal.rotate(lowerCAmelCase__)
															for j, item in enumerate(lowerCAmelCase__):
																					matrix[i][j] += item

        # multiply the matrix with the first signal
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	np.matmul(np.transpose(lowerCAmelCase__)       ,						np.transpose(self.first_signal))

									# rounding-off to two decimal places
									return [round(lowerCAmelCase__       ,						2) for i in final_signal]


if __name__ == "__main__":
						doctest.testmod()






