
from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch





@dataclass
class 							UpperCAmelCase      :




	'''simple docstring'''





	snake_case_					    =     42  # [batch_size x 3]
	snake_case_					    =     42  # [batch_size x 3]
	snake_case_					    =     42  # [batch_size x 3]
	snake_case_					    =     42  # [batch_size x 3]
	snake_case_					    =     42
	snake_case_					    =     42
	snake_case_					    =     42
	snake_case_					    =     42
	snake_case_					    =     42






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
					assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
					assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					return torch.from_numpy(np.array([self.width, self.height]							,dtype=np.floataa ) )






	def 				UpperCamelCase_  (       self					: List[Any] ):
					return torch.from_numpy(np.array([self.x_fov, self.y_fov]							,dtype=np.floataa ) )






	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= torch.arange(self.height * self.width )
					__A   			= torch.stack(
					    [
					        pixel_indices % self.width,
					        torch.div(A							,self.width							,rounding_mode="trunc" ),
					    ]							,axis=1							,)
					return coords






	@property
	def 				UpperCamelCase_  (       self					: Any ):
					__A			,							*__A   			= self.shape
					__A   			= int(np.prod(A ) )

					__A   			= self.get_image_coords()
					__A   			= torch.broadcast_to(coords.unsqueeze(0 )							,[batch_size * inner_batch_size, *coords.shape] )
					__A   			= self.get_camera_rays(A )

					__A   			= rays.view(A							,inner_batch_size * self.height * self.width							,2							,3 )

					return rays






	def 				UpperCamelCase_  (       self					: str							,A					: torch.Tensor ):
					__A			,							*__A			,							__A   			= coords.shape
					assert n_coords == 2
					assert batch_size == self.origin.shape[0]

					__A   			= coords.view(A							,-1							,2 )

					__A   			= self.resolution()
					__A   			= self.fov()

					__A   			= (flat.float() / (res - 1)) * 2 - 1
					__A   			= fracs * torch.tan(fov / 2 )

					__A   			= fracs.view(A							,-1							,2 )
					__A   			= (
					    self.z.view(A							,1							,3 )
					    + self.x.view(A							,1							,3 ) * fracs[:, :, :1]
					    + self.y.view(A							,1							,3 ) * fracs[:, :, 1:]
					)
					__A   			= directions / directions.norm(dim=-1							,keepdim=A )
					__A   			= torch.stack(
					    [
					        torch.broadcast_to(self.origin.view(A							,1							,3 )							,[batch_size, directions.shape[1], 3] ),
					        directions,
					    ]							,dim=2							,)
					return rays.view(A							,*A							,2							,3 )







	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: int							,A					: int ):
					assert width * self.height == height * self.width, "The aspect ratio should not change."
					return DifferentiableProjectiveCamera(
					    origin=self.origin							,x=self.x							,y=self.y							,z=self.z							,width=A							,height=A							,x_fov=self.x_fov							,y_fov=self.y_fov							,)





def 						UpperCAmelCase			(	a_						)		->	DifferentiableProjectiveCamera:







				"""simple docstring"""



				__A   			= []
				__A   			= []
				__A   			= []
				__A   			= []
				for theta in np.linspace(0		,   2 * np.pi		,   num=2_0						):
								__A   			= np.array([np.sin(a_						), np.cos(a_						), -0.5]						)
								z /= np.sqrt(np.sum(z**2						)						)
								__A   			= -z * 4
								__A   			= np.array([np.cos(a_						), -np.sin(a_						), 0.0]						)
								__A   			= np.cross(a_		,   a_						)
								origins.append(a_						)
								xs.append(a_						)
								ys.append(a_						)
								zs.append(a_						)
				return DifferentiableProjectiveCamera(
				    origin=torch.from_numpy(np.stack(a_		,   axis=0						)						).float()		,   x=torch.from_numpy(np.stack(a_		,   axis=0						)						).float()		,   y=torch.from_numpy(np.stack(a_		,   axis=0						)						).float()		,   z=torch.from_numpy(np.stack(a_		,   axis=0						)						).float()		,   width=a_		,   height=a_		,   x_fov=0.7		,   y_fov=0.7		,   shape=(1, len(a_						))		,   )


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			(	a_						)		->	List[str]:







				"""simple docstring"""



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


def 						UpperCAmelCase			(	a_		,   a_						)		->	Tuple:







				"""simple docstring"""



				__A   			= {}

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

								__A   			= key.replace("heads.cmd.mim_head.cls.predictions"		,   "mmm_image_head"						)
								__A   			= key.replace("heads.cmd.mlm_head.cls.predictions"		,   "mmm_text_head"						)
								__A   			= key.replace("heads.cmd.itm_head.cls"		,   "itm_head"						)
								__A   			= key.replace("heads.cmd.itm_head.pooler"		,   "itm_head.pooler"						)
								__A   			= key.replace("heads.cmd.clip_head.logit_scale"		,   "flava.logit_scale"						)
								__A   			= key.replace("heads.fairseq_mlm.cls.predictions"		,   "mlm_head"						)
								__A   			= key.replace("heads.imagenet.mim_head.cls.predictions"		,   "mim_head"						)
								__A   			= key.replace("mm_text_projection"		,   "flava.text_to_mm_projection"						)
								__A   			= key.replace("mm_image_projection"		,   "flava.image_to_mm_projection"						)
								__A   			= key.replace("image_encoder.module"		,   "flava.image_model"						)
								__A   			= key.replace("text_encoder.module"		,   "flava.text_model"						)
								__A   			= key.replace("mm_encoder.module.encoder.cls_token"		,   "flava.multimodal_model.cls_token"						)
								__A   			= key.replace("mm_encoder.module"		,   "flava.multimodal_model"						)
								__A   			= key.replace("text_projection"		,   "flava.text_projection"						)
								__A   			= key.replace("image_projection"		,   "flava.image_projection"						)

								__A   			= value.float()

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

				return upgrade


@torch.no_grad()
def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_=None						)		->	Tuple:







				"""simple docstring"""



				if config_path is not None:
								__A   			= FlavaConfig.from_pretrained(a_						)
				else:
								__A   			= FlavaConfig()

				__A   			= FlavaForPreTraining(a_						).eval()

				__A   			= convert_dalle_checkpoint(a_		,   a_		,   save_checkpoint=a_						)

				if os.path.exists(a_						):
								__A   			= torch.load(a_		,   map_location="cpu"						)
				else:
								__A   			= torch.hub.load_state_dict_from_url(a_		,   map_location="cpu"						)

				__A   			= upgrade_state_dict(a_		,   a_						)
				hf_model.load_state_dict(a_						)
				__A   			= hf_model.state_dict()
				__A   			= count_parameters(a_						)
				__A   			= count_parameters(a_						) + count_parameters(a_						)

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

				hf_model.save_pretrained(a_						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							: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('--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')
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				parser.parse_args()

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