


'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class 					a__ (       __SCREAMING_SNAKE_CASE					):







				"""simple docstring"""



				__UpperCamelCase			:						int		=		42
				__UpperCamelCase			:						List[str]		=		42
				__UpperCamelCase			:						Dict		=		None


class 					a__ (       __SCREAMING_SNAKE_CASE							,  __SCREAMING_SNAKE_CASE					):







				"""simple docstring"""



				__UpperCamelCase			:						Dict		=		2




				@register_to_config
				def __init__(self				,	__lowercase = 0.0_2				,	__lowercase = 1_00				,	__lowercase = 1.0_0_7				,	__lowercase = 80				,	__lowercase = 0.0_5				,	__lowercase = 50				,	):
										# standard deviation of the initial noise distribution
										__lowerCAmelCase											= sigma_max

										# setable values
										__lowerCAmelCase											= None
										__lowerCAmelCase											= None
										__lowerCAmelCase											= None  # sigma(t_i)




				def     _snake_case     (self				,	__lowercase				,	__lowercase = None						):
										return sample




				def     _snake_case     (self				,	__lowercase				,	__lowercase = None						):
										__lowerCAmelCase											= num_inference_steps
										__lowerCAmelCase											= np.arange(0				,	self.num_inference_steps						)[::-1].copy()
										__lowerCAmelCase											= torch.from_numpy(__lowercase						).to(__lowercase						)
										__lowerCAmelCase											= [
										    (
										        self.config.sigma_max**2
										        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
										    )
										    for i in self.timesteps
										]
										__lowerCAmelCase											= torch.tensor(__lowercase				,	dtype=torch.floataa				,	device=__lowercase						)




				def     _snake_case     (self				,	__lowercase				,	__lowercase				,	__lowercase = None						):
										if self.config.s_min <= sigma <= self.config.s_max:
																__lowerCAmelCase											= min(self.config.s_churn / self.num_inference_steps				,	2**0.5 - 1						)
										else:
																__lowerCAmelCase											= 0

										# sample eps ~ N(0, S_noise^2 * I)
										__lowerCAmelCase											= self.config.s_noise * randn_tensor(sample.shape				,	generator=__lowercase						).to(sample.device						)
										__lowerCAmelCase											= sigma + gamma * sigma
										__lowerCAmelCase											= sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

										return sample_hat, sigma_hat




				def     _snake_case     (self				,	__lowercase				,	__lowercase				,	__lowercase				,	__lowercase				,	__lowercase = True				,	):
										__lowerCAmelCase											= sample_hat + sigma_hat * model_output
										__lowerCAmelCase											= (sample_hat - pred_original_sample) / sigma_hat
										__lowerCAmelCase											= sample_hat + (sigma_prev - sigma_hat) * derivative

										if not return_dict:
																return (sample_prev, derivative)

										return KarrasVeOutput(
										    prev_sample=__lowercase				,	derivative=__lowercase				,	pred_original_sample=__lowercase						)




				def     _snake_case     (self				,	__lowercase				,	__lowercase				,	__lowercase				,	__lowercase				,	__lowercase				,	__lowercase				,	__lowercase = True				,	):
										__lowerCAmelCase											= sample_prev + sigma_prev * model_output
										__lowerCAmelCase											= (sample_prev - pred_original_sample) / sigma_prev
										__lowerCAmelCase											= sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

										if not return_dict:
																return (sample_prev, derivative)

										return KarrasVeOutput(
										    prev_sample=__lowercase				,	derivative=__lowercase				,	pred_original_sample=__lowercase						)




				def     _snake_case     (self				,	__lowercase				,	__lowercase				,	__lowercase						):
										raise NotImplementedError()



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)
