
import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				List[Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	s.rsplit(lowerCAmelCase						,  lowerCAmelCase )
		return new.join(lowerCAmelCase )


def 							__A(lowerCAmelCase )		->				Dict:

		"""simple docstring"""






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


def 							__A(lowerCAmelCase )		->				Tuple:

		"""simple docstring"""






		_UpperCamelCase						    =	{}

		_UpperCamelCase						    =	["""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:
								_UpperCamelCase						    =	key.replace(F'{group_key}.'						,  F'{group_key}.group.' )

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

				if key.endswith(""".w""" ):
						_UpperCamelCase						    =	rreplace(lowerCAmelCase						,  """.w"""						,  """.weight"""						,  1 )
				if key.endswith(""".b""" ):
						_UpperCamelCase						    =	rreplace(lowerCAmelCase						,  """.b"""						,  """.bias"""						,  1 )

				_UpperCamelCase						    =	value.float()

		return upgrade


@torch.no_grad()
def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase=None						,  lowerCAmelCase=True )		->				Optional[Any]:

		"""simple docstring"""






		from dall_e import Encoder

		_UpperCamelCase						    =	Encoder()
		if os.path.exists(lowerCAmelCase ):
				_UpperCamelCase						    =	torch.load(lowerCAmelCase )
		else:
				_UpperCamelCase						    =	torch.hub.load_state_dict_from_url(lowerCAmelCase )

		if isinstance(lowerCAmelCase						,  lowerCAmelCase ):
				_UpperCamelCase						    =	ckpt.state_dict()
		encoder.load_state_dict(lowerCAmelCase )

		if config_path is not None:
				_UpperCamelCase						    =	FlavaImageCodebookConfig.from_pretrained(lowerCAmelCase )
		else:
				_UpperCamelCase						    =	FlavaImageCodebookConfig()

		_UpperCamelCase						    =	FlavaImageCodebook(lowerCAmelCase ).eval()
		_UpperCamelCase						    =	encoder.state_dict()

		_UpperCamelCase						    =	upgrade_state_dict(lowerCAmelCase )
		hf_model.load_state_dict(lowerCAmelCase )
		_UpperCamelCase						    =	hf_model.state_dict()
		_UpperCamelCase						    =	count_parameters(lowerCAmelCase )
		_UpperCamelCase						    =	count_parameters(lowerCAmelCase )

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

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


if __name__ == "__main__":
		lowerCamelCase__	=							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__	=							parser.parse_args()

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





import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


lowerCamelCase__	=							logging.get_logger(__name__)


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		_UpperCamelCase						    =	nn.functional.normalize(lowerCAmelCase )
		_UpperCamelCase						    =	nn.functional.normalize(lowerCAmelCase )
		return torch.mm(lowerCAmelCase						,  normalized_text_embeds.t() )







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Tuple	=		CLIPConfig

						UpperCamelCase_							: str	=		["CLIPEncoderLayer"]

						def __init__(			self       ,							a				)					->      Optional[Any]:


								'''simple docstring'''

								super().__init__(a				)

								_UpperCamelCase						    =	CLIPVisionModel(config.vision_config				)
								_UpperCamelCase						    =	nn.Linear(config.vision_config.hidden_size       ,							config.projection_dim       ,							bias=a				)

								_UpperCamelCase						    =	nn.Parameter(torch.ones(17       ,							config.projection_dim				)       ,							requires_grad=a				)
								_UpperCamelCase						    =	nn.Parameter(torch.ones(3       ,							config.projection_dim				)       ,							requires_grad=a				)

								_UpperCamelCase						    =	nn.Parameter(torch.ones(17				)       ,							requires_grad=a				)
								_UpperCamelCase						    =	nn.Parameter(torch.ones(3				)       ,							requires_grad=a				)

						@torch.no_grad()
						def  A_				(			self       ,							a       ,							a				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	self.vision_model(a				)[1]  # pooled_output
								_UpperCamelCase						    =	self.visual_projection(a				)

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								_UpperCamelCase						    =	cosine_distance(a       ,							self.special_care_embeds				).cpu().float().numpy()
								_UpperCamelCase						    =	cosine_distance(a       ,							self.concept_embeds				).cpu().float().numpy()

								_UpperCamelCase						    =	[]
								_UpperCamelCase						    =	image_embeds.shape[0]
								for i in range(a				):
										_UpperCamelCase						    =	{"""special_scores""": {}, """special_care""": [], """concept_scores""": {}, """bad_concepts""": []}

										# increase this value to create a stronger `nfsw` filter
										# at the cost of increasing the possibility of filtering benign images
										_UpperCamelCase						    =	0.0

										for concept_idx in range(len(special_cos_dist[0]				)				):
												_UpperCamelCase						    =	special_cos_dist[i][concept_idx]
												_UpperCamelCase						    =	self.special_care_embeds_weights[concept_idx].item()
												_UpperCamelCase						    =	round(concept_cos - concept_threshold + adjustment       ,							3				)
												if result_img["special_scores"][concept_idx] > 0:
														result_img["special_care"].append({concept_idx, result_img["""special_scores"""][concept_idx]}				)
														_UpperCamelCase						    =	0.01

										for concept_idx in range(len(cos_dist[0]				)				):
												_UpperCamelCase						    =	cos_dist[i][concept_idx]
												_UpperCamelCase						    =	self.concept_embeds_weights[concept_idx].item()
												_UpperCamelCase						    =	round(concept_cos - concept_threshold + adjustment       ,							3				)
												if result_img["concept_scores"][concept_idx] > 0:
														result_img["bad_concepts"].append(a				)

										result.append(a				)

								_UpperCamelCase						    =	[len(res["""bad_concepts"""]				) > 0 for res in result]

								return images, has_nsfw_concepts







						@torch.no_grad()
						def  A_				(			self       ,							a       ,							a				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	self.vision_model(a				)[1]  # pooled_output
								_UpperCamelCase						    =	self.visual_projection(a				)

								_UpperCamelCase						    =	cosine_distance(a       ,							self.special_care_embeds				)
								_UpperCamelCase						    =	cosine_distance(a       ,							self.concept_embeds				)

								# increase this value to create a stronger `nsfw` filter
								# at the cost of increasing the possibility of filtering benign images
								_UpperCamelCase						    =	0.0

								_UpperCamelCase						    =	special_cos_dist - self.special_care_embeds_weights + adjustment
								# special_scores = special_scores.round(decimals=3)
								_UpperCamelCase						    =	torch.any(special_scores > 0       ,							dim=1				)
								_UpperCamelCase						    =	special_care * 0.01
								_UpperCamelCase						    =	special_adjustment.unsqueeze(1				).expand(-1       ,							cos_dist.shape[1]				)

								_UpperCamelCase						    =	(cos_dist - self.concept_embeds_weights) + special_adjustment
								# concept_scores = concept_scores.round(decimals=3)
								_UpperCamelCase						    =	torch.any(concept_scores > 0       ,							dim=1				)

								return images, has_nsfw_concepts



