




"""simple docstring"""






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      _snake_case			(				lowercase__		):
				# 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      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Any    =    {}

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

								_lowerCamelCase     :			List[Any]    =    key.replace('heads.cmd.mim_head.cls.predictions'							,    'mmm_image_head'		)
								_lowerCamelCase     :			Optional[Any]    =    key.replace('heads.cmd.mlm_head.cls.predictions'							,    'mmm_text_head'		)
								_lowerCamelCase     :			Union[str, Any]    =    key.replace('heads.cmd.itm_head.cls'							,    'itm_head'		)
								_lowerCamelCase     :			Optional[Any]    =    key.replace('heads.cmd.itm_head.pooler'							,    'itm_head.pooler'		)
								_lowerCamelCase     :			Tuple    =    key.replace('heads.cmd.clip_head.logit_scale'							,    'flava.logit_scale'		)
								_lowerCamelCase     :			List[Any]    =    key.replace('heads.fairseq_mlm.cls.predictions'							,    'mlm_head'		)
								_lowerCamelCase     :			str    =    key.replace('heads.imagenet.mim_head.cls.predictions'							,    'mim_head'		)
								_lowerCamelCase     :			Union[str, Any]    =    key.replace('mm_text_projection'							,    'flava.text_to_mm_projection'		)
								_lowerCamelCase     :			str    =    key.replace('mm_image_projection'							,    'flava.image_to_mm_projection'		)
								_lowerCamelCase     :			Optional[int]    =    key.replace('image_encoder.module'							,    'flava.image_model'		)
								_lowerCamelCase     :			str    =    key.replace('text_encoder.module'							,    'flava.text_model'		)
								_lowerCamelCase     :			Union[str, Any]    =    key.replace('mm_encoder.module.encoder.cls_token'							,    'flava.multimodal_model.cls_token'		)
								_lowerCamelCase     :			str    =    key.replace('mm_encoder.module'							,    'flava.multimodal_model'		)
								_lowerCamelCase     :			List[str]    =    key.replace('text_projection'							,    'flava.text_projection'		)
								_lowerCamelCase     :			Dict    =    key.replace('image_projection'							,    'flava.image_projection'		)

								_lowerCamelCase     :			str    =    value.float()

				for key, value in codebook_state_dict.items():
								_lowerCamelCase     :			Union[str, Any]    =    value

				return upgrade







@torch.no_grad()
def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__=None		):
				if config_path is not None:
								_lowerCamelCase     :			str    =    FlavaConfig.from_pretrained(lowercase__		)
				else:
								_lowerCamelCase     :			List[str]    =    FlavaConfig()

				_lowerCamelCase     :			Optional[int]    =    FlavaForPreTraining(lowercase__		).eval()

				_lowerCamelCase     :			int    =    convert_dalle_checkpoint(lowercase__							,    lowercase__							,    save_checkpoint=lowercase__		)

				if os.path.exists(lowercase__		):
								_lowerCamelCase     :			Union[str, Any]    =    torch.load(lowercase__							,    map_location='cpu'		)
				else:
								_lowerCamelCase     :			Optional[int]    =    torch.hub.load_state_dict_from_url(lowercase__							,    map_location='cpu'		)

				_lowerCamelCase     :			List[Any]    =    upgrade_state_dict(lowercase__							,    lowercase__		)
				hf_model.load_state_dict(lowercase__		)
				_lowerCamelCase     :			Any    =    hf_model.state_dict()
				_lowerCamelCase     :			Any    =    count_parameters(lowercase__		)
				_lowerCamelCase     :			Optional[int]    =    count_parameters(lowercase__		) + count_parameters(lowercase__		)

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

				hf_model.save_pretrained(lowercase__		)


if __name__ == "__main__":
			lowercase__     						=						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""")
			lowercase__     						=						parser.parse_args()

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





"""simple docstring"""






import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


lowercase__     						=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase=768  ):
							super().__init__(lowercase  )
							_lowerCamelCase     :			Any    =    proj_size

							_lowerCamelCase     :			Dict    =    CLIPVisionModel(lowercase  )
							_lowerCamelCase     :			List[str]    =    PaintByExampleMapper(lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    nn.LayerNorm(config.hidden_size  )
							_lowerCamelCase     :			int    =    nn.Linear(config.hidden_size  ,      self.proj_size  )

							# uncondition for scaling
							_lowerCamelCase     :			str    =    nn.Parameter(torch.randn((1, 1, self.proj_size)  )  )






			def 					A_    (  self  ,      lowercase  ,      lowercase=False  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.model(pixel_values=lowercase  )
							_lowerCamelCase     :			int    =    clip_output.pooler_output
							_lowerCamelCase     :			str    =    self.mapper(latent_states[:, None]  )
							_lowerCamelCase     :			List[Any]    =    self.final_layer_norm(lowercase  )
							_lowerCamelCase     :			Dict    =    self.proj_out(lowercase  )
							if return_uncond_vector:
											return latent_states, self.uncond_vector

							return latent_states






class lowerCAmelCase__	(     nn.Module						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ):
							super().__init__()
							_lowerCamelCase     :			Tuple    =    (config.num_hidden_layers + 1) // 5
							_lowerCamelCase     :			int    =    config.hidden_size
							_lowerCamelCase     :			Optional[Any]    =    1
							_lowerCamelCase     :			str    =    nn.ModuleList(
							    [
							        BasicTransformerBlock(lowercase  ,      lowercase  ,      lowercase  ,      activation_fn='gelu'  ,      attention_bias=lowercase  )
							        for _ in range(lowercase  )
							    ]  )






			def 					A_    (  self  ,      lowercase  ):
							for block in self.blocks:
											_lowerCamelCase     :			Tuple    =    block(lowercase  )

							return hidden_states