"""simple docstring"""






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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block





@dataclass
class 				_A		( lowerCAmelCase			):
      snake_case__    :     torch.FloatTensor






class 				_A		( lowerCAmelCase						,					lowerCAmelCase			):

      @register_to_config
      def __init__(		self	, __lowerCAmelCase = 6_5536	, __lowerCAmelCase = None	, __lowerCAmelCase = 2	, __lowerCAmelCase = 2	, __lowerCAmelCase = 0	, __lowerCAmelCase = "fourier"	, __lowerCAmelCase = True	, __lowerCAmelCase = False	, __lowerCAmelCase = 0.0	, __lowerCAmelCase = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")	, __lowerCAmelCase = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")	, __lowerCAmelCase = "UNetMidBlock1D"	, __lowerCAmelCase = None	, __lowerCAmelCase = (32, 32, 64)	, __lowerCAmelCase = None	, __lowerCAmelCase = 8	, __lowerCAmelCase = 1	, __lowerCAmelCase = False	, ):







            """simple docstring"""



            super().__init__()
            lowercase														=			sample_size

            # time
            if time_embedding_type == "fourier":
                  lowercase														=			GaussianFourierProjection(
                      embedding_size=8	, set_W_to_weight=__lowerCAmelCase	, log=__lowerCAmelCase	, flip_sin_to_cos=__lowerCAmelCase		)
                  lowercase														=			2 * block_out_channels[0]
            elif time_embedding_type == "positional":
                  lowercase														=			Timesteps(
                      block_out_channels[0]	, flip_sin_to_cos=__lowerCAmelCase	, downscale_freq_shift=__lowerCAmelCase		)
                  lowercase														=			block_out_channels[0]

            if use_timestep_embedding:
                  lowercase														=			block_out_channels[0] * 4
                  lowercase														=			TimestepEmbedding(
                      in_channels=__lowerCAmelCase	, time_embed_dim=__lowerCAmelCase	, act_fn=__lowerCAmelCase	, out_dim=block_out_channels[0]	, )

            lowercase														=			nn.ModuleList([]		)
            lowercase														=			None
            lowercase														=			nn.ModuleList([]		)
            lowercase														=			None

            # down
            lowercase														=			in_channels
            for i, down_block_type in enumerate(__lowerCAmelCase		):
                  lowercase														=			output_channel
                  lowercase														=			block_out_channels[i]

                  if i == 0:
                        input_channel += extra_in_channels

                  lowercase														=			i == len(__lowerCAmelCase		) - 1

                  lowercase														=			get_down_block(
                      __lowerCAmelCase	, num_layers=__lowerCAmelCase	, in_channels=__lowerCAmelCase	, out_channels=__lowerCAmelCase	, temb_channels=block_out_channels[0]	, add_downsample=not is_final_block or downsample_each_block	, )
                  self.down_blocks.append(__lowerCAmelCase		)

            # mid
            lowercase														=			get_mid_block(
                __lowerCAmelCase	, in_channels=block_out_channels[-1]	, mid_channels=block_out_channels[-1]	, out_channels=block_out_channels[-1]	, embed_dim=block_out_channels[0]	, num_layers=__lowerCAmelCase	, add_downsample=__lowerCAmelCase	, )

            # up
            lowercase														=			list(reversed(__lowerCAmelCase		)		)
            lowercase														=			reversed_block_out_channels[0]
            if out_block_type is None:
                  lowercase														=			out_channels
            else:
                  lowercase														=			block_out_channels[0]

            for i, up_block_type in enumerate(__lowerCAmelCase		):
                  lowercase														=			output_channel
                  lowercase														=			(
                      reversed_block_out_channels[i + 1] if i < len(__lowerCAmelCase		) - 1 else final_upsample_channels
                  )

                  lowercase														=			i == len(__lowerCAmelCase		) - 1

                  lowercase														=			get_up_block(
                      __lowerCAmelCase	, num_layers=__lowerCAmelCase	, in_channels=__lowerCAmelCase	, out_channels=__lowerCAmelCase	, temb_channels=block_out_channels[0]	, add_upsample=not is_final_block	, )
                  self.up_blocks.append(__lowerCAmelCase		)
                  lowercase														=			output_channel

            # out
            lowercase														=			norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4	, 32		)
            lowercase														=			get_out_block(
                out_block_type=__lowerCAmelCase	, num_groups_out=__lowerCAmelCase	, embed_dim=block_out_channels[0]	, out_channels=__lowerCAmelCase	, act_fn=__lowerCAmelCase	, fc_dim=block_out_channels[-1] // 4	, )







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase = True	, ):







            """simple docstring"""



            lowercase														=			timestep
            if not torch.is_tensor(__lowerCAmelCase		):
                  lowercase														=			torch.tensor([timesteps]	, dtype=torch.long	, device=sample.device		)
            elif torch.is_tensor(__lowerCAmelCase		) and len(timesteps.shape		) == 0:
                  lowercase														=			timesteps[None].to(sample.device		)

            lowercase														=			self.time_proj(__lowerCAmelCase		)
            if self.config.use_timestep_embedding:
                  lowercase														=			self.time_mlp(__lowerCAmelCase		)
            else:
                  lowercase														=			timestep_embed[..., None]
                  lowercase														=			timestep_embed.repeat([1, 1, sample.shape[2]]		).to(sample.dtype		)
                  lowercase														=			timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])		)

            # 2. down
            lowercase														=			()
            for downsample_block in self.down_blocks:
                  lowercase							,							lowercase														=			downsample_block(hidden_states=__lowerCAmelCase	, temb=__lowerCAmelCase		)
                  down_block_res_samples += res_samples

            # 3. mid
            if self.mid_block:
                  lowercase														=			self.mid_block(__lowerCAmelCase	, __lowerCAmelCase		)

            # 4. up
            for i, upsample_block in enumerate(self.up_blocks		):
                  lowercase														=			down_block_res_samples[-1:]
                  lowercase														=			down_block_res_samples[:-1]
                  lowercase														=			upsample_block(__lowerCAmelCase	, res_hidden_states_tuple=__lowerCAmelCase	, temb=__lowerCAmelCase		)

            # 5. post-process
            if self.out_block:
                  lowercase														=			self.out_block(__lowerCAmelCase	, __lowerCAmelCase		)

            if not return_dict:
                  return (sample,)

            return UNetaDOutput(sample=__lowerCAmelCase		)




"""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     UpperCAmelCase__ (       lowerCAmelCase__  :str	)				->					Optional[int]:
      '''simple docstring'''


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


def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :Union[str, Any]	)				->					str:
      '''simple docstring'''


      lowercase														=			{}

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

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

            lowercase														=			value.float()

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

      return upgrade


@torch.no_grad()
def     UpperCAmelCase__ (       lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :Optional[int]      ,	lowerCAmelCase__  :str=None	)				->					int:
      '''simple docstring'''


      if config_path is not None:
            lowercase														=			FlavaConfig.from_pretrained(lowerCAmelCase__	)
      else:
            lowercase														=			FlavaConfig()

      lowercase														=			FlavaForPreTraining(lowerCAmelCase__	).eval()

      lowercase														=			convert_dalle_checkpoint(lowerCAmelCase__      ,	lowerCAmelCase__      ,	save_checkpoint=lowerCAmelCase__	)

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

      lowercase														=			upgrade_state_dict(lowerCAmelCase__      ,	lowerCAmelCase__	)
      hf_model.load_state_dict(lowerCAmelCase__	)
      lowercase														=			hf_model.state_dict()
      lowercase														=			count_parameters(lowerCAmelCase__	)
      lowercase														=			count_parameters(lowerCAmelCase__	) + count_parameters(lowerCAmelCase__	)

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

      hf_model.save_pretrained(lowerCAmelCase__	)


if __name__ == "__main__":
       __lowerCAmelCase					: Tuple        =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""")
       __lowerCAmelCase					: List[str]        =parser.parse_args()

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



