
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 __snake_case		(				lowerCAmelCase					):
    _a		:						torch.FloatTensor






class __snake_case		(				lowerCAmelCase ,     lowerCAmelCase					):
    @register_to_config
    def __init__(  self		,snake_case = 65536		,snake_case = None		,snake_case = 2		,snake_case = 2		,snake_case = 0		,snake_case = "fourier"		,snake_case = True		,snake_case = False		,snake_case = 0.0		,snake_case = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")		,snake_case = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")		,snake_case = "UNetMidBlock1D"		,snake_case = None		,snake_case = (32, 32, 64)		,snake_case = None		,snake_case = 8		,snake_case = 1		,snake_case = False		,):



           '''simple docstring'''







           super().__init__()
           lowercase							:					List[Any]	     =				sample_size

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

           if use_timestep_embedding:
                  lowercase							:					str	     =				block_out_channels[0] * 4
                  lowercase							:					Optional[Any]	     =				TimestepEmbedding(
                      in_channels=snake_case		,time_embed_dim=snake_case		,act_fn=snake_case		,out_dim=block_out_channels[0]		,)

           lowercase							:					Tuple	     =				nn.ModuleList([]				)
           lowercase							:					Optional[Any]	     =				None
           lowercase							:					str	     =				nn.ModuleList([]				)
           lowercase							:					Tuple	     =				None

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

                  if i == 0:
                         input_channel += extra_in_channels

                  lowercase							:					Dict	     =				i == len(snake_case				) - 1

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

           # mid
           lowercase							:					Optional[int]	     =				get_mid_block(
               snake_case		,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=snake_case		,add_downsample=snake_case		,)

           # up
           lowercase							:					str	     =				list(reversed(snake_case				)				)
           lowercase							:					str	     =				reversed_block_out_channels[0]
           if out_block_type is None:
                  lowercase							:					Optional[Any]	     =				out_channels
           else:
                  lowercase							:					Dict	     =				block_out_channels[0]

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

                  lowercase							:					List[str]	     =				i == len(snake_case				) - 1

                  lowercase							:					Optional[Any]	     =				get_up_block(
                      snake_case		,num_layers=snake_case		,in_channels=snake_case		,out_channels=snake_case		,temb_channels=block_out_channels[0]		,add_upsample=not is_final_block		,)
                  self.up_blocks.append(snake_case				)
                  lowercase							:					Tuple	     =				output_channel

           # out
           lowercase							:					str	     =				norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,32				)
           lowercase							:					Any	     =				get_out_block(
               out_block_type=snake_case		,num_groups_out=snake_case		,embed_dim=block_out_channels[0]		,out_channels=snake_case		,act_fn=snake_case		,fc_dim=block_out_channels[-1] // 4		,)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case = True		,):



           '''simple docstring'''







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

           lowercase							:					List[Any]	     =				self.time_proj(snake_case				)
           if self.config.use_timestep_embedding:
                  lowercase							:					Tuple	     =				self.time_mlp(snake_case				)
           else:
                  lowercase							:					int	     =				timestep_embed[..., None]
                  lowercase							:					Any	     =				timestep_embed.repeat([1, 1, sample.shape[2]]				).to(sample.dtype				)
                  lowercase							:					str	     =				timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])				)

           # 2. down
           lowercase							:					str	     =				()
           for downsample_block in self.down_blocks:
                  lowercase     ,      lowercase							:					Union[str, Any]	     =				downsample_block(hidden_states=snake_case		,temb=snake_case				)
                  down_block_res_samples += res_samples

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

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

           # 5. post-process
           if self.out_block:
                  lowercase							:					List[Any]	     =				self.out_block(snake_case		,snake_case				)

           if not return_dict:
                  return (sample,)

           return UNetaDOutput(sample=snake_case				)


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   List[str]:
       # vision encoder
       if "img_encoder.pos_embed" in name:
              lowercase							:					Dict	     =				name.replace("""img_encoder.pos_embed""" ,     """vision_model.embeddings.position_embeddings"""    )
       if "img_encoder.patch_embed.proj" in name:
              lowercase							:					Any	     =				name.replace("""img_encoder.patch_embed.proj""" ,     """vision_model.embeddings.patch_embeddings.projection"""    )
       if "img_encoder.patch_embed.norm" in name:
              lowercase							:					Tuple	     =				name.replace("""img_encoder.patch_embed.norm""" ,     """vision_model.embeddings.layernorm"""    )
       if "img_encoder.layers" in name:
              lowercase							:					Tuple	     =				name.replace("""img_encoder.layers""" ,     """vision_model.encoder.stages"""    )
       if "blocks" in name and "res" not in name:
              lowercase							:					Optional[Any]	     =				name.replace("""blocks""" ,     """layers"""    )
       if "attn" in name and "pre_assign" not in name:
              lowercase							:					Union[str, Any]	     =				name.replace("""attn""" ,     """self_attn"""    )
       if "proj" in name and "self_attn" in name and "text" not in name:
              lowercase							:					Dict	     =				name.replace("""proj""" ,     """out_proj"""    )
       if "pre_assign_attn.attn.proj" in name:
              lowercase							:					Dict	     =				name.replace("""pre_assign_attn.attn.proj""" ,     """pre_assign_attn.attn.out_proj"""    )
       if "norm1" in name:
              lowercase							:					Tuple	     =				name.replace("""norm1""" ,     """layer_norm1"""    )
       if "norm2" in name and "pre_assign" not in name:
              lowercase							:					Tuple	     =				name.replace("""norm2""" ,     """layer_norm2"""    )
       if "img_encoder.norm" in name:
              lowercase							:					List[Any]	     =				name.replace("""img_encoder.norm""" ,     """vision_model.layernorm"""    )
       # text encoder
       if "text_encoder.token_embedding" in name:
              lowercase							:					Tuple	     =				name.replace("""text_encoder.token_embedding""" ,     """text_model.embeddings.token_embedding"""    )
       if "text_encoder.positional_embedding" in name:
              lowercase							:					Any	     =				name.replace("""text_encoder.positional_embedding""" ,     """text_model.embeddings.position_embedding.weight"""    )
       if "text_encoder.transformer.resblocks." in name:
              lowercase							:					Dict	     =				name.replace("""text_encoder.transformer.resblocks.""" ,     """text_model.encoder.layers."""    )
       if "ln_1" in name:
              lowercase							:					List[str]	     =				name.replace("""ln_1""" ,     """layer_norm1"""    )
       if "ln_2" in name:
              lowercase							:					Tuple	     =				name.replace("""ln_2""" ,     """layer_norm2"""    )
       if "c_fc" in name:
              lowercase							:					str	     =				name.replace("""c_fc""" ,     """fc1"""    )
       if "c_proj" in name:
              lowercase							:					Union[str, Any]	     =				name.replace("""c_proj""" ,     """fc2"""    )
       if "text_encoder" in name:
              lowercase							:					Tuple	     =				name.replace("""text_encoder""" ,     """text_model"""    )
       if "ln_final" in name:
              lowercase							:					Optional[Any]	     =				name.replace("""ln_final""" ,     """final_layer_norm"""    )
       # projection layers
       if "img_projector.linear_hidden." in name:
              lowercase							:					List[str]	     =				name.replace("""img_projector.linear_hidden.""" ,     """visual_projection."""    )
       if "img_projector.linear_out." in name:
              lowercase							:					Tuple	     =				name.replace("""img_projector.linear_out.""" ,     """visual_projection.3."""    )
       if "text_projector.linear_hidden" in name:
              lowercase							:					List[Any]	     =				name.replace("""text_projector.linear_hidden""" ,     """text_projection"""    )
       if "text_projector.linear_out" in name:
              lowercase							:					List[Any]	     =				name.replace("""text_projector.linear_out""" ,     """text_projection.3"""    )

       return name







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   List[str]:
       for key in orig_state_dict.copy().keys():
              lowercase							:					List[Any]	     =				orig_state_dict.pop(SCREAMING_SNAKE_CASE__    )

              if "qkv" in key:
                     # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
                     # we need to split them up into separate matrices/vectors
                     lowercase							:					Dict	     =				key.split("""."""    )
                     lowercase     ,      lowercase							:					Optional[Any]	     =				int(key_split[2]    ), int(key_split[4]    )
                     lowercase							:					List[Any]	     =				config.vision_config.hidden_size
                     if "weight" in key:
                            lowercase							:					str	     =				val[:dim, :]
                            lowercase							:					List[str]	     =				val[dim : dim * 2, :]
                            lowercase							:					Optional[int]	     =				val[-dim:, :]
                     else:
                            lowercase							:					Dict	     =				val[:dim]
                            lowercase							:					Dict	     =				val[dim : dim * 2]
                            lowercase							:					Dict	     =				val[-dim:]
              elif "in_proj" in key:
                     # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
                     # we need to split them up into separate matrices/vectors
                     lowercase							:					int	     =				key.split("""."""    )
                     lowercase							:					Tuple	     =				int(key_split[3]    )
                     lowercase							:					str	     =				config.text_config.hidden_size
                     if "weight" in key:
                            lowercase							:					Optional[int]	     =				val[:dim, :]
                            lowercase							:					Optional[Any]	     =				val[
                                dim : dim * 2, :
                            ]
                            lowercase							:					Optional[int]	     =				val[-dim:, :]
                     else:
                            lowercase							:					Optional[int]	     =				val[:dim]
                            lowercase							:					Dict	     =				val[dim : dim * 2]
                            lowercase							:					List[str]	     =				val[-dim:]
              else:
                     lowercase							:					Tuple	     =				rename_key(SCREAMING_SNAKE_CASE__    )
                     # squeeze if necessary
                     if (
                         "text_projection.0" in new_name
                         or "text_projection.3" in new_name
                         or "visual_projection.0" in new_name
                         or "visual_projection.3" in new_name
                     ):
                            lowercase							:					str	     =				val.squeeze_()
                     else:
                            lowercase							:					Any	     =				val

       return orig_state_dict







def 						_snake_case(  )			->   List[Any]:
       lowercase							:					Tuple	     =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       lowercase							:					Any	     =				Image.open(requests.get(SCREAMING_SNAKE_CASE__ ,     stream=SCREAMING_SNAKE_CASE__    ).raw    )
       return im







@torch.no_grad()
def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__="groupvit-gcc-yfcc" ,     SCREAMING_SNAKE_CASE__=False    )			->   str:
       lowercase							:					Dict	     =				GroupViTConfig()
       lowercase							:					Tuple	     =				GroupViTModel(SCREAMING_SNAKE_CASE__    ).eval()

       lowercase							:					List[str]	     =				torch.load(SCREAMING_SNAKE_CASE__ ,     map_location="""cpu"""    )["""model"""]
       lowercase							:					Optional[Any]	     =				convert_state_dict(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       lowercase     ,      lowercase							:					int	     =				model.load_state_dict(SCREAMING_SNAKE_CASE__ ,     strict=SCREAMING_SNAKE_CASE__    )
       assert missing_keys == ["text_model.embeddings.position_ids"]
       assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(SCREAMING_SNAKE_CASE__    ) == 0)

       # verify result
       lowercase							:					Tuple	     =				CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32"""    )
       lowercase							:					Optional[Any]	     =				prepare_img()
       lowercase							:					List[Any]	     =				processor(text=["""a photo of a cat""", """a photo of a dog"""] ,     images=SCREAMING_SNAKE_CASE__ ,     padding=SCREAMING_SNAKE_CASE__ ,     return_tensors="""pt"""    )

       with torch.no_grad():
              lowercase							:					Optional[Any]	     =				model(**SCREAMING_SNAKE_CASE__    )

       if model_name == "groupvit-gcc-yfcc":
              lowercase							:					Optional[int]	     =				torch.tensor([[13.3523, 6.3629]]    )
       elif model_name == "groupvit-gcc-redcaps":
              lowercase							:					int	     =				torch.tensor([[16.1873, 8.6230]]    )
       else:
              raise ValueError(f"Model name {model_name} not supported."    )
       assert torch.allclose(outputs.logits_per_image ,     SCREAMING_SNAKE_CASE__ ,     atol=1e-3    )

       processor.save_pretrained(SCREAMING_SNAKE_CASE__    )
       model.save_pretrained(SCREAMING_SNAKE_CASE__    )
       print("""Successfully saved processor and model to""" ,     SCREAMING_SNAKE_CASE__    )

       if push_to_hub:
              print("""Pushing to the hub..."""    )
              processor.push_to_hub(SCREAMING_SNAKE_CASE__ ,     organization="""nielsr"""    )
              model.push_to_hub(SCREAMING_SNAKE_CASE__ ,     organization="""nielsr"""    )


if __name__ == "__main__":
  lowercase  : List[Any]						   =							argparse.ArgumentParser()
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to dump the processor and PyTorch model."""
  )
  parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to GroupViT checkpoint""")
  parser.add_argument(
      """--model_name""",
      default="""groupvit-gccy-fcc""",
      type=str,
      help="""Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'""",
  )
  parser.add_argument(
      """--push_to_hub""",
      action="""store_true""",
      help="""Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.""",
  )
  lowercase  : Union[str, Any]						   =							parser.parse_args()

  convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
