import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Any:
     a  :    int				=						CLIPSegTextConfig()
     a  :    Tuple				=						CLIPSegVisionConfig(patch_size=1_6       )

     a  :    Optional[int]				=						True if '''refined''' in model_name else False
     a  :    Any				=						1_6 if '''rd16''' in model_name else 6_4

     a  :    List[str]				=						CLIPSegConfig.from_text_vision_configs(
         SCREAMING_SNAKE_CASE__						,      SCREAMING_SNAKE_CASE__						,      use_complex_transposed_convolution=SCREAMING_SNAKE_CASE__						,      reduce_dim=SCREAMING_SNAKE_CASE__						,      )
     return config







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	int:


     if "clip_model" in name:
          a  :    int				=						name.replace('''clip_model'''						,      '''clip'''       )
     if "transformer" in name:
          if "visual" in name:
               a  :    List[Any]				=						name.replace('''visual.transformer'''						,      '''vision_model'''       )
          else:
               a  :    List[str]				=						name.replace('''transformer'''						,      '''text_model'''       )
     if "resblocks" in name:
          a  :    str				=						name.replace('''resblocks'''						,      '''encoder.layers'''       )
     if "ln_1" in name:
          a  :    Union[str, Any]				=						name.replace('''ln_1'''						,      '''layer_norm1'''       )
     if "ln_2" in name:
          a  :    Optional[Any]				=						name.replace('''ln_2'''						,      '''layer_norm2'''       )
     if "c_fc" in name:
          a  :    Dict				=						name.replace('''c_fc'''						,      '''fc1'''       )
     if "c_proj" in name:
          a  :    Optional[int]				=						name.replace('''c_proj'''						,      '''fc2'''       )
     if "attn" in name and "self" not in name:
          a  :    Tuple				=						name.replace('''attn'''						,      '''self_attn'''       )
     # text encoder
     if "token_embedding" in name:
          a  :    Optional[int]				=						name.replace('''token_embedding'''						,      '''text_model.embeddings.token_embedding'''       )
     if "positional_embedding" in name and "visual" not in name:
          a  :    Union[str, Any]				=						name.replace('''positional_embedding'''						,      '''text_model.embeddings.position_embedding.weight'''       )
     if "ln_final" in name:
          a  :    Tuple				=						name.replace('''ln_final'''						,      '''text_model.final_layer_norm'''       )
     # vision encoder
     if "visual.class_embedding" in name:
          a  :    Tuple				=						name.replace('''visual.class_embedding'''						,      '''vision_model.embeddings.class_embedding'''       )
     if "visual.conv1" in name:
          a  :    Tuple				=						name.replace('''visual.conv1'''						,      '''vision_model.embeddings.patch_embedding'''       )
     if "visual.positional_embedding" in name:
          a  :    Optional[Any]				=						name.replace('''visual.positional_embedding'''						,      '''vision_model.embeddings.position_embedding.weight'''       )
     if "visual.ln_pre" in name:
          a  :    Any				=						name.replace('''visual.ln_pre'''						,      '''vision_model.pre_layrnorm'''       )
     if "visual.ln_post" in name:
          a  :    Union[str, Any]				=						name.replace('''visual.ln_post'''						,      '''vision_model.post_layernorm'''       )
     # projection layers
     if "visual.proj" in name:
          a  :    Any				=						name.replace('''visual.proj'''						,      '''visual_projection.weight'''       )
     if "text_projection" in name:
          a  :    List[Any]				=						name.replace('''text_projection'''						,      '''text_projection.weight'''       )
     # decoder
     if "trans_conv" in name:
          a  :    List[Any]				=						name.replace('''trans_conv'''						,      '''transposed_convolution'''       )
     if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
          a  :    Dict				=						'''decoder.''' + name
     if "blocks" in name:
          a  :    Any				=						name.replace('''blocks'''						,      '''decoder.layers'''       )
     if "linear1" in name:
          a  :    List[Any]				=						name.replace('''linear1'''						,      '''mlp.fc1'''       )
     if "linear2" in name:
          a  :    int				=						name.replace('''linear2'''						,      '''mlp.fc2'''       )
     if "norm1" in name and "layer_" not in name:
          a  :    Any				=						name.replace('''norm1'''						,      '''layer_norm1'''       )
     if "norm2" in name and "layer_" not in name:
          a  :    Union[str, Any]				=						name.replace('''norm2'''						,      '''layer_norm2'''       )

     return name







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_       )	->	Any:


     for key in orig_state_dict.copy().keys():
          a  :    Union[str, Any]				=						orig_state_dict.pop(SCREAMING_SNAKE_CASE__       )

          if key.startswith('''clip_model'''       ) and "attn.in_proj" in key:
               a  :    Tuple				=						key.split('''.'''       )
               if "visual" in key:
                    a  :    Optional[int]				=						int(key_split[4]       )
                    a  :    Dict				=						config.vision_config.hidden_size
                    a  :    Any				=						'''vision_model'''
               else:
                    a  :    Dict				=						int(key_split[3]       )
                    a  :    int				=						config.text_config.hidden_size
                    a  :    Dict				=						'''text_model'''

               if "weight" in key:
                    a  :    Optional[int]				=						val[:dim, :]
                    a  :    str				=						val[
                        dim : dim * 2, :
                    ]
                    a  :    List[Any]				=						val[-dim:, :]
               else:
                    a  :    Any				=						val[:dim]
                    a  :    str				=						val[dim : dim * 2]
                    a  :    Optional[int]				=						val[-dim:]
          elif "self_attn" in key and "out_proj" not in key:
               a  :    List[Any]				=						key.split('''.'''       )
               a  :    Dict				=						int(key_split[1]       )
               a  :    Optional[Any]				=						config.reduce_dim
               if "weight" in key:
                    a  :    List[Any]				=						val[:dim, :]
                    a  :    Optional[Any]				=						val[dim : dim * 2, :]
                    a  :    List[str]				=						val[-dim:, :]
               else:
                    a  :    Optional[Any]				=						val[:dim]
                    a  :    Tuple				=						val[dim : dim * 2]
                    a  :    Union[str, Any]				=						val[-dim:]
          else:
               a  :    str				=						rename_key(SCREAMING_SNAKE_CASE__       )
               if "visual_projection" in new_name or "text_projection" in new_name:
                    a  :    Optional[int]				=						val.T
               a  :    Any				=						val

     return orig_state_dict







def 	UpperCAmelCase       (		)	->	int:
     a  :    int				=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     a  :    List[Any]				=						Image.open(requests.get(SCREAMING_SNAKE_CASE__						,      stream=SCREAMING_SNAKE_CASE__       ).raw       )
     return image







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	Dict:
     a  :    Any				=						get_clipseg_config(SCREAMING_SNAKE_CASE__       )
     a  :    int				=						CLIPSegForImageSegmentation(SCREAMING_SNAKE_CASE__       )
     model.eval()

     a  :    List[str]				=						torch.load(SCREAMING_SNAKE_CASE__						,      map_location='''cpu'''       )

     # remove some keys
     for key in state_dict.copy().keys():
          if key.startswith('''model'''       ):
               state_dict.pop(SCREAMING_SNAKE_CASE__						,      SCREAMING_SNAKE_CASE__       )

    # rename some keys
     a  :    List[str]				=						convert_state_dict(SCREAMING_SNAKE_CASE__						,      SCREAMING_SNAKE_CASE__       )
     a  :    Dict				=						model.load_state_dict(SCREAMING_SNAKE_CASE__						,      strict=SCREAMING_SNAKE_CASE__       )

     if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
          raise ValueError('''Missing keys that are not expected: {}'''.format(SCREAMING_SNAKE_CASE__       )       )
     if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
          raise ValueError(f"""Unexpected keys: {unexpected_keys}"""       )

     a  :    Any				=						ViTImageProcessor(size=3_5_2       )
     a  :    List[Any]				=						CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''       )
     a  :    Optional[Any]				=						CLIPSegProcessor(image_processor=SCREAMING_SNAKE_CASE__						,      tokenizer=SCREAMING_SNAKE_CASE__       )

     a  :    List[Any]				=						prepare_img()
     a  :    Union[str, Any]				=						['''a glass''', '''something to fill''', '''wood''', '''a jar''']

     a  :    Any				=						processor(text=SCREAMING_SNAKE_CASE__						,      images=[image] * len(SCREAMING_SNAKE_CASE__       )						,      padding='''max_length'''						,      return_tensors='''pt'''       )

     with torch.no_grad():
          a  :    str				=						model(**SCREAMING_SNAKE_CASE__       )

     # verify values
     a  :    Union[str, Any]				=						torch.tensor([0.11_10, -0.18_82, 0.16_45]       )
     a  :    Union[str, Any]				=						torch.tensor([0.26_92, -0.71_97, -0.13_28]       )
     if model_name == "clipseg-rd64-refined":
          a  :    int				=						torch.tensor(
              [[-10.04_07, -9.94_31, -10.26_46], [-9.97_51, -9.70_64, -9.95_86], [-9.68_91, -9.56_45, -9.96_18]]       )
     elif model_name == "clipseg-rd64":
          a  :    str				=						torch.tensor(
              [[-7.28_77, -7.27_11, -7.24_63], [-7.26_52, -7.27_80, -7.25_20], [-7.22_39, -7.22_04, -7.20_01]]       )
     elif model_name == "clipseg-rd16":
          a  :    Union[str, Any]				=						torch.tensor(
              [[-6.39_55, -6.40_55, -6.41_51], [-6.39_11, -6.40_33, -6.41_00], [-6.34_74, -6.37_02, -6.37_62]]       )
     else:
          raise ValueError(f"""Model name {model_name} not supported."""       )

     assert torch.allclose(outputs.logits[0, :3, :3]						,      SCREAMING_SNAKE_CASE__						,      atol=1e-3       )
     assert torch.allclose(outputs.conditional_embeddings[0, :3]						,      SCREAMING_SNAKE_CASE__						,      atol=1e-3       )
     assert torch.allclose(outputs.pooled_output[0, :3]						,      SCREAMING_SNAKE_CASE__						,      atol=1e-3       )
     print('''Looks ok!'''       )

     if pytorch_dump_folder_path is not None:
          print(f"""Saving model and processor to {pytorch_dump_folder_path}"""       )
          model.save_pretrained(SCREAMING_SNAKE_CASE__       )
          processor.save_pretrained(SCREAMING_SNAKE_CASE__       )

     if push_to_hub:
          print(f"""Pushing model and processor for {model_name} to the hub"""       )
          model.push_to_hub(f"""CIDAS/{model_name}"""       )
          processor.push_to_hub(f"""CIDAS/{model_name}"""       )


if __name__ == "__main__":
  UpperCAmelCase							   =      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default="""clipseg-rd64""",
      type=str,
      choices=["""clipseg-rd16""", """clipseg-rd64""", """clipseg-rd64-refined"""],
      help=(
          """Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning"""
          """ reduce dimension)"""
      ),
  )
  parser.add_argument(
      """--checkpoint_path""",
      default="""/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth""",
      type=str,
      help=(
          """Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and"""
          """ the decoder weights."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
  )

  UpperCAmelCase							   =      parser.parse_args()
  convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
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 UNetMidBlockaD, get_down_block, get_up_block






@dataclass
class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						torch.FloatTensor






class 							_SCREAMING_SNAKE_CASE(   snake_case__       ,      snake_case__	):
   @register_to_config
   def __init__(    self							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = 3							,     lowerCAmelCase_ = 3							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = "positional"							,     lowerCAmelCase_ = 0							,     lowerCAmelCase_ = True							,     lowerCAmelCase_ = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")							,     lowerCAmelCase_ = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")							,     lowerCAmelCase_ = (2_24, 4_48, 6_72, 8_96)							,     lowerCAmelCase_ = 2							,     lowerCAmelCase_ = 1							,     lowerCAmelCase_ = 1							,     lowerCAmelCase_ = "conv"							,     lowerCAmelCase_ = "conv"							,     lowerCAmelCase_ = "silu"							,     lowerCAmelCase_ = 8							,     lowerCAmelCase_ = 32							,     lowerCAmelCase_ = 1e-5							,     lowerCAmelCase_ = "default"							,     lowerCAmelCase_ = True							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     )	->Tuple:
        super().__init__()

        a  :    List[Any]				=						sample_size
        a  :    List[Any]				=						block_out_channels[0] * 4

        # Check inputs
        if len(lowerCAmelCase_    ) != len(lowerCAmelCase_    ):
             raise ValueError(
                 F"""Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."""    )

        if len(lowerCAmelCase_    ) != len(lowerCAmelCase_    ):
             raise ValueError(
                 F"""Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."""    )

        # input
        a  :    List[Any]				=						nn.Convad(lowerCAmelCase_							,     block_out_channels[0]							,     kernel_size=3							,     padding=(1, 1)    )

        # time
        if time_embedding_type == "fourier":
             a  :    List[Any]				=						GaussianFourierProjection(embedding_size=block_out_channels[0]							,     scale=16    )
             a  :    List[Any]				=						2 * block_out_channels[0]
        elif time_embedding_type == "positional":
             a  :    Dict				=						Timesteps(block_out_channels[0]							,     lowerCAmelCase_							,     lowerCAmelCase_    )
             a  :    List[str]				=						block_out_channels[0]

        a  :    List[str]				=						TimestepEmbedding(lowerCAmelCase_							,     lowerCAmelCase_    )

        # class embedding
        if class_embed_type is None and num_class_embeds is not None:
             a  :    Dict				=						nn.Embedding(lowerCAmelCase_							,     lowerCAmelCase_    )
        elif class_embed_type == "timestep":
             a  :    str				=						TimestepEmbedding(lowerCAmelCase_							,     lowerCAmelCase_    )
        elif class_embed_type == "identity":
             a  :    List[str]				=						nn.Identity(lowerCAmelCase_							,     lowerCAmelCase_    )
        else:
             a  :    Optional[Any]				=						None

        a  :    str				=						nn.ModuleList([]    )
        a  :    Any				=						None
        a  :    List[Any]				=						nn.ModuleList([]    )

        # down
        a  :    List[Any]				=						block_out_channels[0]
        for i, down_block_type in enumerate(lowerCAmelCase_    ):
             a  :    Dict				=						output_channel
             a  :    Union[str, Any]				=						block_out_channels[i]
             a  :    int				=						i == len(lowerCAmelCase_    ) - 1

             a  :    Tuple				=						get_down_block(
                 lowerCAmelCase_							,     num_layers=lowerCAmelCase_							,     in_channels=lowerCAmelCase_							,     out_channels=lowerCAmelCase_							,     temb_channels=lowerCAmelCase_							,     add_downsample=not is_final_block							,     resnet_eps=lowerCAmelCase_							,     resnet_act_fn=lowerCAmelCase_							,     resnet_groups=lowerCAmelCase_							,     attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel							,     downsample_padding=lowerCAmelCase_							,     resnet_time_scale_shift=lowerCAmelCase_							,     downsample_type=lowerCAmelCase_							,     )
             self.down_blocks.append(lowerCAmelCase_    )

        # mid
        a  :    int				=						UNetMidBlockaD(
            in_channels=block_out_channels[-1]							,     temb_channels=lowerCAmelCase_							,     resnet_eps=lowerCAmelCase_							,     resnet_act_fn=lowerCAmelCase_							,     output_scale_factor=lowerCAmelCase_							,     resnet_time_scale_shift=lowerCAmelCase_							,     attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]							,     resnet_groups=lowerCAmelCase_							,     add_attention=lowerCAmelCase_							,     )

        # up
        a  :    Optional[int]				=						list(reversed(lowerCAmelCase_    )    )
        a  :    Tuple				=						reversed_block_out_channels[0]
        for i, up_block_type in enumerate(lowerCAmelCase_    ):
             a  :    List[Any]				=						output_channel
             a  :    Optional[Any]				=						reversed_block_out_channels[i]
             a  :    Dict				=						reversed_block_out_channels[min(i + 1							,     len(lowerCAmelCase_    ) - 1    )]

             a  :    Any				=						i == len(lowerCAmelCase_    ) - 1

             a  :    List[Any]				=						get_up_block(
                 lowerCAmelCase_							,     num_layers=layers_per_block + 1							,     in_channels=lowerCAmelCase_							,     out_channels=lowerCAmelCase_							,     prev_output_channel=lowerCAmelCase_							,     temb_channels=lowerCAmelCase_							,     add_upsample=not is_final_block							,     resnet_eps=lowerCAmelCase_							,     resnet_act_fn=lowerCAmelCase_							,     resnet_groups=lowerCAmelCase_							,     attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel							,     resnet_time_scale_shift=lowerCAmelCase_							,     upsample_type=lowerCAmelCase_							,     )
             self.up_blocks.append(lowerCAmelCase_    )
             a  :    Any				=						output_channel

        # out
        a  :    str				=						norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4							,     32    )
        a  :    Tuple				=						nn.GroupNorm(num_channels=block_out_channels[0]							,     num_groups=lowerCAmelCase_							,     eps=lowerCAmelCase_    )
        a  :    Any				=						nn.SiLU()
        a  :    List[Any]				=						nn.Convad(block_out_channels[0]							,     lowerCAmelCase_							,     kernel_size=3							,     padding=1    )


   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = True							,     )	->Union[UNetaDOutput, Tuple]:
        # 0. center input if necessary
        if self.config.center_input_sample:
             a  :    Any				=						2 * sample - 1.0

        # 1. time
        a  :    Optional[Any]				=						timestep
        if not torch.is_tensor(lowerCAmelCase_    ):
             a  :    Dict				=						torch.tensor([timesteps]							,     dtype=torch.long							,     device=sample.device    )
        elif torch.is_tensor(lowerCAmelCase_    ) and len(timesteps.shape    ) == 0:
             a  :    Any				=						timesteps[None].to(sample.device    )

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        a  :    Optional[Any]				=						timesteps * torch.ones(sample.shape[0]							,     dtype=timesteps.dtype							,     device=timesteps.device    )

        a  :    Dict				=						self.time_proj(lowerCAmelCase_    )

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might actually be running in fp16. so we need to cast here.
        # there might be better ways to encapsulate this.
        a  :    Optional[Any]				=						t_emb.to(dtype=self.dtype    )
        a  :    Dict				=						self.time_embedding(lowerCAmelCase_    )

        if self.class_embedding is not None:
             if class_labels is None:
                  raise ValueError('''class_labels should be provided when doing class conditioning'''    )

             if self.config.class_embed_type == "timestep":
                  a  :    List[str]				=						self.time_proj(lowerCAmelCase_    )

             a  :    Any				=						self.class_embedding(lowerCAmelCase_    ).to(dtype=self.dtype    )
             a  :    Optional[int]				=						emb + class_emb

        # 2. pre-process
        a  :    int				=						sample
        a  :    Dict				=						self.conv_in(lowerCAmelCase_    )

        # 3. down
        a  :    Union[str, Any]				=						(sample,)
        for downsample_block in self.down_blocks:
             if hasattr(lowerCAmelCase_							,     '''skip_conv'''    ):
                  a			,							a			,							a  :    str				=						downsample_block(
                      hidden_states=lowerCAmelCase_							,     temb=lowerCAmelCase_							,     skip_sample=lowerCAmelCase_    )
             else:
                  a			,							a  :    List[str]				=						downsample_block(hidden_states=lowerCAmelCase_							,     temb=lowerCAmelCase_    )

             down_block_res_samples += res_samples

        # 4. mid
        a  :    str				=						self.mid_block(lowerCAmelCase_							,     lowerCAmelCase_    )

        # 5. up
        a  :    int				=						None
        for upsample_block in self.up_blocks:
             a  :    List[str]				=						down_block_res_samples[-len(upsample_block.resnets    ) :]
             a  :    Any				=						down_block_res_samples[: -len(upsample_block.resnets    )]

             if hasattr(lowerCAmelCase_							,     '''skip_conv'''    ):
                  a			,							a  :    Any				=						upsample_block(lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )
             else:
                  a  :    Union[str, Any]				=						upsample_block(lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )

        # 6. post-process
        a  :    Tuple				=						self.conv_norm_out(lowerCAmelCase_    )
        a  :    Optional[Any]				=						self.conv_act(lowerCAmelCase_    )
        a  :    Tuple				=						self.conv_out(lowerCAmelCase_    )

        if skip_sample is not None:
             sample += skip_sample

        if self.config.time_embedding_type == "fourier":
             a  :    Optional[Any]				=						timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]    )))    )
             a  :    int				=						sample / timesteps

        if not return_dict:
             return (sample,)

        return UNetaDOutput(sample=lowerCAmelCase_    )