




import argparse

import requests
import torch
from PIL import Image

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





def   a_							(   __snake_case							:    int     )							->		str:



       """simple docstring"""
       lowerCamelCase_							=CLIPSegTextConfig()
       lowerCamelCase_							=CLIPSegVisionConfig(patch_size=16     )

       lowerCamelCase_							=True if '''refined''' in model_name else False
       lowerCamelCase_							=16 if '''rd16''' in model_name else 64

       lowerCamelCase_							=CLIPSegConfig.from_text_vision_configs(
           lowercase__     , lowercase__     , use_complex_transposed_convolution=lowercase__     , reduce_dim=lowercase__     , )
       return config





def   a_							(   __snake_case							:    str     )							->		Tuple:



       """simple docstring"""
       # update prefixes
       if "clip_model" in name:
              lowerCamelCase_							=name.replace('''clip_model'''     , '''clip'''     )
       if "transformer" in name:
              if "visual" in name:
                     lowerCamelCase_							=name.replace('''visual.transformer'''     , '''vision_model'''     )
              else:
                     lowerCamelCase_							=name.replace('''transformer'''     , '''text_model'''     )
       if "resblocks" in name:
              lowerCamelCase_							=name.replace('''resblocks'''     , '''encoder.layers'''     )
       if "ln_1" in name:
              lowerCamelCase_							=name.replace('''ln_1'''     , '''layer_norm1'''     )
       if "ln_2" in name:
              lowerCamelCase_							=name.replace('''ln_2'''     , '''layer_norm2'''     )
       if "c_fc" in name:
              lowerCamelCase_							=name.replace('''c_fc'''     , '''fc1'''     )
       if "c_proj" in name:
              lowerCamelCase_							=name.replace('''c_proj'''     , '''fc2'''     )
       if "attn" in name and "self" not in name:
              lowerCamelCase_							=name.replace('''attn'''     , '''self_attn'''     )
       # text encoder
       if "token_embedding" in name:
              lowerCamelCase_							=name.replace('''token_embedding'''     , '''text_model.embeddings.token_embedding'''     )
       if "positional_embedding" in name and "visual" not in name:
              lowerCamelCase_							=name.replace('''positional_embedding'''     , '''text_model.embeddings.position_embedding.weight'''     )
       if "ln_final" in name:
              lowerCamelCase_							=name.replace('''ln_final'''     , '''text_model.final_layer_norm'''     )
       # vision encoder
       if "visual.class_embedding" in name:
              lowerCamelCase_							=name.replace('''visual.class_embedding'''     , '''vision_model.embeddings.class_embedding'''     )
       if "visual.conv1" in name:
              lowerCamelCase_							=name.replace('''visual.conv1'''     , '''vision_model.embeddings.patch_embedding'''     )
       if "visual.positional_embedding" in name:
              lowerCamelCase_							=name.replace('''visual.positional_embedding'''     , '''vision_model.embeddings.position_embedding.weight'''     )
       if "visual.ln_pre" in name:
              lowerCamelCase_							=name.replace('''visual.ln_pre'''     , '''vision_model.pre_layrnorm'''     )
       if "visual.ln_post" in name:
              lowerCamelCase_							=name.replace('''visual.ln_post'''     , '''vision_model.post_layernorm'''     )
       # projection layers
       if "visual.proj" in name:
              lowerCamelCase_							=name.replace('''visual.proj'''     , '''visual_projection.weight'''     )
       if "text_projection" in name:
              lowerCamelCase_							=name.replace('''text_projection'''     , '''text_projection.weight'''     )
       # decoder
       if "trans_conv" in name:
              lowerCamelCase_							=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:
              lowerCamelCase_							='''decoder.''' + name
       if "blocks" in name:
              lowerCamelCase_							=name.replace('''blocks'''     , '''decoder.layers'''     )
       if "linear1" in name:
              lowerCamelCase_							=name.replace('''linear1'''     , '''mlp.fc1'''     )
       if "linear2" in name:
              lowerCamelCase_							=name.replace('''linear2'''     , '''mlp.fc2'''     )
       if "norm1" in name and "layer_" not in name:
              lowerCamelCase_							=name.replace('''norm1'''     , '''layer_norm1'''     )
       if "norm2" in name and "layer_" not in name:
              lowerCamelCase_							=name.replace('''norm2'''     , '''layer_norm2'''     )

       return name





def   a_							(   __snake_case							:    Tuple     , __snake_case							:    Any     )							->		Any:



       """simple docstring"""
       for key in orig_state_dict.copy().keys():
              lowerCamelCase_							=orig_state_dict.pop(lowercase__     )

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

                     if "weight" in key:
                            lowerCamelCase_							=val[:dim, :]
                            lowerCamelCase_							=val[
                                dim : dim * 2, :
                            ]
                            lowerCamelCase_							=val[-dim:, :]
                     else:
                            lowerCamelCase_							=val[:dim]
                            lowerCamelCase_							=val[dim : dim * 2]
                            lowerCamelCase_							=val[-dim:]
              elif "self_attn" in key and "out_proj" not in key:
                     lowerCamelCase_							=key.split('''.'''     )
                     lowerCamelCase_							=int(key_split[1]     )
                     lowerCamelCase_							=config.reduce_dim
                     if "weight" in key:
                            lowerCamelCase_							=val[:dim, :]
                            lowerCamelCase_							=val[dim : dim * 2, :]
                            lowerCamelCase_							=val[-dim:, :]
                     else:
                            lowerCamelCase_							=val[:dim]
                            lowerCamelCase_							=val[dim : dim * 2]
                            lowerCamelCase_							=val[-dim:]
              else:
                     lowerCamelCase_							=rename_key(lowercase__     )
                     if "visual_projection" in new_name or "text_projection" in new_name:
                            lowerCamelCase_							=val.T
                     lowerCamelCase_							=val

       return orig_state_dict





def   a_							(   )							->		Optional[Any]:



       """simple docstring"""
       lowerCamelCase_							='''http://images.cocodataset.org/val2017/000000039769.jpg'''
       lowerCamelCase_							=Image.open(requests.get(lowercase__     , stream=lowercase__     ).raw     )
       return image





def   a_							(   __snake_case							:    Union[str, Any]     , __snake_case							:    List[str]     , __snake_case							:    Union[str, Any]     , __snake_case							:    Optional[int]     )							->		Any:



       """simple docstring"""
       lowerCamelCase_							=get_clipseg_config(lowercase__     )
       lowerCamelCase_							=CLIPSegForImageSegmentation(lowercase__     )
       model.eval()

       lowerCamelCase_							=torch.load(lowercase__     , map_location='''cpu'''     )

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

    # rename some keys
       lowerCamelCase_							=convert_state_dict(lowercase__     , lowercase__     )
       lowerCamelCase_,   lowerCamelCase_							=model.load_state_dict(lowercase__     , strict=lowercase__     )

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

       lowerCamelCase_							=ViTImageProcessor(size=352     )
       lowerCamelCase_							=CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''     )
       lowerCamelCase_							=CLIPSegProcessor(image_processor=lowercase__     , tokenizer=lowercase__     )

       lowerCamelCase_							=prepare_img()
       lowerCamelCase_							=['''a glass''', '''something to fill''', '''wood''', '''a jar''']

       lowerCamelCase_							=processor(text=lowercase__     , images=[image] * len(lowercase__     )     , padding='''max_length'''     , return_tensors='''pt'''     )

       with torch.no_grad():
              lowerCamelCase_							=model(**lowercase__     )

       # verify values
       lowerCamelCase_							=torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]     )
       lowerCamelCase_							=torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]     )
       if model_name == "clipseg-rd64-refined":
              lowerCamelCase_							=torch.tensor(
                  [[-10.0407, -9.9_4_3_1, -10.2646], [-9.9_7_5_1, -9.7_0_6_4, -9.9_5_8_6], [-9.6_8_9_1, -9.5_6_4_5, -9.9_6_1_8]]     )
       elif model_name == "clipseg-rd64":
              lowerCamelCase_							=torch.tensor(
                  [[-7.2_8_7_7, -7.2_7_1_1, -7.2_4_6_3], [-7.2_6_5_2, -7.2_7_8_0, -7.2_5_2_0], [-7.2_2_3_9, -7.2_2_0_4, -7.2_0_0_1]]     )
       elif model_name == "clipseg-rd16":
              lowerCamelCase_							=torch.tensor(
                  [[-6.3_9_5_5, -6.4_0_5_5, -6.4_1_5_1], [-6.3_9_1_1, -6.4_0_3_3, -6.4_1_0_0], [-6.3_4_7_4, -6.3_7_0_2, -6.3_7_6_2]]     )
       else:
              raise ValueError(F'''Model name {model_name} not supported.'''     )

       assert torch.allclose(outputs.logits[0, :3, :3]     , lowercase__     , atol=1e-3     )
       assert torch.allclose(outputs.conditional_embeddings[0, :3]     , lowercase__     , atol=1e-3     )
       assert torch.allclose(outputs.pooled_output[0, :3]     , lowercase__     , 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(lowercase__     )
              processor.save_pretrained(lowercase__     )

       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__":
     a_   :	int							   = 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."""
     )

     a_   :	List[str]							   = parser.parse_args()
     convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''




import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 						__UpperCamelCase (				lowerCamelCase__       ):
       lowercase			:					Union[str, Any]   		=(HeunDiscreteScheduler,)
       lowercase			:					int   		=10






       def   lowercase__  (							self,	**lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							={
                  '''num_train_timesteps''': 1_100,
                  '''beta_start''': 0.0_0_0_1,
                  '''beta_end''': 0.0_2,
                  '''beta_schedule''': '''linear''',
              }

              config.update(**lowerCAmelCase						)
              return config






       def   lowercase__  (							self						):



              """simple docstring"""







              for timesteps in [10, 50, 100, 1_000]:
                     self.check_over_configs(num_train_timesteps=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1],	[0.0_0_0_2, 0.0_0_2, 0.0_2]						):
                     self.check_over_configs(beta_start=lowerCAmelCase,	beta_end=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              for schedule in ["linear", "scaled_linear", "exp"]:
                     self.check_over_configs(beta_schedule=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              for clip_sample_range in [1.0, 2.0, 3.0]:
                     self.check_over_configs(clip_sample_range=lowerCAmelCase,	clip_sample=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              for prediction_type in ["epsilon", "v_prediction", "sample"]:
                     self.check_over_configs(prediction_type=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.scheduler_classes[0]
              lowerCamelCase_							=self.get_scheduler_config()
              lowerCamelCase_							=scheduler_class(**lowerCAmelCase						)

              scheduler.set_timesteps(self.num_inference_steps						)

              lowerCamelCase_							=self.dummy_model()
              lowerCamelCase_							=self.dummy_sample_deter * scheduler.init_noise_sigma
              lowerCamelCase_							=sample.to(lowerCAmelCase						)

              for i, t in enumerate(scheduler.timesteps						):
                     lowerCamelCase_							=scheduler.scale_model_input(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=model(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     lowerCamelCase_							=output.prev_sample

              lowerCamelCase_							=torch.sum(torch.abs(lowerCAmelCase						)						)
              lowerCamelCase_							=torch.mean(torch.abs(lowerCAmelCase						)						)

              if torch_device in ["cpu", "mps"]:
                     assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3
              else:
                     # CUDA
                     assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.scheduler_classes[0]
              lowerCamelCase_							=self.get_scheduler_config(prediction_type='''v_prediction'''						)
              lowerCamelCase_							=scheduler_class(**lowerCAmelCase						)

              scheduler.set_timesteps(self.num_inference_steps						)

              lowerCamelCase_							=self.dummy_model()
              lowerCamelCase_							=self.dummy_sample_deter * scheduler.init_noise_sigma
              lowerCamelCase_							=sample.to(lowerCAmelCase						)

              for i, t in enumerate(scheduler.timesteps						):
                     lowerCamelCase_							=scheduler.scale_model_input(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=model(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     lowerCamelCase_							=output.prev_sample

              lowerCamelCase_							=torch.sum(torch.abs(lowerCAmelCase						)						)
              lowerCamelCase_							=torch.mean(torch.abs(lowerCAmelCase						)						)

              if torch_device in ["cpu", "mps"]:
                     assert abs(result_sum.item() - 4.6_934e-07						) < 1e-2
                     assert abs(result_mean.item() - 6.1_112e-10						) < 1e-3
              else:
                     # CUDA
                     assert abs(result_sum.item() - 4.693_428_650_170_972e-07						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.scheduler_classes[0]
              lowerCamelCase_							=self.get_scheduler_config()
              lowerCamelCase_							=scheduler_class(**lowerCAmelCase						)

              scheduler.set_timesteps(self.num_inference_steps,	device=lowerCAmelCase						)

              lowerCamelCase_							=self.dummy_model()
              lowerCamelCase_							=self.dummy_sample_deter.to(lowerCAmelCase						) * scheduler.init_noise_sigma

              for t in scheduler.timesteps:
                     lowerCamelCase_							=scheduler.scale_model_input(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=model(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     lowerCamelCase_							=output.prev_sample

              lowerCamelCase_							=torch.sum(torch.abs(lowerCAmelCase						)						)
              lowerCamelCase_							=torch.mean(torch.abs(lowerCAmelCase						)						)

              if str(lowerCAmelCase						).startswith('''cpu'''						):
                     # The following sum varies between 148 and 156 on mps. Why?
                     assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3
              elif str(lowerCAmelCase						).startswith('''mps'''						):
                     # Larger tolerance on mps
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-2
              else:
                     # CUDA
                     assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.scheduler_classes[0]
              lowerCamelCase_							=self.get_scheduler_config()
              lowerCamelCase_							=scheduler_class(**lowerCAmelCase,	use_karras_sigmas=lowerCAmelCase						)

              scheduler.set_timesteps(self.num_inference_steps,	device=lowerCAmelCase						)

              lowerCamelCase_							=self.dummy_model()
              lowerCamelCase_							=self.dummy_sample_deter.to(lowerCAmelCase						) * scheduler.init_noise_sigma
              lowerCamelCase_							=sample.to(lowerCAmelCase						)

              for t in scheduler.timesteps:
                     lowerCamelCase_							=scheduler.scale_model_input(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=model(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     lowerCamelCase_							=output.prev_sample

              lowerCamelCase_							=torch.sum(torch.abs(lowerCAmelCase						)						)
              lowerCamelCase_							=torch.mean(torch.abs(lowerCAmelCase						)						)

              assert abs(result_sum.item() - 0.0_0_0_1_5						) < 1e-2
              assert abs(result_mean.item() - 1.9_869_554_535_034_695e-07						) < 1e-2




