

'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

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




def  UpperCamelCase(	UpperCAmelCase_      ):
 UpperCAmelCase			:      List[Any]		   =					CLIPSegTextConfig()
 UpperCAmelCase			:      Optional[Any]		   =					CLIPSegVisionConfig(patch_size=16      )

 UpperCAmelCase			:      Optional[Any]		   =					True if 'refined' in model_name else False
 UpperCAmelCase			:      Optional[int]		   =					16 if 'rd16' in model_name else 64

 UpperCAmelCase			:      int		   =					CLIPSegConfig.from_text_vision_configs(
     UpperCAmelCase_    ,			UpperCAmelCase_    ,			use_complex_transposed_convolution=UpperCAmelCase_    ,			reduce_dim=UpperCAmelCase_    ,			)
 return config




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

 return name




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 for key in orig_state_dict.copy().keys():
  UpperCAmelCase			:      Tuple		   =					orig_state_dict.pop(UpperCAmelCase_      )

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

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

 return orig_state_dict




def  UpperCamelCase(	):
 UpperCAmelCase			:      Tuple		   =					'http://images.cocodataset.org/val2017/000000039769.jpg'
 UpperCAmelCase			:      str		   =					Image.open(requests.get(UpperCAmelCase_    ,			stream=UpperCAmelCase_      ).raw      )
 return image




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase			:      str		   =					get_clipseg_config(UpperCAmelCase_      )
 UpperCAmelCase			:      Any		   =					CLIPSegForImageSegmentation(UpperCAmelCase_      )
 model.eval()

 UpperCAmelCase			:      List[Any]		   =					torch.load(UpperCAmelCase_    ,			map_location='cpu'      )

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

    # rename some keys
 UpperCAmelCase			:      Optional[Any]		   =					convert_state_dict(UpperCAmelCase_    ,			UpperCAmelCase_      )
 UpperCAmelCase   ,					UpperCAmelCase			:      Tuple		   =					model.load_state_dict(UpperCAmelCase_    ,			strict=UpperCAmelCase_      )

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

 UpperCAmelCase			:      str		   =					ViTImageProcessor(size=3_52      )
 UpperCAmelCase			:      List[str]		   =					CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'      )
 UpperCAmelCase			:      Dict		   =					CLIPSegProcessor(image_processor=UpperCAmelCase_    ,			tokenizer=UpperCAmelCase_      )

 UpperCAmelCase			:      str		   =					prepare_img()
 UpperCAmelCase			:      int		   =					['a glass', 'something to fill', 'wood', 'a jar']

 UpperCAmelCase			:      int		   =					processor(text=UpperCAmelCase_    ,			images=[image] * len(UpperCAmelCase_      )    ,			padding='max_length'    ,			return_tensors='pt'      )

 with torch.no_grad():
  UpperCAmelCase			:      List[Any]		   =					model(**UpperCAmelCase_      )

 # verify values
 UpperCAmelCase			:      List[str]		   =					torch.tensor([0.1110, -0.1882, 0.1645]      )
 UpperCAmelCase			:      List[Any]		   =					torch.tensor([0.2692, -0.7197, -0.1328]      )
 if model_name == "clipseg-rd64-refined":
  UpperCAmelCase			:      int		   =					torch.tensor(
      [[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]      )
 elif model_name == "clipseg-rd64":
  UpperCAmelCase			:      Optional[Any]		   =					torch.tensor(
      [[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]]      )
 elif model_name == "clipseg-rd16":
  UpperCAmelCase			:      str		   =					torch.tensor(
      [[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]]      )
 else:
  raise ValueError(F"""Model name {model_name} not supported."""      )

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

 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__":
 lowercase__            =     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."
 )

 lowercase__            =     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 LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 			A_					(							_snake_case				):






     '''simple docstring'''



     UpperCAmelCase_		:      Dict    						=    (LMSDiscreteScheduler,)
     UpperCAmelCase_		:      Any    						=    10


     def   UpperCAmelCase_			(						self					:	str	,     **lowercase_					:	List[str]			)		->      str:
      UpperCAmelCase			:      Optional[Any]		   =					{
          'num_train_timesteps': 1_100,
          'beta_start': 0.0001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
      }

      config.update(**lowercase_			)
      return config


     def   UpperCAmelCase_			(						self					:	Optional[int]			)		->      Tuple:
      for timesteps in [10, 50, 100, 1_000]:
       self.check_over_configs(num_train_timesteps=lowercase_			)


     def   UpperCAmelCase_			(						self					:	int			)		->      Tuple:
      for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,     [0.0002, 0.002, 0.02]			):
       self.check_over_configs(beta_start=lowercase_	,     beta_end=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Dict			)		->      Union[str, Any]:
      for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Tuple			)		->      List[Any]:
      for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=lowercase_			)


     def   UpperCAmelCase_			(						self					:	str			)		->      Union[str, Any]:
      for t in [0, 500, 800]:
       self.check_over_forward(time_step=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Union[str, Any]			)		->      List[str]:
      UpperCAmelCase			:      Any		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Tuple		   =					self.get_scheduler_config()
      UpperCAmelCase			:      Any		   =					scheduler_class(**lowercase_			)

      scheduler.set_timesteps(self.num_inference_steps			)

      UpperCAmelCase			:      str		   =					self.dummy_model()
      UpperCAmelCase			:      str		   =					self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
       UpperCAmelCase			:      Optional[Any]		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Union[str, Any]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Dict		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      List[str]		   =					output.prev_sample

      UpperCAmelCase			:      Optional[Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Optional[Any]		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 1006.388			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3


     def   UpperCAmelCase_			(						self					:	str			)		->      Dict:
      UpperCAmelCase			:      Any		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Dict		   =					self.get_scheduler_config(prediction_type='v_prediction'			)
      UpperCAmelCase			:      Optional[int]		   =					scheduler_class(**lowercase_			)

      scheduler.set_timesteps(self.num_inference_steps			)

      UpperCAmelCase			:      Optional[Any]		   =					self.dummy_model()
      UpperCAmelCase			:      List[str]		   =					self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
       UpperCAmelCase			:      List[Any]		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Optional[Any]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Dict		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      List[str]		   =					output.prev_sample

      UpperCAmelCase			:      Union[str, Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Any		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 0.0017			) < 1E-2
      assert abs(result_mean.item() - 2.2676E-06			) < 1E-3


     def   UpperCAmelCase_			(						self					:	Optional[Any]			)		->      Dict:
      UpperCAmelCase			:      Dict		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Optional[int]		   =					self.get_scheduler_config()
      UpperCAmelCase			:      List[str]		   =					scheduler_class(**lowercase_			)

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

      UpperCAmelCase			:      Tuple		   =					self.dummy_model()
      UpperCAmelCase			:      Tuple		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      UpperCAmelCase			:      Any		   =					sample.to(lowercase_			)

      for i, t in enumerate(scheduler.timesteps			):
       UpperCAmelCase			:      Dict		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Optional[Any]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Tuple		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      List[Any]		   =					output.prev_sample

      UpperCAmelCase			:      Union[str, Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Union[str, Any]		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 1006.388			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3




     def   UpperCAmelCase_			(						self					:	Dict			)		->      Optional[Any]:
      UpperCAmelCase			:      int		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Union[str, Any]		   =					self.get_scheduler_config()
      UpperCAmelCase			:      Optional[Any]		   =					scheduler_class(**lowercase_	,     use_karras_sigmas=lowercase_			)

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

      UpperCAmelCase			:      List[Any]		   =					self.dummy_model()
      UpperCAmelCase			:      Dict		   =					self.dummy_sample_deter.to(lowercase_			) * scheduler.init_noise_sigma
      UpperCAmelCase			:      Union[str, Any]		   =					sample.to(lowercase_			)

      for t in scheduler.timesteps:
       UpperCAmelCase			:      Optional[int]		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      List[str]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      List[Any]		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      Any		   =					output.prev_sample

      UpperCAmelCase			:      List[Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Any		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 3812.9927			) < 2E-2
      assert abs(result_mean.item() - 4.9648			) < 1E-3




