






"""simple docstring"""


import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate






class __a ( snake_case_					):
  __lowercase     :     Dict       						=       CONFIG_NAME



  @register_to_config
  def __init__(self	:		List[Any]					,      lowercase_	:		bool = True					,      lowercase_	:		int = 8					,      lowercase_	:		str = "lanczos"					,      lowercase_	:		bool = True					,      lowercase_	:		bool = False					,      )		->				int:
   super().__init__()



  @staticmethod
  def     A_					(lowercase_	:		np.ndarray )		->				PIL.Image.Image:
   if images.ndim == 3:
    __A						:							List[Any]      =						images[None, ...]
   __A						:							int      =						(images * 255).round().astype('uint8' )
   if images.shape[-1] == 1:
    # special case for grayscale (single channel) images
    __A						:							List[str]      =						[Image.fromarray(image.squeeze()					,      mode='L' ) for image in images]
   else:
    __A						:							Union[str, Any]      =						[Image.fromarray(lowercase_ ) for image in images]

   return pil_images



  @staticmethod
  def     A_					(lowercase_	:		Union[List[PIL.Image.Image], PIL.Image.Image] )		->				np.ndarray:
   if not isinstance(lowercase_					,      lowercase_ ):
    __A						:							str      =						[images]
   __A						:							Optional[int]      =						[np.array(lowercase_ ).astype(np.floataa ) / 255.0 for image in images]
   __A						:							Optional[int]      =						np.stack(lowercase_					,      axis=0 )

   return images



  @staticmethod
  def     A_					(lowercase_	:		np.ndarray )		->				torch.FloatTensor:
   if images.ndim == 3:
    __A						:							List[str]      =						images[..., None]

   __A						:							Optional[int]      =						torch.from_numpy(images.transpose(0					,      3					,      1					,      2 ) )
   return images



  @staticmethod
  def     A_					(lowercase_	:		torch.FloatTensor )		->				np.ndarray:
   __A						:							Any      =						images.cpu().permute(0					,      2					,      3					,      1 ).float().numpy()
   return images



  @staticmethod
  def     A_					(lowercase_	:		List[Any] )		->				Tuple:
   return 2.0 * images - 1.0



  @staticmethod
  def     A_					(lowercase_	:		Tuple )		->				List[Any]:
   return (images / 2 + 0.5).clamp(0					,      1 )



  @staticmethod
  def     A_					(lowercase_	:		PIL.Image.Image )		->				PIL.Image.Image:
   __A						:							Tuple      =						image.convert('RGB' )
   return image



  def     A_					(self	:		str					,      lowercase_	:		PIL.Image.Image					,      lowercase_	:		Optional[int] = None					,      lowercase_	:		Optional[int] = None					,      )		->				PIL.Image.Image:
   if height is None:
    __A						:							str      =						image.height
   if width is None:
    __A						:							str      =						image.width

   __A     ,       __A						:							List[str]      =						(
       x - x % self.config.vae_scale_factor for x in (width, height)
   )  # resize to integer multiple of vae_scale_factor
   __A						:							List[str]      =						image.resize((width, height)					,      resample=PIL_INTERPOLATION[self.config.resample] )
   return image



  def     A_					(self	:		Tuple					,      lowercase_	:		Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]					,      lowercase_	:		Optional[int] = None					,      lowercase_	:		Optional[int] = None					,      )		->				torch.Tensor:
   __A						:							Tuple      =						(PIL.Image.Image, np.ndarray, torch.Tensor)
   if isinstance(lowercase_					,      lowercase_ ):
    __A						:							Dict      =						[image]
   elif not (isinstance(lowercase_					,      lowercase_ ) and all(isinstance(lowercase_					,      lowercase_ ) for i in image )):
    raise ValueError(
        f"Input is in incorrect format: {[type(lowercase_ ) for i in image]}. Currently, we only support {', '.join(lowercase_ )}" )

   if isinstance(image[0]					,      PIL.Image.Image ):
    if self.config.do_convert_rgb:
     __A						:							Any      =						[self.convert_to_rgb(lowercase_ ) for i in image]
    if self.config.do_resize:
     __A						:							Optional[int]      =						[self.resize(lowercase_					,      lowercase_					,      lowercase_ ) for i in image]
    __A						:							Tuple      =						self.pil_to_numpy(lowercase_ )  # to np
    __A						:							Optional[int]      =						self.numpy_to_pt(lowercase_ )  # to pt

   elif isinstance(image[0]					,      np.ndarray ):
    __A						:							str      =						np.concatenate(lowercase_					,      axis=0 ) if image[0].ndim == 4 else np.stack(lowercase_					,      axis=0 )
    __A						:							Optional[Any]      =						self.numpy_to_pt(lowercase_ )
    __A     ,       __A     ,       __A     ,       __A						:							str      =						image.shape
    if self.config.do_resize and (
        height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
    ):
     raise ValueError(
         f"Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"
         f"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor" )

   elif isinstance(image[0]					,      torch.Tensor ):
    __A						:							Union[str, Any]      =						torch.cat(lowercase_					,      axis=0 ) if image[0].ndim == 4 else torch.stack(lowercase_					,      axis=0 )
    __A     ,       __A     ,       __A     ,       __A						:							int      =						image.shape

    # don't need any preprocess if the image is latents
    if channel == 4:
     return image

    if self.config.do_resize and (
        height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
    ):
     raise ValueError(
         f"Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"
         f"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor" )

        # expected range [0,1], normalize to [-1,1]
   __A						:							List[Any]      =						self.config.do_normalize
   if image.min() < 0:
    warnings.warn(
        'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
        f"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"					,      lowercase_					,      )
    __A						:							Any      =						False

   if do_normalize:
    __A						:							List[str]      =						self.normalize(lowercase_ )

   return image



  def     A_					(self	:		Optional[Any]					,      lowercase_	:		torch.FloatTensor					,      lowercase_	:		str = "pil"					,      lowercase_	:		Optional[List[bool]] = None					,      )		->				Tuple:
   if not isinstance(lowercase_					,      torch.Tensor ):
    raise ValueError(
        f"Input for postprocessing is in incorrect format: {type(lowercase_ )}. We only support pytorch tensor" )
   if output_type not in ["latent", "pt", "np", "pil"]:
    __A						:							Tuple      =						(
        f"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
        '`pil`, `np`, `pt`, `latent`'
    )
    deprecate('Unsupported output_type'					,      '1.0.0'					,      lowercase_					,      standard_warn=lowercase_ )
    __A						:							Tuple      =						'np'

   if output_type == "latent":
    return image

   if do_denormalize is None:
    __A						:							Dict      =						[self.config.do_normalize] * image.shape[0]

   __A						:							str      =						torch.stack(
       [self.denormalize(image[i] ) if do_denormalize[i] else image[i] for i in range(image.shape[0] )] )

   if output_type == "pt":
    return image

   __A						:							Any      =						self.pt_to_numpy(lowercase_ )

   if output_type == "np":
    return image

   if output_type == "pil":
    return self.numpy_to_pil(lowercase_ )






class __a ( snake_case_					):
  __lowercase     :     str       						=       CONFIG_NAME



  @register_to_config
  def __init__(self	:		Dict					,      lowercase_	:		bool = True					,      lowercase_	:		int = 8					,      lowercase_	:		str = "lanczos"					,      lowercase_	:		bool = True					,      )		->				Optional[int]:
   super().__init__()



  @staticmethod
  def     A_					(lowercase_	:		int )		->				str:
   if images.ndim == 3:
    __A						:							Dict      =						images[None, ...]
   __A						:							Union[str, Any]      =						(images * 255).round().astype('uint8' )
   if images.shape[-1] == 1:
    # special case for grayscale (single channel) images
    __A						:							Union[str, Any]      =						[Image.fromarray(image.squeeze()					,      mode='L' ) for image in images]
   else:
    __A						:							Optional[int]      =						[Image.fromarray(image[:, :, :3] ) for image in images]

   return pil_images



  @staticmethod
  def     A_					(lowercase_	:		List[str] )		->				Any:
   return image[:, :, 1] * 2**8 + image[:, :, 2]



  def     A_					(self	:		Union[str, Any]					,      lowercase_	:		int )		->				Tuple:
   if images.ndim == 3:
    __A						:							List[Any]      =						images[None, ...]
   __A						:							Tuple      =						images[:, :, :, 3:]
   if images.shape[-1] == 6:
    __A						:							Union[str, Any]      =						(images_depth * 255).round().astype('uint8' )
    __A						:							Optional[Any]      =						[
        Image.fromarray(self.rgblike_to_depthmap(lowercase_ )					,      mode='I;16' ) for image_depth in images_depth
    ]
   elif images.shape[-1] == 4:
    __A						:							Union[str, Any]      =						(images_depth * 6_5535.0).astype(np.uintaa )
    __A						:							List[Any]      =						[Image.fromarray(lowercase_					,      mode='I;16' ) for image_depth in images_depth]
   else:
    raise Exception('Not supported' )

   return pil_images



  def     A_					(self	:		Optional[Any]					,      lowercase_	:		torch.FloatTensor					,      lowercase_	:		str = "pil"					,      lowercase_	:		Optional[List[bool]] = None					,      )		->				Optional[Any]:
   if not isinstance(lowercase_					,      torch.Tensor ):
    raise ValueError(
        f"Input for postprocessing is in incorrect format: {type(lowercase_ )}. We only support pytorch tensor" )
   if output_type not in ["latent", "pt", "np", "pil"]:
    __A						:							Any      =						(
        f"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
        '`pil`, `np`, `pt`, `latent`'
    )
    deprecate('Unsupported output_type'					,      '1.0.0'					,      lowercase_					,      standard_warn=lowercase_ )
    __A						:							Optional[int]      =						'np'

   if do_denormalize is None:
    __A						:							str      =						[self.config.do_normalize] * image.shape[0]

   __A						:							Dict      =						torch.stack(
       [self.denormalize(image[i] ) if do_denormalize[i] else image[i] for i in range(image.shape[0] )] )

   __A						:							int      =						self.pt_to_numpy(lowercase_ )

   if output_type == "np":
    if image.shape[-1] == 6:
     __A						:							List[Any]      =						np.stack([self.rgblike_to_depthmap(im[:, :, 3:] ) for im in image]					,      axis=0 )
    else:
     __A						:							Optional[Any]      =						image[:, :, :, 3:]
    return image[:, :, :, :3], image_depth

   if output_type == "pil":
    return self.numpy_to_pil(lowercase_ ), self.numpy_to_depth(lowercase_ )
   else:
    raise Exception(f"This type {output_type} is not supported" )













"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

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




def 					a			(  UpperCamelCase_				:					Optional[Any]		)	->		str:
 __A						:							Union[str, Any]      =						CLIPSegTextConfig()
 __A						:							Dict      =						CLIPSegVisionConfig(patch_size=1_6		)

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

 __A						:							Tuple      =						CLIPSegConfig.from_text_vision_configs(
     UpperCamelCase_  ,						UpperCamelCase_  ,						use_complex_transposed_convolution=UpperCamelCase_  ,						reduce_dim=UpperCamelCase_  ,						)
 return config




def 					a			(  UpperCamelCase_				:					List[str]		)	->		Dict:
 # update prefixes
 if "clip_model" in name:
  __A						:							str      =						name.replace('clip_model'  ,						'clip'		)
 if "transformer" in name:
  if "visual" in name:
   __A						:							List[str]      =						name.replace('visual.transformer'  ,						'vision_model'		)
  else:
   __A						:							Dict      =						name.replace('transformer'  ,						'text_model'		)
 if "resblocks" in name:
  __A						:							List[str]      =						name.replace('resblocks'  ,						'encoder.layers'		)
 if "ln_1" in name:
  __A						:							Dict      =						name.replace('ln_1'  ,						'layer_norm1'		)
 if "ln_2" in name:
  __A						:							Any      =						name.replace('ln_2'  ,						'layer_norm2'		)
 if "c_fc" in name:
  __A						:							List[Any]      =						name.replace('c_fc'  ,						'fc1'		)
 if "c_proj" in name:
  __A						:							int      =						name.replace('c_proj'  ,						'fc2'		)
 if "attn" in name and "self" not in name:
  __A						:							Dict      =						name.replace('attn'  ,						'self_attn'		)
 # text encoder
 if "token_embedding" in name:
  __A						:							Dict      =						name.replace('token_embedding'  ,						'text_model.embeddings.token_embedding'		)
 if "positional_embedding" in name and "visual" not in name:
  __A						:							int      =						name.replace('positional_embedding'  ,						'text_model.embeddings.position_embedding.weight'		)
 if "ln_final" in name:
  __A						:							Optional[Any]      =						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						:							List[Any]      =						name.replace('visual.conv1'  ,						'vision_model.embeddings.patch_embedding'		)
 if "visual.positional_embedding" in name:
  __A						:							str      =						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						:							str      =						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						:							int      =						name.replace('text_projection'  ,						'text_projection.weight'		)
 # decoder
 if "trans_conv" in name:
  __A						:							int      =						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						:							List[Any]      =						'decoder.' + name
 if "blocks" in name:
  __A						:							Optional[Any]      =						name.replace('blocks'  ,						'decoder.layers'		)
 if "linear1" in name:
  __A						:							int      =						name.replace('linear1'  ,						'mlp.fc1'		)
 if "linear2" in name:
  __A						:							Tuple      =						name.replace('linear2'  ,						'mlp.fc2'		)
 if "norm1" in name and "layer_" not in name:
  __A						:							List[Any]      =						name.replace('norm1'  ,						'layer_norm1'		)
 if "norm2" in name and "layer_" not in name:
  __A						:							Optional[Any]      =						name.replace('norm2'  ,						'layer_norm2'		)

 return name




def 					a			(  UpperCamelCase_				:					List[str]  ,						UpperCamelCase_				:					Dict		)	->		Any:
 for key in orig_state_dict.copy().keys():
  __A						:							str      =						orig_state_dict.pop(UpperCamelCase_		)

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

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

 return orig_state_dict




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




def 					a			(  UpperCamelCase_				:					List[str]  ,						UpperCamelCase_				:					List[str]  ,						UpperCamelCase_				:					Dict  ,						UpperCamelCase_				:					int		)	->		Optional[int]:
 __A						:							str      =						get_clipseg_config(UpperCamelCase_		)
 __A						:							int      =						CLIPSegForImageSegmentation(UpperCamelCase_		)
 model.eval()

 __A						:							Optional[int]      =						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
 __A						:							int      =						convert_state_dict(UpperCamelCase_  ,						UpperCamelCase_		)
 __A     ,       __A						:							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}"		)

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

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

 __A						:							Optional[Any]      =						processor(text=UpperCamelCase_  ,						images=[image] * len(UpperCamelCase_		)  ,						padding='max_length'  ,						return_tensors='pt'		)

 with torch.no_grad():
  __A						:							int      =						model(**UpperCamelCase_		)

 # verify values
 __A						:							Union[str, Any]      =						torch.tensor([0.1110, -0.1882, 0.1645]		)
 __A						:							List[Any]      =						torch.tensor([0.2692, -0.7197, -0.1328]		)
 if model_name == "clipseg-rd64-refined":
  __A						:							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":
  __A						:							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":
  __A						:							Optional[Any]      =						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__":
   _UpperCamelCase :    str      =			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 :    Tuple      =			parser.parse_args()
   convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





