

"""simple docstring"""




import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)
def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					List[Any]   				=   CLIPSegTextConfig()
 UpperCAmelCase__   :					Tuple   				=   CLIPSegVisionConfig(patch_size=16 )

 UpperCAmelCase__   :					Optional[Any]   				=   True if """refined""" in model_name else False
 UpperCAmelCase__   :					Any   				=   16 if """rd16""" in model_name else 64

 UpperCAmelCase__   :					List[str]   				=   CLIPSegConfig.from_text_vision_configs(
     __UpperCamelCase							,      __UpperCamelCase							,      use_complex_transposed_convolution=__UpperCamelCase							,      reduce_dim=__UpperCamelCase							,      )
 return config
def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





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

 return name
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 for key in orig_state_dict.copy().keys():
  UpperCAmelCase__   :					str   				=   orig_state_dict.pop(__UpperCamelCase )

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

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

 return orig_state_dict
def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 UpperCAmelCase__   :					Optional[int]   				=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
 UpperCAmelCase__   :					Union[str, Any]   				=   Image.open(requests.get(__UpperCamelCase							,      stream=__UpperCamelCase ).raw )
 return image
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Optional[Any]   				=   get_clipseg_config(__UpperCamelCase )
 UpperCAmelCase__   :					int   				=   CLIPSegForImageSegmentation(__UpperCamelCase )
 model.eval()

 UpperCAmelCase__   :					Optional[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__   :					Any   				=   convert_state_dict(__UpperCamelCase							,      __UpperCamelCase )
 UpperCAmelCase__				, UpperCAmelCase__   :					int   				=   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__   :					Tuple   				=   ViTImageProcessor(size=352 )
 UpperCAmelCase__   :					Optional[int]   				=   CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32""" )
 UpperCAmelCase__   :					Tuple   				=   CLIPSegProcessor(image_processor=__UpperCamelCase							,      tokenizer=__UpperCamelCase )

 UpperCAmelCase__   :					Optional[int]   				=   prepare_img()
 UpperCAmelCase__   :					str   				=   ["""a glass""", """something to fill""", """wood""", """a jar"""]

 UpperCAmelCase__   :					str   				=   processor(text=__UpperCamelCase							,      images=[image] * len(__UpperCamelCase )							,      padding="""max_length"""							,      return_tensors="""pt""" )

 with torch.no_grad():
  UpperCAmelCase__   :					Tuple   				=   model(**__UpperCamelCase )

 # verify values
 UpperCAmelCase__   :					Dict   				=   torch.tensor([0.1110, -0.1882, 0.1645] )
 UpperCAmelCase__   :					Tuple   				=   torch.tensor([0.2692, -0.7197, -0.1328] )
 if model_name == "clipseg-rd64-refined":
  UpperCAmelCase__   :					str   				=   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__   :					List[str]   				=   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__   :					Tuple   				=   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	=			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)








"""simple docstring"""




import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_reformer import ReformerTokenizer
else:
      __UpperCAmelCase	=			None


__UpperCAmelCase	=			logging.get_logger(__name__)


__UpperCAmelCase	=			'▁'

__UpperCAmelCase	=			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase	=			{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

__UpperCAmelCase	=			{
    'google/reformer-crime-and-punishment': 52_4288,
}







class        __lowercase     (      __lowerCamelCase   ):
  snake_case_			    =    VOCAB_FILES_NAMES
  snake_case_			    =    PRETRAINED_VOCAB_FILES_MAP
  snake_case_			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case_			    =    ["""input_ids""", """attention_mask"""]
  snake_case_			    =    ReformerTokenizer

  def __init__(					self						:     List[Any]   ,A						:     Dict=None   ,A						:     List[str]=None   ,A						:     Union[str, Any]="</s>"   ,A						:     List[str]="<unk>"   ,A						:     Tuple=[]   ,**A						:     Optional[int]   ,):






   '''simple docstring'''
   super().__init__(
       A   ,tokenizer_file=A   ,eos_token=A   ,unk_token=A   ,additional_special_tokens=A   ,**A   ,)

   UpperCAmelCase__   :					Tuple   				=   vocab_file
   UpperCAmelCase__   :					Optional[Any]   				=   False if not self.vocab_file else True




  def 				__lowercase    (					self						:     Any   ,A						:     str   ,A						:     Optional[str] = None      ):






   '''simple docstring'''
   if not self.can_save_slow_tokenizer:
    raise ValueError(
        """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
        """tokenizer."""      )

   if not os.path.isdir(A      ):
    logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
    return
   UpperCAmelCase__   :					Tuple   				=   os.path.join(
       A   ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

   if os.path.abspath(self.vocab_file      ) != os.path.abspath(A      ):
    copyfile(self.vocab_file   ,A      )

   return (out_vocab_file,)





