


'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

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

def _SCREAMING_SNAKE_CASE				(A	)	->	Dict:






       """simple docstring"""







       lowercase__				      =			CLIPSegTextConfig()
       lowercase__				      =			CLIPSegVisionConfig(patch_size=16	)

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

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

def _SCREAMING_SNAKE_CASE				(A	)	->	Any:






       """simple docstring"""







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

       return name

def _SCREAMING_SNAKE_CASE				(A  ,			A	)	->	Optional[Any]:






       """simple docstring"""







       for key in orig_state_dict.copy().keys():
              lowercase__				      =			orig_state_dict.pop(A	)

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

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

       return orig_state_dict

def _SCREAMING_SNAKE_CASE				()	->	Tuple:






       """simple docstring"""







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

def _SCREAMING_SNAKE_CASE				(A  ,			A  ,			A  ,			A	)	->	Optional[Any]:






       """simple docstring"""







       lowercase__				      =			get_clipseg_config(A	)
       lowercase__				      =			CLIPSegForImageSegmentation(A	)
       model.eval()

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

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

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

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

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

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

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

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

       # verify values
       lowercase__				      =			torch.tensor([0.1_110, -0.1_882, 0.1_645]	)
       lowercase__				      =			torch.tensor([0.2_692, -0.7_197, -0.1_328]	)
       if model_name == "clipseg-rd64-refined":
              lowercase__				      =			torch.tensor(
                  [[-10.0_407, -9.9_431, -10.2_646], [-9.9_751, -9.7_064, -9.9_586], [-9.6_891, -9.5_645, -9.9_618]]	)
       elif model_name == "clipseg-rd64":
              lowercase__				      =			torch.tensor(
                  [[-7.2_877, -7.2_711, -7.2_463], [-7.2_652, -7.2_780, -7.2_520], [-7.2_239, -7.2_204, -7.2_001]]	)
       elif model_name == "clipseg-rd16":
              lowercase__				      =			torch.tensor(
                  [[-6.3_955, -6.4_055, -6.4_151], [-6.3_911, -6.4_033, -6.4_100], [-6.3_474, -6.3_702, -6.3_762]]	)
       else:
              raise ValueError(f"Model name {model_name} not supported."	)

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

       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__":
 lowerCamelCase							:       List[Any]  						=     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.'
 )

 lowerCamelCase							:       str  						=     parser.parse_args()
 convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


lowerCamelCase							:       Tuple  						=     {'configuration_bert_generation': ['BertGenerationConfig']}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:       Union[str, Any]  						=     ['BertGenerationTokenizer']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:       Dict  						=     [
     'BertGenerationDecoder',
     'BertGenerationEncoder',
     'BertGenerationPreTrainedModel',
     'load_tf_weights_in_bert_generation',
 ]


if TYPE_CHECKING:
 from .configuration_bert_generation import BertGenerationConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_bert_generation import BertGenerationTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bert_generation import (
      BertGenerationDecoder,
      BertGenerationEncoder,
      BertGenerationPreTrainedModel,
      load_tf_weights_in_bert_generation,
  )

else:
 import sys

 lowerCamelCase							:       int  						=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




