





"""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						)     ->	int:

 '''simple docstring'''






 snake_case__     				=					CLIPSegTextConfig()
 snake_case__     				=					CLIPSegVisionConfig(patch_size=1_6						)

 snake_case__     				=					True if "refined" in model_name else False
 snake_case__     				=					1_6 if "rd16" in model_name else 6_4

 snake_case__     				=					CLIPSegConfig.from_text_vision_configs(
     _UpperCAmelCase						,  _UpperCAmelCase						,  use_complex_transposed_convolution=_UpperCAmelCase						,  reduce_dim=_UpperCAmelCase						,  )
 return config





def 				a__      (	_UpperCAmelCase						)     ->	Any:

 '''simple docstring'''






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

 return name





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	Tuple:

 '''simple docstring'''






 for key in orig_state_dict.copy().keys():
  snake_case__     				=					orig_state_dict.pop(_UpperCAmelCase						)

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

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

 return orig_state_dict





def 				a__      (	)     ->	str:

 '''simple docstring'''






 snake_case__     				=					"http://images.cocodataset.org/val2017/000000039769.jpg"
 snake_case__     				=					Image.open(requests.get(_UpperCAmelCase						,  stream=_UpperCAmelCase						).raw						)
 return image





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						,  _UpperCAmelCase						,  _UpperCAmelCase						)     ->	str:

 '''simple docstring'''






 snake_case__     				=					get_clipseg_config(_UpperCAmelCase						)
 snake_case__     				=					CLIPSegForImageSegmentation(_UpperCAmelCase						)
 model.eval()

 snake_case__     				=					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
 snake_case__     				=					convert_state_dict(_UpperCAmelCase						,  _UpperCAmelCase						)
 snake_case__				, snake_case__     				=					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}"						)

 snake_case__     				=					ViTImageProcessor(size=3_5_2						)
 snake_case__     				=					CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"						)
 snake_case__     				=					CLIPSegProcessor(image_processor=_UpperCAmelCase						,  tokenizer=_UpperCAmelCase						)

 snake_case__     				=					prepare_img()
 snake_case__     				=					["a glass", "something to fill", "wood", "a jar"]

 snake_case__     				=					processor(text=_UpperCAmelCase						,  images=[image] * len(_UpperCAmelCase						)						,  padding="max_length"						,  return_tensors="pt"						)

 with torch.no_grad():
  snake_case__     				=					model(**_UpperCAmelCase						)

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

   a__       :  int       		=					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_tokenizers_available, is_torch_available


a__       :  List[Any]       		=					{
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__       :  Union[str, Any]       		=					["""RealmTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__       :  Dict       		=					[
       """REALM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """RealmEmbedder""",
       """RealmForOpenQA""",
       """RealmKnowledgeAugEncoder""",
       """RealmPreTrainedModel""",
       """RealmReader""",
       """RealmScorer""",
       """load_tf_weights_in_realm""",
   ]
   a__       :  Any       		=					["""RealmRetriever"""]


if TYPE_CHECKING:
   from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
   from .tokenization_realm import RealmTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_realm import RealmTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_realm import (
          REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
          RealmEmbedder,
          RealmForOpenQA,
          RealmKnowledgeAugEncoder,
          RealmPreTrainedModel,
          RealmReader,
          RealmScorer,
          load_tf_weights_in_realm,
      )
      from .retrieval_realm import RealmRetriever


else:
   import sys

   a__       :  Any       		=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

