




import argparse

import requests
import torch
from PIL import Image

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

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase)    ->  str:
     a											=   CLIPSegTextConfig()
     a											=   CLIPSegVisionConfig(patch_size=16)

     a											=   True if "refined" in model_name else False
     a											=   16 if "rd16" in model_name else 64

     a											=   CLIPSegConfig.from_text_vision_configs(
         __snake_case	,      __snake_case	,      use_complex_transposed_convolution=__snake_case	,      reduce_dim=__snake_case	,      )
     return config

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

     return name

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase)    ->  str:
     for key in orig_state_dict.copy().keys():
          a											=   orig_state_dict.pop(__snake_case)

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

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

     return orig_state_dict

def       SCREAMING_SNAKE_CASE							(     )    ->  Any:
     a											=   "http://images.cocodataset.org/val2017/000000039769.jpg"
     a											=   Image.open(requests.get(__snake_case	,      stream=__snake_case).raw)
     return image

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase)    ->  int:
     a											=   get_clipseg_config(__snake_case)
     a											=   CLIPSegForImageSegmentation(__snake_case)
     model.eval()

     a											=   torch.load(__snake_case	,      map_location="cpu")

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

          # rename some keys
     a											=   convert_state_dict(__snake_case	,      __snake_case)
     a						,   a											=   model.load_state_dict(__snake_case	,      strict=__snake_case)

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

     a											=   ViTImageProcessor(size=3_52)
     a											=   CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
     a											=   CLIPSegProcessor(image_processor=__snake_case	,      tokenizer=__snake_case)

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

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

     with torch.no_grad():
          a											=   model(**__snake_case)

     # verify values
     a											=   torch.tensor([0.1_110, -0.1_882, 0.1_645])
     a											=   torch.tensor([0.2_692, -0.7_197, -0.1_328])
     if model_name == "clipseg-rd64-refined":
          a											=   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":
          a											=   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":
          a											=   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]	,      __snake_case	,      atol=1e-3)
     assert torch.allclose(outputs.conditional_embeddings[0, :3]	,      __snake_case	,      atol=1e-3)
     assert torch.allclose(outputs.pooled_output[0, :3]	,      __snake_case	,      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(__snake_case)
          processor.save_pretrained(__snake_case)

     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__   :    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."
   )

   lowercase__   :    Any 					=      parser.parse_args()
   convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







import unittest

from transformers.image_processing_utils import get_size_dict




class     a__ (      unittest.TestCase   ):







     def     lowerCAmelCase_       (			self				)						->					Optional[int]:



          '''simple docstring'''



          a											=   {"wrong_key": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          a											=   {"height": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          a											=   {"width": 224, "shortest_edge": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          # Test a dict with the correct keys is returned as is
          a											=   {"height": 224, "width": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	A				)

          a											=   {"shortest_edge": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	{"shortest_edge": 224}				)

          a											=   {"longest_edge": 224, "shortest_edge": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	{"longest_edge": 224, "shortest_edge": 224}				)

          # Test a single int value which  represents (size, size)
          a											=   get_size_dict(224				)
          self.assertEqual(A						,	{"height": 224, "width": 224}				)

          # Test a single int value which represents the shortest edge
          a											=   get_size_dict(224						,	default_to_square=A				)
          self.assertEqual(A						,	{"shortest_edge": 224}				)

          # Test a tuple of ints which represents (height, width)
          a											=   get_size_dict((150, 200)				)
          self.assertEqual(A						,	{"height": 150, "width": 200}				)

          # Test a tuple of ints which represents (width, height)
          a											=   get_size_dict((150, 200)						,	height_width_order=A				)
          self.assertEqual(A						,	{"height": 200, "width": 150}				)

          # Test an int representing the shortest edge and max_size which represents the longest edge
          a											=   get_size_dict(224						,	max_size=256						,	default_to_square=A				)
          self.assertEqual(A						,	{"shortest_edge": 224, "longest_edge": 256}				)

          # Test int with default_to_square=True and max_size fails
          with self.assertRaises(A				):
               get_size_dict(224						,	max_size=256						,	default_to_square=A				)

