"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

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







def        __a (		A   ):






     '''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        __a (		A   ):






     '''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        __a (		A     ,					A   ):






     '''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        __a (		):






     '''simple docstring'''



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







def        __a (		A     ,					A     ,					A     ,					A   ):






     '''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=3_52   )
     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([0.1110, -0.1882, 0.1645]   )
     lowercase__								=				torch.tensor([0.2692, -0.7197, -0.1328]   )
     if model_name == "clipseg-rd64-refined":
          lowercase__								=				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":
          lowercase__								=				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":
          lowercase__								=				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]     ,					__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_:       Optional[int]			     =       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_:       Any			     =       parser.parse_args()
 convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve





def 				__a							(       __A			,							__A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    np.stack((horizontal_flow, vertical_flow)			,							2						)

    # Create a grid of all pixel coordinates and subtract the flow to get the
    # target pixels coordinates
    SCREAMING_SNAKE_CASE__         =    np.stack(
        np.meshgrid(np.arange(0			,							image.shape[1]						)			,							np.arange(0			,							image.shape[0]						)						)			,							2						)
    SCREAMING_SNAKE_CASE__         =    np.round(grid - flow						).astype(np.intaa						)

    # Find the locations outside of the original image
    SCREAMING_SNAKE_CASE__         =    (grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]						))
    SCREAMING_SNAKE_CASE__         =    0

    SCREAMING_SNAKE_CASE__         =    image[grid[:, :, 1], grid[:, :, 0]]

    # Set pixels at invalid locations to 0
    SCREAMING_SNAKE_CASE__         =    0

    return warped





def 				__a							(       __A			,							__A			,							__A			,							__A = None			,							):
    if alpha is None:
        SCREAMING_SNAKE_CASE__         =    0.1

    # Initialize flow
    SCREAMING_SNAKE_CASE__         =    np.zeros_like(__A						)
    SCREAMING_SNAKE_CASE__         =    np.zeros_like(__A						)

    # Prepare kernels for the calculation of the derivatives and the average velocity
    SCREAMING_SNAKE_CASE__         =    np.array([[-1, 1], [-1, 1]]						) * 0.25
    SCREAMING_SNAKE_CASE__         =    np.array([[-1, -1], [1, 1]]						) * 0.25
    SCREAMING_SNAKE_CASE__         =    np.array([[1, 1], [1, 1]]						) * 0.25
    SCREAMING_SNAKE_CASE__         =    np.array(
        [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]]						)

    # Iteratively refine the flow
    for _ in range(__A						):
        SCREAMING_SNAKE_CASE__         =    warp(__A			,							__A			,							__A						)
        SCREAMING_SNAKE_CASE__         =    convolve(__A			,							__A						) + convolve(__A			,							__A						)
        SCREAMING_SNAKE_CASE__         =    convolve(__A			,							__A						) + convolve(__A			,							__A						)
        SCREAMING_SNAKE_CASE__         =    convolve(__A			,							__A						) + convolve(__A			,							-kernel_t						)

        SCREAMING_SNAKE_CASE__         =    convolve(__A			,							__A						)
        SCREAMING_SNAKE_CASE__         =    convolve(__A			,							__A						)

        # This updates the flow as proposed in the paper (Step 12)
        SCREAMING_SNAKE_CASE__         =    (
            derivative_x * avg_horizontal_velocity
            + derivative_y * avg_vertical_velocity
            + derivative_t
        )
        SCREAMING_SNAKE_CASE__         =    update / (alpha**2 + derivative_x**2 + derivative_y**2)

        SCREAMING_SNAKE_CASE__         =    avg_horizontal_velocity - derivative_x * update
        SCREAMING_SNAKE_CASE__         =    avg_vertical_velocity - derivative_y * update

    return horizontal_flow, vertical_flow


if __name__ == "__main__":
  import doctest

  doctest.testmod()


