
import argparse

import requests
import torch
from PIL import Image

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





def        lowerCAmelCase       (				snake_case__   :      Dict )->			Optional[int]:
    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        lowerCAmelCase       (				snake_case__   :      str )->			Tuple:
    # 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        lowerCAmelCase       (				snake_case__   :      Union[str, Any]				,					snake_case__   :      Optional[int] )->			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        lowerCAmelCase       (				)->			int:
    A_								=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A_								=					Image.open(requests.get(snake_case__				,					stream=snake_case__ ).raw )
    return image





def        lowerCAmelCase       (				snake_case__   :      int				,					snake_case__   :      str				,					snake_case__   :      Any				,					snake_case__   :      Tuple )->			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_								=					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=352 )
    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_1_1_0, -0.1_8_8_2, 0.1_6_4_5] )
    A_								=					torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8] )
    if model_name == "clipseg-rd64-refined":
        A_								=					torch.tensor(
            [[-10.0407, -9.9_4_3_1, -10.2646], [-9.9_7_5_1, -9.7_0_6_4, -9.9_5_8_6], [-9.6_8_9_1, -9.5_6_4_5, -9.9_6_1_8]] )
    elif model_name == "clipseg-rd64":
        A_								=					torch.tensor(
            [[-7.2_8_7_7, -7.2_7_1_1, -7.2_4_6_3], [-7.2_6_5_2, -7.2_7_8_0, -7.2_5_2_0], [-7.2_2_3_9, -7.2_2_0_4, -7.2_0_0_1]] )
    elif model_name == "clipseg-rd16":
        A_								=					torch.tensor(
            [[-6.3_9_5_5, -6.4_0_5_5, -6.4_1_5_1], [-6.3_9_1_1, -6.4_0_3_3, -6.4_1_0_0], [-6.3_4_7_4, -6.3_7_0_2, -6.3_7_6_2]] )
    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__":
     __magic_name__  :  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.'
     )

     __magic_name__  :  List[Any]     			=   parser.parse_args()
     convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







from transformers import ProcessorMixin






class     __UpperCAmelCase(    A__				):






 """simple docstring"""




 __magic_name__          =   """AutoFeatureExtractor"""
 __magic_name__          =   """AutoTokenizer"""


