import argparse

import requests
import torch
from PIL import Image

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

def   a_					(_lowerCAmelCase  :   Union[str, Any]       )->				Optional[int]:
    snake_case:						Any         =   CLIPSegTextConfig()
    snake_case:						List[Any]         =   CLIPSegVisionConfig(patch_size=16       )

    snake_case:						Any         =   True if """refined""" in model_name else False
    snake_case:						int         =   16 if """rd16""" in model_name else 64

    snake_case:						List[Any]         =   CLIPSegConfig.from_text_vision_configs(
        _lowerCAmelCase			,	_lowerCAmelCase			,	use_complex_transposed_convolution=_lowerCAmelCase			,	reduce_dim=_lowerCAmelCase			,	)
    return config

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

    return name

def   a_					(_lowerCAmelCase  :   List[str]			,	_lowerCAmelCase  :   Optional[int]       )->				Union[str, Any]:
    for key in orig_state_dict.copy().keys():
        snake_case:						List[Any]         =   orig_state_dict.pop(_lowerCAmelCase       )

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

            if "weight" in key:
                snake_case:						Optional[Any]         =   val[:dim, :]
                snake_case:						Dict         =   val[
                    dim : dim * 2, :
                ]
                snake_case:						str         =   val[-dim:, :]
            else:
                snake_case:						Any         =   val[:dim]
                snake_case:						Dict         =   val[dim : dim * 2]
                snake_case:						Optional[Any]         =   val[-dim:]
        elif "self_attn" in key and "out_proj" not in key:
            snake_case:						int         =   key.split("""."""       )
            snake_case:						Optional[Any]         =   int(key_split[1]       )
            snake_case:						Tuple         =   config.reduce_dim
            if "weight" in key:
                snake_case:						Optional[int]         =   val[:dim, :]
                snake_case:						Tuple         =   val[dim : dim * 2, :]
                snake_case:						List[Any]         =   val[-dim:, :]
            else:
                snake_case:						Dict         =   val[:dim]
                snake_case:						Tuple         =   val[dim : dim * 2]
                snake_case:						Optional[Any]         =   val[-dim:]
        else:
            snake_case:						List[Any]         =   rename_key(_lowerCAmelCase       )
            if "visual_projection" in new_name or "text_projection" in new_name:
                snake_case:						Tuple         =   val.T
            snake_case:						Optional[int]         =   val

    return orig_state_dict

def   a_					()->				Any:
    snake_case:						Dict         =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
    snake_case:						str         =   Image.open(requests.get(_lowerCAmelCase			,	stream=_lowerCAmelCase       ).raw       )
    return image

def   a_					(_lowerCAmelCase  :   Tuple			,	_lowerCAmelCase  :   str			,	_lowerCAmelCase  :   Optional[Any]			,	_lowerCAmelCase  :   int       )->				Any:
    snake_case:						Tuple         =   get_clipseg_config(_lowerCAmelCase       )
    snake_case:						Optional[Any]         =   CLIPSegForImageSegmentation(_lowerCAmelCase       )
    model.eval()

    snake_case:						Any         =   torch.load(_lowerCAmelCase			,	map_location="""cpu"""       )

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

    # rename some keys
    snake_case:						Any         =   convert_state_dict(_lowerCAmelCase			,	_lowerCAmelCase       )
    snake_case    , snake_case:						List[Any]         =   model.load_state_dict(_lowerCAmelCase			,	strict=_lowerCAmelCase       )

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

    snake_case:						List[Any]         =   ViTImageProcessor(size=352       )
    snake_case:						Union[str, Any]         =   CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""       )
    snake_case:						List[Any]         =   CLIPSegProcessor(image_processor=_lowerCAmelCase			,	tokenizer=_lowerCAmelCase       )

    snake_case:						Optional[Any]         =   prepare_img()
    snake_case:						Optional[int]         =   ["""a glass""", """something to fill""", """wood""", """a jar"""]

    snake_case:						Tuple         =   processor(text=_lowerCAmelCase			,	images=[image] * len(_lowerCAmelCase       )			,	padding="""max_length"""			,	return_tensors="""pt"""       )

    with torch.no_grad():
        snake_case:						Union[str, Any]         =   model(**_lowerCAmelCase       )

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

    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    :  Dict										= 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    :  Tuple										= parser.parse_args()
    convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)



def   a_					(_lowerCAmelCase  :   str       )->				int:
    snake_case:						List[str]         =   0
    for letter in data:
        snake_case:						int         =   (hash_ << 4) + ord(_lowerCAmelCase       )
        snake_case:						Optional[int]         =   hash_ & 0xf_0_0_0_0_0_0_0
        if x != 0:
            hash_ ^= x >> 24
        hash_ &= ~x
    return hash_


if __name__ == "__main__":
    import doctest

    doctest.testmod()


