
import argparse

import requests
import torch
from PIL import Image

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


def       UpperCAmelCase_					(  __lowerCAmelCase		)	->  List[Any]:
    __lowercase						:     Tuple		      =    CLIPSegTextConfig()
    __lowercase						:     str		      =    CLIPSegVisionConfig(patch_size=16		)

    __lowercase						:     Any		      =    True if '''refined''' in model_name else False
    __lowercase						:     Optional[Any]		      =    16 if '''rd16''' in model_name else 64

    __lowercase						:     Union[str, Any]		      =    CLIPSegConfig.from_text_vision_configs(
        __lowerCAmelCase		,   __lowerCAmelCase		,   use_complex_transposed_convolution=__lowerCAmelCase		,   reduce_dim=__lowerCAmelCase		,   )
    return config


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

    return name


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  Union[str, Any]:
    for key in orig_state_dict.copy().keys():
        __lowercase						:     Union[str, Any]		      =    orig_state_dict.pop(__lowerCAmelCase		)

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

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

    return orig_state_dict


def       UpperCAmelCase_					(  )	->  Any:
    __lowercase						:     Tuple		      =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __lowercase						:     List[str]		      =    Image.open(requests.get(__lowerCAmelCase		,   stream=__lowerCAmelCase		).raw		)
    return image


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		)	->  Optional[Any]:
    __lowercase						:     Any		      =    get_clipseg_config(__lowerCAmelCase		)
    __lowercase						:     List[Any]		      =    CLIPSegForImageSegmentation(__lowerCAmelCase		)
    model.eval()

    __lowercase						:     Union[str, 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
    __lowercase						:     Dict		      =    convert_state_dict(__lowerCAmelCase		,   __lowerCAmelCase		)
    __lowercase						:     int		      =    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}'		)

    __lowercase						:     Union[str, Any]		      =    ViTImageProcessor(size=352		)
    __lowercase						:     Union[str, Any]		      =    CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''		)
    __lowercase						:     Union[str, Any]		      =    CLIPSegProcessor(image_processor=__lowerCAmelCase		,   tokenizer=__lowerCAmelCase		)

    __lowercase						:     List[str]		      =    prepare_img()
    __lowercase						:     str		      =    ['''a glass''', '''something to fill''', '''wood''', '''a jar''']

    __lowercase						:     Optional[Any]		      =    processor(text=__lowerCAmelCase		,   images=[image] * len(__lowerCAmelCase		)		,   padding='''max_length'''		,   return_tensors='''pt'''		)

    with torch.no_grad():
        __lowercase						:     Tuple		      =    model(**__lowerCAmelCase		)

    # verify values
    __lowercase						:     Any		      =    torch.tensor([0.1110, -0.1882, 0.1645]		)
    __lowercase						:     List[str]		      =    torch.tensor([0.2692, -0.7197, -0.1328]		)
    if model_name == "clipseg-rd64-refined":
        __lowercase						:     Dict		      =    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						:     Optional[Any]		      =    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						:     Tuple		      =    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]		,   __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   :     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."
 )

 __lowerCAmelCase   :     Optional[int]      =						parser.parse_args()
 convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




from __future__ import annotations

import math
from itertools import permutations


def       UpperCAmelCase_					(  __lowerCAmelCase		)	->  bool:

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5		,   int(math.sqrt(__lowerCAmelCase		) + 1		)		,   6		):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


def       UpperCAmelCase_					(  __lowerCAmelCase = 7		)	->  int:
    __lowercase						:     Union[str, Any]		      =    ''''''.join(str(__lowerCAmelCase		) for i in range(1		,   n + 1		)		)
    __lowercase						:     Dict		      =    [int(''''''.join(__lowerCAmelCase		)		) for i in permutations(__lowerCAmelCase		,   __lowerCAmelCase		)]
    __lowercase						:     int		      =    [num for num in perm_list if is_prime(__lowerCAmelCase		)]
    return max(__lowerCAmelCase		) if pandigitals else 0


if __name__ == "__main__":
 print(F'{solution() = }')


