






import argparse

import requests
import torch
from PIL import Image

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



def 	_SCREAMING_SNAKE_CASE  (       __lowercase      :	List[str]			)  ->					int:





    """simple docstring"""

    __A   			=		CLIPSegTextConfig()
    __A   			=		CLIPSegVisionConfig(patch_size=1_6			)

    __A   			=		True if """refined""" in model_name else False
    __A   			=		1_6 if """rd16""" in model_name else 6_4

    __A   			=		CLIPSegConfig.from_text_vision_configs(
        lowerCAmelCase_	, lowerCAmelCase_	, use_complex_transposed_convolution=lowerCAmelCase_	, reduce_dim=lowerCAmelCase_	, )
    return config



def 	_SCREAMING_SNAKE_CASE  (       __lowercase      :	Union[str, Any]			)  ->					Union[str, Any]:





    """simple docstring"""

    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  (       __lowercase      :	Optional[int]	, __lowercase      :	Union[str, Any]			)  ->					List[Any]:





    """simple docstring"""

    for key in orig_state_dict.copy().keys():
        __A   			=		orig_state_dict.pop(lowerCAmelCase_			)

        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(lowerCAmelCase_			)
            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  (       )  ->					Optional[Any]:





    """simple docstring"""

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



def 	_SCREAMING_SNAKE_CASE  (       __lowercase      :	Optional[int]	, __lowercase      :	List[str]	, __lowercase      :	Union[str, Any]	, __lowercase      :	List[str]			)  ->					Optional[Any]:





    """simple docstring"""

    __A   			=		get_clipseg_config(lowerCAmelCase_			)
    __A   			=		CLIPSegForImageSegmentation(lowerCAmelCase_			)
    model.eval()

    __A   			=		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
    __A   			=		convert_state_dict(lowerCAmelCase_	, lowerCAmelCase_			)
    __A			,		__A   			=		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}"			)

    __A   			=		ViTImageProcessor(size=3_5_2			)
    __A   			=		CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""			)
    __A   			=		CLIPSegProcessor(image_processor=lowerCAmelCase_	, tokenizer=lowerCAmelCase_			)

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

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

    with torch.no_grad():
        __A   			=		model(**lowerCAmelCase_			)

    # 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]	, 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__":
  __a					:				Optional[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."
  )

  __a					:				Union[str, Any]      =      parser.parse_args()
  convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)









def        __a     (    lowerCAmelCase_   :					int				)      -> int:






						'''simple docstring'''



						if number != int(lowerCAmelCase_				):
												raise ValueError("""factorial() only accepts integral values"""				)
						if number < 0:
												raise ValueError("""factorial() not defined for negative values"""				)
						UpperCAmelCase_=   1
						for i in range(1			,number + 1				):
												value *= i
						return value



def        __a     (    lowerCAmelCase_   :					int				)      -> int:






						'''simple docstring'''



						if not isinstance(lowerCAmelCase_			,lowerCAmelCase_				):
												raise ValueError("""factorial() only accepts integral values"""				)
						if n < 0:
												raise ValueError("""factorial() not defined for negative values"""				)
						return 1 if n == 0 or n == 1 else n * factorial(n - 1				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	__A				    =						int(input('''Enter a positive integer: ''').strip() or 0)
	print(f'factorial{n} is {factorial(n)}')




