
'''simple docstring'''



import argparse

import requests
import torch
from PIL import Image

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





def  _A		(							lowercase__	):
						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(
						    lowercase__		,		lowercase__		,		use_complex_transposed_convolution=lowercase__		,		reduce_dim=lowercase__		,		)
						return config





def  _A		(							lowercase__	):
						# update prefixes
						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		(							lowercase__		,		lowercase__	):
						for key in orig_state_dict.copy().keys():
												lowercase__							 =       orig_state_dict.pop(lowercase__	)

												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(lowercase__	)
																		if "visual_projection" in new_name or "text_projection" in new_name:
																								lowercase__							 =       val.T
																		lowercase__							 =       val

						return orig_state_dict





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





def  _A		(							lowercase__		,		lowercase__		,		lowercase__		,		lowercase__	):
						lowercase__							 =       get_clipseg_config(lowercase__	)
						lowercase__							 =       CLIPSegForImageSegmentation(lowercase__	)
						model.eval()

						lowercase__							 =       torch.load(lowercase__		,		map_location="""cpu"""	)

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

    # rename some keys
						lowercase__							 =       convert_state_dict(lowercase__		,		lowercase__	)
						lowercase__ ,						lowercase__							 =       model.load_state_dict(lowercase__		,		strict=lowercase__	)

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

						lowercase__							 =       ViTImageProcessor(size=352	)
						lowercase__							 =       CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""	)
						lowercase__							 =       CLIPSegProcessor(image_processor=lowercase__		,		tokenizer=lowercase__	)

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

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

						with torch.no_grad():
												lowercase__							 =       model(**lowercase__	)

						# verify values
						lowercase__							 =       torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]	)
						lowercase__							 =       torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]	)
						if model_name == "clipseg-rd64-refined":
												lowercase__							 =       torch.tensor(
												    [[-1_0.0_4_0_7, -9.9_4_3_1, -1_0.2_6_4_6], [-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":
												lowercase__							 =       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":
												lowercase__							 =       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]		,		lowercase__		,		atol=1e-3	)
						assert torch.allclose(outputs.conditional_embeddings[0, :3]		,		lowercase__		,		atol=1e-3	)
						assert torch.allclose(outputs.pooled_output[0, :3]		,		lowercase__		,		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(lowercase__	)
												processor.save_pretrained(lowercase__	)

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








'''simple docstring'''



def  _A		(							lowercase__	):

						# validate:
						if not isinstance(lowercase__		,		lowercase__	) or not all(
						    isinstance(lowercase__		,		lowercase__	) and item >= 0 for item in citations	):
												raise ValueError("""The citations should be a list of non negative integers."""	)

						citations.sort()
						lowercase__							 =       len(lowercase__	)

						for i in range(lowercase__	):
												if citations[len_citations - 1 - i] <= i:
																		return i

						return len_citations


if __name__ == "__main__":
				import doctest

				doctest.testmod()






