
'''simple docstring'''
import argparse

import requests
import torch
from PIL import Image

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




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Any:


		'''simple docstring'''


		snake_case     :					int     =	CLIPSegTextConfig()
		snake_case     :					Tuple     =	CLIPSegVisionConfig(patch_size=16 )

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

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




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       int:


		'''simple docstring'''


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


		'''simple docstring'''


		for key in orig_state_dict.copy().keys():
				snake_case     :					Union[str, Any]     =	orig_state_dict.pop(SCREAMING_SNAKE_CASE__ )

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

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

		return orig_state_dict




def       _UpperCamelCase	(					)     ->       int:


		'''simple docstring'''


		snake_case     :					int     =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		snake_case     :					List[Any]     =	Image.open(requests.get(SCREAMING_SNAKE_CASE__   ,					stream=SCREAMING_SNAKE_CASE__ ).raw )
		return image




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Dict:


		'''simple docstring'''


		snake_case     :					Any     =	get_clipseg_config(SCREAMING_SNAKE_CASE__ )
		snake_case     :					int     =	CLIPSegForImageSegmentation(SCREAMING_SNAKE_CASE__ )
		model.eval()

		snake_case     :					List[str]     =	torch.load(SCREAMING_SNAKE_CASE__   ,					map_location='''cpu''' )

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

    # rename some keys
		snake_case     :					List[str]     =	convert_state_dict(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
		snake_case     :					Dict     =	model.load_state_dict(SCREAMING_SNAKE_CASE__   ,					strict=SCREAMING_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(SCREAMING_SNAKE_CASE__ ) )
		if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
				raise ValueError(F'Unexpected keys: {unexpected_keys}' )

		snake_case     :					Any     =	ViTImageProcessor(size=352 )
		snake_case     :					List[Any]     =	CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32''' )
		snake_case     :					Optional[Any]     =	CLIPSegProcessor(image_processor=SCREAMING_SNAKE_CASE__   ,					tokenizer=SCREAMING_SNAKE_CASE__ )

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

		snake_case     :					Any     =	processor(text=SCREAMING_SNAKE_CASE__   ,					images=[image] * len(SCREAMING_SNAKE_CASE__ )   ,					padding='''max_length'''   ,					return_tensors='''pt''' )

		with torch.no_grad():
				snake_case     :					str     =	model(**SCREAMING_SNAKE_CASE__ )

		# verify values
		snake_case     :					Union[str, Any]     =	torch.tensor([0.1110, -0.1882, 0.1645] )
		snake_case     :					Union[str, Any]     =	torch.tensor([0.2692, -0.7197, -0.1328] )
		if model_name == "clipseg-rd64-refined":
				snake_case     :					int     =	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":
				snake_case     :					str     =	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":
				snake_case     :					Union[str, Any]     =	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]   ,					SCREAMING_SNAKE_CASE__   ,					atol=1E-3 )
		assert torch.allclose(outputs.conditional_embeddings[0, :3]   ,					SCREAMING_SNAKE_CASE__   ,					atol=1E-3 )
		assert torch.allclose(outputs.pooled_output[0, :3]   ,					SCREAMING_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(SCREAMING_SNAKE_CASE__ )
				processor.save_pretrained(SCREAMING_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__":
						lowercase__						=			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."
						)

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




'''simple docstring'''
def       _UpperCamelCase	(					)     ->       int:


		'''simple docstring'''


		for a in range(300 ):
				for b in range(a + 1   ,					400 ):
						for c in range(b + 1   ,					500 ):
								if (a + b + c) == 1000 and (a**2) + (b**2) == (c**2):
										return a * b * c

		return -1




def       _UpperCamelCase	(					)     ->       int:


		'''simple docstring'''


		for a in range(300 ):
				for b in range(400 ):
						snake_case     :					Dict     =	1000 - a - b
						if a < b < c and (a**2) + (b**2) == (c**2):
								return a * b * c

		return -1




def       _UpperCamelCase	(					)     ->       None:


		'''simple docstring'''


		import timeit

		print(
		    timeit.timeit('''solution()'''   ,					setup='''from __main__ import solution'''   ,					number=1000 ) )
		print(
		    timeit.timeit(
		        '''solution_fast()'''   ,					setup='''from __main__ import solution_fast'''   ,					number=1000 ) )


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


