






import argparse

import requests
import torch
from PIL import Image

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





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  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(
	    lowercase_,  lowercase_,  use_complex_transposed_convolution=lowercase_,  reduce_dim=lowercase_,  )
	return config





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  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__						(   snake_case_,  snake_case_  )   ->  int:


	"""simple docstring"""



	for key in orig_state_dict.copy().keys():
		a		=			orig_state_dict.pop(lowercase_  )

		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(lowercase_  )
			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__						(   )   ->  int:


	"""simple docstring"""



	a		=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	a		=			Image.open(requests.get(lowercase_,  stream=lowercase_  ).raw  )
	return image





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_,  snake_case_  )   ->  List[Any]:


	"""simple docstring"""



	a		=			get_clipseg_config(lowercase_  )
	a		=			CLIPSegForImageSegmentation(lowercase_  )
	model.eval()

	a		=			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
	a		=			convert_state_dict(lowercase_,  lowercase_  )
	a		=			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}"""  )

	a		=			ViTImageProcessor(size=3_5_2  )
	a		=			CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''  )
	a		=			CLIPSegProcessor(image_processor=lowercase_,  tokenizer=lowercase_  )

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

	a		=			processor(text=lowercase_,  images=[image] * len(lowercase_  ),  padding='''max_length''',  return_tensors='''pt'''  )

	with torch.no_grad():
		a		=			model(**lowercase_  )

	# verify values
	a		=			torch.tensor([0.1110, -0.1882, 0.1645]  )
	a		=			torch.tensor([0.2692, -0.7197, -0.1328]  )
	if model_name == "clipseg-rd64-refined":
		a		=			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":
		a		=			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":
		a		=			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],  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__":
				UpperCamelCase__						:      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."""
				)

				UpperCamelCase__						:      Dict    		=					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 sys







class 				lowerCamelCase_     :
							def __init__(						self		:					Optional[int]							,__lowerCamelCase		:					str							,__lowerCamelCase		:					int					):





								'''simple docstring'''



								a		=			letter
								a		=			freq
								a		=			{}




							def __repr__(						self		:					List[str]					):





								'''simple docstring'''



								return F"""{self.letter}:{self.freq}"""







class 				lowerCamelCase_     :
							def __init__(						self		:					Dict							,__lowerCamelCase		:					int							,__lowerCamelCase		:					Letter | TreeNode							,__lowerCamelCase		:					Letter | TreeNode					):





								'''simple docstring'''



								a		=			freq
								a		=			left
								a		=			right





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  list[Letter]:


	"""simple docstring"""



	a		=			{}
	with open(snake_case_  ) as f:
		while True:
			a		=			f.read(1  )
			if not c:
				break
			a		=			chars[c] + 1 if c in chars else 1
	return sorted((Letter(snake_case_,  snake_case_  ) for c, f in chars.items()),  key=lambda snake_case_  : x.freq  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  Letter | TreeNode:


	"""simple docstring"""



	a		=			letters  # type: ignore
	while len(snake_case_  ) > 1:
		a		=			response.pop(0  )
		a		=			response.pop(0  )
		a		=			left.freq + right.freq
		a		=			TreeNode(snake_case_,  snake_case_,  snake_case_  )
		response.append(snake_case_  )
		response.sort(key=lambda snake_case_  : x.freq  )
	return response[0]





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  list[Letter]:


	"""simple docstring"""



	if isinstance(snake_case_,  snake_case_  ):
		a		=			bitstring
		return [root]
	a		=			root  # type: ignore
	a		=			[]
	letters += traverse_tree(treenode.left,  bitstring + '''0'''  )
	letters += traverse_tree(treenode.right,  bitstring + '''1'''  )
	return letters





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  None:


	"""simple docstring"""



	a		=			parse_file(snake_case_  )
	a		=			build_tree(snake_case_  )
	a		=			{
	    k: v for letter in traverse_tree(snake_case_,  ''''''  ) for k, v in letter.bitstring.items()
	}
	print(f"""Huffman Coding  of {file_path}: """  )
	with open(snake_case_  ) as f:
		while True:
			a		=			f.read(1  )
			if not c:
				break
			print(letters[c],  end=''' '''  )
	print()


if __name__ == "__main__":
				# pass the file path to the huffman function
				huffman(sys.argv[1])





