


'''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     )      ->int:






				'''simple docstring'''

				__lowerCamelCase		:   str           =       CLIPSegTextConfig()
				__lowerCamelCase		:   Dict           =       CLIPSegVisionConfig(patch_size=16     )

				__lowerCamelCase		:   int           =       True if "refined" in model_name else False
				__lowerCamelCase		:   int           =       16 if "rd16" in model_name else 64

				__lowerCamelCase		:   int           =       CLIPSegConfig.from_text_vision_configs(
				    lowercase				,    lowercase				,    use_complex_transposed_convolution=lowercase				,    reduce_dim=lowercase				,    )
				return config





def 				a_							(    lowercase     )      ->Dict:






				'''simple docstring'''

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

				return name





def 				a_							(    lowercase				,    lowercase     )      ->Optional[Any]:






				'''simple docstring'''

				for key in orig_state_dict.copy().keys():
								__lowerCamelCase		:   List[str]           =       orig_state_dict.pop(lowercase     )

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

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

				return orig_state_dict





def 				a_							(    )      ->Optional[int]:






				'''simple docstring'''

				__lowerCamelCase		:   Tuple           =       "http://images.cocodataset.org/val2017/000000039769.jpg"
				__lowerCamelCase		:   Union[str, Any]           =       Image.open(requests.get(lowercase				,    stream=lowercase     ).raw     )
				return image





def 				a_							(    lowercase				,    lowercase				,    lowercase				,    lowercase     )      ->str:






				'''simple docstring'''

				__lowerCamelCase		:   Union[str, Any]           =       get_clipseg_config(lowercase     )
				__lowerCamelCase		:   Dict           =       CLIPSegForImageSegmentation(lowercase     )
				model.eval()

				__lowerCamelCase		:   int           =       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
				__lowerCamelCase		:   List[str]           =       convert_state_dict(lowercase				,    lowercase     )
				__lowerCamelCase    ,       __lowerCamelCase		:   int           =       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}'     )

				__lowerCamelCase		:   List[Any]           =       ViTImageProcessor(size=352     )
				__lowerCamelCase		:   int           =       CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"     )
				__lowerCamelCase		:   Dict           =       CLIPSegProcessor(image_processor=lowercase				,    tokenizer=lowercase     )

				__lowerCamelCase		:   Union[str, Any]           =       prepare_img()
				__lowerCamelCase		:   Any           =       ["a glass", "something to fill", "wood", "a jar"]

				__lowerCamelCase		:   int           =       processor(text=lowercase				,    images=[image] * len(lowercase     )				,    padding="max_length"				,    return_tensors="pt"     )

				with torch.no_grad():
								__lowerCamelCase		:   Any           =       model(**lowercase     )

				# verify values
				__lowerCamelCase		:   Dict           =       torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]     )
				__lowerCamelCase		:   Tuple           =       torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]     )
				if model_name == "clipseg-rd64-refined":
								__lowerCamelCase		:   Tuple           =       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":
								__lowerCamelCase		:   str           =       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":
								__lowerCamelCase		:   Dict           =       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__":
				__lowercase	:   List[str]   		=					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	:   Any   		=					parser.parse_args()
				convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''




#
# Sort large text files in a minimum amount of memory
#
import argparse
import os





class _snake_case :
							"""simple docstring"""





							__a				       =  '''block_{0}.dat'''







							def __init__( self      ,					__A						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   Dict           =       filename
											__lowerCamelCase		:   Union[str, Any]           =       []







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       self.BLOCK_FILENAME_FORMAT.format(__A						)
											with open(__A      ,					"w"						) as file:
															file.write(__A						)
											self.block_filenames.append(__A						)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											return self.block_filenames







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A=None						)  ->Dict:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       0
											with open(self.filename						) as file:
															while True:
																			__lowerCamelCase		:   Union[str, Any]           =       file.readlines(__A						)

																			if lines == []:
																							break

																			if sort_key is None:
																							lines.sort()
																			else:
																							lines.sort(key=__A						)

																			self.write_block("".join(__A						)      ,					__A						)
																			i += 1







							def 		__SCREAMING_SNAKE_CASE( self						)  ->List[Any]:



											"""simple docstring"""







											map(os.remove      ,					self.block_filenames						)





class _snake_case :
							"""simple docstring"""





							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->Optional[int]:



											"""simple docstring"""







											__lowerCamelCase		:   Union[str, Any]           =       -1
											__lowerCamelCase		:   Optional[Any]           =       None

											for i in range(len(__A						)						):
															if min_str is None or choices[i] < min_str:
																			__lowerCamelCase		:   Tuple           =       i

											return min_index





class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A						)  ->List[Any]:



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       files
											__lowerCamelCase		:   Tuple           =       set()
											__lowerCamelCase		:   Optional[Any]           =       len(__A						)
											__lowerCamelCase		:   List[str]           =       {i: None for i in range(self.num_buffers						)}







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Any:



											"""simple docstring"""







											return {
											    i: self.buffers[i] for i in range(self.num_buffers						) if i not in self.empty
											}







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Dict:



											"""simple docstring"""







											for i in range(self.num_buffers						):
															if self.buffers[i] is None and i not in self.empty:
																			__lowerCamelCase		:   Tuple           =       self.files[i].readline()

																			if self.buffers[i] == "":
																							self.empty.add(__A						)
																							self.files[i].close()

											if len(self.empty						) == self.num_buffers:
															return False

											return True







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->Any:



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       self.buffers[index]
											__lowerCamelCase		:   List[Any]           =       None

											return value





class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A						)  ->List[str]:



											"""simple docstring"""







											__lowerCamelCase		:   List[Any]           =       merge_strategy







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A						)  ->Optional[int]:



											"""simple docstring"""







											__lowerCamelCase		:   List[str]           =       FilesArray(self.get_file_handles(__A      ,					__A						)						)
											with open(__A      ,					"w"      ,					__A						) as outfile:
															while buffers.refresh():
																			__lowerCamelCase		:   Optional[Any]           =       self.merge_strategy.select(buffers.get_dict()						)
																			outfile.write(buffers.unshift(__A						)						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->str:



											"""simple docstring"""







											__lowerCamelCase		:   Union[str, Any]           =       {}

											for i in range(len(__A						)						):
															__lowerCamelCase		:   Tuple           =       open(filenames[i]      ,					"r"      ,					__A						)  # noqa: UP015

											return files





class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A						)  ->str:



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       block_size







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A=None						)  ->List[str]:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       self.get_number_blocks(__A      ,					self.block_size						)
											__lowerCamelCase		:   Optional[int]           =       FileSplitter(__A						)
											splitter.split(self.block_size      ,					__A						)

											__lowerCamelCase		:   List[str]           =       FileMerger(NWayMerge()						)
											__lowerCamelCase		:   str           =       self.block_size / (num_blocks + 1)
											merger.merge(splitter.get_block_filenames()      ,					filename + ".out"      ,					__A						)

											splitter.cleanup()







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->List[str]:



											"""simple docstring"""







											return (os.stat(__A						).st_size / block_size) + 1





def 				a_							(    lowercase     )      ->List[Any]:






				'''simple docstring'''

				if string[-1].lower() == "k":
								return int(string[:-1]     ) * 1024
				elif string[-1].lower() == "m":
								return int(string[:-1]     ) * 1024 * 1024
				elif string[-1].lower() == "g":
								return int(string[:-1]     ) * 1024 * 1024 * 1024
				else:
								return int(lowercase     )





def 				a_							(    )      ->Optional[Any]:






				'''simple docstring'''

				__lowerCamelCase		:   List[str]           =       argparse.ArgumentParser()
				parser.add_argument(
				    "-m"				,    "--mem"				,    help="amount of memory to use for sorting"				,    default="100M"     )
				parser.add_argument(
				    "filename"				,    metavar="<filename>"				,    nargs=1				,    help="name of file to sort"     )
				__lowerCamelCase		:   List[str]           =       parser.parse_args()

				__lowerCamelCase		:   Tuple           =       ExternalSort(parse_memory(args.mem     )     )
				sorter.sort(args.filename[0]     )


if __name__ == "__main__":
				main()





