



import argparse

import requests
import torch
from PIL import Image

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

def 			lowerCAmelCase_  (						_lowercase       :     List[str]	)							-> Any:







			"""simple docstring"""
			a__     :		int		 =						CLIPSegTextConfig()
			a__     :		Tuple		 =						CLIPSegVisionConfig(patch_size=16	)

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

			a__     :		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 			lowerCAmelCase_  (						_lowercase       :     Any	)							-> int:







			"""simple docstring"""


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

			return name

def 			lowerCAmelCase_  (						_lowercase       :     List[str]		,     _lowercase       :     List[Any]	)							-> Any:







			"""simple docstring"""


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

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

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

			return orig_state_dict

def 			lowerCAmelCase_  (						)							-> int:







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

def 			lowerCAmelCase_  (						_lowercase       :     Tuple		,     _lowercase       :     Union[str, Any]		,     _lowercase       :     List[str]		,     _lowercase       :     Optional[int]	)							-> Dict:







			"""simple docstring"""
			a__     :		Any		 =						get_clipseg_config(SCREAMING_SNAKE_CASE__	)
			a__     :		int		 =						CLIPSegForImageSegmentation(SCREAMING_SNAKE_CASE__	)
			model.eval()

			a__     :		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
			a__     :		List[str]		 =						convert_state_dict(SCREAMING_SNAKE_CASE__		,     SCREAMING_SNAKE_CASE__	)
			a__     :		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}'''	)

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

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

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

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

			# verify values
			a__     :		Union[str, Any]		 =						torch.tensor([0.1110, -0.1882, 0.1645]	)
			a__     :		Union[str, Any]		 =						torch.tensor([0.2692, -0.7197, -0.1328]	)
			if model_name == "clipseg-rd64-refined":
						a__     :		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":
						a__     :		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":
						a__     :		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	:   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."
		)

		_lowercase	:   Optional[int]    	=parser.parse_args()
		convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)









import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		_lowercase	:   Optional[int]    	=None


_lowercase	:   Union[str, Any]    	=logging.get_logger(__name__)


_lowercase	:   Any    	="▁"

_lowercase	:   Union[str, Any]    	={"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase	:   List[str]    	={
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

_lowercase	:   List[str]    	={
    "google/reformer-crime-and-punishment": 52_4288,
}

class      snake_case__       (A__	):







						"""simple docstring"""




						__lowerCAmelCase  :Union[str, Any]			      =  VOCAB_FILES_NAMES
						__lowerCAmelCase  :int			      =  PRETRAINED_VOCAB_FILES_MAP
						__lowerCAmelCase  :List[Any]			      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__lowerCAmelCase  :Any			      =  ["input_ids", "attention_mask"]
						__lowerCAmelCase  :Tuple			      =  ReformerTokenizer

						def __init__(     self      ,     __lowercase=None      ,     __lowercase=None      ,     __lowercase="</s>"      ,     __lowercase="<unk>"      ,     __lowercase=[]      ,     **__lowercase      ,     )      ->   int:
									"""simple docstring"""




									super().__init__(
									    __lowercase      ,     tokenizer_file=__lowercase      ,     eos_token=__lowercase      ,     unk_token=__lowercase      ,     additional_special_tokens=__lowercase      ,     **__lowercase      ,     )

									a__     :		Union[str, Any]		 =						vocab_file
									a__     :		str		 =						False if not self.vocab_file else True

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase = None						)      ->   Tuple[str]:
									"""simple docstring"""




									if not self.can_save_slow_tokenizer:
												raise ValueError(
												    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
												    """tokenizer."""						)

									if not os.path.isdir(__lowercase						):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
												return
									a__     :		Tuple		 =						os.path.join(
									    __lowercase      ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

									if os.path.abspath(self.vocab_file						) != os.path.abspath(__lowercase						):
												copyfile(self.vocab_file      ,     __lowercase						)

									return (out_vocab_file,)




