




'''simple docstring'''







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

from tokenizers import processors

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


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

require_version('tokenizers>=0.13.3')

if is_sentencepiece_available():
						from .tokenization_llama import LlamaTokenizer
else:
						_lowerCamelCase							:						Dict							     =  None

_lowerCamelCase							:						Tuple							     =  logging.get_logger(__name__)
_lowerCamelCase							:						Optional[Any]							     =  {'vocab_file': 'tokenizer.model', 'tokenizer_file': 'tokenizer.json'}

_lowerCamelCase     ,_lowerCamelCase							:						Dict							     =  '[INST]', '[/INST]'
_lowerCamelCase     ,_lowerCamelCase							:						int							     =  '<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
_lowerCamelCase							:						Optional[int]							     =  'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'





class 					lowerCamelCase_							(	UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__   =			VOCAB_FILES_NAMES
					lowercase__   =			LlamaTokenizer
					lowercase__   =			"""left"""
					lowercase__   =			["""input_ids""", """attention_mask"""]






					def __init__(   self,  __UpperCamelCase=None,  __UpperCamelCase=None,  __UpperCamelCase=False,  __UpperCamelCase="<unk>",  __UpperCamelCase="<s>",  __UpperCamelCase="</s>",  __UpperCamelCase=True,  __UpperCamelCase=False,  **__UpperCamelCase,  ):
							super().__init__(
							    vocab_file=__UpperCamelCase,  tokenizer_file=__UpperCamelCase,  clean_up_tokenization_spaces=__UpperCamelCase,  unk_token=__UpperCamelCase,  bos_token=__UpperCamelCase,  eos_token=__UpperCamelCase,  **__UpperCamelCase,  )
							_snake_case     	=	add_bos_token
							_snake_case     	=	add_eos_token
							self.update_post_processor()

							_snake_case     	=	vocab_file
							_snake_case     	=	False if not self.vocab_file else True






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	self.bos_token
							_snake_case     	=	self.bos_token_id

							_snake_case     	=	self.eos_token
							_snake_case     	=	self.eos_token_id

							_snake_case     	=	f'''{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}'''
							_snake_case     	=	f'''{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}'''

							_snake_case     	=	[]
							if self.add_bos_token:
									special_tokens.append((bos, bos_token_id)			)
							if self.add_eos_token:
									special_tokens.append((eos, eos_token_id)			)
							_snake_case     	=	processors.TemplateProcessing(
							    single=__UpperCamelCase,  pair=__UpperCamelCase,  special_tokens=__UpperCamelCase			)






					@property
					def     __SCREAMING_SNAKE_CASE     (   self			):
							return self._add_eos_token






					@property
					def     __SCREAMING_SNAKE_CASE     (   self			):
							return self._add_bos_token






					@add_eos_token.setter
					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase			):
							_snake_case     	=	value
							self.update_post_processor()






					@add_bos_token.setter
					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase			):
							_snake_case     	=	value
							self.update_post_processor()






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase,  __UpperCamelCase = None			):
							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(__UpperCamelCase			):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
									return
							_snake_case     	=	os.path.join(
							    __UpperCamelCase,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)

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

							return (out_vocab_file,)






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase			):
							_snake_case     	=	list(conversation.iter_texts()			)
							if not all([is_user for is_user, msg in dialogue[::2]]			) or not all(
							    [not is_user for is_user, msg in dialogue[1::2]]			):
									raise ValueError(
									    "The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"			)

							_snake_case     	=	[]
							if len(conversation.past_user_inputs			) > 0:
									if not conversation.past_user_inputs[0].startswith(__UpperCamelCase			) or E_SYS not in conversation.past_user_inputs[0]:
											_snake_case     	=	(
											    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
											)
							elif not dialogue[0][1].startswith(__UpperCamelCase			) or E_SYS not in dialogue[0][1]:
									_snake_case     	=	(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

							dialog_tokens += sum(
							    [
							        [self.bos_token_id]
							        + self.encode(
							            f'''{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} ''',  add_special_tokens=__UpperCamelCase			)
							        + [self.eos_token_id]
							        for prompt, answer in zip(dialogue[::2],  dialogue[1::2]			)
							    ],  [],  )
							if not (dialogue[-1][0]):
									raise ValueError(f'''Last message must be from user, got {dialogue[-1]['role']}'''			)
							dialog_tokens += [self.bos_token_id] + self.encode(
							    f'''{B_INST} {(dialogue[-1][1]).strip()} {E_INST}''',  add_special_tokens=__UpperCamelCase			)
							return dialog_tokens











'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

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






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	CLIPSegTextConfig()
		_snake_case     	=	CLIPSegVisionConfig(patch_size=16  )

		_snake_case     	=	True if "refined" in model_name else False
		_snake_case     	=	16 if "rd16" in model_name else 64

		_snake_case     	=	CLIPSegConfig.from_text_vision_configs(
		    lowercase_						,							lowercase_						,							use_complex_transposed_convolution=lowercase_						,							reduce_dim=lowercase_						,							)
		return config






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







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

		return name






def 		_lowercase		(     lowercase_						,							lowercase_  ):



		'''simple docstring'''







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

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

						if "weight" in key:
								_snake_case     	=	val[:dim, :]
								_snake_case     	=	val[
								    dim : dim * 2, :
								]
								_snake_case     	=	val[-dim:, :]
						else:
								_snake_case     	=	val[:dim]
								_snake_case     	=	val[dim : dim * 2]
								_snake_case     	=	val[-dim:]
				elif "self_attn" in key and "out_proj" not in key:
						_snake_case     	=	key.split("."  )
						_snake_case     	=	int(key_split[1]  )
						_snake_case     	=	config.reduce_dim
						if "weight" in key:
								_snake_case     	=	val[:dim, :]
								_snake_case     	=	val[dim : dim * 2, :]
								_snake_case     	=	val[-dim:, :]
						else:
								_snake_case     	=	val[:dim]
								_snake_case     	=	val[dim : dim * 2]
								_snake_case     	=	val[-dim:]
				else:
						_snake_case     	=	rename_key(lowercase_  )
						if "visual_projection" in new_name or "text_projection" in new_name:
								_snake_case     	=	val.T
						_snake_case     	=	val

		return orig_state_dict






def 		_lowercase		(     ):



		'''simple docstring'''







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






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







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

		_snake_case     	=	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
		_snake_case     	=	convert_state_dict(lowercase_						,							lowercase_  )
		_snake_case	,   _snake_case     	=	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}'''  )

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

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

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

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

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

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





