




'''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:
		a__         =     None

a__         =     logging.get_logger(__name__)
a__         =     {'''vocab_file''': '''tokenizer.model''', '''tokenizer_file''': '''tokenizer.json'''}

a__    , a__         =     '''[INST]''', '''[/INST]'''
a__    , a__         =     '''<<SYS>>\n''', '''\n<</SYS>>\n\n'''

# fmt: off
a__         =     '''You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don\'t know the answer to a question, please don\'t share false information.'''





class __magic_name__(						__lowerCAmelCase      ):
						UpperCAmelCase_		:					str    =  VOCAB_FILES_NAMES
						UpperCAmelCase_		:					List[Any]    =  LlamaTokenizer
						UpperCAmelCase_		:					Any    =  """left"""
						UpperCAmelCase_		:					Optional[int]    =  ["""input_ids""", """attention_mask"""]






						def __init__(   self						:						Any					,   __UpperCamelCase						:						Tuple=None					,   __UpperCamelCase						:						Any=None					,   __UpperCamelCase						:						List[str]=False					,   __UpperCamelCase						:						List[Any]="<unk>"					,   __UpperCamelCase						:						Optional[int]="<s>"					,   __UpperCamelCase						:						Tuple="</s>"					,   __UpperCamelCase						:						Any=True					,   __UpperCamelCase						:						Any=False					,   **__UpperCamelCase						:						Tuple					,   ):



									'''simple docstring'''






									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       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									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       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									return self._add_eos_token






						@property
						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									return self._add_bos_token






						@add_eos_token.setter
						def       __lowerCAmelCase(   self						:						Union[str, Any]					,   __UpperCamelCase						:						Any       ):



									'''simple docstring'''






									snake_case__          =						value
									self.update_post_processor()






						@add_bos_token.setter
						def       __lowerCAmelCase(   self						:						int					,   __UpperCamelCase						:						Any       ):



									'''simple docstring'''






									snake_case__          =						value
									self.update_post_processor()






						def       __lowerCAmelCase(   self						:						Any					,   __UpperCamelCase						:						str					,   __UpperCamelCase						:						Optional[str] = None       ):



									'''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(__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       __lowerCAmelCase(   self						:						Optional[int]					,   __UpperCamelCase						:						"Conversation"       ):



									'''simple docstring'''






									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 					a			(  UpperCamelCase_				:					Optional[Any]		)	->		str:
 __A						:							Union[str, Any]      =						CLIPSegTextConfig()
 __A						:							Dict      =						CLIPSegVisionConfig(patch_size=1_6		)

 __A						:							Dict      =						True if 'refined' in model_name else False
 __A						:							Optional[int]      =						1_6 if 'rd16' in model_name else 6_4

 __A						:							Tuple      =						CLIPSegConfig.from_text_vision_configs(
     UpperCamelCase_  ,						UpperCamelCase_  ,						use_complex_transposed_convolution=UpperCamelCase_  ,						reduce_dim=UpperCamelCase_  ,						)
 return config




def 					a			(  UpperCamelCase_				:					List[str]		)	->		Dict:
 # update prefixes
 if "clip_model" in name:
  __A						:							str      =						name.replace('clip_model'  ,						'clip'		)
 if "transformer" in name:
  if "visual" in name:
   __A						:							List[str]      =						name.replace('visual.transformer'  ,						'vision_model'		)
  else:
   __A						:							Dict      =						name.replace('transformer'  ,						'text_model'		)
 if "resblocks" in name:
  __A						:							List[str]      =						name.replace('resblocks'  ,						'encoder.layers'		)
 if "ln_1" in name:
  __A						:							Dict      =						name.replace('ln_1'  ,						'layer_norm1'		)
 if "ln_2" in name:
  __A						:							Any      =						name.replace('ln_2'  ,						'layer_norm2'		)
 if "c_fc" in name:
  __A						:							List[Any]      =						name.replace('c_fc'  ,						'fc1'		)
 if "c_proj" in name:
  __A						:							int      =						name.replace('c_proj'  ,						'fc2'		)
 if "attn" in name and "self" not in name:
  __A						:							Dict      =						name.replace('attn'  ,						'self_attn'		)
 # text encoder
 if "token_embedding" in name:
  __A						:							Dict      =						name.replace('token_embedding'  ,						'text_model.embeddings.token_embedding'		)
 if "positional_embedding" in name and "visual" not in name:
  __A						:							int      =						name.replace('positional_embedding'  ,						'text_model.embeddings.position_embedding.weight'		)
 if "ln_final" in name:
  __A						:							Optional[Any]      =						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						:							List[Any]      =						name.replace('visual.conv1'  ,						'vision_model.embeddings.patch_embedding'		)
 if "visual.positional_embedding" in name:
  __A						:							str      =						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						:							str      =						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						:							int      =						name.replace('text_projection'  ,						'text_projection.weight'		)
 # decoder
 if "trans_conv" in name:
  __A						:							int      =						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						:							List[Any]      =						'decoder.' + name
 if "blocks" in name:
  __A						:							Optional[Any]      =						name.replace('blocks'  ,						'decoder.layers'		)
 if "linear1" in name:
  __A						:							int      =						name.replace('linear1'  ,						'mlp.fc1'		)
 if "linear2" in name:
  __A						:							Tuple      =						name.replace('linear2'  ,						'mlp.fc2'		)
 if "norm1" in name and "layer_" not in name:
  __A						:							List[Any]      =						name.replace('norm1'  ,						'layer_norm1'		)
 if "norm2" in name and "layer_" not in name:
  __A						:							Optional[Any]      =						name.replace('norm2'  ,						'layer_norm2'		)

 return name




def 					a			(  UpperCamelCase_				:					List[str]  ,						UpperCamelCase_				:					Dict		)	->		Any:
 for key in orig_state_dict.copy().keys():
  __A						:							str      =						orig_state_dict.pop(UpperCamelCase_		)

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

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

 return orig_state_dict




def 					a			(  )	->		Dict:
 __A						:							List[Any]      =						'http://images.cocodataset.org/val2017/000000039769.jpg'
 __A						:							Any      =						Image.open(requests.get(UpperCamelCase_  ,						stream=UpperCamelCase_		).raw		)
 return image




def 					a			(  UpperCamelCase_				:					List[str]  ,						UpperCamelCase_				:					List[str]  ,						UpperCamelCase_				:					Dict  ,						UpperCamelCase_				:					int		)	->		Optional[int]:
 __A						:							str      =						get_clipseg_config(UpperCamelCase_		)
 __A						:							int      =						CLIPSegForImageSegmentation(UpperCamelCase_		)
 model.eval()

 __A						:							Optional[int]      =						torch.load(UpperCamelCase_  ,						map_location='cpu'		)

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

    # rename some keys
 __A						:							int      =						convert_state_dict(UpperCamelCase_  ,						UpperCamelCase_		)
 __A     ,       __A						:							Tuple      =						model.load_state_dict(UpperCamelCase_  ,						strict=UpperCamelCase_		)

 if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
  raise ValueError('Missing keys that are not expected: {}'.format(UpperCamelCase_		)		)
 if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
  raise ValueError(F"Unexpected keys: {unexpected_keys}"		)

 __A						:							Optional[Any]      =						ViTImageProcessor(size=3_5_2		)
 __A						:							List[Any]      =						CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'		)
 __A						:							Optional[int]      =						CLIPSegProcessor(image_processor=UpperCamelCase_  ,						tokenizer=UpperCamelCase_		)

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

 __A						:							Optional[Any]      =						processor(text=UpperCamelCase_  ,						images=[image] * len(UpperCamelCase_		)  ,						padding='max_length'  ,						return_tensors='pt'		)

 with torch.no_grad():
  __A						:							int      =						model(**UpperCamelCase_		)

 # verify values
 __A						:							Union[str, Any]      =						torch.tensor([0.1110, -0.1882, 0.1645]		)
 __A						:							List[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						:							Optional[Any]      =						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						:							Optional[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]  ,						UpperCamelCase_  ,						atol=1E-3		)
 assert torch.allclose(outputs.conditional_embeddings[0, :3]  ,						UpperCamelCase_  ,						atol=1E-3		)
 assert torch.allclose(outputs.pooled_output[0, :3]  ,						UpperCamelCase_  ,						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(UpperCamelCase_		)
  processor.save_pretrained(UpperCamelCase_		)

 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 :    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."""
   )

   _UpperCamelCase :    Tuple      =			parser.parse_args()
   convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





