

'''simple docstring'''


import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

lowerCAmelCase__										= logging.get_logger(__name__)


lowerCAmelCase__										= {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowerCAmelCase__										= {
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

lowerCAmelCase__										= {"facebook/blenderbot-3B": 128}







@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 						SCREAMING_SNAKE_CASE(  )    ->    int:
	UpperCAmelCase_		:      List[Any]       	=					(
	    list(range(ord('!'  )   ,ord('~'  ) + 1  )  ) + list(range(ord('¡'  )   ,ord('¬'  ) + 1  )  ) + list(range(ord('®'  )   ,ord('ÿ'  ) + 1  )  )
	)
	UpperCAmelCase_		:      List[Any]       	=					bs[:]
	UpperCAmelCase_		:      Optional[int]       	=					0
	for b in range(2**8  ):
		if b not in bs:
			bs.append(UpperCamelCase  )
			cs.append(2**8 + n  )
			n += 1
	UpperCAmelCase_		:      Dict       	=					[chr(UpperCamelCase  ) for n in cs]
	return dict(zip(UpperCamelCase   ,UpperCamelCase  )  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    str:
	UpperCAmelCase_		:      int       	=					set()
	UpperCAmelCase_		:      Dict       	=					word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)  )
		UpperCAmelCase_		:      List[Any]       	=					char
	return pairs







class 					lowercase						(  a_ ):
							_lowerCamelCase		:      Optional[Any]=   VOCAB_FILES_NAMES
							_lowerCamelCase		:      int=   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase		:      str=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase		:      List[str]=   ["input_ids", "attention_mask"]


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case="replace"    ,			_snake_case="<s>"    ,			_snake_case="</s>"    ,			_snake_case="</s>"    ,			_snake_case="<s>"    ,			_snake_case="<unk>"    ,			_snake_case="<pad>"    ,			_snake_case="<mask>"    ,			_snake_case=False    ,			**_snake_case    ,			)							->				int:
								UpperCAmelCase_		:      List[str]       	=					AddedToken(_snake_case    ,			lstrip=_snake_case    ,			rstrip=_snake_case) if isinstance(_snake_case    ,			_snake_case) else bos_token
								UpperCAmelCase_		:      Tuple       	=					AddedToken(_snake_case    ,			lstrip=_snake_case    ,			rstrip=_snake_case) if isinstance(_snake_case    ,			_snake_case) else eos_token
								UpperCAmelCase_		:      Any       	=					AddedToken(_snake_case    ,			lstrip=_snake_case    ,			rstrip=_snake_case) if isinstance(_snake_case    ,			_snake_case) else sep_token
								UpperCAmelCase_		:      Tuple       	=					AddedToken(_snake_case    ,			lstrip=_snake_case    ,			rstrip=_snake_case) if isinstance(_snake_case    ,			_snake_case) else cls_token
								UpperCAmelCase_		:      Optional[Any]       	=					AddedToken(_snake_case    ,			lstrip=_snake_case    ,			rstrip=_snake_case) if isinstance(_snake_case    ,			_snake_case) else unk_token
								UpperCAmelCase_		:      Any       	=					AddedToken(_snake_case    ,			lstrip=_snake_case    ,			rstrip=_snake_case) if isinstance(_snake_case    ,			_snake_case) else pad_token

								# Mask token behave like a normal word, i.e. include the space before it
								UpperCAmelCase_		:      Any       	=					AddedToken(_snake_case    ,			lstrip=_snake_case    ,			rstrip=_snake_case) if isinstance(_snake_case    ,			_snake_case) else mask_token

								super().__init__(
								    errors=_snake_case    ,			bos_token=_snake_case    ,			eos_token=_snake_case    ,			unk_token=_snake_case    ,			sep_token=_snake_case    ,			cls_token=_snake_case    ,			pad_token=_snake_case    ,			mask_token=_snake_case    ,			add_prefix_space=_snake_case    ,			**_snake_case    ,			)

								with open(_snake_case    ,			encoding='utf-8') as vocab_handle:
									UpperCAmelCase_		:      Dict       	=					json.load(_snake_case)
								UpperCAmelCase_		:      str       	=					{v: k for k, v in self.encoder.items()}
								UpperCAmelCase_		:      Tuple       	=					errors  # how to handle errors in decoding
								UpperCAmelCase_		:      Optional[Any]       	=					bytes_to_unicode()
								UpperCAmelCase_		:      List[str]       	=					{v: k for k, v in self.byte_encoder.items()}
								with open(_snake_case    ,			encoding='utf-8') as merges_handle:
									UpperCAmelCase_		:      Union[str, Any]       	=					merges_handle.read().split('\n')[1:-1]
								UpperCAmelCase_		:      Tuple       	=					[tuple(merge.split()) for merge in bpe_merges]
								UpperCAmelCase_		:      Tuple       	=					dict(zip(_snake_case    ,			range(len(_snake_case))))
								UpperCAmelCase_		:      Any       	=					{}
								UpperCAmelCase_		:      Optional[Any]       	=					add_prefix_space

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								UpperCAmelCase_		:      Tuple       	=					re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+')


							@property
							# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
							def 						_snake_case							(			self)							->				Tuple:
								return len(self.encoder)


							def 						_snake_case							(			self)							->				List[Any]:
								return dict(self.encoder    ,			**self.added_tokens_encoder)


							def 						_snake_case							(			self    ,			_snake_case)							->				Optional[int]:
								if token in self.cache:
									return self.cache[token]
								UpperCAmelCase_		:      Optional[Any]       	=					tuple(_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					get_pairs(_snake_case)

								if not pairs:
									return token

								while True:
									UpperCAmelCase_		:      Optional[Any]       	=					min(_snake_case    ,			key=lambda _snake_case: self.bpe_ranks.get(_snake_case    ,			float('inf')))
									if bigram not in self.bpe_ranks:
										break
									UpperCAmelCase_	,    UpperCAmelCase_		:      Union[str, Any]       	=					bigram
									UpperCAmelCase_		:      List[Any]       	=					[]
									UpperCAmelCase_		:      str       	=					0
									while i < len(_snake_case):
										try:
											UpperCAmelCase_		:      Any       	=					word.index(_snake_case    ,			_snake_case)
										except ValueError:
											new_word.extend(word[i:])
											break
										else:
											new_word.extend(word[i:j])
											UpperCAmelCase_		:      Tuple       	=					j

										if word[i] == first and i < len(_snake_case) - 1 and word[i + 1] == second:
											new_word.append(first + second)
											i += 2
										else:
											new_word.append(word[i])
											i += 1
									UpperCAmelCase_		:      Union[str, Any]       	=					tuple(_snake_case)
									UpperCAmelCase_		:      str       	=					new_word
									if len(_snake_case) == 1:
										break
									else:
										UpperCAmelCase_		:      Tuple       	=					get_pairs(_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					' '.join(_snake_case)
								UpperCAmelCase_		:      int       	=					word
								return word


							def 						_snake_case							(			self    ,			_snake_case)							->				Dict:
								UpperCAmelCase_		:      Optional[Any]       	=					[]
								for token in re.findall(self.pat    ,			_snake_case):
									UpperCAmelCase_		:      int       	=					''.join(
									    self.byte_encoder[b] for b in token.encode('utf-8'))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
									bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_snake_case).split(' '))
								return bpe_tokens


							def 						_snake_case							(			self    ,			_snake_case)							->				Tuple:
								return self.encoder.get(_snake_case    ,			self.encoder.get(self.unk_token))


							def 						_snake_case							(			self    ,			_snake_case)							->				Tuple:
								return self.decoder.get(_snake_case)


							def 						_snake_case							(			self    ,			_snake_case)							->				List[Any]:
								UpperCAmelCase_		:      Optional[Any]       	=					''.join(_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					bytearray([self.byte_decoder[c] for c in text]).decode('utf-8'    ,			errors=self.errors)
								return text


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None)							->				Tuple[str]:
								if not os.path.isdir(_snake_case):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
									return
								UpperCAmelCase_		:      Tuple       	=					os.path.join(
								    _snake_case    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
								UpperCAmelCase_		:      Any       	=					os.path.join(
								    _snake_case    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

								with open(_snake_case    ,			'w'    ,			encoding='utf-8') as f:
									f.write(json.dumps(self.encoder    ,			indent=2    ,			sort_keys=_snake_case    ,			ensure_ascii=_snake_case) + '\n')

								UpperCAmelCase_		:      int       	=					0
								with open(_snake_case    ,			'w'    ,			encoding='utf-8') as writer:
									writer.write('#version: 0.2\n')
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,			key=lambda _snake_case: kv[1]):
										if index != token_index:
											logger.warning(
											    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
											    ' Please check that the tokenizer is not corrupted!')
											UpperCAmelCase_		:      Union[str, Any]       	=					token_index
										writer.write(' '.join(_snake_case) + '\n')
										index += 1

								return vocab_file, merge_file


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None    ,			_snake_case = False)							->				List[int]:
								if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=_snake_case    ,			token_ids_a=_snake_case    ,			already_has_special_tokens=_snake_case)

								if token_ids_a is None:
									return [1] + ([0] * len(_snake_case)) + [1]
								return [1] + ([0] * len(_snake_case)) + [1, 1] + ([0] * len(_snake_case)) + [1]


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None)							->				List[int]:
								UpperCAmelCase_		:      List[Any]       	=					[self.sep_token_id]
								UpperCAmelCase_		:      List[Any]       	=					[self.cls_token_id]

								if token_ids_a is None:
									return len(cls + token_ids_a + sep) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case=False    ,			**_snake_case)							->				Any:
								UpperCAmelCase_		:      int       	=					kwargs.pop('add_prefix_space'    ,			self.add_prefix_space)
								if (is_split_into_words or add_prefix_space) and (len(_snake_case) > 0 and not text[0].isspace()):
									UpperCAmelCase_		:      Dict       	=					' ' + text
								return (text, kwargs)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None)							->				List[str]:
								return token_ids_a + [self.eos_token_id]





							def 						_snake_case							(			self    ,			_snake_case)							->				List[int]:
								UpperCAmelCase_		:      Optional[Any]       	=					[]
								for is_user, text in conversation.iter_texts():
									if is_user:
										# We need to space prefix as it's being done within blenderbot
										inputs.append(' ' + text)
									else:
										# Generated responses should contain them already.
										inputs.append(_snake_case)

								UpperCAmelCase_		:      Any       	=					'  '.join(_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					self.encode(_snake_case)
								if len(_snake_case) > self.model_max_length:
									UpperCAmelCase_		:      int       	=					input_ids[-self.model_max_length :]
									logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens.""")
								return input_ids









'''simple docstring'''


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
						lowerCAmelCase__										= argparse.ArgumentParser()

						parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

						parser.add_argument(
						    "--txt2img_unclip",
						    default="kakaobrain/karlo-v1-alpha",
						    type=str,
						    required=False,
						    help="The pretrained txt2img unclip.",
						)

						lowerCAmelCase__										= parser.parse_args()

						lowerCAmelCase__										= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

						lowerCAmelCase__										= CLIPImageProcessor()
						lowerCAmelCase__										= CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

						lowerCAmelCase__										= UnCLIPImageVariationPipeline(
						    decoder=txtaimg.decoder,
						    text_encoder=txtaimg.text_encoder,
						    tokenizer=txtaimg.tokenizer,
						    text_proj=txtaimg.text_proj,
						    feature_extractor=feature_extractor,
						    image_encoder=image_encoder,
						    super_res_first=txtaimg.super_res_first,
						    super_res_last=txtaimg.super_res_last,
						    decoder_scheduler=txtaimg.decoder_scheduler,
						    super_res_scheduler=txtaimg.super_res_scheduler,
						)

						imgaimg.save_pretrained(args.dump_path)






