

'''simple docstring'''


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


lowerCAmelCase__						:       Any										=						logging.get_logger(__name__)

lowerCAmelCase__						:       Optional[Any]										=						{"""tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase__						:       str										=						{
    """tokenizer_file""": {
        """bigscience/tokenizer""": """https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json""",
        """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json""",
        """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json""",
        """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json""",
        """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json""",
        """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json""",
        """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json""",
    },
}




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    VOCAB_FILES_NAMES
						__UpperCAmelCase			     =    PRETRAINED_VOCAB_FILES_MAP
						__UpperCAmelCase			     =    ["""input_ids""", """attention_mask"""]
						__UpperCAmelCase			     =    None







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



									'''simple docstring'''



									super().__init__(
									    snake_case_			,			snake_case_			,			tokenizer_file=snake_case_			,			unk_token=snake_case_			,			bos_token=snake_case_			,			eos_token=snake_case_			,			pad_token=snake_case_			,			add_prefix_space=snake_case_			,			clean_up_tokenization_spaces=snake_case_			,			**snake_case_			,			)
									snake_case__						:       Tuple       						=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
									if pre_tok_state.get('''add_prefix_space'''			,			snake_case_  ) != add_prefix_space:
												snake_case__						:       Any       						=  getattr(snake_case_			,			pre_tok_state.pop('''type'''  )  )
												snake_case__						:       str       						=  add_prefix_space
												snake_case__						:       Tuple       						=  pre_tok_class(**snake_case_  )

									snake_case__						:       List[Any]       						=  add_prefix_space







						def       __magic_name__							(							self					:      Optional[Any]			,			*snake_case_					:      int			,			**snake_case_					:      int  ):



									'''simple docstring'''



									snake_case__						:       int       						=  kwargs.get('''is_split_into_words'''			,			snake_case_  )
									if not (self.add_prefix_space or not is_split_into_words):
												raise Exception(
												    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
												    ''' pretokenized inputs.'''  )

									return super()._batch_encode_plus(*snake_case_			,			**snake_case_  )







						def       __magic_name__							(							self					:      int			,			*snake_case_					:      Any			,			**snake_case_					:      Any  ):



									'''simple docstring'''



									snake_case__						:       Any       						=  kwargs.get('''is_split_into_words'''			,			snake_case_  )

									if not (self.add_prefix_space or not is_split_into_words):
												raise Exception(
												    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
												    ''' pretokenized inputs.'''  )

									return super()._encode_plus(*snake_case_			,			**snake_case_  )







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      str			,			snake_case_					:      Optional[str] = None  ):



									'''simple docstring'''



									snake_case__						:       Optional[Any]       						=  self._tokenizer.model.save(snake_case_			,			name=snake_case_  )
									return tuple(snake_case_  )







						def       __magic_name__							(							self					:      str			,			snake_case_					:      "Conversation"  ):



									'''simple docstring'''



									snake_case__						:       List[str]       						=  []
									for is_user, text in conversation.iter_texts():
												input_ids.extend(self.encode(snake_case_			,			add_special_tokens=snake_case_  ) + [self.eos_token_id]  )

									if len(snake_case_  ) > self.model_max_length:
												snake_case__						:       List[str]       						=  input_ids[-self.model_max_length :]
									return input_ids






'''simple docstring'''


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
					lowerCAmelCase__						:       Dict										=						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__						:       List[Any]										=						parser.parse_args()

					lowerCAmelCase__						:       Optional[Any]										=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

					lowerCAmelCase__						:       Optional[int]										=						CLIPImageProcessor()
					lowerCAmelCase__						:       str										=						CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

					lowerCAmelCase__						:       Dict										=						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)



