





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


UpperCAmelCase__						:			Optional[int]									=logging.get_logger(__name__)

UpperCAmelCase__						:			List[Any]									={'''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__						:			Dict									={
    '''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     (						a    ):
				__A      =				VOCAB_FILES_NAMES
				__A      =				PRETRAINED_VOCAB_FILES_MAP
				__A      =				["""input_ids""", """attention_mask"""]
				__A      =				None


				def __init__(		self   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_="<unk>"   , UpperCAmelCase_="<s>"   , UpperCAmelCase_="</s>"   , UpperCAmelCase_="<pad>"   , UpperCAmelCase_=False   , UpperCAmelCase_=False   , **UpperCAmelCase_   , ):
								super().__init__(
								    UpperCAmelCase_   , UpperCAmelCase_   , tokenizer_file=UpperCAmelCase_   , unk_token=UpperCAmelCase_   , bos_token=UpperCAmelCase_   , eos_token=UpperCAmelCase_   , pad_token=UpperCAmelCase_   , add_prefix_space=UpperCAmelCase_   , clean_up_tokenization_spaces=UpperCAmelCase_   , **UpperCAmelCase_   , )
								lowerCamelCase	 =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
								if pre_tok_state.get("""add_prefix_space"""   , UpperCAmelCase_							) != add_prefix_space:
												lowerCamelCase	 =getattr(UpperCAmelCase_   , pre_tok_state.pop("""type"""							)							)
												lowerCamelCase	 =add_prefix_space
												lowerCamelCase	 =pre_tok_class(**UpperCAmelCase_							)

								lowerCamelCase	 =add_prefix_space


				def _snake_case       (		self   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								lowerCamelCase	 =kwargs.get("""is_split_into_words"""   , UpperCAmelCase_							)
								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(*UpperCAmelCase_   , **UpperCAmelCase_							)


				def _snake_case       (		self   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								lowerCamelCase	 =kwargs.get("""is_split_into_words"""   , UpperCAmelCase_							)

								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(*UpperCAmelCase_   , **UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_ = None							):
								lowerCamelCase	 =self._tokenizer.model.save(UpperCAmelCase_   , name=UpperCAmelCase_							)
								return tuple(UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =[]
								for is_user, text in conversation.iter_texts():
												input_ids.extend(self.encode(UpperCAmelCase_   , add_special_tokens=UpperCAmelCase_							) + [self.eos_token_id]							)

								if len(UpperCAmelCase_							) > self.model_max_length:
												lowerCamelCase	 =input_ids[-self.model_max_length :]
								return input_ids












from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
							if not (is_transformers_available() and is_torch_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
							from .pipeline_kandinsky import KandinskyPipeline
							from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
							from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
							from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
							from .text_encoder import MultilingualCLIP





