

'''simple docstring'''


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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation


_snake_case				:				List[Any]       							=		logging.get_logger(__name__)

_snake_case				:				List[str]       							=		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_snake_case				:				Dict       							=		{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

_snake_case				:				Any       							=		{
    'gpt-neox-20b': 2048,
}

class       A   (		A__   ):
							lowercase_          =    VOCAB_FILES_NAMES
							lowercase_          =    PRETRAINED_VOCAB_FILES_MAP
							lowercase_          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_          =    ['input_ids', 'attention_mask']







							def __init__(  self	:  Dict							,      lowerCAmelCase_	:  Dict=None							,      lowerCAmelCase_	:  Any=None							,      lowerCAmelCase_	:  Union[str, Any]=None							,      lowerCAmelCase_	:  Optional[Any]="<|endoftext|>"							,      lowerCAmelCase_	:  Tuple="<|endoftext|>"							,      lowerCAmelCase_	:  Tuple="<|endoftext|>"							,      lowerCAmelCase_	:  Union[str, Any]=False							,      **lowerCAmelCase_	:  List[str]							,      )      ->							Optional[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							,      add_prefix_space=__snake_case							,      **__snake_case							,      )

														_a     					=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
														if pre_tok_state.get('''add_prefix_space'''							,      __snake_case ) != add_prefix_space:
																					_a     					=    getattr(__snake_case							,      pre_tok_state.pop('''type''' ) )
																					_a     					=    add_prefix_space
																					_a     					=    pre_tok_class(**__snake_case )

														_a     					=    add_prefix_space







							def        __lowerCAmelCase      (  self	:  Tuple							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  Optional[str] = None )      ->							Tuple[str]:


														"""simple docstring"""


														_a     					=    self._tokenizer.model.save(__snake_case							,      name=__snake_case )
														return tuple(__snake_case )







							def        __lowerCAmelCase      (  self	:  Union[str, Any]							,      lowerCAmelCase_	:  "Conversation" )      ->							List[int]:


														"""simple docstring"""


														_a     					=    []
														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:
																					_a     					=    input_ids[-self.model_max_length :]
														return input_ids







'''simple docstring'''


from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available

@dataclass
class       A   (		_a   ):
							lowercase_          =    42
							lowercase_          =    42


if is_transformers_available() and is_torch_available():
				from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline

