

"""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


a       :int     =			logging.get_logger(__name__)

a       :str     =			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

a       :Optional[int]     =			{
    "tokenizer_file": {
        "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
    },
}

a       :int     =			{
    "gpt-neox-20b": 2_048,
}

class   __a				(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE    :Any							=			VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE    :Dict							=			PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE    :Tuple							=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE    :List[Any]							=			["""input_ids""", """attention_mask"""]

     def __init__(    self						,     _a=None						,     _a=None						,     _a=None						,     _a="<|endoftext|>"						,     _a="<|endoftext|>"						,     _a="<|endoftext|>"						,     _a=False						,     **_a						,     )	->   Optional[Any]:


           """simple docstring"""


           super().__init__(
               UpperCamelCase_						,     UpperCamelCase_						,     tokenizer_file=UpperCamelCase_						,     unk_token=UpperCamelCase_						,     bos_token=UpperCamelCase_						,     eos_token=UpperCamelCase_						,     add_prefix_space=UpperCamelCase_						,     **UpperCamelCase_						,     )

           SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
           if pre_tok_state.get("""add_prefix_space"""						,     UpperCamelCase_	) != add_prefix_space:
                 SCREAMING_SNAKE_CASE__ :   str   		=						getattr(UpperCamelCase_						,     pre_tok_state.pop("""type"""	)	)
                 SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						add_prefix_space
                 SCREAMING_SNAKE_CASE__ :   List[Any]   		=						pre_tok_class(**UpperCamelCase_	)

           SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						add_prefix_space

     def        _a      (    self						,     _a						,     _a = None	)	->   str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   List[str]   		=						self._tokenizer.model.save(UpperCamelCase_						,     name=UpperCamelCase_	)
           return tuple(UpperCamelCase_	)





     def        _a      (    self						,     _a	)	->   List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Tuple   		=						[]
           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:
                 SCREAMING_SNAKE_CASE__ :   str   		=						input_ids[-self.model_max_length :]
           return input_ids






"""simple docstring"""



import torch

from diffusers import StableDiffusionPipeline


UpperCamelCase__          =	'path-to-your-trained-model'
UpperCamelCase__          =	StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')

UpperCamelCase__          =	'A photo of sks dog in a bucket'
UpperCamelCase__          =	pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('dog-bucket.png')

