from typing import Dict, List, Optional

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


SCREAMING_SNAKE_CASE		:     Tuple         =	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE		:     List[Any]         =	{
    """nielsr/canine-s""": 2048,
}

# Unicode defines 1,114,112 total “codepoints”
SCREAMING_SNAKE_CASE		:     Tuple         =	1114112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
SCREAMING_SNAKE_CASE		:     List[str]         =	0

SCREAMING_SNAKE_CASE		:     int         =	0xE0_00
SCREAMING_SNAKE_CASE		:     Dict         =	0xE0_01
SCREAMING_SNAKE_CASE		:     Optional[int]         =	0xE0_02
SCREAMING_SNAKE_CASE		:     str         =	0xE0_03
SCREAMING_SNAKE_CASE		:     Optional[int]         =	0xE0_04

# Maps special codepoints to human-readable names.
SCREAMING_SNAKE_CASE		:     Dict[int, str]         =	{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
SCREAMING_SNAKE_CASE		:     Dict[str, int]         =	{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}

class        UpperCamelCase						(				__a				):
						a__		:Dict      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





						def __init__(self			,     __UpperCamelCase=chr(__lowerCAmelCase						)			,     __UpperCamelCase=chr(__lowerCAmelCase						)			,     __UpperCamelCase=chr(__lowerCAmelCase						)			,     __UpperCamelCase=chr(__lowerCAmelCase						)			,     __UpperCamelCase=chr(__lowerCAmelCase						)			,     __UpperCamelCase=chr(__lowerCAmelCase						)			,     __UpperCamelCase=False			,     __UpperCamelCase=2_048			,     **__UpperCamelCase			,     )				->				int:
												UpperCamelCase_		:							List[Any]  =		AddedToken(__lowerCAmelCase			,     lstrip=__lowerCAmelCase			,     rstrip=__lowerCAmelCase						) if isinstance(__lowerCAmelCase			,     __lowerCAmelCase						) else bos_token
												UpperCamelCase_		:							int  =		AddedToken(__lowerCAmelCase			,     lstrip=__lowerCAmelCase			,     rstrip=__lowerCAmelCase						) if isinstance(__lowerCAmelCase			,     __lowerCAmelCase						) else eos_token
												UpperCamelCase_		:							int  =		AddedToken(__lowerCAmelCase			,     lstrip=__lowerCAmelCase			,     rstrip=__lowerCAmelCase						) if isinstance(__lowerCAmelCase			,     __lowerCAmelCase						) else sep_token
												UpperCamelCase_		:							Optional[int]  =		AddedToken(__lowerCAmelCase			,     lstrip=__lowerCAmelCase			,     rstrip=__lowerCAmelCase						) if isinstance(__lowerCAmelCase			,     __lowerCAmelCase						) else cls_token
												UpperCamelCase_		:							Optional[Any]  =		AddedToken(__lowerCAmelCase			,     lstrip=__lowerCAmelCase			,     rstrip=__lowerCAmelCase						) if isinstance(__lowerCAmelCase			,     __lowerCAmelCase						) else pad_token

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

												super().__init__(
												    bos_token=__lowerCAmelCase			,     eos_token=__lowerCAmelCase			,     sep_token=__lowerCAmelCase			,     cls_token=__lowerCAmelCase			,     pad_token=__lowerCAmelCase			,     mask_token=__lowerCAmelCase			,     add_prefix_space=__lowerCAmelCase			,     model_max_length=__lowerCAmelCase			,     **__lowerCAmelCase			,     )

												# Creates a mapping for looking up the IDs of special symbols.
												UpperCamelCase_		:							List[Any]  =		{}
												for codepoint, name in SPECIAL_CODEPOINTS.items():
																		UpperCamelCase_		:							Any  =		codepoint

												# Creates a mapping for looking up the string forms of special symbol IDs.
												UpperCamelCase_		:							Union[str, Any]  =		{
												    codepoint: name for name, codepoint in self._special_codepoints.items()
												}

												UpperCamelCase_		:							Union[str, Any]  =		UNICODE_VOCAB_SIZE
												UpperCamelCase_		:							int  =		len(self._special_codepoints						)





						@property
						def        A_							(self						)				->				List[Any]:
												return self._unicode_vocab_size





						def        A_							(self			,     __UpperCamelCase						)				->				Tuple:
												return list(__lowerCAmelCase						)





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



												try:
																		return ord(__lowerCAmelCase						)
												except TypeError:
																		raise ValueError(f'''invalid token: \'{token}\''''						)





						def        A_							(self			,     __UpperCamelCase						)				->				Optional[Any]:



												try:
																		if index in SPECIAL_CODEPOINTS:
																								return SPECIAL_CODEPOINTS[index]
																		return chr(__lowerCAmelCase						)
												except TypeError:
																		raise ValueError(f'''invalid id: {index}'''						)





						def        A_							(self			,     __UpperCamelCase						)				->				Optional[Any]:
												return "".join(__lowerCAmelCase						)





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None						)				->				List[Any]:
												UpperCamelCase_		:							Tuple  =		[self.sep_token_id]
												UpperCamelCase_		:							List[str]  =		[self.cls_token_id]

												UpperCamelCase_		:							List[Any]  =		cls + token_ids_a + sep
												if token_ids_a is not None:
																		result += token_ids_a + sep
												return result





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None			,     __UpperCamelCase = False						)				->				Optional[int]:



												if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=__lowerCAmelCase			,     token_ids_a=__lowerCAmelCase			,     already_has_special_tokens=__lowerCAmelCase						)

												UpperCamelCase_		:							int  =		[1] + ([0] * len(__lowerCAmelCase						)) + [1]
												if token_ids_a is not None:
																		result += ([0] * len(__lowerCAmelCase						)) + [1]
												return result





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None						)				->				Any:
												UpperCamelCase_		:							Dict  =		[self.sep_token_id]
												UpperCamelCase_		:							Union[str, Any]  =		[self.cls_token_id]

												UpperCamelCase_		:							Tuple  =		len(cls + token_ids_a + sep						) * [0]
												if token_ids_a is not None:
																		result += len(token_ids_a + sep						) * [1]
												return result





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None						)				->				Tuple:
												return ()







"""simple docstring"""






import torch

from diffusers import StableDiffusionPipeline


__lowerCAmelCase       :			str   	="""path-to-your-trained-model"""
__lowerCAmelCase       :			int   	=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

__lowerCAmelCase       :			Optional[int]   	="""A photo of sks dog in a bucket"""
__lowerCAmelCase       :			int   	=pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]

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



