

"""simple docstring"""

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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


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

a__ :	Union[str, Any]						= {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

a__ :	int						= {
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

a__ :	List[str]						= {
    """microsoft/deberta-base""": 512,
    """microsoft/deberta-large""": 512,
    """microsoft/deberta-xlarge""": 512,
    """microsoft/deberta-base-mnli""": 512,
    """microsoft/deberta-large-mnli""": 512,
    """microsoft/deberta-xlarge-mnli""": 512,
}

a__ :	Dict						= {
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}


class 			_snake_case     (						UpperCamelCase       ):
						__snake_case 			=     VOCAB_FILES_NAMES
						__snake_case 			=     PRETRAINED_VOCAB_FILES_MAP
						__snake_case 			=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__snake_case 			=     ['input_ids', 'attention_mask', 'token_type_ids']
						__snake_case 			=     DebertaTokenizer







						def __init__(self     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_="replace"     ,							UpperCAmelCase_="[CLS]"     ,							UpperCAmelCase_="[SEP]"     ,							UpperCAmelCase_="[SEP]"     ,							UpperCAmelCase_="[CLS]"     ,							UpperCAmelCase_="[UNK]"     ,							UpperCAmelCase_="[PAD]"     ,							UpperCAmelCase_="[MASK]"     ,							UpperCAmelCase_=False     ,							**UpperCAmelCase_     ,							)			->						Union[str, Any]:
											super().__init__(
											    UpperCAmelCase_     ,							UpperCAmelCase_     ,							tokenizer_file=UpperCAmelCase_     ,							errors=UpperCAmelCase_     ,							bos_token=UpperCAmelCase_     ,							eos_token=UpperCAmelCase_     ,							unk_token=UpperCAmelCase_     ,							sep_token=UpperCAmelCase_     ,							cls_token=UpperCAmelCase_     ,							pad_token=UpperCAmelCase_     ,							mask_token=UpperCAmelCase_     ,							add_prefix_space=UpperCAmelCase_     ,							**UpperCAmelCase_     ,							)
											__lowercase												=			kwargs.pop('''add_bos_token'''     ,							UpperCAmelCase_							)

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

											__lowercase												=			add_prefix_space







						@property
						def        _UpperCamelCase    (self							)			->						str:
											if self._mask_token is None:
																if self.verbose:
																					logger.error('''Using mask_token, but it is not set yet.'''							)
																return None
											return str(self._mask_token							)







						@mask_token.setter
						def        _UpperCamelCase    (self     ,							UpperCAmelCase_							)			->						Optional[int]:
											__lowercase												=			AddedToken(UpperCAmelCase_     ,							lstrip=UpperCAmelCase_     ,							rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_     ,							UpperCAmelCase_							) else value
											__lowercase												=			value







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = None							)			->						List[int]:
											if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											__lowercase												=			[self.cls_token_id]
											__lowercase												=			[self.sep_token_id]
											return cls + token_ids_a + sep + token_ids_a + sep







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = None							)			->						List[int]:
											__lowercase												=			[self.sep_token_id]
											__lowercase												=			[self.cls_token_id]

											if token_ids_a is None:
																return len(cls + token_ids_a + sep							) * [0]
											return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]







						def        _UpperCamelCase    (self     ,							*UpperCAmelCase_     ,							**UpperCAmelCase_							)			->						BatchEncoding:
											__lowercase												=			kwargs.get('''is_split_into_words'''     ,							UpperCAmelCase_							)
											assert self.add_prefix_space or not is_split_into_words, (
											    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        _UpperCamelCase    (self     ,							*UpperCAmelCase_     ,							**UpperCAmelCase_							)			->						BatchEncoding:
											__lowercase												=			kwargs.get('''is_split_into_words'''     ,							UpperCAmelCase_							)

											assert self.add_prefix_space or not is_split_into_words, (
											    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        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = None							)			->						Tuple[str]:
											__lowercase												=			self._tokenizer.model.save(UpperCAmelCase_     ,							name=UpperCAmelCase_							)
											return tuple(UpperCAmelCase_							)







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_							)			->						List[int]:
											__lowercase												=			[]
											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:
																__lowercase												=			input_ids[-self.model_max_length :]
											return input_ids





"""simple docstring"""

from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


a__ :	int						= logging.get_logger(__name__)  # pylint: disable=invalid-name


class 			_snake_case     (						UpperCamelCase       ):
						__snake_case 			=     ['safety_checker', 'feature_extractor']







						def __init__(self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = True     ,							)			->						Optional[int]:
											super().__init__()
											if hasattr(scheduler.config     ,							'''steps_offset'''							) and scheduler.config.steps_offset != 1:
																__lowercase												=			(
																    f'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
																    f''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
																    '''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
																    ''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
																    ''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
																    ''' file'''
																)
																deprecate('''steps_offset!=1'''     ,							'''1.0.0'''     ,							UpperCAmelCase_     ,							standard_warn=UpperCAmelCase_							)
																__lowercase												=			dict(scheduler.config							)
																__lowercase												=			1
																__lowercase												=			FrozenDict(UpperCAmelCase_							)

											self.register_modules(
											    vae=UpperCAmelCase_     ,							text_encoder=UpperCAmelCase_     ,							tokenizer=UpperCAmelCase_     ,							unet=UpperCAmelCase_     ,							scheduler=UpperCAmelCase_     ,							safety_checker=UpperCAmelCase_     ,							feature_extractor=UpperCAmelCase_     ,							)
											self.register_to_config(requires_safety_checker=UpperCAmelCase_							)







						@property
						def        _UpperCamelCase    (self							)			->						Dict[str, Any]:
											return {k: getattr(self     ,							UpperCAmelCase_							) for k in self.config.keys() if not k.startswith('''_'''							)}







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_ = "auto"							)			->						str:
											if slice_size == "auto":
																# half the attention head size is usually a good trade-off between
																# speed and memory
																__lowercase												=			self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(UpperCAmelCase_							)







						def        _UpperCamelCase    (self							)			->						int:
											self.enable_attention_slicing(UpperCAmelCase_							)







						@torch.no_grad()
						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = 0.8     ,							UpperCAmelCase_ = 5_0     ,							UpperCAmelCase_ = 7.5     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							UpperCAmelCase_ = 0.0     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = "pil"     ,							UpperCAmelCase_ = True     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							)			->						str:
											# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
											return StableDiffusionInpaintPipelineLegacy(**self.components							)(
											    prompt=UpperCAmelCase_     ,							image=UpperCAmelCase_     ,							mask_image=UpperCAmelCase_     ,							strength=UpperCAmelCase_     ,							num_inference_steps=UpperCAmelCase_     ,							guidance_scale=UpperCAmelCase_     ,							negative_prompt=UpperCAmelCase_     ,							num_images_per_prompt=UpperCAmelCase_     ,							eta=UpperCAmelCase_     ,							generator=UpperCAmelCase_     ,							output_type=UpperCAmelCase_     ,							return_dict=UpperCAmelCase_     ,							callback=UpperCAmelCase_     ,							)







						@torch.no_grad()
						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = 0.8     ,							UpperCAmelCase_ = 5_0     ,							UpperCAmelCase_ = 7.5     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							UpperCAmelCase_ = 0.0     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = "pil"     ,							UpperCAmelCase_ = True     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							**UpperCAmelCase_     ,							)			->						List[str]:
											# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
											return StableDiffusionImgaImgPipeline(**self.components							)(
											    prompt=UpperCAmelCase_     ,							image=UpperCAmelCase_     ,							strength=UpperCAmelCase_     ,							num_inference_steps=UpperCAmelCase_     ,							guidance_scale=UpperCAmelCase_     ,							negative_prompt=UpperCAmelCase_     ,							num_images_per_prompt=UpperCAmelCase_     ,							eta=UpperCAmelCase_     ,							generator=UpperCAmelCase_     ,							output_type=UpperCAmelCase_     ,							return_dict=UpperCAmelCase_     ,							callback=UpperCAmelCase_     ,							callback_steps=UpperCAmelCase_     ,							)







						@torch.no_grad()
						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = 5_1_2     ,							UpperCAmelCase_ = 5_1_2     ,							UpperCAmelCase_ = 5_0     ,							UpperCAmelCase_ = 7.5     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							UpperCAmelCase_ = 0.0     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = "pil"     ,							UpperCAmelCase_ = True     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							)			->						Optional[int]:
											# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
											return StableDiffusionPipeline(**self.components							)(
											    prompt=UpperCAmelCase_     ,							height=UpperCAmelCase_     ,							width=UpperCAmelCase_     ,							num_inference_steps=UpperCAmelCase_     ,							guidance_scale=UpperCAmelCase_     ,							negative_prompt=UpperCAmelCase_     ,							num_images_per_prompt=UpperCAmelCase_     ,							eta=UpperCAmelCase_     ,							generator=UpperCAmelCase_     ,							latents=UpperCAmelCase_     ,							output_type=UpperCAmelCase_     ,							return_dict=UpperCAmelCase_     ,							callback=UpperCAmelCase_     ,							callback_steps=UpperCAmelCase_     ,							)


