




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


UpperCamelCase__            =     logging.get_logger(__name__)

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

UpperCamelCase__            =     {
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """allenai/longformer-base-4096""": (
            """https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
        ),
    },
}

UpperCamelCase__            =     {
    """allenai/longformer-base-4096""": 4096,
    """allenai/longformer-large-4096""": 4096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4096,
}
class      a__       (   snake_case__ ):
						_a				:       Optional[Any]   =       VOCAB_FILES_NAMES
						_a				:       List[str]   =       PRETRAINED_VOCAB_FILES_MAP
						_a				:       Optional[int]   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a				:       Union[str, Any]   =       ["""input_ids""", """attention_mask"""]
						_a				:       Optional[Any]   =       LongformerTokenizer






						def __init__(  self						,     _A=None						,     _A=None						,     _A=None						,     _A="replace"						,     _A="<s>"						,     _A="</s>"						,     _A="</s>"						,     _A="<s>"						,     _A="<unk>"						,     _A="<pad>"						,     _A="<mask>"						,     _A=False						,     _A=True						,     **_A						,     ):




											"""simple docstring"""



											super().__init__(
											    _A						,     _A						,     tokenizer_file=_A						,     errors=_A						,     bos_token=_A						,     eos_token=_A						,     sep_token=_A						,     cls_token=_A						,     unk_token=_A						,     pad_token=_A						,     mask_token=_A						,     add_prefix_space=_A						,     trim_offsets=_A						,     **_A						,     )

											__lowerCAmelCase							  =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
											if pre_tok_state.get("add_prefix_space"						,     _A     ) != add_prefix_space:
																__lowerCAmelCase							  =			getattr(_A						,     pre_tok_state.pop("type"     )     )
																__lowerCAmelCase							  =			add_prefix_space
																__lowerCAmelCase							  =			pre_tok_class(**_A     )

											__lowerCAmelCase							  =			add_prefix_space

											__lowerCAmelCase							  =			"post_processor"
											__lowerCAmelCase							  =			getattr(self.backend_tokenizer						,     _A						,     _A     )
											if tokenizer_component_instance:
																__lowerCAmelCase							  =			json.loads(tokenizer_component_instance.__getstate__()     )

																# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																if "sep" in state:
																					__lowerCAmelCase							  =			tuple(state["sep"]     )
																if "cls" in state:
																					__lowerCAmelCase							  =			tuple(state["cls"]     )

																__lowerCAmelCase							  =			False

																if state.get("add_prefix_space"						,     _A     ) != add_prefix_space:
																					__lowerCAmelCase							  =			add_prefix_space
																					__lowerCAmelCase							  =			True

																if state.get("trim_offsets"						,     _A     ) != trim_offsets:
																					__lowerCAmelCase							  =			trim_offsets
																					__lowerCAmelCase							  =			True

																if changes_to_apply:
																					__lowerCAmelCase							  =			getattr(_A						,     state.pop("type"     )     )
																					__lowerCAmelCase							  =			component_class(**_A     )
																					setattr(self.backend_tokenizer						,     _A						,     _A     )






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											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 							__SCREAMING_SNAKE_CASE(  self						,     _A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			AddedToken(_A						,     lstrip=_A						,     rstrip=_A     ) if isinstance(_A						,     _A     ) else value
											__lowerCAmelCase							  =			value






						def 							__SCREAMING_SNAKE_CASE(  self						,     *_A						,     **_A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			kwargs.get("is_split_into_words"						,     _A     )
											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(*_A						,     **_A     )






						def 							__SCREAMING_SNAKE_CASE(  self						,     *_A						,     **_A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			kwargs.get("is_split_into_words"						,     _A     )

											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(*_A						,     **_A     )






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A = None     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self._tokenizer.model.save(_A						,     name=_A     )
											return tuple(_A     )






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A=None     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
											if token_ids_a is None:
																return output

											return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A = None     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			[self.sep_token_id]
											__lowerCAmelCase							  =			[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 + sep + token_ids_a + sep     ) * [0]










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


UpperCamelCase__            =     logging.get_logger(__name__)  # pylint: disable=invalid-name
class      a__       (   snake_case__ ):
						_a				:       Tuple   =       ["""safety_checker""", """feature_extractor"""]






						def __init__(  self						,     _A						,     _A						,     _A						,     _A						,     _A						,     _A						,     _A						,     _A = True						,     ):




											"""simple docstring"""



											super().__init__()
											if hasattr(scheduler.config						,     "steps_offset"     ) and scheduler.config.steps_offset != 1:
																__lowerCAmelCase							  =			(
																    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"						,     _A						,     standard_warn=_A     )
																__lowerCAmelCase							  =			dict(scheduler.config     )
																__lowerCAmelCase							  =			1
																__lowerCAmelCase							  =			FrozenDict(_A     )

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






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return {k: getattr(self						,     _A     ) for k in self.config.keys() if not k.startswith("_"     )}






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A = "auto"     ):




											"""simple docstring"""



											if slice_size == "auto":
																# half the attention head size is usually a good trade-off between
																# speed and memory
																__lowerCAmelCase							  =			self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(_A     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											self.enable_attention_slicing(_A     )






						@torch.no_grad()
						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A						,     _A = 0.8						,     _A = 5_0						,     _A = 7.5						,     _A = None						,     _A = 1						,     _A = 0.0						,     _A = None						,     _A = "pil"						,     _A = True						,     _A = None						,     _A = 1						,     ):




											"""simple docstring"""



											return StableDiffusionInpaintPipelineLegacy(**self.components     )(
											    prompt=_A						,     image=_A						,     mask_image=_A						,     strength=_A						,     num_inference_steps=_A						,     guidance_scale=_A						,     negative_prompt=_A						,     num_images_per_prompt=_A						,     eta=_A						,     generator=_A						,     output_type=_A						,     return_dict=_A						,     callback=_A						,     )






						@torch.no_grad()
						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A = 0.8						,     _A = 5_0						,     _A = 7.5						,     _A = None						,     _A = 1						,     _A = 0.0						,     _A = None						,     _A = "pil"						,     _A = True						,     _A = None						,     _A = 1						,     **_A						,     ):




											"""simple docstring"""



											return StableDiffusionImgaImgPipeline(**self.components     )(
											    prompt=_A						,     image=_A						,     strength=_A						,     num_inference_steps=_A						,     guidance_scale=_A						,     negative_prompt=_A						,     num_images_per_prompt=_A						,     eta=_A						,     generator=_A						,     output_type=_A						,     return_dict=_A						,     callback=_A						,     callback_steps=_A						,     )






						@torch.no_grad()
						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A = 5_1_2						,     _A = 5_1_2						,     _A = 5_0						,     _A = 7.5						,     _A = None						,     _A = 1						,     _A = 0.0						,     _A = None						,     _A = None						,     _A = "pil"						,     _A = True						,     _A = None						,     _A = 1						,     ):




											"""simple docstring"""



											return StableDiffusionPipeline(**self.components     )(
											    prompt=_A						,     height=_A						,     width=_A						,     num_inference_steps=_A						,     guidance_scale=_A						,     negative_prompt=_A						,     num_images_per_prompt=_A						,     eta=_A						,     generator=_A						,     latents=_A						,     output_type=_A						,     return_dict=_A						,     callback=_A						,     callback_steps=_A						,     )




