




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


class       _a  (     snake_case__			):
       __a    :					Optional[Any]           = ['safety_checker', 'feature_extractor']
       def __init__(     self						:     Optional[Any]       ,    lowercase						:     AutoencoderKL       ,    lowercase						:     CLIPTextModel       ,    lowercase						:     CLIPTokenizer       ,    lowercase						:     UNetaDConditionModel       ,    lowercase						:     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]       ,    lowercase						:     StableDiffusionSafetyChecker       ,    lowercase						:     CLIPImageProcessor       ,    lowercase						:     bool = True       ,    ):



           '''simple docstring'''

           super().__init__()
           if hasattr(scheduler.config       ,    '''steps_offset'''    ) and scheduler.config.steps_offset != 1:
               UpperCAmelCase      	=		(
                   f"The configuration file of this scheduler: {scheduler} is outdated.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'''       ,    lowercase       ,    standard_warn=lowercase    )
               UpperCAmelCase      	=		dict(scheduler.config    )
               UpperCAmelCase      	=		1
               UpperCAmelCase      	=		FrozenDict(lowercase    )

           self.register_modules(
               vae=lowercase       ,    text_encoder=lowercase       ,    tokenizer=lowercase       ,    unet=lowercase       ,    scheduler=lowercase       ,    safety_checker=lowercase       ,    feature_extractor=lowercase       ,    )
           self.register_to_config(requires_safety_checker=lowercase    )
       @property
       def      A				(     self						:     int    ):



           '''simple docstring'''

           return {k: getattr(self       ,    lowercase    ) for k in self.config.keys() if not k.startswith('''_'''    )}
       def      A				(     self						:     str       ,    lowercase						:     Optional[Union[str, int]] = "auto"    ):



           '''simple docstring'''

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



           '''simple docstring'''

           self.enable_attention_slicing(lowercase    )
       @torch.no_grad()
       def      A				(     self						:     Union[str, Any]       ,    lowercase						:     Union[str, List[str]]       ,    lowercase						:     Union[torch.FloatTensor, PIL.Image.Image]       ,    lowercase						:     Union[torch.FloatTensor, PIL.Image.Image]       ,    lowercase						:     float = 0.8       ,    lowercase						:     Optional[int] = 50       ,    lowercase						:     Optional[float] = 7.5       ,    lowercase						:     Optional[Union[str, List[str]]] = None       ,    lowercase						:     Optional[int] = 1       ,    lowercase						:     Optional[float] = 0.0       ,    lowercase						:     Optional[torch.Generator] = None       ,    lowercase						:     Optional[str] = "pil"       ,    lowercase						:     bool = True       ,    lowercase						:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,    lowercase						:     int = 1       ,    ):



           '''simple docstring'''

           return StableDiffusionInpaintPipelineLegacy(**self.components    )(
               prompt=lowercase       ,    image=lowercase       ,    mask_image=lowercase       ,    strength=lowercase       ,    num_inference_steps=lowercase       ,    guidance_scale=lowercase       ,    negative_prompt=lowercase       ,    num_images_per_prompt=lowercase       ,    eta=lowercase       ,    generator=lowercase       ,    output_type=lowercase       ,    return_dict=lowercase       ,    callback=lowercase       ,    )
       @torch.no_grad()
       def      A				(     self						:     str       ,    lowercase						:     Union[str, List[str]]       ,    lowercase						:     Union[torch.FloatTensor, PIL.Image.Image]       ,    lowercase						:     float = 0.8       ,    lowercase						:     Optional[int] = 50       ,    lowercase						:     Optional[float] = 7.5       ,    lowercase						:     Optional[Union[str, List[str]]] = None       ,    lowercase						:     Optional[int] = 1       ,    lowercase						:     Optional[float] = 0.0       ,    lowercase						:     Optional[torch.Generator] = None       ,    lowercase						:     Optional[str] = "pil"       ,    lowercase						:     bool = True       ,    lowercase						:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,    lowercase						:     int = 1       ,    **lowercase						:     List[str]       ,    ):



           '''simple docstring'''

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







       @torch.no_grad()
       def      A				(     self						:     List[str]       ,    lowercase						:     Union[str, List[str]]       ,    lowercase						:     int = 512       ,    lowercase						:     int = 512       ,    lowercase						:     int = 50       ,    lowercase						:     float = 7.5       ,    lowercase						:     Optional[Union[str, List[str]]] = None       ,    lowercase						:     Optional[int] = 1       ,    lowercase						:     float = 0.0       ,    lowercase						:     Optional[torch.Generator] = None       ,    lowercase						:     Optional[torch.FloatTensor] = None       ,    lowercase						:     Optional[str] = "pil"       ,    lowercase						:     bool = True       ,    lowercase						:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,    lowercase						:     int = 1       ,    ):



           '''simple docstring'''

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










'''simple docstring'''







import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase


@require_tokenizers
class       _a  (     unittest.TestCase			):
       __a    :					Optional[int]           = EsmTokenizer
       def    A				(     self						:     Tuple    ):



           '''simple docstring'''

           super().setUp()
           UpperCAmelCase      	=		tempfile.mkdtemp()
           # fmt: off
           UpperCAmelCase      	=		["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
           # fmt: on
           UpperCAmelCase      	=		os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['''vocab_file''']    )
           with open(self.vocab_file       ,    '''w'''       ,    encoding='''utf-8'''    ) as vocab_writer:
               vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]    )    )
       def    A				(     self						:     Union[str, Any]       ,    **lowercase						:     Tuple    ):



           '''simple docstring'''

           return [self.get_tokenizer(**lowercase    )]
       def    A				(     self						:     Optional[Any]       ,    **lowercase						:     str    ):



           '''simple docstring'''

           return self.tokenizer_class.from_pretrained(self.tmpdirname       ,    **lowercase    )
       def    A				(     self						:     Optional[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class(self.vocab_file    )

           UpperCAmelCase      	=		tokenizer.tokenize('''LAGVS'''    )
           self.assertListEqual(lowercase       ,    ['''L''', '''A''', '''G''', '''V''', '''S''']    )
           self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase    )       ,    [4, 5, 6, 7, 8]    )
       def    A				(     self						:     str    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class(self.vocab_file    )

           UpperCAmelCase      	=		'''LAGVS'''
           self.assertListEqual(tokenizer.encode(lowercase    )       ,    [0, 4, 5, 6, 7, 8, 2]    )
       def    A				(     self						:     Optional[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class(self.vocab_file    )

           UpperCAmelCase      	=		['''LAGVS''', '''WCB''']
           UpperCAmelCase      	=		tokenizer(lowercase       ,    padding=lowercase    )['''input_ids''']

           self.assertListEqual(lowercase       ,    [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]    )
       def    A				(     self						:     Union[str, Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class(self.vocab_file    )

           UpperCAmelCase      	=		['''LAGVS''', '''WCB''']
           UpperCAmelCase      	=		tokenizer(lowercase       ,    padding=lowercase    )['''input_ids''']

           self.assertListEqual(lowercase       ,    [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]    )







       def    A				(     self						:     Any    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.get_tokenizers(fast=lowercase    )
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"    ):
                   UpperCAmelCase      	=		'''<unk>'''
                   UpperCAmelCase      	=		'''<mask>'''

                   UpperCAmelCase      	=		tokenizer.tokenize(lowercase    )
                   UpperCAmelCase      	=		tokenizer.tokenize(lowercase    )

                   self.assertEqual(len(lowercase    )       ,    1    )
                   self.assertEqual(len(lowercase    )       ,    1    )
                   self.assertEqual(token_a[0]       ,    lowercase    )
                   self.assertEqual(token_a[0]       ,    lowercase    )




