






# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()

class        snake_case_				(							lowercase_   ,      lowercase_   ,      lowercase_   ,      unittest.TestCase   ):







   """simple docstring"""



   __lowerCAmelCase  =    StableDiffusionControlNetInpaintPipeline
   __lowerCAmelCase  =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   __lowerCAmelCase  =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   __lowerCAmelCase  =    frozenset({"""control_image"""}   )  # skip `image` and `mask` for now, only test for control_image
   __lowerCAmelCase  =    TEXT_TO_IMAGE_IMAGE_PARAMS

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str]						)	->		str:


       """simple docstring"""



       torch.manual_seed(0						)
       _lowercase     			=						UNetaDConditionModel(
           block_out_channels=(32, 64) ,							layers_per_block=2 ,							sample_size=32 ,							in_channels=9 ,							out_channels=4 ,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,							cross_attention_dim=32 ,							)
       torch.manual_seed(0						)
       _lowercase     			=						ControlNetModel(
           block_out_channels=(32, 64) ,							layers_per_block=2 ,							in_channels=4 ,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,							cross_attention_dim=32 ,							conditioning_embedding_out_channels=(16, 32) ,							)
       torch.manual_seed(0						)
       _lowercase     			=						DDIMScheduler(
           beta_start=0.0_00_85 ,							beta_end=0.0_12 ,							beta_schedule='scaled_linear' ,							clip_sample=snake_case__ ,							set_alpha_to_one=snake_case__ ,							)
       torch.manual_seed(0						)
       _lowercase     			=						AutoencoderKL(
           block_out_channels=[32, 64] ,							in_channels=3 ,							out_channels=3 ,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,							latent_channels=4 ,							)
       torch.manual_seed(0						)
       _lowercase     			=						CLIPTextConfig(
           bos_token_id=0 ,							eos_token_id=2 ,							hidden_size=32 ,							intermediate_size=37 ,							layer_norm_eps=1E-05 ,							num_attention_heads=4 ,							num_hidden_layers=5 ,							pad_token_id=1 ,							vocab_size=1_000 ,							)
       _lowercase     			=						CLIPTextModel(snake_case__						)
       _lowercase     			=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

       _lowercase     			=						{
           'unet': unet,
           'controlnet': controlnet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }
       return components

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						Optional[Any] ,							snake_case__:						Any=0						)	->		Optional[Any]:


       """simple docstring"""



       if str(snake_case__						).startswith('mps'						):
           _lowercase     			=						torch.manual_seed(snake_case__						)
       else:
           _lowercase     			=						torch.Generator(device=snake_case__						).manual_seed(snake_case__						)

       _lowercase     			=						2
       _lowercase     			=						randn_tensor(
           (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,							generator=snake_case__ ,							device=torch.device(snake_case__						) ,							)
       _lowercase     			=						floats_tensor((1, 3, 32, 32) ,							rng=random.Random(snake_case__						)						).to(snake_case__						)
       _lowercase     			=						init_image.cpu().permute(0 ,							2 ,							3 ,							1						)[0]

       _lowercase     			=						Image.fromarray(np.uinta(snake_case__						)						).convert('RGB'						).resize((64, 64)						)
       _lowercase     			=						Image.fromarray(np.uinta(init_image + 4						)						).convert('RGB'						).resize((64, 64)						)

       _lowercase     			=						{
           'prompt': 'A painting of a squirrel eating a burger',
           'generator': generator,
           'num_inference_steps': 2,
           'guidance_scale': 6.0,
           'output_type': 'numpy',
           'image': image,
           'mask_image': mask_image,
           'control_image': control_image,
       }

       return inputs

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str]						)	->		Any:


       """simple docstring"""



       return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3						)

   @unittest.skipIf(
       torch_device != 'cuda' or not is_xformers_available() ,							reason='XFormers attention is only available with CUDA and `xformers` installed' ,							)
   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any]						)	->		int:


       """simple docstring"""



       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Dict						)	->		Tuple:


       """simple docstring"""



       self._test_inference_batch_single_identical(expected_max_diff=2E-3						)






class        snake_case_				(							lowercase_   ):







   """simple docstring"""



   __lowerCAmelCase  =    StableDiffusionControlNetInpaintPipeline
   __lowerCAmelCase  =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   __lowerCAmelCase  =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   __lowerCAmelCase  =    frozenset([]   )

   def       SCREAMING_SNAKE_CASE__					(     self:						Tuple						)	->		List[Any]:


       """simple docstring"""



       torch.manual_seed(0						)
       _lowercase     			=						UNetaDConditionModel(
           block_out_channels=(32, 64) ,							layers_per_block=2 ,							sample_size=32 ,							in_channels=4 ,							out_channels=4 ,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,							cross_attention_dim=32 ,							)
       torch.manual_seed(0						)
       _lowercase     			=						ControlNetModel(
           block_out_channels=(32, 64) ,							layers_per_block=2 ,							in_channels=4 ,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,							cross_attention_dim=32 ,							conditioning_embedding_out_channels=(16, 32) ,							)
       torch.manual_seed(0						)
       _lowercase     			=						DDIMScheduler(
           beta_start=0.0_00_85 ,							beta_end=0.0_12 ,							beta_schedule='scaled_linear' ,							clip_sample=snake_case__ ,							set_alpha_to_one=snake_case__ ,							)
       torch.manual_seed(0						)
       _lowercase     			=						AutoencoderKL(
           block_out_channels=[32, 64] ,							in_channels=3 ,							out_channels=3 ,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,							latent_channels=4 ,							)
       torch.manual_seed(0						)
       _lowercase     			=						CLIPTextConfig(
           bos_token_id=0 ,							eos_token_id=2 ,							hidden_size=32 ,							intermediate_size=37 ,							layer_norm_eps=1E-05 ,							num_attention_heads=4 ,							num_hidden_layers=5 ,							pad_token_id=1 ,							vocab_size=1_000 ,							)
       _lowercase     			=						CLIPTextModel(snake_case__						)
       _lowercase     			=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

       _lowercase     			=						{
           'unet': unet,
           'controlnet': controlnet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }
       return components






class        snake_case_				(							lowercase_   ,      lowercase_   ,      unittest.TestCase   ):







   """simple docstring"""



   __lowerCAmelCase  =    StableDiffusionControlNetInpaintPipeline
   __lowerCAmelCase  =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   __lowerCAmelCase  =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any]						)	->		Union[str, Any]:


       """simple docstring"""



       torch.manual_seed(0						)
       _lowercase     			=						UNetaDConditionModel(
           block_out_channels=(32, 64) ,							layers_per_block=2 ,							sample_size=32 ,							in_channels=9 ,							out_channels=4 ,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,							cross_attention_dim=32 ,							)
       torch.manual_seed(0						)

       def init_weights(snake_case__:						Optional[Any]						):
           if isinstance(snake_case__ ,							torch.nn.Convad						):
               torch.nn.init.normal(m.weight						)
               m.bias.data.fill_(1.0						)

       _lowercase     			=						ControlNetModel(
           block_out_channels=(32, 64) ,							layers_per_block=2 ,							in_channels=4 ,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,							cross_attention_dim=32 ,							conditioning_embedding_out_channels=(16, 32) ,							)
       controlneta.controlnet_down_blocks.apply(snake_case__						)

       torch.manual_seed(0						)
       _lowercase     			=						ControlNetModel(
           block_out_channels=(32, 64) ,							layers_per_block=2 ,							in_channels=4 ,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,							cross_attention_dim=32 ,							conditioning_embedding_out_channels=(16, 32) ,							)
       controlneta.controlnet_down_blocks.apply(snake_case__						)

       torch.manual_seed(0						)
       _lowercase     			=						DDIMScheduler(
           beta_start=0.0_00_85 ,							beta_end=0.0_12 ,							beta_schedule='scaled_linear' ,							clip_sample=snake_case__ ,							set_alpha_to_one=snake_case__ ,							)
       torch.manual_seed(0						)
       _lowercase     			=						AutoencoderKL(
           block_out_channels=[32, 64] ,							in_channels=3 ,							out_channels=3 ,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,							latent_channels=4 ,							)
       torch.manual_seed(0						)
       _lowercase     			=						CLIPTextConfig(
           bos_token_id=0 ,							eos_token_id=2 ,							hidden_size=32 ,							intermediate_size=37 ,							layer_norm_eps=1E-05 ,							num_attention_heads=4 ,							num_hidden_layers=5 ,							pad_token_id=1 ,							vocab_size=1_000 ,							)
       _lowercase     			=						CLIPTextModel(snake_case__						)
       _lowercase     			=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

       _lowercase     			=						MultiControlNetModel([controlneta, controlneta]						)

       _lowercase     			=						{
           'unet': unet,
           'controlnet': controlnet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }
       return components

   def       SCREAMING_SNAKE_CASE__					(     self:						str ,							snake_case__:						int ,							snake_case__:						Any=0						)	->		Tuple:


       """simple docstring"""



       if str(snake_case__						).startswith('mps'						):
           _lowercase     			=						torch.manual_seed(snake_case__						)
       else:
           _lowercase     			=						torch.Generator(device=snake_case__						).manual_seed(snake_case__						)

       _lowercase     			=						2

       _lowercase     			=						[
           randn_tensor(
               (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,							generator=snake_case__ ,							device=torch.device(snake_case__						) ,							),
           randn_tensor(
               (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,							generator=snake_case__ ,							device=torch.device(snake_case__						) ,							),
       ]
       _lowercase     			=						floats_tensor((1, 3, 32, 32) ,							rng=random.Random(snake_case__						)						).to(snake_case__						)
       _lowercase     			=						init_image.cpu().permute(0 ,							2 ,							3 ,							1						)[0]

       _lowercase     			=						Image.fromarray(np.uinta(snake_case__						)						).convert('RGB'						).resize((64, 64)						)
       _lowercase     			=						Image.fromarray(np.uinta(init_image + 4						)						).convert('RGB'						).resize((64, 64)						)

       _lowercase     			=						{
           'prompt': 'A painting of a squirrel eating a burger',
           'generator': generator,
           'num_inference_steps': 2,
           'guidance_scale': 6.0,
           'output_type': 'numpy',
           'image': image,
           'mask_image': mask_image,
           'control_image': control_image,
       }

       return inputs

   def       SCREAMING_SNAKE_CASE__					(     self:						Any						)	->		Tuple:


       """simple docstring"""



       _lowercase     			=						self.get_dummy_components()
       _lowercase     			=						self.pipeline_class(**snake_case__						)
       pipe.to(snake_case__						)

       _lowercase     			=						10.0
       _lowercase     			=						4

       _lowercase     			=						self.get_dummy_inputs(snake_case__						)
       _lowercase     			=						steps
       _lowercase     			=						scale
       _lowercase     			=						pipe(**snake_case__						)[0]

       _lowercase     			=						self.get_dummy_inputs(snake_case__						)
       _lowercase     			=						steps
       _lowercase     			=						scale
       _lowercase     			=						pipe(**snake_case__ ,							control_guidance_start=0.1 ,							control_guidance_end=0.2						)[0]

       _lowercase     			=						self.get_dummy_inputs(snake_case__						)
       _lowercase     			=						steps
       _lowercase     			=						scale
       _lowercase     			=						pipe(**snake_case__ ,							control_guidance_start=[0.1, 0.3] ,							control_guidance_end=[0.2, 0.7]						)[0]

       _lowercase     			=						self.get_dummy_inputs(snake_case__						)
       _lowercase     			=						steps
       _lowercase     			=						scale
       _lowercase     			=						pipe(**snake_case__ ,							control_guidance_start=0.4 ,							control_guidance_end=[0.5, 0.8]						)[0]

       # make sure that all outputs are different
       assert np.sum(np.abs(output_a - output_a						)						) > 1E-3
       assert np.sum(np.abs(output_a - output_a						)						) > 1E-3
       assert np.sum(np.abs(output_a - output_a						)						) > 1E-3

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str]						)	->		Optional[int]:


       """simple docstring"""



       return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3						)

   @unittest.skipIf(
       torch_device != 'cuda' or not is_xformers_available() ,							reason='XFormers attention is only available with CUDA and `xformers` installed' ,							)
   def       SCREAMING_SNAKE_CASE__					(     self:						str						)	->		List[Any]:


       """simple docstring"""



       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3						)

   def       SCREAMING_SNAKE_CASE__					(     self:						List[Any]						)	->		Tuple:


       """simple docstring"""



       self._test_inference_batch_single_identical(expected_max_diff=2E-3						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		Dict:


       """simple docstring"""



       _lowercase     			=						self.get_dummy_components()
       _lowercase     			=						self.pipeline_class(**snake_case__						)
       pipe.to(snake_case__						)
       pipe.set_progress_bar_config(disable=snake_case__						)
       with tempfile.TemporaryDirectory() as tmpdir:
           try:
               # save_pretrained is not implemented for Multi-ControlNet
               pipe.save_pretrained(snake_case__						)
           except NotImplementedError:
               pass






@slow
@require_torch_gpu
class        snake_case_				(							unittest.TestCase   ):







   """simple docstring"""



   def       SCREAMING_SNAKE_CASE__					(     self:						Any						)	->		List[str]:


       """simple docstring"""



       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str]						)	->		Any:


       """simple docstring"""



       _lowercase     			=						ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'						)

       _lowercase     			=						StableDiffusionControlNetInpaintPipeline.from_pretrained(
           'runwayml/stable-diffusion-inpainting' ,							safety_checker=snake_case__ ,							controlnet=snake_case__						)
       pipe.enable_model_cpu_offload()
       pipe.set_progress_bar_config(disable=snake_case__						)

       _lowercase     			=						torch.Generator(device='cpu'						).manual_seed(0						)
       _lowercase     			=						load_image(
           'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'						).resize((512, 512)						)

       _lowercase     			=						load_image(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
           '/stable_diffusion_inpaint/input_bench_mask.png'						).resize((512, 512)						)

       _lowercase     			=						'pitch black hole'

       _lowercase     			=						load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'						).resize((512, 512)						)

       _lowercase     			=						pipe(
           snake_case__ ,							image=snake_case__ ,							mask_image=snake_case__ ,							control_image=snake_case__ ,							generator=snake_case__ ,							output_type='np' ,							num_inference_steps=3 ,							)

       _lowercase     			=						output.images[0]

       assert image.shape == (512, 512, 3)

       _lowercase     			=						load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy'						)

       assert np.abs(expected_image - image						).max() < 9E-2

   def       SCREAMING_SNAKE_CASE__					(     self:						Tuple						)	->		Optional[int]:


       """simple docstring"""



       _lowercase     			=						ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'						)

       _lowercase     			=						StableDiffusionControlNetInpaintPipeline.from_pretrained(
           'runwayml/stable-diffusion-v1-5' ,							safety_checker=snake_case__ ,							controlnet=snake_case__						)
       _lowercase     			=						DDIMScheduler.from_config(pipe.scheduler.config						)
       pipe.enable_model_cpu_offload()
       pipe.set_progress_bar_config(disable=snake_case__						)

       _lowercase     			=						torch.Generator(device='cpu'						).manual_seed(33						)

       _lowercase     			=						load_image(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'						)
       _lowercase     			=						init_image.resize((512, 512)						)

       _lowercase     			=						load_image(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'						)
       _lowercase     			=						mask_image.resize((512, 512)						)

       _lowercase     			=						'a handsome man with ray-ban sunglasses'

       def make_inpaint_condition(snake_case__:						Dict ,							snake_case__:						Optional[Any]						):
           _lowercase     			=						np.array(image.convert('RGB'						)						).astype(np.floataa						) / 2_55.0
           _lowercase     			=						np.array(image_mask.convert('L'						)						).astype(np.floataa						) / 2_55.0

           assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
           _lowercase     			=						-1.0  # set as masked pixel
           _lowercase     			=						np.expand_dims(snake_case__ ,							0						).transpose(0 ,							3 ,							1 ,							2						)
           _lowercase     			=						torch.from_numpy(snake_case__						)
           return image

       _lowercase     			=						make_inpaint_condition(snake_case__ ,							snake_case__						)

       _lowercase     			=						pipe(
           snake_case__ ,							image=snake_case__ ,							mask_image=snake_case__ ,							control_image=snake_case__ ,							guidance_scale=9.0 ,							eta=1.0 ,							generator=snake_case__ ,							num_inference_steps=20 ,							output_type='np' ,							)
       _lowercase     			=						output.images[0]

       assert image.shape == (512, 512, 3)

       _lowercase     			=						load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy'						)

       assert np.abs(expected_image - image						).max() < 9E-2








import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


lowerCamelCase					    =	threading.Lock()
lowerCamelCase					    =	None

lowerCamelCase					    =	{
    '''debug''': logging.DEBUG,
    '''info''': logging.INFO,
    '''warning''': logging.WARNING,
    '''error''': logging.ERROR,
    '''critical''': logging.CRITICAL,
}

lowerCamelCase					    =	logging.WARNING

lowerCamelCase					    =	True
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _lowercase     			=						os.getenv('DIFFUSERS_VERBOSITY',   _lowercase   )
    if env_level_str:
        if env_level_str in log_levels:
            return log_levels[env_level_str]
        else:
            logging.getLogger().warning(
                f'''Unknown option DIFFUSERS_VERBOSITY={env_level_str}, '''
                f'''has to be one of: { ", ".join(log_levels.keys()   ) }'''   )
    return _default_log_level
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    return __name__.split('.'   )[0]
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    return logging.getLogger(_get_library_name()   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    global _default_handler

    with _lock:
        if _default_handler:
            # This library has already configured the library root logger.
            return
        _lowercase     			=						logging.StreamHandler()  # Set sys.stderr as stream.
        _lowercase     			=						sys.stderr.flush

        # Apply our default configuration to the library root logger.
        _lowercase     			=						_get_library_root_logger()
        library_root_logger.addHandler(_default_handler   )
        library_root_logger.setLevel(_get_default_logging_level()   )
        _lowercase     			=						False
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    global _default_handler

    with _lock:
        if not _default_handler:
            return

        _lowercase     			=						_get_library_root_logger()
        library_root_logger.removeHandler(_default_handler   )
        library_root_logger.setLevel(logging.NOTSET   )
        _lowercase     			=						None
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    return log_levels
def 							UpperCAmelCase   ( _lowercase = None   ):


    '''simple docstring'''



    if name is None:
        _lowercase     			=						_get_library_name()

    _configure_library_root_logger()
    return logging.getLogger(_lowercase   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _configure_library_root_logger()
    return _get_library_root_logger().getEffectiveLevel()
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    _configure_library_root_logger()
    _get_library_root_logger().setLevel(_lowercase   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    return set_verbosity(_lowercase   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    return set_verbosity(_lowercase   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    return set_verbosity(_lowercase   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    return set_verbosity(_lowercase   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _configure_library_root_logger()

    assert _default_handler is not None
    _get_library_root_logger().removeHandler(_default_handler   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _configure_library_root_logger()

    assert _default_handler is not None
    _get_library_root_logger().addHandler(_default_handler   )
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    _configure_library_root_logger()

    assert handler is not None
    _get_library_root_logger().addHandler(_lowercase   )
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    _configure_library_root_logger()

    assert handler is not None and handler not in _get_library_root_logger().handlers
    _get_library_root_logger().removeHandler(_lowercase   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _configure_library_root_logger()
    _lowercase     			=						False
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _configure_library_root_logger()
    _lowercase     			=						True
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _lowercase     			=						_get_library_root_logger().handlers

    for handler in handlers:
        _lowercase     			=						logging.Formatter('[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s'   )
        handler.setFormatter(_lowercase   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _lowercase     			=						_get_library_root_logger().handlers

    for handler in handlers:
        handler.setFormatter(_lowercase   )
def 							UpperCAmelCase   ( self,   *_lowercase,   **_lowercase   ):


    '''simple docstring'''



    _lowercase     			=						os.getenv('DIFFUSERS_NO_ADVISORY_WARNINGS',   _lowercase   )
    if no_advisory_warnings:
        return
    self.warning(*_lowercase,   **_lowercase   )


lowerCamelCase					    =	warning_advice

class        snake_case_				:







   """simple docstring"""



   def __init__(     self:						int ,							*snake_case__:						str ,							**snake_case__:						Any						)	->		Any:  # pylint: disable=unused-argument


       """simple docstring"""



       _lowercase     			=						args[0] if args else None

   def __iter__(     self:						Union[str, Any]						)	->		str:


       """simple docstring"""



       return iter(self._iterator						)

   def __getattr__(     self:						int ,							snake_case__:						Any						)	->		List[Any]:


       """simple docstring"""



       def empty_fn(*snake_case__:						Optional[int] ,							**snake_case__:						Union[str, Any]						):  # pylint: disable=unused-argument
           return

       return empty_fn

   def __enter__(     self:						List[Any]						)	->		Tuple:


       """simple docstring"""



       return self

   def __exit__(     self:						List[str] ,							snake_case__:						Optional[Any] ,							snake_case__:						Dict ,							snake_case__:						List[str]						)	->		Optional[Any]:


       """simple docstring"""



       return






class        snake_case_				:







   """simple docstring"""



   def __call__(     self:						List[str] ,							*snake_case__:						Tuple ,							**snake_case__:						Union[str, Any]						)	->		List[str]:


       """simple docstring"""



       if _tqdm_active:
           return tqdm_lib.tqdm(*snake_case__ ,							**snake_case__						)
       else:
           return EmptyTqdm(*snake_case__ ,							**snake_case__						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Tuple ,							*snake_case__:						Union[str, Any] ,							**snake_case__:						str						)	->		Optional[int]:


       """simple docstring"""



       _lowercase     			=						None
       if _tqdm_active:
           return tqdm_lib.tqdm.set_lock(*snake_case__ ,							**snake_case__						)

   def       SCREAMING_SNAKE_CASE__					(     self:						int						)	->		int:


       """simple docstring"""



       if _tqdm_active:
           return tqdm_lib.tqdm.get_lock()






lowerCamelCase					    =	_tqdm_cls()
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    global _tqdm_active
    return bool(_tqdm_active   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    global _tqdm_active
    _lowercase     			=						True
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    global _tqdm_active
    _lowercase     			=						False
