






# 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 				_UpperCAmelCase (	A__		,A__		,A__		,unittest.TestCase     ):

				"""simple docstring"""




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




				def     lowercase__       (					self						:   Optional[int]  ):

								'''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=0.00085,  beta_end=0.012,  beta_schedule='''scaled_linear''',  clip_sample=lowerCamelCase,  set_alpha_to_one=lowerCamelCase,  )
								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(lowerCamelCase  )
								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     lowercase__       (					self						:   int,  lowerCamelCase						:   List[str],  lowerCamelCase						:   Tuple=0  ):

								'''simple docstring'''
								if str(lowerCamelCase  ).startswith('''mps'''  ):
												lowercase__       			=      torch.manual_seed(lowerCamelCase  )
								else:
												lowercase__       			=      torch.Generator(device=lowerCamelCase  ).manual_seed(lowerCamelCase  )

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

								lowercase__       			=      Image.fromarray(np.uinta(lowerCamelCase  )  ).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     lowercase__       (					self						:   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     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )




				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								self._test_inference_batch_single_identical(expected_max_diff=2E-3  )



class 				_UpperCAmelCase (	A__     ):

				"""simple docstring"""




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




				def     lowercase__       (					self						:   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=0.00085,  beta_end=0.012,  beta_schedule='''scaled_linear''',  clip_sample=lowerCamelCase,  set_alpha_to_one=lowerCamelCase,  )
								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(lowerCamelCase  )
								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 				_UpperCAmelCase (	A__		,A__		,unittest.TestCase     ):

				"""simple docstring"""




				lowercase__    							=	StableDiffusionControlNetInpaintPipeline
				lowercase__    							=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				lowercase__    							=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS




				def     lowercase__       (					self						:   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(lowerCamelCase						:   Optional[Any]  ):
												if isinstance(lowerCamelCase,  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(lowerCamelCase  )

								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(lowerCamelCase  )

								torch.manual_seed(0  )
								lowercase__       			=      DDIMScheduler(
								    beta_start=0.00085,  beta_end=0.012,  beta_schedule='''scaled_linear''',  clip_sample=lowerCamelCase,  set_alpha_to_one=lowerCamelCase,  )
								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(lowerCamelCase  )
								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     lowercase__       (					self						:   Optional[int],  lowerCamelCase						:   List[str],  lowerCamelCase						:   Dict=0  ):

								'''simple docstring'''
								if str(lowerCamelCase  ).startswith('''mps'''  ):
												lowercase__       			=      torch.manual_seed(lowerCamelCase  )
								else:
												lowercase__       			=      torch.Generator(device=lowerCamelCase  ).manual_seed(lowerCamelCase  )

								lowercase__       			=      2

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

								lowercase__       			=      Image.fromarray(np.uinta(lowerCamelCase  )  ).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     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								lowercase__       			=      self.get_dummy_components()
								lowercase__       			=      self.pipeline_class(**lowerCamelCase  )
								pipe.to(lowerCamelCase  )

								lowercase__       			=      10.0
								lowercase__       			=      4

								lowercase__       			=      self.get_dummy_inputs(lowerCamelCase  )
								lowercase__       			=      steps
								lowercase__       			=      scale
								lowercase__       			=      pipe(**lowerCamelCase  )[0]

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

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

								lowercase__       			=      self.get_dummy_inputs(lowerCamelCase  )
								lowercase__       			=      steps
								lowercase__       			=      scale
								lowercase__       			=      pipe(**lowerCamelCase,  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     lowercase__       (					self						:   List[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     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )




				def     lowercase__       (					self						:   Optional[int]  ):

								'''simple docstring'''
								self._test_inference_batch_single_identical(expected_max_diff=2E-3  )




				def     lowercase__       (					self						:   Optional[Any]  ):

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



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

				"""simple docstring"""




				def     lowercase__       (					self						:   int  ):

								'''simple docstring'''
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




				def     lowercase__       (					self						:   Any  ):

								'''simple docstring'''
								lowercase__       			=      ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''  )

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

								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(
								    lowerCamelCase,  image=lowerCamelCase,  mask_image=lowerCamelCase,  control_image=lowerCamelCase,  generator=lowerCamelCase,  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     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								lowercase__       			=      ControlNetModel.from_pretrained('''lllyasviel/control_v11p_sd15_inpaint'''  )

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

								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(lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Tuple  ):
												lowercase__       			=      np.array(image.convert('''RGB'''  )  ).astype(np.floataa  ) / 255.0
												lowercase__       			=      np.array(image_mask.convert('''L'''  )  ).astype(np.floataa  ) / 255.0

												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(lowerCamelCase,  0  ).transpose(0,  3,  1,  2  )
												lowercase__       			=      torch.from_numpy(lowerCamelCase  )
												return image

								lowercase__       			=      make_inpaint_condition(lowerCamelCase,  lowerCamelCase  )

								lowercase__       			=      pipe(
								    lowerCamelCase,  image=lowerCamelCase,  mask_image=lowerCamelCase,  control_image=lowerCamelCase,  guidance_scale=9.0,  eta=1.0,  generator=lowerCamelCase,  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












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A__     :      int             =			{
    'configuration_sam': [
        'SAM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SamConfig',
        'SamMaskDecoderConfig',
        'SamPromptEncoderConfig',
        'SamVisionConfig',
    ],
    'processing_sam': ['SamProcessor'],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      List[Any]             =			[
							    'SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SamModel',
							    'SamPreTrainedModel',
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      int             =			[
							    'TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFSamModel',
							    'TFSamPreTrainedModel',
							]
try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      Tuple             =			['SamImageProcessor']


if TYPE_CHECKING:
							from .configuration_sam import (
							    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SamConfig,
							    SamMaskDecoderConfig,
							    SamPromptEncoderConfig,
							    SamVisionConfig,
							)
							from .processing_sam import SamProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_sam import SamImageProcessor

else:
							import sys

							A__     :      Dict             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




