
"""simple docstring"""

import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)






def 							A		(       snake_case   :List[Any]       ,      snake_case   :Optional[Any]       ,      snake_case   :List[str]	)   ->			Any:
	if isinstance(lowerCAmelCase__       ,      torch.Tensor	):
		return image
	elif isinstance(lowerCAmelCase__       ,      PIL.Image.Image	):
		__UpperCamelCase       						= [image]

	if isinstance(image[0]       ,      PIL.Image.Image	):
		__UpperCamelCase       						= [np.array(i.resize((w, h)       ,      resample=PIL_INTERPOLATION['lanczos']	)	)[None, :] for i in image]
		__UpperCamelCase       						= np.concatenate(lowerCAmelCase__       ,      axis=0	)
		__UpperCamelCase       						= np.array(lowerCAmelCase__	).astype(np.floataa	) / 2_5_5.0
		__UpperCamelCase       						= image.transpose(0       ,      3       ,      1       ,      2	)
		__UpperCamelCase       						= 2.0 * image - 1.0
		__UpperCamelCase       						= torch.from_numpy(lowerCAmelCase__	)
	elif isinstance(image[0]       ,      torch.Tensor	):
		__UpperCamelCase       						= torch.cat(lowerCAmelCase__       ,      dim=0	)
	return image






def 							A		(       snake_case   :Union[str, Any]       ,      snake_case   :str       ,      snake_case   :Tuple       ,      snake_case   :Optional[int]=0.9_995	)   ->			int:
	if not isinstance(lowerCAmelCase__       ,      np.ndarray	):
		__UpperCamelCase       						= True
		__UpperCamelCase       						= va.device
		__UpperCamelCase       						= va.cpu().numpy()
		__UpperCamelCase       						= va.cpu().numpy()

	__UpperCamelCase       						= np.sum(va * va / (np.linalg.norm(lowerCAmelCase__	) * np.linalg.norm(lowerCAmelCase__	))	)
	if np.abs(lowerCAmelCase__	) > DOT_THRESHOLD:
		__UpperCamelCase       						= (1 - t) * va + t * va
	else:
		__UpperCamelCase       						= np.arccos(lowerCAmelCase__	)
		__UpperCamelCase       						= np.sin(lowerCAmelCase__	)
		__UpperCamelCase       						= theta_a * t
		__UpperCamelCase       						= np.sin(lowerCAmelCase__	)
		__UpperCamelCase       						= np.sin(theta_a - theta_t	) / sin_theta_a
		__UpperCamelCase       						= sin_theta_t / sin_theta_a
		__UpperCamelCase       						= sa * va + sa * va

	if inputs_are_torch:
		__UpperCamelCase       						= torch.from_numpy(lowerCAmelCase__	).to(lowerCAmelCase__	)

	return va






def 							A		(       snake_case   :Optional[Any]       ,      snake_case   :List[Any]	)   ->			int:
	__UpperCamelCase       						= F.normalize(lowerCAmelCase__       ,      dim=-1	)
	__UpperCamelCase       						= F.normalize(lowerCAmelCase__       ,      dim=-1	)
	return (x - y).norm(dim=-1	).div(2	).arcsin().pow(2	).mul(2	)






def 							A		(       snake_case   :Optional[Any]       ,      snake_case   :Any	)   ->			List[str]:
	for param in model.parameters():
		__UpperCamelCase       						= value





class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





					def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  ):

						'''simple docstring'''




						super().__init__()
						self.register_modules(
						    vae=_a			,  text_encoder=_a			,  clip_model=_a			,  tokenizer=_a			,  unet=_a			,  scheduler=_a			,  feature_extractor=_a			,  coca_model=_a			,  coca_tokenizer=_a			,  coca_transform=_a			,  )
						__UpperCamelCase       						= (
						    feature_extractor.size
						    if isinstance(feature_extractor.size			,  _a   )
						    else feature_extractor.size['shortest_edge']
						)
						__UpperCamelCase       						= transforms.Normalize(mean=feature_extractor.image_mean			,  std=feature_extractor.image_std   )
						set_requires_grad(self.text_encoder			,  _a   )
						set_requires_grad(self.clip_model			,  _a   )





					def 		UpperCAmelCase      (			self			,  __UpperCAmelCase = "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(_a   )





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						self.enable_attention_slicing(_a   )





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						set_requires_grad(self.vae			,  _a   )





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						set_requires_grad(self.vae			,  _a   )





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						set_requires_grad(self.unet			,  _a   )





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						set_requires_grad(self.unet			,  _a   )





					def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

						'''simple docstring'''




						__UpperCamelCase       						= min(int(num_inference_steps * strength   )			,  _a   )

						__UpperCamelCase       						= max(num_inference_steps - init_timestep			,  0   )
						__UpperCamelCase       						= self.scheduler.timesteps[t_start:]

						return timesteps, num_inference_steps - t_start





					def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase=None   ):

						'''simple docstring'''




						if not isinstance(_a			,  torch.Tensor   ):
							raise ValueError(F'`image` has to be of type `torch.Tensor` but is {type(_a   )}'   )

						__UpperCamelCase       						= image.to(device=_a			,  dtype=_a   )

						if isinstance(_a			,  _a   ):
							__UpperCamelCase       						= [
							    self.vae.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(_a   )
							]
							__UpperCamelCase       						= torch.cat(_a			,  dim=0   )
						else:
							__UpperCamelCase       						= self.vae.encode(_a   ).latent_dist.sample(_a   )

						# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
						__UpperCamelCase       						= 0.1_8_2_1_5 * init_latents
						__UpperCamelCase       						= init_latents.repeat_interleave(_a			,  dim=0   )

						__UpperCamelCase       						= randn_tensor(init_latents.shape			,  generator=_a			,  device=_a			,  dtype=_a   )

						# get latents
						__UpperCamelCase       						= self.scheduler.add_noise(_a			,  _a			,  _a   )
						__UpperCamelCase       						= init_latents

						return latents





					def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

						'''simple docstring'''




						__UpperCamelCase       						= self.coca_transform(_a   ).unsqueeze(0   )
						with torch.no_grad(), torch.cuda.amp.autocast():
							__UpperCamelCase       						= self.coca_model.generate(transformed_image.to(device=self.device			,  dtype=self.coca_model.dtype   )   )
						__UpperCamelCase       						= self.coca_tokenizer.decode(generated[0].cpu().numpy()   )
						return generated.split('<end_of_text>'   )[0].replace('<start_of_text>'			,  ''   ).rstrip(' .,'   )





					def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

						'''simple docstring'''




						__UpperCamelCase       						= self.feature_extractor.preprocess(_a   )
						__UpperCamelCase       						= torch.from_numpy(clip_image_input['pixel_values'][0]   ).unsqueeze(0   ).to(self.device   ).half()
						__UpperCamelCase       						= self.clip_model.get_image_features(_a   )
						__UpperCamelCase       						= image_embeddings_clip / image_embeddings_clip.norm(p=2			,  dim=-1			,  keepdim=_a   )
						__UpperCamelCase       						= image_embeddings_clip.repeat_interleave(_a			,  dim=0   )
						return image_embeddings_clip





					@torch.enable_grad()
					def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  ):

						'''simple docstring'''




						__UpperCamelCase       						= latents.detach().requires_grad_()

						__UpperCamelCase       						= self.scheduler.scale_model_input(_a			,  _a   )

						# predict the noise residual
						__UpperCamelCase       						= self.unet(_a			,  _a			,  encoder_hidden_states=_a   ).sample

						if isinstance(self.scheduler			,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)   ):
							__UpperCamelCase       						= self.scheduler.alphas_cumprod[timestep]
							__UpperCamelCase       						= 1 - alpha_prod_t
							# compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							__UpperCamelCase       						= (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

							__UpperCamelCase       						= torch.sqrt(_a   )
							__UpperCamelCase       						= pred_original_sample * (fac) + latents * (1 - fac)
						elif isinstance(self.scheduler			,  _a   ):
							__UpperCamelCase       						= self.scheduler.sigmas[index]
							__UpperCamelCase       						= latents - sigma * noise_pred
						else:
							raise ValueError(F'scheduler type {type(self.scheduler   )} not supported'   )

						# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
						__UpperCamelCase       						= 1 / 0.1_8_2_1_5 * sample
						__UpperCamelCase       						= self.vae.decode(_a   ).sample
						__UpperCamelCase       						= (image / 2 + 0.5).clamp(0			,  1   )

						__UpperCamelCase       						= transforms.Resize(self.feature_extractor_size   )(_a   )
						__UpperCamelCase       						= self.normalize(_a   ).to(latents.dtype   )

						__UpperCamelCase       						= self.clip_model.get_image_features(_a   )
						__UpperCamelCase       						= image_embeddings_clip / image_embeddings_clip.norm(p=2			,  dim=-1			,  keepdim=_a   )

						__UpperCamelCase       						= spherical_dist_loss(_a			,  _a   ).mean() * clip_guidance_scale

						__UpperCamelCase       						= -torch.autograd.grad(_a			,  _a   )[0]

						if isinstance(self.scheduler			,  _a   ):
							__UpperCamelCase       						= latents.detach() + grads * (sigma**2)
							__UpperCamelCase       						= noise_pred_original
						else:
							__UpperCamelCase       						= noise_pred_original - torch.sqrt(_a   ) * grads
						return noise_pred, latents







					@torch.no_grad()
					def __call__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase = None			,  __UpperCAmelCase = None			,  __UpperCAmelCase = 512			,  __UpperCAmelCase = 512			,  __UpperCAmelCase = 0.6			,  __UpperCAmelCase = 50			,  __UpperCAmelCase = 7.5			,  __UpperCAmelCase = 1			,  __UpperCAmelCase = 0.0			,  __UpperCAmelCase = 100			,  __UpperCAmelCase = None			,  __UpperCAmelCase = "pil"			,  __UpperCAmelCase = True			,  __UpperCAmelCase = 0.8			,  __UpperCAmelCase = 0.1			,  __UpperCAmelCase = 0.1			,  ):

						'''simple docstring'''




						if isinstance(_a			,  _a   ) and len(_a   ) != batch_size:
							raise ValueError(F'You have passed {batch_size} batch_size, but only {len(_a   )} generators.'   )

						if height % 8 != 0 or width % 8 != 0:
							raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.'   )

						if isinstance(_a			,  torch.Generator   ) and batch_size > 1:
							__UpperCamelCase       						= [generator] + [None] * (batch_size - 1)

						__UpperCamelCase       						= [
						    ('model', self.coca_model is None),
						    ('tokenizer', self.coca_tokenizer is None),
						    ('transform', self.coca_transform is None),
						]
						__UpperCamelCase       						= [x[0] for x in coca_is_none if x[1]]
						__UpperCamelCase       						= ', '.join(_a   )
						# generate prompts with coca model if prompt is None
						if content_prompt is None:
							if len(_a   ):
								raise ValueError(
								    F'Content prompt is None and CoCa [{coca_is_none_str}] is None.'
								    F'Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'   )
							__UpperCamelCase       						= self.get_image_description(_a   )
						if style_prompt is None:
							if len(_a   ):
								raise ValueError(
								    F'Style prompt is None and CoCa [{coca_is_none_str}] is None.'
								    F' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'   )
							__UpperCamelCase       						= self.get_image_description(_a   )

						# get prompt text embeddings for content and style
						__UpperCamelCase       						= self.tokenizer(
						    _a			,  padding='max_length'			,  max_length=self.tokenizer.model_max_length			,  truncation=_a			,  return_tensors='pt'			,  )
						__UpperCamelCase       						= self.text_encoder(content_text_input.input_ids.to(self.device   )   )[0]

						__UpperCamelCase       						= self.tokenizer(
						    _a			,  padding='max_length'			,  max_length=self.tokenizer.model_max_length			,  truncation=_a			,  return_tensors='pt'			,  )
						__UpperCamelCase       						= self.text_encoder(style_text_input.input_ids.to(self.device   )   )[0]

						__UpperCamelCase       						= slerp(_a			,  _a			,  _a   )

						# duplicate text embeddings for each generation per prompt
						__UpperCamelCase       						= text_embeddings.repeat_interleave(_a			,  dim=0   )

						# set timesteps
						__UpperCamelCase       						= 'offset' in set(inspect.signature(self.scheduler.set_timesteps   ).parameters.keys()   )
						__UpperCamelCase       						= {}
						if accepts_offset:
							__UpperCamelCase       						= 1

						self.scheduler.set_timesteps(_a			,  **_a   )
						# Some schedulers like PNDM have timesteps as arrays
						# It's more optimized to move all timesteps to correct device beforehand
						self.scheduler.timesteps.to(self.device   )

						__UpperCamelCase						,			__UpperCamelCase       						= self.get_timesteps(_a			,  _a			,  self.device   )
						__UpperCamelCase       						= timesteps[:1].repeat(_a   )

						# Preprocess image
						__UpperCamelCase       						= preprocess(_a			,  _a			,  _a   )
						__UpperCamelCase       						= self.prepare_latents(
						    _a			,  _a			,  _a			,  text_embeddings.dtype			,  self.device			,  _a   )

						__UpperCamelCase       						= preprocess(_a			,  _a			,  _a   )
						__UpperCamelCase       						= self.prepare_latents(
						    _a			,  _a			,  _a			,  text_embeddings.dtype			,  self.device			,  _a   )

						__UpperCamelCase       						= slerp(_a			,  _a			,  _a   )

						if clip_guidance_scale > 0:
							__UpperCamelCase       						= self.get_clip_image_embeddings(_a			,  _a   )
							__UpperCamelCase       						= self.get_clip_image_embeddings(_a			,  _a   )
							__UpperCamelCase       						= slerp(
							    _a			,  _a			,  _a   )

						# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
						# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
						# corresponds to doing no classifier free guidance.
						__UpperCamelCase       						= guidance_scale > 1.0
						# get unconditional embeddings for classifier free guidance
						if do_classifier_free_guidance:
							__UpperCamelCase       						= content_text_input.input_ids.shape[-1]
							__UpperCamelCase       						= self.tokenizer(['']			,  padding='max_length'			,  max_length=_a			,  return_tensors='pt'   )
							__UpperCamelCase       						= self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
							# duplicate unconditional embeddings for each generation per prompt
							__UpperCamelCase       						= uncond_embeddings.repeat_interleave(_a			,  dim=0   )

							# For classifier free guidance, we need to do two forward passes.
							# Here we concatenate the unconditional and text embeddings into a single batch
							# to avoid doing two forward passes
							__UpperCamelCase       						= torch.cat([uncond_embeddings, text_embeddings]   )

						# get the initial random noise unless the user supplied it

						# Unlike in other pipelines, latents need to be generated in the target device
						# for 1-to-1 results reproducibility with the CompVis implementation.
						# However this currently doesn't work in `mps`.
						__UpperCamelCase       						= (batch_size, self.unet.config.in_channels, height // 8, width // 8)
						__UpperCamelCase       						= text_embeddings.dtype
						if latents is None:
							if self.device.type == "mps":
								# randn does not work reproducibly on mps
								__UpperCamelCase       						= torch.randn(_a			,  generator=_a			,  device='cpu'			,  dtype=_a   ).to(
								    self.device   )
							else:
								__UpperCamelCase       						= torch.randn(_a			,  generator=_a			,  device=self.device			,  dtype=_a   )
						else:
							if latents.shape != latents_shape:
								raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'   )
							__UpperCamelCase       						= latents.to(self.device   )

						# scale the initial noise by the standard deviation required by the scheduler
						__UpperCamelCase       						= latents * self.scheduler.init_noise_sigma

						# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
						# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
						# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
						# and should be between [0, 1]
						__UpperCamelCase       						= 'eta' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
						__UpperCamelCase       						= {}
						if accepts_eta:
							__UpperCamelCase       						= eta

						# check if the scheduler accepts generator
						__UpperCamelCase       						= 'generator' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
						if accepts_generator:
							__UpperCamelCase       						= generator

						with self.progress_bar(total=_a   ):
							for i, t in enumerate(_a   ):
								# expand the latents if we are doing classifier free guidance
								__UpperCamelCase       						= torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
								__UpperCamelCase       						= self.scheduler.scale_model_input(_a			,  _a   )

								# predict the noise residual
								__UpperCamelCase       						= self.unet(_a			,  _a			,  encoder_hidden_states=_a   ).sample

								# perform classifier free guidance
								if do_classifier_free_guidance:
									__UpperCamelCase						,			__UpperCamelCase       						= noise_pred.chunk(2   )
									__UpperCamelCase       						= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

								# perform clip guidance
								if clip_guidance_scale > 0:
									__UpperCamelCase       						= (
									    text_embeddings.chunk(2   )[1] if do_classifier_free_guidance else text_embeddings
									)
									__UpperCamelCase						,			__UpperCamelCase       						= self.cond_fn(
									    _a			,  _a			,  _a			,  _a			,  _a			,  _a			,  _a			,  )

								# compute the previous noisy sample x_t -> x_t-1
								__UpperCamelCase       						= self.scheduler.step(_a			,  _a			,  _a			,  **_a   ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
						__UpperCamelCase       						= 1 / 0.1_8_2_1_5 * latents
						__UpperCamelCase       						= self.vae.decode(_a   ).sample

						__UpperCamelCase       						= (image / 2 + 0.5).clamp(0			,  1   )
						__UpperCamelCase       						= image.cpu().permute(0			,  2			,  3			,  1   ).numpy()

						if output_type == "pil":
							__UpperCamelCase       						= self.numpy_to_pil(_a   )

						if not return_dict:
							return (image, None)

						return StableDiffusionPipelineOutput(images=_a			,  nsfw_content_detected=_a   )





"""simple docstring"""

from typing import TYPE_CHECKING

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


UpperCamelCase			:       Dict									=				{
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       List[str]									=				["LxmertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       Optional[Any]									=				[
          "LxmertEncoder",
          "LxmertForPreTraining",
          "LxmertForQuestionAnswering",
          "LxmertModel",
          "LxmertPreTrainedModel",
          "LxmertVisualFeatureEncoder",
          "LxmertXLayer",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       List[Any]									=				[
          "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFLxmertForPreTraining",
          "TFLxmertMainLayer",
          "TFLxmertModel",
          "TFLxmertPreTrainedModel",
          "TFLxmertVisualFeatureEncoder",
      ]


if TYPE_CHECKING:
      from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
      from .tokenization_lxmert import LxmertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_lxmert_fast import LxmertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_lxmert import (
                LxmertEncoder,
                LxmertForPreTraining,
                LxmertForQuestionAnswering,
                LxmertModel,
                LxmertPreTrainedModel,
                LxmertVisualFeatureEncoder,
                LxmertXLayer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_lxmert import (
                TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFLxmertForPreTraining,
                TFLxmertMainLayer,
                TFLxmertModel,
                TFLxmertPreTrainedModel,
                TFLxmertVisualFeatureEncoder,
            )

else:
      import sys

      UpperCamelCase			:       Any									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



