


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase	: List[str]  				=logging.get_logger(__name__)


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Any					=		SwinConfig(
						    embed_dim=192    ,       depths=(2, 2, 18, 2)    ,       num_heads=(6, 12, 24, 48)    ,       window_size=12    ,       out_features=['''stage2''', '''stage3''', '''stage4''']    ,       )

						__SCREAMING_SNAKE_CASE						:     Tuple					=		DetaConfig(
						    backbone_config=lowercase__    ,       num_queries=900    ,       encoder_ffn_dim=2048    ,       decoder_ffn_dim=2048    ,       num_feature_levels=5    ,       assign_first_stage=lowercase__    ,       with_box_refine=lowercase__    ,       two_stage=lowercase__    ,       )

						# set labels
						__SCREAMING_SNAKE_CASE						:     Tuple					=		'''huggingface/label-files'''
						if "o365" in model_name:
												__SCREAMING_SNAKE_CASE						:     Dict					=		366
												__SCREAMING_SNAKE_CASE						:     Any					=		'''object365-id2label.json'''
						else:
												__SCREAMING_SNAKE_CASE						:     List[str]					=		91
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		'''coco-detection-id2label.json'''

						__SCREAMING_SNAKE_CASE						:     List[str]					=		num_labels
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		json.load(open(cached_download(hf_hub_url(lowercase__    ,       lowercase__    ,       repo_type='''dataset'''      )      )    ,       '''r'''      )      )
						__SCREAMING_SNAKE_CASE						:     Dict					=		{int(lowercase__      ): v for k, v in idalabel.items()}
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		idalabel
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		{v: k for k, v in idalabel.items()}

						return config


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     int					=		[]

						# stem
						# fmt: off
						rename_keys.append(('''backbone.0.body.patch_embed.proj.weight''', '''model.backbone.model.embeddings.patch_embeddings.projection.weight''')      )
						rename_keys.append(('''backbone.0.body.patch_embed.proj.bias''', '''model.backbone.model.embeddings.patch_embeddings.projection.bias''')      )
						rename_keys.append(('''backbone.0.body.patch_embed.norm.weight''', '''model.backbone.model.embeddings.norm.weight''')      )
						rename_keys.append(('''backbone.0.body.patch_embed.norm.bias''', '''model.backbone.model.embeddings.norm.bias''')      )
						# stages
						for i in range(len(config.backbone_config.depths      )      ):
												for j in range(config.backbone_config.depths[i]      ):
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')      )
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')      )
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')      )
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')      )
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')      )
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')      )
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')      )
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')      )
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')      )
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')      )
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')      )
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')      )

												if i < 3:
																		rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.reduction.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight''')      )
																		rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight''')      )
																		rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.bias''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias''')      )

						rename_keys.append(('''backbone.0.body.norm1.weight''', '''model.backbone.model.hidden_states_norms.stage2.weight''')      )
						rename_keys.append(('''backbone.0.body.norm1.bias''', '''model.backbone.model.hidden_states_norms.stage2.bias''')      )
						rename_keys.append(('''backbone.0.body.norm2.weight''', '''model.backbone.model.hidden_states_norms.stage3.weight''')      )
						rename_keys.append(('''backbone.0.body.norm2.bias''', '''model.backbone.model.hidden_states_norms.stage3.bias''')      )
						rename_keys.append(('''backbone.0.body.norm3.weight''', '''model.backbone.model.hidden_states_norms.stage4.weight''')      )
						rename_keys.append(('''backbone.0.body.norm3.bias''', '''model.backbone.model.hidden_states_norms.stage4.bias''')      )

						# transformer encoder
						for i in range(config.encoder_layers      ):
												rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')      )
												rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')      )
												rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', F'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')      )
												rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', F'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')      )
												rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', F'''model.encoder.layers.{i}.self_attn.value_proj.weight''')      )
												rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', F'''model.encoder.layers.{i}.self_attn.value_proj.bias''')      )
												rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', F'''model.encoder.layers.{i}.self_attn.output_proj.weight''')      )
												rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', F'''model.encoder.layers.{i}.self_attn.output_proj.bias''')      )
												rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.weight''', F'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')      )
												rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')      )
												rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''model.encoder.layers.{i}.fc1.weight''')      )
												rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''model.encoder.layers.{i}.fc1.bias''')      )
												rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''model.encoder.layers.{i}.fc2.weight''')      )
												rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''model.encoder.layers.{i}.fc2.bias''')      )
												rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''model.encoder.layers.{i}.final_layer_norm.weight''')      )
												rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''model.encoder.layers.{i}.final_layer_norm.bias''')      )

						# transformer decoder
						for i in range(config.decoder_layers      ):
												rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')      )
												rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')      )
												rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')      )
												rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')      )
												rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')      )
												rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')      )
												rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')      )
												rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')      )
												rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.weight''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')      )
												rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')      )
												rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''model.decoder.layers.{i}.self_attn.out_proj.weight''')      )
												rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''model.decoder.layers.{i}.self_attn.out_proj.bias''')      )
												rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.weight''', F'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')      )
												rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.bias''', F'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')      )
												rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''model.decoder.layers.{i}.fc1.weight''')      )
												rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''model.decoder.layers.{i}.fc1.bias''')      )
												rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''model.decoder.layers.{i}.fc2.weight''')      )
												rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''model.decoder.layers.{i}.fc2.bias''')      )
												rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''model.decoder.layers.{i}.final_layer_norm.weight''')      )
												rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''model.decoder.layers.{i}.final_layer_norm.bias''')      )

						# fmt: on

						return rename_keys


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Tuple					=		dct.pop(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Any					=		val


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     str					=		[int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
						for i in range(len(backbone_config.depths      )      ):
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		num_features[i]
												for j in range(backbone_config.depths[i]      ):
																		# fmt: off
																		# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																		__SCREAMING_SNAKE_CASE						:     Optional[int]					=		state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''      )
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'''      )
																		# next, add query, keys and values (in that order) to the state dict
																		__SCREAMING_SNAKE_CASE						:     Any					=		in_proj_weight[:dim, :]
																		__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		in_proj_bias[: dim]
																		__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		in_proj_weight[
																		    dim : dim * 2, :
																		]
																		__SCREAMING_SNAKE_CASE						:     str					=		in_proj_bias[
																		    dim : dim * 2
																		]
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		in_proj_weight[
																		    -dim :, :
																		]
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		in_proj_bias[-dim :]
																		# fmt: on


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						# transformer decoder self-attention layers
						__SCREAMING_SNAKE_CASE						:     Any					=		config.d_model
						for i in range(config.decoder_layers      ):
												# read in weights + bias of input projection layer of self-attention
												__SCREAMING_SNAKE_CASE						:     List[str]					=		state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''      )
												__SCREAMING_SNAKE_CASE						:     Any					=		state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''      )
												# next, add query, keys and values (in that order) to the state dict
												__SCREAMING_SNAKE_CASE						:     Any					=		in_proj_weight[:hidden_size, :]
												__SCREAMING_SNAKE_CASE						:     Tuple					=		in_proj_bias[:hidden_size]
												__SCREAMING_SNAKE_CASE						:     str					=		in_proj_weight[
												    hidden_size : hidden_size * 2, :
												]
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		in_proj_bias[hidden_size : hidden_size * 2]
												__SCREAMING_SNAKE_CASE						:     int					=		in_proj_weight[-hidden_size:, :]
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		in_proj_bias[-hidden_size:]


def        _UpperCamelCase							(      ):
						__SCREAMING_SNAKE_CASE						:     int					=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		Image.open(requests.get(lowercase__    ,       stream=lowercase__      ).raw      )

						return im


@torch.no_grad()
def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Tuple					=		get_deta_config(lowercase__      )

						# load original state dict
						if model_name == "deta-swin-large":
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		hf_hub_download(repo_id='''nielsr/deta-checkpoints'''    ,       filename='''adet_swin_ft.pth'''      )
						elif model_name == "deta-swin-large-o365":
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		hf_hub_download(repo_id='''jozhang97/deta-swin-l-o365'''    ,       filename='''deta_swin_pt_o365.pth'''      )
						else:
												raise ValueError(F'''Model name {model_name} not supported'''      )

						__SCREAMING_SNAKE_CASE						:     Dict					=		torch.load(lowercase__    ,       map_location='''cpu'''      )['''model''']

						# original state dict
						for name, param in state_dict.items():
												print(lowercase__    ,       param.shape      )

						# rename keys
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		create_rename_keys(lowercase__      )
						for src, dest in rename_keys:
												rename_key(lowercase__    ,       lowercase__    ,       lowercase__      )
						read_in_swin_q_k_v(lowercase__    ,       config.backbone_config      )
						read_in_decoder_q_k_v(lowercase__    ,       lowercase__      )

						# fix some prefixes
						for key in state_dict.copy().keys():
												if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		state_dict.pop(lowercase__      )
																		__SCREAMING_SNAKE_CASE						:     List[str]					=		val
												if "input_proj" in key:
																		__SCREAMING_SNAKE_CASE						:     List[str]					=		state_dict.pop(lowercase__      )
																		__SCREAMING_SNAKE_CASE						:     str					=		val
												if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
																		__SCREAMING_SNAKE_CASE						:     Optional[int]					=		state_dict.pop(lowercase__      )
																		__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		val

    # finally, create HuggingFace model and load state dict
						__SCREAMING_SNAKE_CASE						:     List[str]					=		DetaForObjectDetection(lowercase__      )
						model.load_state_dict(lowercase__      )
						model.eval()

						__SCREAMING_SNAKE_CASE						:     List[Any]					=		'''cuda''' if torch.cuda.is_available() else '''cpu'''
						model.to(lowercase__      )

						# load image processor
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		DetaImageProcessor(format='''coco_detection'''      )

						# verify our conversion on image
						__SCREAMING_SNAKE_CASE						:     Tuple					=		prepare_img()
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		processor(images=lowercase__    ,       return_tensors='''pt'''      )
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		encoding['''pixel_values''']
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		model(pixel_values.to(lowercase__      )      )

						# verify logits
						print('''Logits:'''    ,       outputs.logits[0, :3, :3]      )
						print('''Boxes:'''    ,       outputs.pred_boxes[0, :3, :3]      )
						if model_name == "deta-swin-large":
												__SCREAMING_SNAKE_CASE						:     str					=		torch.tensor(
												    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]      )
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]      )
						elif model_name == "deta-swin-large-o365":
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		torch.tensor(
												    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]      )
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]      )
						assert torch.allclose(outputs.logits[0, :3, :3]    ,       expected_logits.to(lowercase__      )    ,       atol=1e-4      )
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]    ,       expected_boxes.to(lowercase__      )    ,       atol=1e-4      )
						print('''Everything ok!'''      )

						if pytorch_dump_folder_path:
												# Save model and processor
												logger.info(F'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''      )
												Path(lowercase__      ).mkdir(exist_ok=lowercase__      )
												model.save_pretrained(lowercase__      )
												processor.save_pretrained(lowercase__      )

						# Push to hub
						if push_to_hub:
												print('''Pushing model and processor to hub...'''      )
												model.push_to_hub(F'''jozhang97/{model_name}'''      )
												processor.push_to_hub(F'''jozhang97/{model_name}'''      )


if __name__ == "__main__":
						__lowerCAmelCase	: str  				=argparse.ArgumentParser()

						parser.add_argument(
						    '--model_name',
						    type=str,
						    default='deta-swin-large',
						    choices=['deta-swin-large', 'deta-swin-large-o365'],
						    help='Name of the model you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path',
						    default=None,
						    type=str,
						    help='Path to the folder to output PyTorch model.',
						)
						parser.add_argument(
						    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
						)
						__lowerCAmelCase	: List[str]  				=parser.parse_args()
						convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase	: Any  				={'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase	: int  				=[
						    'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'ViTMSNModel',
						    'ViTMSNForImageClassification',
						    'ViTMSNPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_msn import (
												    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTMSNForImageClassification,
												    ViTMSNModel,
												    ViTMSNPreTrainedModel,
												)

else:
						import sys

						__lowerCAmelCase	: Union[str, Any]  				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
