"""simple docstring"""





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_     =	logging.get_logger(__name__)



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    List[Any]:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    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		:						Optional[Any]			  =    DetaConfig(
							    backbone_config=__SCREAMING_SNAKE_CASE     ,	num_queries=900     ,	encoder_ffn_dim=2_048     ,	decoder_ffn_dim=2_048     ,	num_feature_levels=5     ,	assign_first_stage=__SCREAMING_SNAKE_CASE     ,	with_box_refine=__SCREAMING_SNAKE_CASE     ,	two_stage=__SCREAMING_SNAKE_CASE     ,	)

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

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

							return config



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Dict:
							_SCREAMING_SNAKE_CASE		:						Dict			  =    []

							# 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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    dct.pop(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    val



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						int			  =    [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		:						Dict			  =    state_dict.pop(F"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"""  )
																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    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		:						List[Any]			  =    in_proj_weight[:dim, :]
																					_SCREAMING_SNAKE_CASE		:						Any			  =    in_proj_bias[: dim]
																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    in_proj_weight[
																					    dim : dim * 2, :
																					]
																					_SCREAMING_SNAKE_CASE		:						Any			  =    in_proj_bias[
																					    dim : dim * 2
																					]
																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    in_proj_weight[
																					    -dim :, :
																					]
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    in_proj_bias[-dim :]
																					# fmt: on



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Tuple:
							# transformer decoder self-attention layers
							_SCREAMING_SNAKE_CASE		:						int			  =    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[Any]			  =    state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""  )
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    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		:						str			  =    in_proj_bias[:hidden_size]
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    in_proj_weight[
														    hidden_size : hidden_size * 2, :
														]
														_SCREAMING_SNAKE_CASE		:						int			  =    in_proj_bias[hidden_size : hidden_size * 2]
														_SCREAMING_SNAKE_CASE		:						str			  =    in_proj_weight[-hidden_size:, :]
														_SCREAMING_SNAKE_CASE		:						str			  =    in_proj_bias[-hidden_size:]



def      lowerCamelCase_()->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Any			  =    """http://images.cocodataset.org/val2017/000000039769.jpg"""
							_SCREAMING_SNAKE_CASE		:						Dict			  =    Image.open(requests.get(__SCREAMING_SNAKE_CASE     ,	stream=__SCREAMING_SNAKE_CASE  ).raw  )

							return im



@torch.no_grad()
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							_SCREAMING_SNAKE_CASE		:						Dict			  =    get_deta_config(__SCREAMING_SNAKE_CASE  )

							# load original state dict
							if model_name == "deta-swin-large":
														_SCREAMING_SNAKE_CASE		:						str			  =    hf_hub_download(repo_id="""nielsr/deta-checkpoints"""     ,	filename="""adet_swin_ft.pth"""  )
							elif model_name == "deta-swin-large-o365":
														_SCREAMING_SNAKE_CASE		:						List[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		:						List[Any]			  =    torch.load(__SCREAMING_SNAKE_CASE     ,	map_location="""cpu"""  )["""model"""]

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

							# rename keys
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    create_rename_keys(__SCREAMING_SNAKE_CASE  )
							for src, dest in rename_keys:
														rename_key(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							read_in_swin_q_k_v(__SCREAMING_SNAKE_CASE     ,	config.backbone_config  )
							read_in_decoder_q_k_v(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

							# 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		:						Optional[int]			  =    state_dict.pop(__SCREAMING_SNAKE_CASE  )
																					_SCREAMING_SNAKE_CASE		:						str			  =    val
														if "input_proj" in key:
																					_SCREAMING_SNAKE_CASE		:						int			  =    state_dict.pop(__SCREAMING_SNAKE_CASE  )
																					_SCREAMING_SNAKE_CASE		:						int			  =    val
														if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    state_dict.pop(__SCREAMING_SNAKE_CASE  )
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    val

    # finally, create HuggingFace model and load state dict
							_SCREAMING_SNAKE_CASE		:						int			  =    DetaForObjectDetection(__SCREAMING_SNAKE_CASE  )
							model.load_state_dict(__SCREAMING_SNAKE_CASE  )
							model.eval()

							_SCREAMING_SNAKE_CASE		:						Dict			  =    """cuda""" if torch.cuda.is_available() else """cpu"""
							model.to(__SCREAMING_SNAKE_CASE  )

							# load image processor
							_SCREAMING_SNAKE_CASE		:						Dict			  =    DetaImageProcessor(format="""coco_detection"""  )

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

							# 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		:						Tuple			  =    torch.tensor(
														    [[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]]  )
														_SCREAMING_SNAKE_CASE		:						Dict			  =    torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]]  )
							elif model_name == "deta-swin-large-o365":
														_SCREAMING_SNAKE_CASE		:						str			  =    torch.tensor(
														    [[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]]  )
														_SCREAMING_SNAKE_CASE		:						Dict			  =    torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]]  )
							assert torch.allclose(outputs.logits[0, :3, :3]     ,	expected_logits.to(__SCREAMING_SNAKE_CASE  )     ,	atol=1e-4  )
							assert torch.allclose(outputs.pred_boxes[0, :3, :3]     ,	expected_boxes.to(__SCREAMING_SNAKE_CASE  )     ,	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(__SCREAMING_SNAKE_CASE  ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE  )
														model.save_pretrained(__SCREAMING_SNAKE_CASE  )
														processor.save_pretrained(__SCREAMING_SNAKE_CASE  )

							# 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_     =	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_     =	parser.parse_args()
			convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase_     =	{
    '''configuration_clipseg''': [
        '''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPSegConfig''',
        '''CLIPSegTextConfig''',
        '''CLIPSegVisionConfig''',
    ],
    '''processing_clipseg''': ['''CLIPSegProcessor'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	[
			    '''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''CLIPSegModel''',
			    '''CLIPSegPreTrainedModel''',
			    '''CLIPSegTextModel''',
			    '''CLIPSegVisionModel''',
			    '''CLIPSegForImageSegmentation''',
			]

if TYPE_CHECKING:
			from .configuration_clipseg import (
			    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    CLIPSegConfig,
			    CLIPSegTextConfig,
			    CLIPSegVisionConfig,
			)
			from .processing_clipseg import CLIPSegProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_clipseg import (
						    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CLIPSegForImageSegmentation,
						    CLIPSegModel,
						    CLIPSegPreTrainedModel,
						    CLIPSegTextModel,
						    CLIPSegVisionModel,
						)

else:
			import sys

			lowerCAmelCase_     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


