
import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[int]						,  _lowerCamelCase :			Dict)					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[int]			   =					XCLIPTextConfig()

							# derive patch size from model name
							__UpperCamelCase	:   Union[str, Any]			   =					model_name.find("patch")
							__UpperCamelCase	:   Optional[Any]			   =					int(model_name[start_idx + len("patch") : start_idx + len("patch") + 2])
							__UpperCamelCase	:   Optional[int]			   =					XCLIPVisionConfig(patch_size=__UpperCAmelCase						,  num_frames=__UpperCAmelCase)

							if "large" in model_name:
														__UpperCamelCase	:   List[Any]			   =					768
														__UpperCamelCase	:   Optional[Any]			   =					3_072
														__UpperCamelCase	:   Optional[Any]			   =					12

														__UpperCamelCase	:   List[Any]			   =					1_024
														__UpperCamelCase	:   List[str]			   =					4_096
														__UpperCamelCase	:   List[Any]			   =					16
														__UpperCamelCase	:   str			   =					24
														__UpperCamelCase	:   Tuple			   =					768
														__UpperCamelCase	:   Tuple			   =					3_072

							if model_name == "xclip-large-patch14-16-frames":
														__UpperCamelCase	:   Dict			   =					336

							__UpperCamelCase	:   Union[str, Any]			   =					XCLIPConfig.from_text_vision_configs(__UpperCAmelCase						,  __UpperCAmelCase)

							if "large" in model_name:
														__UpperCamelCase	:   Any			   =					768

							return config




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Union[str, Any])					->    Optional[int]:






							'''simple docstring'''





							if name == "token_embedding.weight":
														__UpperCamelCase	:   List[Any]			   =					name.replace("token_embedding.weight"						,  "text_model.embeddings.token_embedding.weight")
							if name == "positional_embedding":
														__UpperCamelCase	:   Union[str, Any]			   =					name.replace("positional_embedding"						,  "text_model.embeddings.position_embedding.weight")
							if "ln_1" in name:
														__UpperCamelCase	:   str			   =					name.replace("ln_1"						,  "layer_norm1")
							if "ln_2" in name:
														__UpperCamelCase	:   List[Any]			   =					name.replace("ln_2"						,  "layer_norm2")
							if "c_fc" in name:
														__UpperCamelCase	:   List[Any]			   =					name.replace("c_fc"						,  "fc1")
							if "c_proj" in name:
														__UpperCamelCase	:   List[Any]			   =					name.replace("c_proj"						,  "fc2")
							if name.startswith("transformer.resblocks"):
														__UpperCamelCase	:   Any			   =					name.replace("transformer.resblocks"						,  "text_model.encoder.layers")
							if "attn.out_proj" in name and "message" not in name:
														__UpperCamelCase	:   Any			   =					name.replace("attn.out_proj"						,  "self_attn.out_proj")
							if "ln_final" in name:
														__UpperCamelCase	:   List[str]			   =					name.replace("ln_final"						,  "text_model.final_layer_norm")
							# visual encoder
							if name == "visual.class_embedding":
														__UpperCamelCase	:   Optional[int]			   =					name.replace("visual.class_embedding"						,  "vision_model.embeddings.class_embedding")
							if name == "visual.positional_embedding":
														__UpperCamelCase	:   str			   =					name.replace("visual.positional_embedding"						,  "vision_model.embeddings.position_embedding.weight")
							if name.startswith("visual.transformer.resblocks"):
														__UpperCamelCase	:   Tuple			   =					name.replace("visual.transformer.resblocks"						,  "vision_model.encoder.layers")
							if "visual.conv1" in name:
														__UpperCamelCase	:   Any			   =					name.replace("visual.conv1"						,  "vision_model.embeddings.patch_embedding")
							if "visual.ln_pre" in name:
														__UpperCamelCase	:   Tuple			   =					name.replace("visual.ln_pre"						,  "vision_model.pre_layernorm")
							if "visual.ln_post" in name:
														__UpperCamelCase	:   Optional[Any]			   =					name.replace("visual.ln_post"						,  "vision_model.post_layernorm")
							if "visual.proj" in name:
														__UpperCamelCase	:   Any			   =					name.replace("visual.proj"						,  "visual_projection.weight")
							if "text_projection" in name:
														__UpperCamelCase	:   Optional[Any]			   =					name.replace("text_projection"						,  "text_projection.weight")
							# things on top
							if "prompts_visual_proj" in name:
														__UpperCamelCase	:   List[Any]			   =					name.replace("prompts_visual_proj"						,  "prompts_visual_projection")
							if "prompts_visual_ln" in name:
														__UpperCamelCase	:   Union[str, Any]			   =					name.replace("prompts_visual_ln"						,  "prompts_visual_layernorm")
							# mit
							if name == "mit.positional_embedding":
														__UpperCamelCase	:   Optional[Any]			   =					name.replace("positional"						,  "position")
							if name.startswith("mit.resblocks"):
														__UpperCamelCase	:   List[str]			   =					name.replace("mit.resblocks"						,  "mit.encoder.layers")
							# prompts generator
							if name.startswith("prompts_generator.norm"):
														__UpperCamelCase	:   List[str]			   =					name.replace("prompts_generator.norm"						,  "prompts_generator.layernorm")

							return name




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Dict						,  _lowerCamelCase :			List[str])					->    Dict:






							'''simple docstring'''





							for key in orig_state_dict.copy().keys():
														__UpperCamelCase	:   str			   =					orig_state_dict.pop(__UpperCAmelCase)

														if "attn.in_proj" in key:
																					__UpperCamelCase	:   List[str]			   =					key.split(".")
																					if key.startswith("visual"):
																												__UpperCamelCase	:   Union[str, Any]			   =					key_split[3]
																												__UpperCamelCase	:   Optional[Any]			   =					config.vision_config.hidden_size
																												if "message_attn" in key:
																																			if "weight" in key:
																																										__UpperCamelCase	:   List[str]			   =					val[
																																										    :dim, :
																																										]
																																										__UpperCamelCase	:   Optional[int]			   =					val[
																																										    dim : dim * 2, :
																																										]
																																										__UpperCamelCase	:   Optional[Any]			   =					val[
																																										    -dim:, :
																																										]
																																			else:
																																										__UpperCamelCase	:   Optional[int]			   =					val[
																																										    :dim
																																										]
																																										__UpperCamelCase	:   Optional[Any]			   =					val[
																																										    dim : dim * 2
																																										]
																																										__UpperCamelCase	:   Optional[Any]			   =					val[
																																										    -dim:
																																										]
																												else:
																																			if "weight" in key:
																																										__UpperCamelCase	:   int			   =					val[
																																										    :dim, :
																																										]
																																										__UpperCamelCase	:   Union[str, Any]			   =					val[
																																										    dim : dim * 2, :
																																										]
																																										__UpperCamelCase	:   List[Any]			   =					val[
																																										    -dim:, :
																																										]
																																			else:
																																										__UpperCamelCase	:   str			   =					val[:dim]
																																										__UpperCamelCase	:   Any			   =					val[
																																										    dim : dim * 2
																																										]
																																										__UpperCamelCase	:   Dict			   =					val[-dim:]
																					elif key.startswith("mit"):
																												__UpperCamelCase	:   Tuple			   =					key_split[2]
																												__UpperCamelCase	:   Tuple			   =					config.vision_config.mit_hidden_size
																												if "weight" in key:
																																			__UpperCamelCase	:   str			   =					val[:dim, :]
																																			__UpperCamelCase	:   List[Any]			   =					val[dim : dim * 2, :]
																																			__UpperCamelCase	:   List[str]			   =					val[-dim:, :]
																												else:
																																			__UpperCamelCase	:   Any			   =					val[:dim]
																																			__UpperCamelCase	:   Optional[Any]			   =					val[dim : dim * 2]
																																			__UpperCamelCase	:   Optional[int]			   =					val[-dim:]
																					else:
																												__UpperCamelCase	:   Tuple			   =					key_split[2]
																												__UpperCamelCase	:   str			   =					config.text_config.hidden_size
																												if "weight" in key:
																																			__UpperCamelCase	:   int			   =					val[:dim, :]
																																			__UpperCamelCase	:   Any			   =					val[
																																			    dim : dim * 2, :
																																			]
																																			__UpperCamelCase	:   int			   =					val[-dim:, :]
																												else:
																																			__UpperCamelCase	:   str			   =					val[:dim]
																																			__UpperCamelCase	:   Optional[int]			   =					val[
																																			    dim : dim * 2
																																			]
																																			__UpperCamelCase	:   Optional[Any]			   =					val[-dim:]
														else:
																					__UpperCamelCase	:   Dict			   =					rename_key(__UpperCAmelCase)
																					if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																												__UpperCamelCase	:   Union[str, Any]			   =					val.T
																					__UpperCamelCase	:   Dict			   =					val

							return orig_state_dict




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[Any])					->    Tuple:






							'''simple docstring'''





							if num_frames == 8:
														__UpperCamelCase	:   Optional[Any]			   =					'''eating_spaghetti_8_frames.npy'''
							elif num_frames == 16:
														__UpperCamelCase	:   str			   =					'''eating_spaghetti.npy'''
							elif num_frames == 32:
														__UpperCamelCase	:   str			   =					'''eating_spaghetti_32_frames.npy'''
							__UpperCamelCase	:   List[Any]			   =					hf_hub_download(
							    repo_id="hf-internal-testing/spaghetti-video"						,  filename=__UpperCAmelCase						,  repo_type="dataset"						,  )
							__UpperCamelCase	:   str			   =					np.load(__UpperCAmelCase)
							return list(__UpperCAmelCase)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[Any]						,  _lowerCamelCase :			Any=None						,  _lowerCamelCase :			str=False)					->    List[Any]:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[Any]			   =					{
							    # fully supervised kinetics-400 checkpoints
							    '''xclip-base-patch32''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth''',
							    '''xclip-base-patch32-16-frames''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'''
							    ),
							    '''xclip-base-patch16''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth''',
							    '''xclip-base-patch16-16-frames''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'''
							    ),
							    '''xclip-large-patch14''': '''https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb''',
							    '''xclip-large-patch14-16-frames''': '''https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f''',
							    # fully supervised kinetics-600 checkpoints
							    '''xclip-base-patch16-kinetics-600''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'''
							    ),
							    '''xclip-base-patch16-kinetics-600-16-frames''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'''
							    ),
							    '''xclip-large-patch14-kinetics-600''': '''https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be''',
							    # few shot
							    '''xclip-base-patch16-hmdb-2-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'''
							    ),
							    '''xclip-base-patch16-hmdb-4-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'''
							    ),
							    '''xclip-base-patch16-hmdb-8-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'''
							    ),
							    '''xclip-base-patch16-hmdb-16-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'''
							    ),
							    '''xclip-base-patch16-ucf-2-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'''
							    ),
							    '''xclip-base-patch16-ucf-4-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'''
							    ),
							    '''xclip-base-patch16-ucf-8-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'''
							    ),
							    '''xclip-base-patch16-ucf-16-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'''
							    ),
							    # zero shot
							    '''xclip-base-patch16-zero-shot''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth''',
							}

							__UpperCamelCase	:   Optional[Any]			   =					model_to_url[model_name]
							__UpperCamelCase	:   Dict			   =					8
							if "16-frames" in model_name:
														__UpperCamelCase	:   Optional[Any]			   =					16
							elif "shot" in model_name:
														__UpperCamelCase	:   Any			   =					32

							__UpperCamelCase	:   Any			   =					get_xclip_config(__UpperCAmelCase						,  __UpperCAmelCase)
							__UpperCamelCase	:   Any			   =					XCLIPModel(__UpperCAmelCase)
							model.eval()

							if "drive" in checkpoint_url:
														__UpperCamelCase	:   int			   =					'''pytorch_model.bin'''
														gdown.cached_download(__UpperCAmelCase						,  __UpperCAmelCase						,  quiet=__UpperCAmelCase)
														__UpperCamelCase	:   Optional[int]			   =					torch.load(__UpperCAmelCase						,  map_location="cpu")['''model''']
							else:
														__UpperCamelCase	:   Optional[int]			   =					torch.hub.load_state_dict_from_url(__UpperCAmelCase)['''model''']

							__UpperCamelCase	:   Any			   =					convert_state_dict(__UpperCAmelCase						,  __UpperCAmelCase)

							__UpperCamelCase	:   Any			   =					XCLIPModel(__UpperCAmelCase)
							__UpperCamelCase	:   Dict			   =					model.load_state_dict(__UpperCAmelCase						,  strict=__UpperCAmelCase)
							assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
							model.eval()

							__UpperCamelCase	:   Union[str, Any]			   =					336 if model_name == '''xclip-large-patch14-16-frames''' else 224
							__UpperCamelCase	:   str			   =					VideoMAEImageProcessor(size=__UpperCAmelCase)
							__UpperCamelCase	:   List[str]			   =					CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
							__UpperCamelCase	:   List[str]			   =					CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32")
							__UpperCamelCase	:   Union[str, Any]			   =					XCLIPProcessor(image_processor=__UpperCAmelCase						,  tokenizer=__UpperCAmelCase)

							__UpperCamelCase	:   str			   =					prepare_video(__UpperCAmelCase)
							__UpperCamelCase	:   List[str]			   =					processor(
							    text=["playing sports", "eating spaghetti", "go shopping"]						,  videos=__UpperCAmelCase						,  return_tensors="pt"						,  padding=__UpperCAmelCase)

							print("Shape of pixel values:"						,  inputs.pixel_values.shape)

							with torch.no_grad():
														__UpperCamelCase	:   Any			   =					model(**__UpperCAmelCase)

							# Verify outputs
							__UpperCamelCase	:   Any			   =					outputs.logits_per_video
							__UpperCamelCase	:   str			   =					logits_per_video.softmax(dim=1)
							print("Probs:"						,  __UpperCAmelCase)
							# kinetics-400
							if model_name == "xclip-base-patch32":
														__UpperCamelCase	:   Tuple			   =					torch.tensor([[0.0_0_1_9, 0.9_9_5_1, 0.0_0_3_0]])
							elif model_name == "xclip-base-patch32-16-frames":
														__UpperCamelCase	:   int			   =					torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]])
							elif model_name == "xclip-base-patch16":
														__UpperCamelCase	:   List[str]			   =					torch.tensor([[0.0_0_8_3, 0.9_6_8_1, 0.0_2_3_6]])
							elif model_name == "xclip-base-patch16-16-frames":
														__UpperCamelCase	:   Optional[Any]			   =					torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]])
							elif model_name == "xclip-large-patch14":
														__UpperCamelCase	:   Tuple			   =					torch.tensor([[0.0_0_6_2, 0.9_8_6_4, 0.0_0_7_5]])
							elif model_name == "xclip-large-patch14-16-frames":
														__UpperCamelCase	:   Union[str, Any]			   =					torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]])
							# kinetics-600
							elif model_name == "xclip-base-patch16-kinetics-600":
														__UpperCamelCase	:   List[str]			   =					torch.tensor([[0.0_5_5_5, 0.8_9_1_4, 0.0_5_3_1]])
							elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
														__UpperCamelCase	:   Dict			   =					torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]])
							elif model_name == "xclip-large-patch14-kinetics-600":
														__UpperCamelCase	:   Dict			   =					torch.tensor([[0.0_0_3_6, 0.9_9_2_0, 0.0_0_4_5]])
							# few shot
							elif model_name == "xclip-base-patch16-hmdb-2-shot":
														__UpperCamelCase	:   Optional[int]			   =					torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]])
							elif model_name == "xclip-base-patch16-hmdb-4-shot":
														__UpperCamelCase	:   List[str]			   =					torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]])
							elif model_name == "xclip-base-patch16-hmdb-8-shot":
														__UpperCamelCase	:   Dict			   =					torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]])
							elif model_name == "xclip-base-patch16-hmdb-16-shot":
														__UpperCamelCase	:   List[Any]			   =					torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]])
							elif model_name == "xclip-base-patch16-ucf-2-shot":
														__UpperCamelCase	:   Any			   =					torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]])
							elif model_name == "xclip-base-patch16-ucf-4-shot":
														__UpperCamelCase	:   List[Any]			   =					torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]])
							elif model_name == "xclip-base-patch16-ucf-8-shot":
														__UpperCamelCase	:   int			   =					torch.tensor([[0.0_0_2_7, 0.9_9_0_4, 0.0_0_7_0]])
							elif model_name == "xclip-base-patch16-ucf-16-shot":
														__UpperCamelCase	:   Optional[int]			   =					torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]])
							# zero shot
							elif model_name == "xclip-base-patch16-zero-shot":
														__UpperCamelCase	:   int			   =					torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]])
							else:
														raise ValueError(F'Model name {model_name} not supported')
							assert torch.allclose(__UpperCAmelCase						,  __UpperCAmelCase						,  atol=1e-3)
							print("Looks ok!")

							if pytorch_dump_folder_path is not None:
														print(F'Saving model {model_name} to {pytorch_dump_folder_path}')
														model.save_pretrained(__UpperCAmelCase)

							if push_to_hub:
														print("Pushing model, processor and slow tokenizer files to the hub...")
														model.push_to_hub(__UpperCAmelCase						,  organization="nielsr")
														processor.push_to_hub(__UpperCAmelCase						,  organization="nielsr")
														slow_tokenizer.push_to_hub(__UpperCAmelCase						,  organization="nielsr")


if __name__ == "__main__":
		lowercase  :					Union[str, Any]													=       argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--model_name',
		    default='xclip-base-patch32',
		    type=str,
		    help='Name of the model.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
		)
		parser.add_argument(
		    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
		)

		lowercase  :					Optional[Any]													=       parser.parse_args()
		convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
"""simple docstring"""






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__A			   = logging.get_logger(__name__)

__A			   = {
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Dict						    = "deta"
    _UpperCAmelCase	:Any						    = {
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }






    def __init__(   self    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=900    ,			_UpperCAmelCase=2048    ,			_UpperCAmelCase=6    ,			_UpperCAmelCase=2048    ,			_UpperCAmelCase=8    ,			_UpperCAmelCase=6    ,			_UpperCAmelCase=1024    ,			_UpperCAmelCase=8    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase="relu"    ,			_UpperCAmelCase=256    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=0.02    ,			_UpperCAmelCase=1.0    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase="sine"    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=300    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.25    ,			**_UpperCAmelCase    ,			):
       if backbone_config is None:
          logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''							)
          lowercase__:   str					=      CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']							)
       else:
          if isinstance(_UpperCAmelCase    ,			_UpperCAmelCase							):
             lowercase__:   Tuple					=      backbone_config.pop('''model_type'''							)
             lowercase__:   str					=      CONFIG_MAPPING[backbone_model_type]
             lowercase__:   Optional[int]					=      config_class.from_dict(_UpperCAmelCase							)

       lowercase__:   int					=      backbone_config
       lowercase__:   Any					=      num_queries
       lowercase__:   List[str]					=      max_position_embeddings
       lowercase__:   Optional[Any]					=      d_model
       lowercase__:   List[Any]					=      encoder_ffn_dim
       lowercase__:   Tuple					=      encoder_layers
       lowercase__:   Dict					=      encoder_attention_heads
       lowercase__:   Any					=      decoder_ffn_dim
       lowercase__:   Union[str, Any]					=      decoder_layers
       lowercase__:   List[Any]					=      decoder_attention_heads
       lowercase__:   int					=      dropout
       lowercase__:   List[str]					=      attention_dropout
       lowercase__:   Tuple					=      activation_dropout
       lowercase__:   Tuple					=      activation_function
       lowercase__:   int					=      init_std
       lowercase__:   Optional[Any]					=      init_xavier_std
       lowercase__:   Optional[Any]					=      encoder_layerdrop
       lowercase__:   Optional[int]					=      auxiliary_loss
       lowercase__:   Union[str, Any]					=      position_embedding_type
       # deformable attributes
       lowercase__:   List[str]					=      num_feature_levels
       lowercase__:   Optional[Any]					=      encoder_n_points
       lowercase__:   int					=      decoder_n_points
       lowercase__:   str					=      two_stage
       lowercase__:   Optional[int]					=      two_stage_num_proposals
       lowercase__:   Tuple					=      with_box_refine
       lowercase__:   str					=      assign_first_stage
       if two_stage is True and with_box_refine is False:
          raise ValueError('''If two_stage is True, with_box_refine must be True.'''							)
       # Hungarian matcher
       lowercase__:   Union[str, Any]					=      class_cost
       lowercase__:   Optional[int]					=      bbox_cost
       lowercase__:   int					=      giou_cost
       # Loss coefficients
       lowercase__:   Optional[int]					=      mask_loss_coefficient
       lowercase__:   List[str]					=      dice_loss_coefficient
       lowercase__:   str					=      bbox_loss_coefficient
       lowercase__:   Union[str, Any]					=      giou_loss_coefficient
       lowercase__:   Optional[int]					=      eos_coefficient
       lowercase__:   str					=      focal_alpha
       super().__init__(is_encoder_decoder=_UpperCAmelCase    ,			**_UpperCAmelCase							)






    @property
    def _snake_case			(   self							):
       return self.encoder_attention_heads






    @property
    def _snake_case			(   self							):
       return self.d_model






    def _snake_case			(   self							):
       lowercase__:   Union[str, Any]					=      copy.deepcopy(self.__dict__							)
       lowercase__:   Dict					=      self.backbone_config.to_dict()
       lowercase__:   Union[str, Any]					=      self.__class__.model_type
       return output
