




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)

def 			a   (_lowerCAmelCase     ):
							# initialize config
							if "resnet-50" in model_name:
														SCREAMING_SNAKE_CASE_   	=    ResNetConfig.from_pretrained('''microsoft/resnet-50'''     )
							elif "resnet-101" in model_name:
														SCREAMING_SNAKE_CASE_   	=    ResNetConfig.from_pretrained('''microsoft/resnet-101'''     )
							else:
														raise ValueError('''Model name should include either resnet50 or resnet101'''     )

							SCREAMING_SNAKE_CASE_   	=    DetrConfig(use_timm_backbone=_lowerCAmelCase  ,    backbone_config=_lowerCAmelCase     )

							# set label attributes
							SCREAMING_SNAKE_CASE_   	=    '''panoptic''' in model_name
							if is_panoptic:
														SCREAMING_SNAKE_CASE_   	=    2_5_0
							else:
														SCREAMING_SNAKE_CASE_   	=    9_1
														SCREAMING_SNAKE_CASE_   	=    '''huggingface/label-files'''
														SCREAMING_SNAKE_CASE_   	=    '''coco-detection-id2label.json'''
														SCREAMING_SNAKE_CASE_   	=    json.load(open(hf_hub_download(_lowerCAmelCase  ,    _lowerCAmelCase  ,    repo_type='''dataset'''     )  ,    '''r'''     )     )
														SCREAMING_SNAKE_CASE_   	=    {int(_lowerCAmelCase     ): v for k, v in idalabel.items()}
														SCREAMING_SNAKE_CASE_   	=    idalabel
														SCREAMING_SNAKE_CASE_   	=    {v: k for k, v in idalabel.items()}

							return config, is_panoptic

def 			a   (_lowerCAmelCase     ):
							# here we list all keys to be renamed (original name on the left, our name on the right)
							SCREAMING_SNAKE_CASE_   	=    []

							# stem
							# fmt: off
							rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight''')     )
							rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight''')     )
							rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias''')     )
							rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean''')     )
							rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var''')     )
							# stages
							for stage_idx in range(len(config.backbone_config.depths     )     ):
														for layer_idx in range(config.backbone_config.depths[stage_idx]     ):
																					# shortcut
																					if layer_idx == 0:
																												rename_keys.append(
																												    (
																												        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
																												        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
																												    )     )
																												rename_keys.append(
																												    (
																												        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
																												        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
																												    )     )
																												rename_keys.append(
																												    (
																												        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
																												        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
																												    )     )
																												rename_keys.append(
																												    (
																												        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
																												        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
																												    )     )
																												rename_keys.append(
																												    (
																												        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
																												        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
																												    )     )
																					# 3 convs
																					for i in range(3     ):
																												rename_keys.append(
																												    (
																												        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
																												        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
																												    )     )
																												rename_keys.append(
																												    (
																												        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
																												        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
																												    )     )
																												rename_keys.append(
																												    (
																												        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
																												        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
																												    )     )
																												rename_keys.append(
																												    (
																												        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
																												        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
																												    )     )
																												rename_keys.append(
																												    (
																												        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
																												        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
																												    )     )
    # fmt: on

							for i in range(config.encoder_layers     ):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append(
														    (
														        F"transformer.encoder.layers.{i}.self_attn.out_proj.weight",
														        F"encoder.layers.{i}.self_attn.out_proj.weight",
														    )     )
														rename_keys.append(
														    (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")     )
														rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight")     )
														rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias")     )
														rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight")     )
														rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias")     )
														rename_keys.append(
														    (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")     )
														rename_keys.append(
														    (F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias")     )
														rename_keys.append(
														    (F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight")     )
														rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias")     )
														# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
														rename_keys.append(
														    (
														        F"transformer.decoder.layers.{i}.self_attn.out_proj.weight",
														        F"decoder.layers.{i}.self_attn.out_proj.weight",
														    )     )
														rename_keys.append(
														    (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")     )
														rename_keys.append(
														    (
														        F"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
														        F"decoder.layers.{i}.encoder_attn.out_proj.weight",
														    )     )
														rename_keys.append(
														    (
														        F"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
														        F"decoder.layers.{i}.encoder_attn.out_proj.bias",
														    )     )
														rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight")     )
														rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias")     )
														rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight")     )
														rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias")     )
														rename_keys.append(
														    (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")     )
														rename_keys.append(
														    (F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias")     )
														rename_keys.append(
														    (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")     )
														rename_keys.append(
														    (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")     )
														rename_keys.append(
														    (F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight")     )
														rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias")     )

							# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
							rename_keys.extend(
							    [
							        ('''input_proj.weight''', '''input_projection.weight'''),
							        ('''input_proj.bias''', '''input_projection.bias'''),
							        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
							        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
							        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
							        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
							        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
							        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
							        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
							        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
							        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
							        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
							        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
							    ]     )

							return rename_keys

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    state_dict.pop(_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    val

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase=False     ):
							SCREAMING_SNAKE_CASE_   	=    ''''''
							if is_panoptic:
														SCREAMING_SNAKE_CASE_   	=    '''detr.'''

							# first: transformer encoder
							for i in range(6     ):
														# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
														SCREAMING_SNAKE_CASE_   	=    state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"     )
														SCREAMING_SNAKE_CASE_   	=    state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"     )
														# next, add query, keys and values (in that order) to the state dict
														SCREAMING_SNAKE_CASE_   	=    in_proj_weight[:2_5_6, :]
														SCREAMING_SNAKE_CASE_   	=    in_proj_bias[:2_5_6]
														SCREAMING_SNAKE_CASE_   	=    in_proj_weight[2_5_6:5_1_2, :]
														SCREAMING_SNAKE_CASE_   	=    in_proj_bias[2_5_6:5_1_2]
														SCREAMING_SNAKE_CASE_   	=    in_proj_weight[-2_5_6:, :]
														SCREAMING_SNAKE_CASE_   	=    in_proj_bias[-2_5_6:]
							# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
							for i in range(6     ):
														# read in weights + bias of input projection layer of self-attention
														SCREAMING_SNAKE_CASE_   	=    state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"     )
														SCREAMING_SNAKE_CASE_   	=    state_dict.pop(F"{prefix}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_   	=    in_proj_weight[:2_5_6, :]
														SCREAMING_SNAKE_CASE_   	=    in_proj_bias[:2_5_6]
														SCREAMING_SNAKE_CASE_   	=    in_proj_weight[2_5_6:5_1_2, :]
														SCREAMING_SNAKE_CASE_   	=    in_proj_bias[2_5_6:5_1_2]
														SCREAMING_SNAKE_CASE_   	=    in_proj_weight[-2_5_6:, :]
														SCREAMING_SNAKE_CASE_   	=    in_proj_bias[-2_5_6:]
														# read in weights + bias of input projection layer of cross-attention
														SCREAMING_SNAKE_CASE_   	=    state_dict.pop(
														    F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"     )
														SCREAMING_SNAKE_CASE_   	=    state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"     )
														# next, add query, keys and values (in that order) of cross-attention to the state dict
														SCREAMING_SNAKE_CASE_   	=    in_proj_weight_cross_attn[:2_5_6, :]
														SCREAMING_SNAKE_CASE_   	=    in_proj_bias_cross_attn[:2_5_6]
														SCREAMING_SNAKE_CASE_   	=    in_proj_weight_cross_attn[2_5_6:5_1_2, :]
														SCREAMING_SNAKE_CASE_   	=    in_proj_bias_cross_attn[2_5_6:5_1_2]
														SCREAMING_SNAKE_CASE_   	=    in_proj_weight_cross_attn[-2_5_6:, :]
														SCREAMING_SNAKE_CASE_   	=    in_proj_bias_cross_attn[-2_5_6:]

def 			a   ():
							SCREAMING_SNAKE_CASE_   	=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							SCREAMING_SNAKE_CASE_   	=    Image.open(requests.get(_lowerCAmelCase  ,    stream=_lowerCAmelCase     ).raw     )

							return im

@torch.no_grad()
def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase=None  ,    _lowerCAmelCase=False     ):
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    get_detr_config(_lowerCAmelCase     )

							# load original model from torch hub
							SCREAMING_SNAKE_CASE_   	=    {
							    '''detr-resnet-50''': '''detr_resnet50''',
							    '''detr-resnet-101''': '''detr_resnet101''',
							}
							logger.info(F"Converting model {model_name}..."     )
							SCREAMING_SNAKE_CASE_   	=    torch.hub.load('''facebookresearch/detr'''  ,    model_name_to_original_name[model_name]  ,    pretrained=_lowerCAmelCase     ).eval()
							SCREAMING_SNAKE_CASE_   	=    detr.state_dict()
							# rename keys
							for src, dest in create_rename_keys(_lowerCAmelCase     ):
														if is_panoptic:
																					SCREAMING_SNAKE_CASE_   	=    '''detr.''' + src
														rename_key(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
							# query, key and value matrices need special treatment
							read_in_q_k_v(_lowerCAmelCase  ,    is_panoptic=_lowerCAmelCase     )
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							SCREAMING_SNAKE_CASE_   	=    '''detr.model.''' if is_panoptic else '''model.'''
							for key in state_dict.copy().keys():
														if is_panoptic:
																					if (
																					    key.startswith('''detr'''     )
																					    and not key.startswith('''class_labels_classifier'''     )
																					    and not key.startswith('''bbox_predictor'''     )
																					):
																												SCREAMING_SNAKE_CASE_   	=    state_dict.pop(_lowerCAmelCase     )
																												SCREAMING_SNAKE_CASE_   	=    val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												SCREAMING_SNAKE_CASE_   	=    state_dict.pop(_lowerCAmelCase     )
																												SCREAMING_SNAKE_CASE_   	=    val
																					elif key.startswith('''bbox_attention'''     ) or key.startswith('''mask_head'''     ):
																												continue
																					else:
																												SCREAMING_SNAKE_CASE_   	=    state_dict.pop(_lowerCAmelCase     )
																												SCREAMING_SNAKE_CASE_   	=    val
														else:
																					if not key.startswith('''class_labels_classifier'''     ) and not key.startswith('''bbox_predictor'''     ):
																												SCREAMING_SNAKE_CASE_   	=    state_dict.pop(_lowerCAmelCase     )
																												SCREAMING_SNAKE_CASE_   	=    val

    # finally, create HuggingFace model and load state dict
							SCREAMING_SNAKE_CASE_   	=    DetrForSegmentation(_lowerCAmelCase     ) if is_panoptic else DetrForObjectDetection(_lowerCAmelCase     )
							model.load_state_dict(_lowerCAmelCase     )
							model.eval()

							# verify our conversion on an image
							SCREAMING_SNAKE_CASE_   	=    '''coco_panoptic''' if is_panoptic else '''coco_detection'''
							SCREAMING_SNAKE_CASE_   	=    DetrImageProcessor(format=_lowerCAmelCase     )

							SCREAMING_SNAKE_CASE_   	=    processor(images=prepare_img()  ,    return_tensors='''pt'''     )
							SCREAMING_SNAKE_CASE_   	=    encoding['''pixel_values''']

							SCREAMING_SNAKE_CASE_   	=    detr(_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    model(_lowerCAmelCase     )

							assert torch.allclose(outputs.logits  ,    original_outputs['''pred_logits''']  ,    atol=1e-3     )
							assert torch.allclose(outputs.pred_boxes  ,    original_outputs['''pred_boxes''']  ,    atol=1e-3     )
							if is_panoptic:
														assert torch.allclose(outputs.pred_masks  ,    original_outputs['''pred_masks''']  ,    atol=1e-4     )
							print('''Looks ok!'''     )

							if pytorch_dump_folder_path is not None:
														# Save model and image processor
														logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."     )
														Path(_lowerCAmelCase     ).mkdir(exist_ok=_lowerCAmelCase     )
														model.save_pretrained(_lowerCAmelCase     )
														processor.save_pretrained(_lowerCAmelCase     )

							if push_to_hub:
														# Upload model and image processor to the hub
														logger.info('''Uploading PyTorch model and image processor to the hub...'''     )
														model.push_to_hub(F"nielsr/{model_name}"     )
														processor.push_to_hub(F"nielsr/{model_name}"     )


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE 						=argparse.ArgumentParser()

			parser.add_argument(
			    """--model_name""",
			    default="""detr-resnet-50""",
			    type=str,
			    choices=["""detr-resnet-50""", """detr-resnet-101"""],
			    help="""Name of the DETR 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 to push the model to the hub or not.""")
			__SCREAMING_SNAKE_CASE 						=parser.parse_args()
			convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE 						="""%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))

			print("""Googling.....""")

			__SCREAMING_SNAKE_CASE 						=f"""https://www.google.com/search?q={query}&num=100"""

			__SCREAMING_SNAKE_CASE 						=requests.get(
			    url,
			    headers={"""User-Agent""": str(UserAgent().random)},
			)

			try:
						__SCREAMING_SNAKE_CASE 						=(
						    BeautifulSoup(res.text, """html.parser""")
						    .find("""div""", attrs={"""class""": """yuRUbf"""})
						    .find("""a""")
						    .get("""href""")
						)

			except AttributeError:
						__SCREAMING_SNAKE_CASE 						=parse_qs(
						    BeautifulSoup(res.text, """html.parser""")
						    .find("""div""", attrs={"""class""": """kCrYT"""})
						    .find("""a""")
						    .get("""href""")
						)["""url"""][0]

			webbrowser.open(link)


