






import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase  							=					[]
for i in range(6):
	# 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}.cross_attn.out_proj.weight',
	        f'decoder.layers.{i}.encoder_attn.out_proj.weight',
	    )
	)
	rename_keys.append(
	    (
	        f'transformer.decoder.layers.{i}.cross_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'))

	# q, k, v projections in self/cross-attention in decoder for conditional DETR
	rename_keys.append(
	    (f'transformer.decoder.layers.{i}.sa_qcontent_proj.weight', f'decoder.layers.{i}.sa_qcontent_proj.weight')
	)
	rename_keys.append(
	    (f'transformer.decoder.layers.{i}.sa_kcontent_proj.weight', f'decoder.layers.{i}.sa_kcontent_proj.weight')
	)
	rename_keys.append(
	    (f'transformer.decoder.layers.{i}.sa_qpos_proj.weight', f'decoder.layers.{i}.sa_qpos_proj.weight')
	)
	rename_keys.append(
	    (f'transformer.decoder.layers.{i}.sa_kpos_proj.weight', f'decoder.layers.{i}.sa_kpos_proj.weight')
	)
	rename_keys.append((f'transformer.decoder.layers.{i}.sa_v_proj.weight', f'decoder.layers.{i}.sa_v_proj.weight'))
	rename_keys.append(
	    (f'transformer.decoder.layers.{i}.ca_qcontent_proj.weight', f'decoder.layers.{i}.ca_qcontent_proj.weight')
	)
	# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
	rename_keys.append(
	    (f'transformer.decoder.layers.{i}.ca_kcontent_proj.weight', f'decoder.layers.{i}.ca_kcontent_proj.weight')
	)
	rename_keys.append(
	    (f'transformer.decoder.layers.{i}.ca_kpos_proj.weight', f'decoder.layers.{i}.ca_kpos_proj.weight')
	)
	rename_keys.append((f'transformer.decoder.layers.{i}.ca_v_proj.weight', f'decoder.layers.{i}.ca_v_proj.weight'))
	rename_keys.append(
	    (f'transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight', f'decoder.layers.{i}.ca_qpos_sine_proj.weight')
	)

	rename_keys.append(
	    (f'transformer.decoder.layers.{i}.sa_qcontent_proj.bias', f'decoder.layers.{i}.sa_qcontent_proj.bias')
	)
	rename_keys.append(
	    (f'transformer.decoder.layers.{i}.sa_kcontent_proj.bias', f'decoder.layers.{i}.sa_kcontent_proj.bias')
	)
	rename_keys.append((f'transformer.decoder.layers.{i}.sa_qpos_proj.bias', f'decoder.layers.{i}.sa_qpos_proj.bias'))
	rename_keys.append((f'transformer.decoder.layers.{i}.sa_kpos_proj.bias', f'decoder.layers.{i}.sa_kpos_proj.bias'))
	rename_keys.append((f'transformer.decoder.layers.{i}.sa_v_proj.bias', f'decoder.layers.{i}.sa_v_proj.bias'))
	rename_keys.append(
	    (f'transformer.decoder.layers.{i}.ca_qcontent_proj.bias', f'decoder.layers.{i}.ca_qcontent_proj.bias')
	)
	# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
	rename_keys.append(
	    (f'transformer.decoder.layers.{i}.ca_kcontent_proj.bias', f'decoder.layers.{i}.ca_kcontent_proj.bias')
	)
	rename_keys.append((f'transformer.decoder.layers.{i}.ca_kpos_proj.bias', f'decoder.layers.{i}.ca_kpos_proj.bias'))
	rename_keys.append((f'transformer.decoder.layers.{i}.ca_v_proj.bias', f'decoder.layers.{i}.ca_v_proj.bias'))
	rename_keys.append(
	    (f'transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias', f'decoder.layers.{i}.ca_qpos_sine_proj.bias')
	)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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"),
        ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
        ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
        ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
        ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
        ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
        ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
        ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
        ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
        ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
        ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
    ]
)





def 						__UpperCAmelCase			(     a_							,     a_							,     a_):
							snake_case_				=							state_dict.pop(a_)
							snake_case_				=							val





def 						__UpperCAmelCase			(     a_):
							snake_case_				=							OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					snake_case_				=							key.replace('backbone.0.body'							,     'backbone.conv_encoder.model')
																					snake_case_				=							value
														else:
																					snake_case_				=							value

							return new_state_dict





def 						__UpperCAmelCase			(     a_							,     a_=False):
							snake_case_				=							''
							if is_panoptic:
														snake_case_				=							'conditional_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)
														snake_case_				=							state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''')
														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
														snake_case_				=							in_proj_weight[:2_56, :]
														snake_case_				=							in_proj_bias[:2_56]
														snake_case_				=							in_proj_weight[2_56:5_12, :]
														snake_case_				=							in_proj_bias[2_56:5_12]
														snake_case_				=							in_proj_weight[-2_56:, :]
														snake_case_				=							in_proj_bias[-2_56:]





def 						__UpperCAmelCase			(     ):
							snake_case_				=							'http://images.cocodataset.org/val2017/000000039769.jpg'
							snake_case_				=							Image.open(requests.get(a_							,     stream=a_).raw)

							return im





@torch.no_grad()
def 						__UpperCAmelCase			(     a_							,     a_):
							snake_case_				=							ConditionalDetrConfig()
							# set backbone and dilation attributes
							if "resnet101" in model_name:
														snake_case_				=							'resnet101'
							if "dc5" in model_name:
														snake_case_				=							True
							snake_case_				=							'panoptic' in model_name
							if is_panoptic:
														snake_case_				=							2_50
							else:
														snake_case_				=							91
														snake_case_				=							'huggingface/label-files'
														snake_case_				=							'coco-detection-id2label.json'
														snake_case_				=							json.load(open(hf_hub_download(a_							,     a_							,     repo_type='dataset')							,     'r'))
														snake_case_				=							{int(a_): v for k, v in idalabel.items()}
														snake_case_				=							idalabel
														snake_case_				=							{v: k for k, v in idalabel.items()}

							# load image processor
							snake_case_				=							'coco_panoptic' if is_panoptic else 'coco_detection'
							snake_case_				=							ConditionalDetrImageProcessor(format=a_)

							# prepare image
							snake_case_				=							prepare_img()
							snake_case_				=							image_processor(images=a_							,     return_tensors='pt')
							snake_case_				=							encoding['pixel_values']

							logger.info(f'''Converting model {model_name}...''')

							# load original model from torch hub
							snake_case_				=							torch.hub.load('DeppMeng/ConditionalDETR'							,     a_							,     pretrained=a_).eval()
							snake_case_				=							conditional_detr.state_dict()
							# rename keys
							for src, dest in rename_keys:
														if is_panoptic:
																					snake_case_				=							'conditional_detr.' + src
														rename_key(a_							,     a_							,     a_)
							snake_case_				=							rename_backbone_keys(a_)
							# query, key and value matrices need special treatment
							read_in_q_k_v(a_							,     is_panoptic=a_)
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							snake_case_				=							'conditional_detr.model.' if is_panoptic else 'model.'
							for key in state_dict.copy().keys():
														if is_panoptic:
																					if (
																					    key.startswith('conditional_detr')
																					    and not key.startswith('class_labels_classifier')
																					    and not key.startswith('bbox_predictor')
																					):
																												snake_case_				=							state_dict.pop(a_)
																												snake_case_				=							val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												snake_case_				=							state_dict.pop(a_)
																												snake_case_				=							val
																					elif key.startswith('bbox_attention') or key.startswith('mask_head'):
																												continue
																					else:
																												snake_case_				=							state_dict.pop(a_)
																												snake_case_				=							val
														else:
																					if not key.startswith('class_labels_classifier') and not key.startswith('bbox_predictor'):
																												snake_case_				=							state_dict.pop(a_)
																												snake_case_				=							val
    # finally, create HuggingFace model and load state dict
							snake_case_				=							ConditionalDetrForSegmentation(a_) if is_panoptic else ConditionalDetrForObjectDetection(a_)
							model.load_state_dict(a_)
							model.eval()
							model.push_to_hub(repo_id=a_							,     organization='DepuMeng'							,     commit_message='Add model')
							# verify our conversion
							snake_case_				=							conditional_detr(a_)
							snake_case_				=							model(a_)
							assert torch.allclose(outputs.logits							,     original_outputs['pred_logits']							,     atol=1E-4)
							assert torch.allclose(outputs.pred_boxes							,     original_outputs['pred_boxes']							,     atol=1E-4)
							if is_panoptic:
														assert torch.allclose(outputs.pred_masks							,     original_outputs['pred_masks']							,     atol=1E-4)

							# Save model and image processor
							logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''')
							Path(a_).mkdir(exist_ok=a_)
							model.save_pretrained(a_)
							image_processor.save_pretrained(a_)


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

	parser.add_argument(
	    "--model_name",
	    default="conditional_detr_resnet50",
	    type=str,
	    help="Name of the CONDITIONAL_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."
	)
	lowercase  							=					parser.parse_args()
	convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)













from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= ['''image_processor''', '''tokenizer''']
	lowerCAmelCase													= '''BlipImageProcessor'''
	lowerCAmelCase													= ('''BertTokenizer''', '''BertTokenizerFast''')




	def __init__(       self       ,			a       ,			a					)       ->    Tuple:
								snake_case_				=							False
								super().__init__(a       ,			a					)
								snake_case_				=							self.image_processor




	def __call__(       self       ,			a = None       ,			a = None       ,			a = True       ,			a = False       ,			a = None       ,			a = None       ,			a = 0       ,			a = None       ,			a = None       ,			a = False       ,			a = False       ,			a = False       ,			a = False       ,			a = False       ,			a = True       ,			a = None       ,			**a       ,			)       ->    BatchEncoding:
								if images is None and text is None:
															raise ValueError('You have to specify either images or text.'					)

								# Get only text
								if images is None:
															snake_case_				=							self.tokenizer
															snake_case_				=							self.tokenizer(
															    text=a       ,			add_special_tokens=a       ,			padding=a       ,			truncation=a       ,			max_length=a       ,			stride=a       ,			pad_to_multiple_of=a       ,			return_attention_mask=a       ,			return_overflowing_tokens=a       ,			return_special_tokens_mask=a       ,			return_offsets_mapping=a       ,			return_token_type_ids=a       ,			return_length=a       ,			verbose=a       ,			return_tensors=a       ,			**a       ,			)
															return text_encoding

								# add pixel_values
								snake_case_				=							self.image_processor(a       ,			return_tensors=a					)

								if text is not None:
															snake_case_				=							self.tokenizer(
															    text=a       ,			add_special_tokens=a       ,			padding=a       ,			truncation=a       ,			max_length=a       ,			stride=a       ,			pad_to_multiple_of=a       ,			return_attention_mask=a       ,			return_overflowing_tokens=a       ,			return_special_tokens_mask=a       ,			return_offsets_mapping=a       ,			return_token_type_ids=a       ,			return_length=a       ,			verbose=a       ,			return_tensors=a       ,			**a       ,			)
								else:
															snake_case_				=							None

								if text_encoding is not None:
															encoding_image_processor.update(a					)

								return encoding_image_processor




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    int:
								return self.tokenizer.batch_decode(*a       ,			**a					)




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    Any:
								return self.tokenizer.decode(*a       ,			**a					)






	@property
	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.tokenizer.model_input_names
								snake_case_				=							self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)





