






'''simple docstring'''


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()
lowerCamelCase_				: Dict    	=							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase_				: List[str]    	=							[]
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 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = state_dict.pop(_lowerCamelCase					)
						UpperCamelCase__         = val







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		UpperCamelCase__         = key.replace("""backbone.0.body"""						,      """backbone.conv_encoder.model"""					)
																		UpperCamelCase__         = value
												else:
																		UpperCamelCase__         = value

						return new_state_dict







def 	__magic_name__( _A						,      _A=False					):
						'''simple docstring'''





						UpperCamelCase__         = ''
						if is_panoptic:
												UpperCamelCase__         = '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)
												UpperCamelCase__         = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"					)
												UpperCamelCase__         = 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
												UpperCamelCase__         = in_proj_weight[:256, :]
												UpperCamelCase__         = in_proj_bias[:256]
												UpperCamelCase__         = in_proj_weight[256:512, :]
												UpperCamelCase__         = in_proj_bias[256:512]
												UpperCamelCase__         = in_proj_weight[-256:, :]
												UpperCamelCase__         = in_proj_bias[-256:]







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = 'http://images.cocodataset.org/val2017/000000039769.jpg'
						UpperCamelCase__         = Image.open(requests.get(_lowerCamelCase						,      stream=_lowerCamelCase					).raw					)

						return im







@torch.no_grad()
def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = ConditionalDetrConfig()
						# set backbone and dilation attributes
						if "resnet101" in model_name:
												UpperCamelCase__         = 'resnet101'
						if "dc5" in model_name:
												UpperCamelCase__         = True
						UpperCamelCase__         = 'panoptic' in model_name
						if is_panoptic:
												UpperCamelCase__         = 250
						else:
												UpperCamelCase__         = 91
												UpperCamelCase__         = 'huggingface/label-files'
												UpperCamelCase__         = 'coco-detection-id2label.json'
												UpperCamelCase__         = json.load(open(hf_hub_download(_lowerCamelCase						,      _lowerCamelCase						,      repo_type="""dataset"""					)						,      """r"""					)					)
												UpperCamelCase__         = {int(_lowerCamelCase					): v for k, v in idalabel.items()}
												UpperCamelCase__         = idalabel
												UpperCamelCase__         = {v: k for k, v in idalabel.items()}

						# load image processor
						UpperCamelCase__         = 'coco_panoptic' if is_panoptic else 'coco_detection'
						UpperCamelCase__         = ConditionalDetrImageProcessor(format=_lowerCamelCase					)

						# prepare image
						UpperCamelCase__         = prepare_img()
						UpperCamelCase__         = image_processor(images=_lowerCamelCase						,      return_tensors="""pt"""					)
						UpperCamelCase__         = encoding['pixel_values']

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

						# load original model from torch hub
						UpperCamelCase__         = torch.hub.load("""DeppMeng/ConditionalDETR"""						,      _lowerCamelCase						,      pretrained=_lowerCamelCase					).eval()
						UpperCamelCase__         = conditional_detr.state_dict()
						# rename keys
						for src, dest in rename_keys:
												if is_panoptic:
																		UpperCamelCase__         = 'conditional_detr.' + src
												rename_key(_lowerCamelCase						,      _lowerCamelCase						,      _lowerCamelCase					)
						UpperCamelCase__         = rename_backbone_keys(_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
						UpperCamelCase__         = '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"""					)
																		):
																								UpperCamelCase__         = state_dict.pop(_lowerCamelCase					)
																								UpperCamelCase__         = val
																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
																								UpperCamelCase__         = state_dict.pop(_lowerCamelCase					)
																								UpperCamelCase__         = val
																		elif key.startswith("""bbox_attention"""					) or key.startswith("""mask_head"""					):
																								continue
																		else:
																								UpperCamelCase__         = state_dict.pop(_lowerCamelCase					)
																								UpperCamelCase__         = val
												else:
																		if not key.startswith("""class_labels_classifier"""					) and not key.startswith("""bbox_predictor"""					):
																								UpperCamelCase__         = state_dict.pop(_lowerCamelCase					)
																								UpperCamelCase__         = val
    # finally, create HuggingFace model and load state dict
						UpperCamelCase__         = ConditionalDetrForSegmentation(_lowerCamelCase					) if is_panoptic else ConditionalDetrForObjectDetection(_lowerCamelCase					)
						model.load_state_dict(_lowerCamelCase					)
						model.eval()
						model.push_to_hub(repo_id=_lowerCamelCase						,      organization="""DepuMeng"""						,      commit_message="""Add model"""					)
						# verify our conversion
						UpperCamelCase__         = conditional_detr(_lowerCamelCase					)
						UpperCamelCase__         = model(_lowerCamelCase					)
						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(_lowerCamelCase					).mkdir(exist_ok=_lowerCamelCase					)
						model.save_pretrained(_lowerCamelCase					)
						image_processor.save_pretrained(_lowerCamelCase					)


if __name__ == "__main__":
					lowerCamelCase_				: int    	=							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.'''
					)
					lowerCamelCase_				: Optional[int]    	=							parser.parse_args()
					convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








'''simple docstring'''


import numpy as np

from transformers import Pipeline







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = np.max(_A						,      axis=-1						,      keepdims=_A					)
						UpperCamelCase__         = np.exp(outputs - maxes					)
						return shifted_exp / shifted_exp.sum(axis=-1						,      keepdims=_A					)







class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			def 				A      (   self							:						int ,      **lowercase							:						List[Any]			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = {}
									if "second_text" in kwargs:
															UpperCamelCase__         = kwargs["""second_text"""]
									return preprocess_kwargs, {}, {}






			def 				A      (   self							:						Optional[int] ,      lowercase							:						Union[str, Any] ,      lowercase							:						Any=None			)							->    Optional[int]:

									'''simple docstring'''







									return self.tokenizer(lowercase ,      text_pair=lowercase ,      return_tensors=self.framework			)






			def 				A      (   self							:						Union[str, Any] ,      lowercase							:						Optional[int]			)							->    Any:

									'''simple docstring'''







									return self.model(**lowercase			)







			def 				A      (   self							:						List[str] ,      lowercase							:						Any			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = model_outputs.logits[0].numpy()
									UpperCamelCase__         = softmax(lowercase			)

									UpperCamelCase__         = np.argmax(lowercase			)
									UpperCamelCase__         = self.model.config.idalabel[best_class]
									UpperCamelCase__         = probabilities[best_class].item()
									UpperCamelCase__         = logits.tolist()
									return {"label": label, "score": score, "logits": logits}
