





"""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()
__magic_name__       :					str						=			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__magic_name__       :					Union[str, Any]						=			[]
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							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[str]				   =			state_dict.pop(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		List[Any]				   =			val

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Optional[Any]				   =			OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		UpperCamelCase				:		Tuple				   =			key.replace("""backbone.0.body"""							,     """backbone.conv_encoder.model"""     )
																		UpperCamelCase				:		Optional[int]				   =			value
												else:
																		UpperCamelCase				:		Any				   =			value

						return new_state_dict

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=False     ):
						UpperCamelCase				:		Tuple				   =			""""""
						if is_panoptic:
												UpperCamelCase				:		Any				   =			"""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				:		Optional[int]				   =			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""     )
												UpperCamelCase				:		int				   =			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				:		Any				   =			in_proj_weight[:256, :]
												UpperCamelCase				:		str				   =			in_proj_bias[:256]
												UpperCamelCase				:		int				   =			in_proj_weight[256:512, :]
												UpperCamelCase				:		Dict				   =			in_proj_bias[256:512]
												UpperCamelCase				:		str				   =			in_proj_weight[-256:, :]
												UpperCamelCase				:		str				   =			in_proj_bias[-256:]

def      UpperCamelCase							():
						UpperCamelCase				:		int				   =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCamelCase				:		Union[str, Any]				   =			Image.open(requests.get(SCREAMING_SNAKE_CASE							,     stream=SCREAMING_SNAKE_CASE     ).raw     )

						return im

@torch.no_grad()
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		int				   =			ConditionalDetrConfig()
						# set backbone and dilation attributes
						if "resnet101" in model_name:
												UpperCamelCase				:		Tuple				   =			"""resnet101"""
						if "dc5" in model_name:
												UpperCamelCase				:		List[str]				   =			True
						UpperCamelCase				:		Optional[int]				   =			"""panoptic""" in model_name
						if is_panoptic:
												UpperCamelCase				:		int				   =			250
						else:
												UpperCamelCase				:		Tuple				   =			91
												UpperCamelCase				:		Optional[int]				   =			"""huggingface/label-files"""
												UpperCamelCase				:		Optional[int]				   =			"""coco-detection-id2label.json"""
												UpperCamelCase				:		Tuple				   =			json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     repo_type="""dataset"""     )							,     """r"""     )     )
												UpperCamelCase				:		Union[str, Any]				   =			{int(SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
												UpperCamelCase				:		Union[str, Any]				   =			idalabel
												UpperCamelCase				:		Tuple				   =			{v: k for k, v in idalabel.items()}

						# load image processor
						UpperCamelCase				:		Dict				   =			"""coco_panoptic""" if is_panoptic else """coco_detection"""
						UpperCamelCase				:		Union[str, Any]				   =			ConditionalDetrImageProcessor(format=SCREAMING_SNAKE_CASE     )

						# prepare image
						UpperCamelCase				:		Optional[int]				   =			prepare_img()
						UpperCamelCase				:		List[Any]				   =			image_processor(images=SCREAMING_SNAKE_CASE							,     return_tensors="""pt"""     )
						UpperCamelCase				:		List[Any]				   =			encoding["""pixel_values"""]

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

						# load original model from torch hub
						UpperCamelCase				:		List[str]				   =			torch.hub.load("""DeppMeng/ConditionalDETR"""							,     SCREAMING_SNAKE_CASE							,     pretrained=SCREAMING_SNAKE_CASE     ).eval()
						UpperCamelCase				:		str				   =			conditional_detr.state_dict()
						# rename keys
						for src, dest in rename_keys:
												if is_panoptic:
																		UpperCamelCase				:		Union[str, Any]				   =			"""conditional_detr.""" + src
												rename_key(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Union[str, Any]				   =			rename_backbone_keys(SCREAMING_SNAKE_CASE     )
						# query, key and value matrices need special treatment
						read_in_q_k_v(SCREAMING_SNAKE_CASE							,     is_panoptic=SCREAMING_SNAKE_CASE     )
						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
						UpperCamelCase				:		List[Any]				   =			"""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				:		Optional[int]				   =			state_dict.pop(SCREAMING_SNAKE_CASE     )
																								UpperCamelCase				:		List[str]				   =			val
																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
																								UpperCamelCase				:		Optional[int]				   =			state_dict.pop(SCREAMING_SNAKE_CASE     )
																								UpperCamelCase				:		str				   =			val
																		elif key.startswith("""bbox_attention"""     ) or key.startswith("""mask_head"""     ):
																								continue
																		else:
																								UpperCamelCase				:		Any				   =			state_dict.pop(SCREAMING_SNAKE_CASE     )
																								UpperCamelCase				:		str				   =			val
												else:
																		if not key.startswith("""class_labels_classifier"""     ) and not key.startswith("""bbox_predictor"""     ):
																								UpperCamelCase				:		List[Any]				   =			state_dict.pop(SCREAMING_SNAKE_CASE     )
																								UpperCamelCase				:		Any				   =			val
    # finally, create HuggingFace model and load state dict
						UpperCamelCase				:		Dict				   =			ConditionalDetrForSegmentation(SCREAMING_SNAKE_CASE     ) if is_panoptic else ConditionalDetrForObjectDetection(SCREAMING_SNAKE_CASE     )
						model.load_state_dict(SCREAMING_SNAKE_CASE     )
						model.eval()
						model.push_to_hub(repo_id=SCREAMING_SNAKE_CASE							,     organization="""DepuMeng"""							,     commit_message="""Add model"""     )
						# verify our conversion
						UpperCamelCase				:		Dict				   =			conditional_detr(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Tuple				   =			model(SCREAMING_SNAKE_CASE     )
						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(SCREAMING_SNAKE_CASE     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE     )
						model.save_pretrained(SCREAMING_SNAKE_CASE     )
						image_processor.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
		__magic_name__       :					List[str]						=			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."""
		)
		__magic_name__       :					str						=			parser.parse_args()
		convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










"""simple docstring"""


import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__magic_name__       :					Dict						=			logging.getLogger(__name__)






@dataclass
class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=0.0					,     metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""}	)
			__lowerCAmelCase     :				bool										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Whether to SortishSamler or not."""}	)
			__lowerCAmelCase     :				bool										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}	)
			__lowerCAmelCase     :				bool										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """whether to use adafactor"""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Attention dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[str]										=  field(
			    default="""linear"""					,     metadata={"""help""": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()	)}'''}					,     )



