




'''simple docstring'''




import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase   :					Any   							=					logging.get_logger(__name__)

def 	__UpperCAmelCase       ( __magic_name__						)->    Union[str, Any]:



			"""simple docstring"""



			snake_case_				:					int   =       torch.load(__magic_name__     ,map_location="cpu"						)
			if "model" in sd.keys():
						snake_case_				:					Union[str, Any]   =       torch.load(__magic_name__     ,map_location="cpu"						)['''model''']

			# pop unnecessary weights
			snake_case_				:					List[str]   =       [
			    '''decoder.version''',
			    '''decoder.output_projection.weight''',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(__magic_name__						)

			snake_case_				:					Any   =       {
			    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
			    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
			    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
			    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									snake_case_				:					Dict   =       sd.pop(__magic_name__						)

			snake_case_				:					Optional[Any]   =       list(sd.keys()						)
			for key in keys:
						if ".qkv_proj." in key:
									snake_case_				:					Tuple   =       sd[key]
									# We split QKV in separate Q,K,V

									snake_case_				:					Optional[Any]   =       key.replace(".qkv_proj."     ,".q_proj."						)
									snake_case_				:					Optional[Any]   =       key.replace(".qkv_proj."     ,".k_proj."						)
									snake_case_				:					Any   =       key.replace(".qkv_proj."     ,".v_proj."						)

									snake_case_				:					Dict   =       value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									snake_case_				:					Union[str, Any]   =       torch.split(__magic_name__     ,depth // 3     ,dim=0						)

									snake_case_				:					Optional[int]   =       q
									snake_case_				:					str   =       k
									snake_case_				:					List[str]   =       v
									del sd[key]

			return sd

@torch.no_grad()
def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__=None						)->    Dict:



			"""simple docstring"""



			snake_case_				:					Any   =       load_checkpoint(__magic_name__						)

			if config is not None:
						snake_case_				:					str   =       OPTConfig.from_pretrained(__magic_name__						)
			else:
						snake_case_				:					Optional[int]   =       OPTConfig()

			snake_case_				:					Dict   =       OPTModel(__magic_name__						).half().eval()
			model.load_state_dict(__magic_name__						)

			# Check results
			Path(__magic_name__						).mkdir(exist_ok=__magic_name__						)
			model.save_pretrained(__magic_name__						)


if __name__ == "__main__":
		__lowerCamelCase   :					List[str]   							=					argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--fairseq_path''',
		    type=str,
		    help=(
		        '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
		        ''' https://huggingface.co/models?other=opt_metasq'''
		    ),
		)
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
		__lowerCamelCase   :					List[Any]   							=					parser.parse_args()
		convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)












'''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   :					Union[str, Any]   							=					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCamelCase   :					Optional[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       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    int:



			"""simple docstring"""



			snake_case_				:					Optional[Any]   =       state_dict.pop(__magic_name__						)
			snake_case_				:					Any   =       val

def 	__UpperCAmelCase       ( __magic_name__						)->    Optional[Any]:



			"""simple docstring"""



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

			return new_state_dict

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=False						)->    Optional[int]:



			"""simple docstring"""



			snake_case_				:					str   =       ""
			if is_panoptic:
						snake_case_				:					Dict   =       "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_				:					Any   =       state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''						)
						snake_case_				:					Optional[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
						snake_case_				:					Tuple   =       in_proj_weight[:256, :]
						snake_case_				:					List[Any]   =       in_proj_bias[:256]
						snake_case_				:					Optional[Any]   =       in_proj_weight[256:512, :]
						snake_case_				:					Optional[int]   =       in_proj_bias[256:512]
						snake_case_				:					Optional[int]   =       in_proj_weight[-256:, :]
						snake_case_				:					str   =       in_proj_bias[-256:]

def 	__UpperCAmelCase       ( )->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					Optional[int]   =       "http://images.cocodataset.org/val2017/000000039769.jpg"
			snake_case_				:					Optional[Any]   =       Image.open(requests.get(__magic_name__     ,stream=__magic_name__						).raw						)

			return im

@torch.no_grad()
def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    List[str]:



			"""simple docstring"""



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

			# load image processor
			snake_case_				:					Optional[int]   =       "coco_panoptic" if is_panoptic else "coco_detection"
			snake_case_				:					str   =       ConditionalDetrImageProcessor(format=__magic_name__						)

			# prepare image
			snake_case_				:					str   =       prepare_img()
			snake_case_				:					int   =       image_processor(images=__magic_name__     ,return_tensors="pt"						)
			snake_case_				:					Union[str, Any]   =       encoding["pixel_values"]

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

			# load original model from torch hub
			snake_case_				:					Union[str, Any]   =       torch.hub.load("DeppMeng/ConditionalDETR"     ,__magic_name__     ,pretrained=__magic_name__						).eval()
			snake_case_				:					Any   =       conditional_detr.state_dict()
			# rename keys
			for src, dest in rename_keys:
						if is_panoptic:
									snake_case_				:					Any   =       "conditional_detr." + src
						rename_key(__magic_name__     ,__magic_name__     ,__magic_name__						)
			snake_case_				:					Tuple   =       rename_backbone_keys(__magic_name__						)
			# query, key and value matrices need special treatment
			read_in_q_k_v(__magic_name__     ,is_panoptic=__magic_name__						)
			# 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_				:					int   =       "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_				:					Any   =       state_dict.pop(__magic_name__						)
												snake_case_				:					Optional[int]   =       val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												snake_case_				:					Tuple   =       state_dict.pop(__magic_name__						)
												snake_case_				:					Any   =       val
									elif key.startswith("bbox_attention"						) or key.startswith("mask_head"						):
												continue
									else:
												snake_case_				:					Union[str, Any]   =       state_dict.pop(__magic_name__						)
												snake_case_				:					List[Any]   =       val
						else:
									if not key.startswith("class_labels_classifier"						) and not key.startswith("bbox_predictor"						):
												snake_case_				:					Any   =       state_dict.pop(__magic_name__						)
												snake_case_				:					List[Any]   =       val
    # finally, create HuggingFace model and load state dict
			snake_case_				:					Optional[int]   =       ConditionalDetrForSegmentation(__magic_name__						) if is_panoptic else ConditionalDetrForObjectDetection(__magic_name__						)
			model.load_state_dict(__magic_name__						)
			model.eval()
			model.push_to_hub(repo_id=__magic_name__     ,organization="DepuMeng"     ,commit_message="Add model"						)
			# verify our conversion
			snake_case_				:					Dict   =       conditional_detr(__magic_name__						)
			snake_case_				:					Union[str, Any]   =       model(__magic_name__						)
			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(__magic_name__						).mkdir(exist_ok=__magic_name__						)
			model.save_pretrained(__magic_name__						)
			image_processor.save_pretrained(__magic_name__						)


if __name__ == "__main__":
		__lowerCamelCase   :					Tuple   							=					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   :					int   							=					parser.parse_args()
		convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




