


"""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()
a_       							=    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a_       							=    []
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      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	List[str]:
				_A					  =			state_dict.pop(__lowercase   )
				_A					  =			val






def      a__ (				__lowercase   )	->	List[str]:
				_A					  =			OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												_A					  =			key.replace("backbone.0.body"     ,			"backbone.conv_encoder.model"   )
												_A					  =			value
								else:
												_A					  =			value

				return new_state_dict






def      a__ (				__lowercase     ,			__lowercase=False   )	->	Any:
				_A					  =			""
				if is_panoptic:
								_A					  =			"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)
								_A					  =			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
								_A					  =			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
								_A					  =			in_proj_weight[:256, :]
								_A					  =			in_proj_bias[:256]
								_A					  =			in_proj_weight[256:512, :]
								_A					  =			in_proj_bias[256:512]
								_A					  =			in_proj_weight[-256:, :]
								_A					  =			in_proj_bias[-256:]






def      a__ (				)	->	int:
				_A					  =			"http://images.cocodataset.org/val2017/000000039769.jpg"
				_A					  =			Image.open(requests.get(__lowercase     ,			stream=__lowercase   ).raw   )

				return im






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

				# load image processor
				_A					  =			"coco_panoptic" if is_panoptic else "coco_detection"
				_A					  =			ConditionalDetrImageProcessor(format=__lowercase   )

				# prepare image
				_A					  =			prepare_img()
				_A					  =			image_processor(images=__lowercase     ,			return_tensors="pt"   )
				_A					  =			encoding["pixel_values"]

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

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


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



"""simple docstring"""


import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


a_       							=    logging.get_logger(__name__)
@dataclass
class snake_case      :
		__UpperCamelCase           =			field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys())})
		__UpperCamelCase           =			field(
		    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
		__UpperCamelCase           =			field(
		    default=128     ,				metadata={
		        'help': (
		            'The maximum total input sequence length after tokenization. Sequences longer '
		            'than this will be truncated, sequences shorter will be padded.'
		        )
		    }     ,				)
		__UpperCamelCase           =			field(
		    default=_UpperCamelCase     ,				metadata={'help': 'Overwrite the cached training and evaluation sets'})







		def  a_ (					self      :       Optional[Any]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			self.task_name.lower()





class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			'train'
		__UpperCamelCase           =			'dev'
		__UpperCamelCase           =			'test'





class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			42
		__UpperCamelCase           =			42
		__UpperCamelCase           =			42







		def __init__(					self      :       Optional[int]    ,   a__      :       GlueDataTrainingArguments    ,   a__      :       PreTrainedTokenizerBase    ,   a__      :       Optional[int] = None    ,   a__      :       Union[str, Split] = Split.train    ,   a__      :       Optional[str] = None    ,   )       ->						Tuple:





						'''simple docstring'''







						warnings.warn(
						    "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
						    "library. You can have a look at this example script for pointers: "
						    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"    ,   a__    ,   )
						_A					  =			args
						_A					  =			glue_processors[args.task_name]()
						_A					  =			glue_output_modes[args.task_name]
						if isinstance(a__    ,   a__		):
										try:
														_A					  =			Split[mode]
										except KeyError:
														raise KeyError("mode is not a valid split name"		)
        # Load data features from cache or dataset file
						_A					  =			os.path.join(
						    cache_dir if cache_dir is not None else args.data_dir    ,   F"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""    ,   )
						_A					  =			self.processor.get_labels()
						if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
						    "RobertaTokenizer",
						    "RobertaTokenizerFast",
						    "XLMRobertaTokenizer",
						    "BartTokenizer",
						    "BartTokenizerFast",
						):
										# HACK(label indices are swapped in RoBERTa pretrained model)
										_A  ,		_A					  =			label_list[2], label_list[1]
						_A					  =			label_list

						# Make sure only the first process in distributed training processes the dataset,
						# and the others will use the cache.
						_A					  =			cached_features_file + ".lock"
						with FileLock(a__		):
										if os.path.exists(a__		) and not args.overwrite_cache:
														_A					  =			time.time()
														_A					  =			torch.load(a__		)
														logger.info(
														    F"""Loading features from cached file {cached_features_file} [took %.3f s]"""    ,   time.time() - start		)
										else:
														logger.info(F"""Creating features from dataset file at {args.data_dir}"""		)

														if mode == Split.dev:
																		_A					  =			self.processor.get_dev_examples(args.data_dir		)
														elif mode == Split.test:
																		_A					  =			self.processor.get_test_examples(args.data_dir		)
														else:
																		_A					  =			self.processor.get_train_examples(args.data_dir		)
														if limit_length is not None:
																		_A					  =			examples[:limit_length]
														_A					  =			glue_convert_examples_to_features(
														    a__    ,   a__    ,   max_length=args.max_seq_length    ,   label_list=a__    ,   output_mode=self.output_mode    ,   )
														_A					  =			time.time()
														torch.save(self.features    ,   a__		)
														# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
														logger.info(
														    F"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""		)







		def __len__(					self      :       List[Any]		)       ->						Any:





						'''simple docstring'''







						return len(self.features		)







		def __getitem__(					self      :       Tuple    ,   a__      :       Union[str, Any]		)       ->						InputFeatures:





						'''simple docstring'''







						return self.features[i]







		def  a_ (					self      :       Optional[int]		)       ->						List[Any]:





						'''simple docstring'''







						return self.label_list