






"""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       :	Optional[int]             = logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__a       :	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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  state_dict.pop(lowerCamelCase_)
					a__          =  val





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_=False):
					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        SCREAMING_SNAKE_CASE       (      ):
					a__          =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					a__          =  Image.open(requests.get(lowerCamelCase_ ,     stream=lowerCamelCase_).raw)

					return im





@torch.no_grad()
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					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(lowerCamelCase_ ,     lowerCamelCase_ ,     repo_type='''dataset''') ,     '''r'''))
										a__          =  {int(lowerCamelCase_): 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=lowerCamelCase_)

					# prepare image
					a__          =  prepare_img()
					a__          =  image_processor(images=lowerCamelCase_ ,     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''' ,     lowerCamelCase_ ,     pretrained=lowerCamelCase_).eval()
					a__          =  conditional_detr.state_dict()
					# rename keys
					for src, dest in rename_keys:
										if is_panoptic:
															a__          =  '''conditional_detr.''' + src
										rename_key(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					a__          =  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
					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(lowerCamelCase_)
																				a__          =  val
															elif "class_labels_classifier" in key or "bbox_predictor" in key:
																				a__          =  state_dict.pop(lowerCamelCase_)
																				a__          =  val
															elif key.startswith('''bbox_attention''') or key.startswith('''mask_head'''):
																				continue
															else:
																				a__          =  state_dict.pop(lowerCamelCase_)
																				a__          =  val
										else:
															if not key.startswith('''class_labels_classifier''') and not key.startswith('''bbox_predictor'''):
																				a__          =  state_dict.pop(lowerCamelCase_)
																				a__          =  val
    # finally, create HuggingFace model and load state dict
					a__          =  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
					a__          =  conditional_detr(lowerCamelCase_)
					a__          =  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__":
	__a       :	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.'
	)
	__a       :	Union[str, Any]             = parser.parse_args()
	convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










"""simple docstring"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_albert import AlbertTokenizer
else:
	__a       :	int             = None

__a       :	List[str]             = logging.get_logger(__name__)
__a       :	Union[str, Any]             = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__a       :	str             = {
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

__a       :	Optional[int]             = {
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

__a       :	Union[str, Any]             = '▁'







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =VOCAB_FILES_NAMES
							_SCREAMING_SNAKE_CASE    =PRETRAINED_VOCAB_FILES_MAP
							_SCREAMING_SNAKE_CASE    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_SCREAMING_SNAKE_CASE    =AlbertTokenizer




							def __init__(   self:     List[Any]				,		__A:     List[Any]=None				,		__A:     Tuple=None				,		__A:     Union[str, Any]=True				,		__A:     int=True				,		__A:     Optional[int]=False				,		__A:     List[Any]="[CLS]"				,		__A:     Optional[int]="[SEP]"				,		__A:     Union[str, Any]="<unk>"				,		__A:     int="[SEP]"				,		__A:     Optional[Any]="<pad>"				,		__A:     Optional[int]="[CLS]"				,		__A:     str="[MASK]"				,		**__A:     List[Any]				,		):

												'''simple docstring'''

												a__          =  (
												    AddedToken(__A				,		lstrip=__A				,		rstrip=__A				,		normalized=__A    )
												    if isinstance(__A				,		__A    )
												    else mask_token
												)

												super().__init__(
												    __A				,		tokenizer_file=__A				,		do_lower_case=__A				,		remove_space=__A				,		keep_accents=__A				,		bos_token=__A				,		eos_token=__A				,		unk_token=__A				,		sep_token=__A				,		pad_token=__A				,		cls_token=__A				,		mask_token=__A				,		**__A				,		)

												a__          =  do_lower_case
												a__          =  remove_space
												a__          =  keep_accents
												a__          =  vocab_file
												a__          =  False if not self.vocab_file else True




							def  lowercase		(   self:     Union[str, Any]				,		__A:     List[int]				,		__A:     Optional[List[int]] = None    ):

												'''simple docstring'''

												a__          =  [self.sep_token_id]
												a__          =  [self.cls_token_id]
												if token_ids_a is None:
																	return cls + token_ids_a + sep
												return cls + token_ids_a + sep + token_ids_a + sep




							def  lowercase		(   self:     List[Any]				,		__A:     List[int]				,		__A:     Optional[List[int]] = None    ):

												'''simple docstring'''

												a__          =  [self.sep_token_id]
												a__          =  [self.cls_token_id]

												if token_ids_a is None:
																	return len(cls + token_ids_a + sep    ) * [0]
												return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]





							def  lowercase		(   self:     Union[str, Any]				,		__A:     str				,		__A:     Optional[str] = None    ):

												'''simple docstring'''

												if not self.can_save_slow_tokenizer:
																	raise ValueError(
																	    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																	    '''tokenizer.'''    )

												if not os.path.isdir(__A    ):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
																	return
												a__          =  os.path.join(
												    __A				,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

												if os.path.abspath(self.vocab_file    ) != os.path.abspath(__A    ):
																	copyfile(self.vocab_file				,		__A    )

												return (out_vocab_file,)


