


from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'canine'




						def __init__(	self						:   int   ,     __A						:   Tuple=768   ,     __A						:   str=12   ,     __A						:   str=12   ,     __A						:   str=3072   ,     __A						:   Any="gelu"   ,     __A						:   Optional[int]=0.1   ,     __A						:   Any=0.1   ,     __A						:   Any=16384   ,     __A						:   Union[str, Any]=16   ,     __A						:   Optional[Any]=0.02   ,     __A						:   Optional[int]=1E-12   ,     __A						:   str=0   ,     __A						:   str=0Xe_000   ,     __A						:   Tuple=0Xe_001   ,     __A						:   Optional[int]=4   ,     __A						:   List[str]=4   ,     __A						:   Optional[Any]=8   ,     __A						:   Optional[int]=16384   ,     __A						:   Tuple=128   ,     **__A						:   List[Any]   ,     )      ->Dict:






									"""simple docstring"""






									super().__init__(pad_token_id=__A   ,     bos_token_id=__A   ,     eos_token_id=__A   ,     **__A						)

									a__ :Tuple						  =    max_position_embeddings
									a__ :Dict						  =    hidden_size
									a__ :List[Any]						  =    num_hidden_layers
									a__ :Tuple						  =    num_attention_heads
									a__ :Dict						  =    intermediate_size
									a__ :str						  =    hidden_act
									a__ :Optional[int]						  =    hidden_dropout_prob
									a__ :List[Any]						  =    attention_probs_dropout_prob
									a__ :Optional[Any]						  =    initializer_range
									a__ :Tuple						  =    type_vocab_size
									a__ :List[str]						  =    layer_norm_eps

									# Character config:
									a__ :Tuple						  =    downsampling_rate
									a__ :List[Any]						  =    upsampling_kernel_size
									a__ :int						  =    num_hash_functions
									a__ :List[str]						  =    num_hash_buckets
									a__ :List[str]						  =    local_transformer_stride






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()
snake_case__			 =						logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case__			 =						[]
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 				lowerCamelCase__	(      a	:   Any	,  a	:   Any	,  a	:   List[Any]				)    ->	Any:





			"""simple docstring"""
			a__ :Optional[Any]						  =    state_dict.pop(a				)
			a__ :Tuple						  =    val


def 				lowerCamelCase__	(      a	:   List[str]				)    ->	str:





			"""simple docstring"""
			a__ :Any						  =    OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									a__ :List[Any]						  =    key.replace("backbone.0.body"	,  "backbone.conv_encoder.model"				)
									a__ :List[Any]						  =    value
						else:
									a__ :Dict						  =    value

			return new_state_dict


def 				lowerCamelCase__	(      a	:   str	,  a	:   Dict=False				)    ->	List[Any]:





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


def 				lowerCamelCase__	(      )    ->	Optional[Any]:





			"""simple docstring"""
			a__ :str						  =    "http://images.cocodataset.org/val2017/000000039769.jpg"
			a__ :Optional[Any]						  =    Image.open(requests.get(a	,  stream=a				).raw				)

			return im


@torch.no_grad()
def 				lowerCamelCase__	(      a	:   Tuple	,  a	:   Union[str, Any]				)    ->	List[Any]:





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

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

			# prepare image
			a__ :int						  =    prepare_img()
			a__ :Optional[int]						  =    image_processor(images=a	,  return_tensors="pt"				)
			a__ :int						  =    encoding["pixel_values"]

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

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


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


