






"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE    :    Tuple        =							logging.get_logger(__name__)



def 			__lowerCamelCase       (       lowerCAmelCase__					):


				if "resnet-50" in model_name:
								A__							=							ResNetConfig.from_pretrained('microsoft/resnet-50'					)
				elif "resnet-101" in model_name:
								A__							=							ResNetConfig.from_pretrained('microsoft/resnet-101'					)
				else:
								raise ValueError('Model name should include either resnet50 or resnet101'					)

				A__							=							DetrConfig(use_timm_backbone=SCREAMING_SNAKE_CASE__							,backbone_config=SCREAMING_SNAKE_CASE__					)

				# set label attributes
				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(SCREAMING_SNAKE_CASE__							,SCREAMING_SNAKE_CASE__							,repo_type='dataset'					)							,'r'					)					)
								A__							=							{int(SCREAMING_SNAKE_CASE__					): v for k, v in idalabel.items()}
								A__							=							idalabel
								A__							=							{v: k for k, v in idalabel.items()}

				return config, is_panoptic



def 			__lowerCamelCase       (       lowerCAmelCase__					):
				A__							=							[]

				# stem
				# fmt: off
				rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')					)
				rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')					)
				rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')					)
				rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')					)
				rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')					)
				# stages
				for stage_idx in range(len(config.backbone_config.depths					)					):
								for layer_idx in range(config.backbone_config.depths[stage_idx]					):
												# shortcut
												if layer_idx == 0:
																rename_keys.append(
																    (
																        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
																        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
																    )					)
																rename_keys.append(
																    (
																        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
																        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
																    )					)
																rename_keys.append(
																    (
																        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
																        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
																    )					)
																rename_keys.append(
																    (
																        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
																        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
																    )					)
																rename_keys.append(
																    (
																        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
																        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
																    )					)
												# 3 convs
												for i in range(3					):
																rename_keys.append(
																    (
																        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
																        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
																    )					)
																rename_keys.append(
																    (
																        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
																        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
																    )					)
																rename_keys.append(
																    (
																        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
																        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
																    )					)
																rename_keys.append(
																    (
																        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
																        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
																    )					)
																rename_keys.append(
																    (
																        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
																        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
																    )					)
    # fmt: on

				for i in range(config.encoder_layers					):
								# 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}.multihead_attn.out_proj.weight''',
								        f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
								    )					)
								rename_keys.append(
								    (
								        f'''transformer.decoder.layers.{i}.multihead_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''')					)

				# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
				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'),
				    ]					)

				return rename_keys



def 			__lowerCamelCase       (       lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__					):
				A__							=							state_dict.pop(SCREAMING_SNAKE_CASE__					)
				A__							=							val



def 			__lowerCamelCase       (       lowerCAmelCase__							,lowerCAmelCase__=False					):
				A__							=							''''''
				if is_panoptic:
								A__							=							'''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:]
				# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
				for i in range(6					):
								# read in weights + bias of input projection layer of self-attention
								A__							=							state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''					)
								A__							=							state_dict.pop(f'''{prefix}transformer.decoder.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:]
								# read in weights + bias of input projection layer of cross-attention
								A__							=							state_dict.pop(
								    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''					)
								A__							=							state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''					)
								# next, add query, keys and values (in that order) of cross-attention to the state dict
								A__							=							in_proj_weight_cross_attn[:256, :]
								A__							=							in_proj_bias_cross_attn[:256]
								A__							=							in_proj_weight_cross_attn[256:512, :]
								A__							=							in_proj_bias_cross_attn[256:512]
								A__							=							in_proj_weight_cross_attn[-256:, :]
								A__							=							in_proj_bias_cross_attn[-256:]



def 			__lowerCamelCase       (       ):
				A__							=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				A__							=							Image.open(requests.get(SCREAMING_SNAKE_CASE__							,stream=SCREAMING_SNAKE_CASE__					).raw					)

				return im



@torch.no_grad()
def 			__lowerCamelCase       (       lowerCAmelCase__							,lowerCAmelCase__=None							,lowerCAmelCase__=False					):
				A__							=							get_detr_config(SCREAMING_SNAKE_CASE__					)

				# load original model from torch hub
				A__							=							{
				    '''detr-resnet-50''': '''detr_resnet50''',
				    '''detr-resnet-101''': '''detr_resnet101''',
				}
				logger.info(f'''Converting model {model_name}...'''					)
				A__							=							torch.hub.load('facebookresearch/detr'							,model_name_to_original_name[model_name]							,pretrained=SCREAMING_SNAKE_CASE__					).eval()
				A__							=							detr.state_dict()
				# rename keys
				for src, dest in create_rename_keys(SCREAMING_SNAKE_CASE__					):
								if is_panoptic:
												A__							=							'''detr.''' + src
								rename_key(SCREAMING_SNAKE_CASE__							,SCREAMING_SNAKE_CASE__							,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
				A__							=							'''detr.model.''' if is_panoptic else '''model.'''
				for key in state_dict.copy().keys():
								if is_panoptic:
												if (
												    key.startswith('detr'					)
												    and not key.startswith('class_labels_classifier'					)
												    and not key.startswith('bbox_predictor'					)
												):
																A__							=							state_dict.pop(SCREAMING_SNAKE_CASE__					)
																A__							=							val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																A__							=							state_dict.pop(SCREAMING_SNAKE_CASE__					)
																A__							=							val
												elif key.startswith('bbox_attention'					) or key.startswith('mask_head'					):
																continue
												else:
																A__							=							state_dict.pop(SCREAMING_SNAKE_CASE__					)
																A__							=							val
								else:
												if not key.startswith('class_labels_classifier'					) and not key.startswith('bbox_predictor'					):
																A__							=							state_dict.pop(SCREAMING_SNAKE_CASE__					)
																A__							=							val

    # finally, create HuggingFace model and load state dict
				A__							=							DetrForSegmentation(SCREAMING_SNAKE_CASE__					) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE__					)
				model.load_state_dict(SCREAMING_SNAKE_CASE__					)
				model.eval()

				# verify our conversion on an image
				A__							=							'''coco_panoptic''' if is_panoptic else '''coco_detection'''
				A__							=							DetrImageProcessor(format=SCREAMING_SNAKE_CASE__					)

				A__							=							processor(images=prepare_img()							,return_tensors='pt'					)
				A__							=							encoding['''pixel_values''']

				A__							=							detr(SCREAMING_SNAKE_CASE__					)
				A__							=							model(SCREAMING_SNAKE_CASE__					)

				assert torch.allclose(outputs.logits							,original_outputs['pred_logits']							,atol=1E-3					)
				assert torch.allclose(outputs.pred_boxes							,original_outputs['pred_boxes']							,atol=1E-3					)
				if is_panoptic:
								assert torch.allclose(outputs.pred_masks							,original_outputs['pred_masks']							,atol=1E-4					)
				print('Looks ok!'					)

				if pytorch_dump_folder_path is not None:
								# 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__					)
								processor.save_pretrained(SCREAMING_SNAKE_CASE__					)

				if push_to_hub:
								# Upload model and image processor to the hub
								logger.info('Uploading PyTorch model and image processor to the hub...'					)
								model.push_to_hub(f'''nielsr/{model_name}'''					)
								processor.push_to_hub(f'''nielsr/{model_name}'''					)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE    :    Any        =							argparse.ArgumentParser()

					parser.add_argument(
					    '''--model_name''',
					    default='''detr-resnet-50''',
					    type=str,
					    choices=['''detr-resnet-50''', '''detr-resnet-101'''],
					    help='''Name of the 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.'''
					)
					parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
					SCREAMING_SNAKE_CASE    :    Any        =							parser.parse_args()
					convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''
from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						42







class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		@register_to_config
		def __init__(      self				:			List[str]  ,	UpperCamelCase__				:			int = 16  ,	UpperCamelCase__				:			int = 88  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			int = 1  ,	UpperCamelCase__				:			float = 0.0  ,	UpperCamelCase__				:			int = 32  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			bool = False  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			str = "geglu"  ,	UpperCamelCase__				:			bool = True  ,	UpperCamelCase__				:			bool = True  ,	)						->			List[Any]:






				"""simple docstring"""







				super().__init__()
				snake_case  :       Optional[int]     			=						num_attention_heads
				snake_case  :       int     			=						attention_head_dim
				snake_case  :       Dict     			=						num_attention_heads * attention_head_dim

				snake_case  :       Tuple     			=						in_channels

				snake_case  :       Optional[int]     			=						torch.nn.GroupNorm(num_groups=UpperCamelCase__  ,	num_channels=UpperCamelCase__  ,	eps=1e-6  ,	affine=UpperCamelCase__      )
				snake_case  :       List[Any]     			=						nn.Linear(UpperCamelCase__  ,	UpperCamelCase__      )

				# 3. Define transformers blocks
				snake_case  :       Union[str, Any]     			=						nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	dropout=UpperCamelCase__  ,	cross_attention_dim=UpperCamelCase__  ,	activation_fn=UpperCamelCase__  ,	attention_bias=UpperCamelCase__  ,	double_self_attention=UpperCamelCase__  ,	norm_elementwise_affine=UpperCamelCase__  ,	)
				        for d in range(UpperCamelCase__      )
				    ]      )

				snake_case  :       Any     			=						nn.Linear(UpperCamelCase__  ,	UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			Tuple  ,	UpperCamelCase__				:			Any=None  ,	UpperCamelCase__				:			Dict=None  ,	UpperCamelCase__				:			Dict=None  ,	UpperCamelCase__				:			Union[str, Any]=1  ,	UpperCamelCase__				:			Optional[int]=None  ,	UpperCamelCase__				:			bool = True  ,	)						->			Any:






				"""simple docstring"""







				snake_case			,snake_case			,snake_case			,snake_case  :       Tuple     			=						hidden_states.shape
				snake_case  :       List[Any]     			=						batch_frames // num_frames

				snake_case  :       Union[str, Any]     			=						hidden_states

				snake_case  :       Optional[Any]     			=						hidden_states[None, :].reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
				snake_case  :       int     			=						hidden_states.permute(0  ,	2  ,	1  ,	3  ,	4      )

				snake_case  :       Tuple     			=						self.norm(UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						hidden_states.permute(0  ,	3  ,	4  ,	2  ,	1      ).reshape(batch_size * height * width  ,	UpperCamelCase__  ,	UpperCamelCase__      )

				snake_case  :       Optional[Any]     			=						self.proj_in(UpperCamelCase__      )

				# 2. Blocks
				for block in self.transformer_blocks:
						snake_case  :       Union[str, Any]     			=						block(
						    UpperCamelCase__  ,	encoder_hidden_states=UpperCamelCase__  ,	timestep=UpperCamelCase__  ,	cross_attention_kwargs=UpperCamelCase__  ,	class_labels=UpperCamelCase__  ,	)

				# 3. Output
				snake_case  :       Dict     			=						self.proj_out(UpperCamelCase__      )
				snake_case  :       Tuple     			=						(
				    hidden_states[None, None, :]
				    .reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
				    .permute(0  ,	3  ,	4  ,	1  ,	2      )
				    .contiguous()
				)
				snake_case  :       List[Any]     			=						hidden_states.reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )

				snake_case  :       Any     			=						hidden_states + residual

				if not return_dict:
						return (output,)

				return TransformerTemporalModelOutput(sample=UpperCamelCase__      )


