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()
__UpperCamelCase	: Union[str, Any]    		=  logging.get_logger(__name__)
def    A			(					_lowercase		):
						# initialize config
						if "resnet-50" in model_name:
												SCREAMING_SNAKE_CASE							:					str           =							ResNetConfig.from_pretrained('''microsoft/resnet-50'''		)
						elif "resnet-101" in model_name:
												SCREAMING_SNAKE_CASE							:					str           =							ResNetConfig.from_pretrained('''microsoft/resnet-101'''		)
						else:
												raise ValueError('''Model name should include either resnet50 or resnet101'''		)

						SCREAMING_SNAKE_CASE							:					int           =							DetrConfig(use_timm_backbone=_lowercase	,						backbone_config=_lowercase		)

						# set label attributes
						SCREAMING_SNAKE_CASE							:					Dict           =							'''panoptic''' in model_name
						if is_panoptic:
												SCREAMING_SNAKE_CASE							:					str           =							250
						else:
												SCREAMING_SNAKE_CASE							:					Any           =							91
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							'''huggingface/label-files'''
												SCREAMING_SNAKE_CASE							:					Dict           =							'''coco-detection-id2label.json'''
												SCREAMING_SNAKE_CASE							:					Tuple           =							json.load(open(hf_hub_download(_lowercase	,						_lowercase	,						repo_type='''dataset'''		)	,						'''r'''		)		)
												SCREAMING_SNAKE_CASE							:					int           =							{int(_lowercase		): v for k, v in idalabel.items()}
												SCREAMING_SNAKE_CASE							:					str           =							idalabel
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							{v: k for k, v in idalabel.items()}

						return config, is_panoptic
def    A			(					_lowercase		):
						# here we list all keys to be renamed (original name on the left, our name on the right)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							[]

						# 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    A			(					_lowercase	,						_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					int           =							state_dict.pop(_lowercase		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							val
def    A			(					_lowercase	,						_lowercase=False		):
						SCREAMING_SNAKE_CASE							:					Tuple           =							''''''
						if is_panoptic:
												SCREAMING_SNAKE_CASE							:					List[Any]           =							'''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)
												SCREAMING_SNAKE_CASE							:					int           =							state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							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
												SCREAMING_SNAKE_CASE							:					Any           =							in_proj_weight[:256, :]
												SCREAMING_SNAKE_CASE							:					int           =							in_proj_bias[:256]
												SCREAMING_SNAKE_CASE							:					Any           =							in_proj_weight[256:512, :]
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							in_proj_bias[256:512]
												SCREAMING_SNAKE_CASE							:					int           =							in_proj_weight[-256:, :]
												SCREAMING_SNAKE_CASE							:					int           =							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
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""		)
												SCREAMING_SNAKE_CASE							:					str           =							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
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							in_proj_weight[:256, :]
												SCREAMING_SNAKE_CASE							:					Tuple           =							in_proj_bias[:256]
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							in_proj_weight[256:512, :]
												SCREAMING_SNAKE_CASE							:					int           =							in_proj_bias[256:512]
												SCREAMING_SNAKE_CASE							:					List[str]           =							in_proj_weight[-256:, :]
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							in_proj_bias[-256:]
												# read in weights + bias of input projection layer of cross-attention
												SCREAMING_SNAKE_CASE							:					Dict           =							state_dict.pop(
												    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""		)
												SCREAMING_SNAKE_CASE							:					Any           =							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
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							in_proj_weight_cross_attn[:256, :]
												SCREAMING_SNAKE_CASE							:					int           =							in_proj_bias_cross_attn[:256]
												SCREAMING_SNAKE_CASE							:					Tuple           =							in_proj_weight_cross_attn[256:512, :]
												SCREAMING_SNAKE_CASE							:					List[Any]           =							in_proj_bias_cross_attn[256:512]
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							in_proj_weight_cross_attn[-256:, :]
												SCREAMING_SNAKE_CASE							:					Any           =							in_proj_bias_cross_attn[-256:]
def    A			(					):
						SCREAMING_SNAKE_CASE							:					Tuple           =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						SCREAMING_SNAKE_CASE							:					Dict           =							Image.open(requests.get(_lowercase	,						stream=_lowercase		).raw		)

						return im
@torch.no_grad()
def    A			(					_lowercase	,						_lowercase=None	,						_lowercase=False		):
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					str           =							get_detr_config(_lowercase		)

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

    # finally, create HuggingFace model and load state dict
						SCREAMING_SNAKE_CASE							:					List[str]           =							DetrForSegmentation(_lowercase		) if is_panoptic else DetrForObjectDetection(_lowercase		)
						model.load_state_dict(_lowercase		)
						model.eval()

						# verify our conversion on an image
						SCREAMING_SNAKE_CASE							:					List[Any]           =							'''coco_panoptic''' if is_panoptic else '''coco_detection'''
						SCREAMING_SNAKE_CASE							:					List[str]           =							DetrImageProcessor(format=_lowercase		)

						SCREAMING_SNAKE_CASE							:					List[str]           =							processor(images=prepare_img()	,						return_tensors='''pt'''		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							encoding['''pixel_values''']

						SCREAMING_SNAKE_CASE							:					List[str]           =							detr(_lowercase		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							model(_lowercase		)

						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(_lowercase		).mkdir(exist_ok=_lowercase		)
												model.save_pretrained(_lowercase		)
												processor.save_pretrained(_lowercase		)

						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__":
					__UpperCamelCase	: List[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.')
					__UpperCamelCase	: List[str]    		=  parser.parse_args()
					convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase	: Tuple    		=  {
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Optional[Any]    		=  ['MaskFormerFeatureExtractor']
					__UpperCamelCase	: List[Any]    		=  ['MaskFormerImageProcessor']


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Optional[int]    		=  [
					    'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MaskFormerForInstanceSegmentation',
					    'MaskFormerModel',
					    'MaskFormerPreTrainedModel',
					]
					__UpperCamelCase	: Union[str, Any]    		=  [
					    'MaskFormerSwinBackbone',
					    'MaskFormerSwinModel',
					    'MaskFormerSwinPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
					from .configuration_maskformer_swin import MaskFormerSwinConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_maskformer import MaskFormerFeatureExtractor
										from .image_processing_maskformer import MaskFormerImageProcessor
					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskformer import (
										    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskFormerForInstanceSegmentation,
										    MaskFormerModel,
										    MaskFormerPreTrainedModel,
										)
										from .modeling_maskformer_swin import (
										    MaskFormerSwinBackbone,
										    MaskFormerSwinModel,
										    MaskFormerSwinPreTrainedModel,
										)


else:
					import sys

					__UpperCamelCase	: List[str]    		=  _LazyModule(__name__, globals()['__file__'], _import_structure)





