



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

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






						if "resnet-50" in model_name:
												snake_case   :			List[Any]						    = ResNetConfig.from_pretrained("microsoft/resnet-50" )
						elif "resnet-101" in model_name:
												snake_case   :			Tuple						    = ResNetConfig.from_pretrained("microsoft/resnet-101" )
						else:
												raise ValueError("Model name should include either resnet50 or resnet101" )

						snake_case   :			Dict						    = DetrConfig(use_timm_backbone=lowerCamelCase_     ,		backbone_config=lowerCamelCase_ )

						# set label attributes
						snake_case   :			Optional[int]						    = "panoptic" in model_name
						if is_panoptic:
												snake_case   :			Dict						    = 2_5_0
						else:
												snake_case   :			Dict						    = 9_1
												snake_case   :			Union[str, Any]						    = "huggingface/label-files"
												snake_case   :			Tuple						    = "coco-detection-id2label.json"
												snake_case   :			str						    = json.load(open(hf_hub_download(lowerCamelCase_     ,		lowerCamelCase_     ,		repo_type="dataset" )     ,		"r" ) )
												snake_case   :			Tuple						    = {int(lowerCamelCase_ ): v for k, v in idalabel.items()}
												snake_case   :			int						    = idalabel
												snake_case   :			int						    = {v: k for k, v in idalabel.items()}

						return config, is_panoptic

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Tuple ):
						"""simple docstring"""






						snake_case   :			str						    = []

						# 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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Any     ,		lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							Any ):
						"""simple docstring"""






						snake_case   :			Dict						    = state_dict.pop(lowerCamelCase_ )
						snake_case   :			Optional[Any]						    = val

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Dict     ,		lowerCamelCase_:							Dict=False ):
						"""simple docstring"""






						snake_case   :			int						    = ""
						if is_panoptic:
												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)
												snake_case   :			str						    = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
												snake_case   :			List[Any]						    = 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
												snake_case   :			Optional[int]						    = in_proj_weight[:2_5_6, :]
												snake_case   :			Dict						    = in_proj_bias[:2_5_6]
												snake_case   :			int						    = in_proj_weight[2_5_6:5_1_2, :]
												snake_case   :			Tuple						    = in_proj_bias[2_5_6:5_1_2]
												snake_case   :			str						    = in_proj_weight[-2_5_6:, :]
												snake_case   :			int						    = in_proj_bias[-2_5_6:]
						# 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
												snake_case   :			Optional[Any]						    = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
												snake_case   :			Union[str, Any]						    = 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
												snake_case   :			Optional[Any]						    = in_proj_weight[:2_5_6, :]
												snake_case   :			List[Any]						    = in_proj_bias[:2_5_6]
												snake_case   :			Union[str, Any]						    = in_proj_weight[2_5_6:5_1_2, :]
												snake_case   :			Optional[int]						    = in_proj_bias[2_5_6:5_1_2]
												snake_case   :			List[str]						    = in_proj_weight[-2_5_6:, :]
												snake_case   :			Tuple						    = in_proj_bias[-2_5_6:]
												# read in weights + bias of input projection layer of cross-attention
												snake_case   :			Union[str, Any]						    = state_dict.pop(
												    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
												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
												snake_case   :			Optional[int]						    = in_proj_weight_cross_attn[:2_5_6, :]
												snake_case   :			str						    = in_proj_bias_cross_attn[:2_5_6]
												snake_case   :			List[str]						    = in_proj_weight_cross_attn[2_5_6:5_1_2, :]
												snake_case   :			Any						    = in_proj_bias_cross_attn[2_5_6:5_1_2]
												snake_case   :			Union[str, Any]						    = in_proj_weight_cross_attn[-2_5_6:, :]
												snake_case   :			Union[str, Any]						    = in_proj_bias_cross_attn[-2_5_6:]

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						snake_case   :			List[Any]						    = "http://images.cocodataset.org/val2017/000000039769.jpg"
						snake_case   :			Optional[Any]						    = Image.open(requests.get(lowerCamelCase_     ,		stream=lowerCamelCase_ ).raw )

						return im

@torch.no_grad()
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any]     ,		lowerCamelCase_:							int=None     ,		lowerCamelCase_:							List[str]=False ):
						"""simple docstring"""






						snake_case    ,			snake_case   :			List[Any]						    = get_detr_config(lowerCamelCase_ )

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

    # finally, create HuggingFace model and load state dict
						snake_case   :			Any						    = DetrForSegmentation(lowerCamelCase_ ) if is_panoptic else DetrForObjectDetection(lowerCamelCase_ )
						model.load_state_dict(lowerCamelCase_ )
						model.eval()

						# verify our conversion on an image
						snake_case   :			Dict						    = "coco_panoptic" if is_panoptic else "coco_detection"
						snake_case   :			str						    = DetrImageProcessor(format=lowerCamelCase_ )

						snake_case   :			List[Any]						    = processor(images=prepare_img()     ,		return_tensors="pt" )
						snake_case   :			str						    = encoding["pixel_values"]

						snake_case   :			Dict						    = detr(lowerCamelCase_ )
						snake_case   :			Dict						    = model(lowerCamelCase_ )

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

						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__":
			A		=						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.')
			A		=						parser.parse_args()
			convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


A		=						{}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						['GPTSw3Tokenizer']


if TYPE_CHECKING:
			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_gpt_swa import GPTSwaTokenizer

else:
			import sys

			A		=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



