



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     				=						logging.get_logger(__name__)







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							List[Any]:
		# initialize config
		if "resnet-50" in model_name:
				UpperCAmelCase_						=				ResNetConfig.from_pretrained("microsoft/resnet-50" )
		elif "resnet-101" in model_name:
				UpperCAmelCase_						=				ResNetConfig.from_pretrained("microsoft/resnet-101" )
		else:
				raise ValueError("Model name should include either resnet50 or resnet101" )

		UpperCAmelCase_						=				DetrConfig(use_timm_backbone=_lowercase			,     backbone_config=_lowercase )

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

		return config, is_panoptic







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							str:
		# here we list all keys to be renamed (original name on the left, our name on the right)
		UpperCAmelCase_						=				[]

		# 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 snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Dict:
		UpperCAmelCase_						=				state_dict.pop(_lowercase )
		UpperCAmelCase_						=				val







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=False )		->							Optional[int]:
		UpperCAmelCase_						=				''''''
		if is_panoptic:
				UpperCAmelCase_						=				'''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)
				UpperCAmelCase_						=				state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
				UpperCAmelCase_						=				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
				UpperCAmelCase_						=				in_proj_weight[:256, :]
				UpperCAmelCase_						=				in_proj_bias[:256]
				UpperCAmelCase_						=				in_proj_weight[256:512, :]
				UpperCAmelCase_						=				in_proj_bias[256:512]
				UpperCAmelCase_						=				in_proj_weight[-256:, :]
				UpperCAmelCase_						=				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
				UpperCAmelCase_						=				state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
				UpperCAmelCase_						=				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
				UpperCAmelCase_						=				in_proj_weight[:256, :]
				UpperCAmelCase_						=				in_proj_bias[:256]
				UpperCAmelCase_						=				in_proj_weight[256:512, :]
				UpperCAmelCase_						=				in_proj_bias[256:512]
				UpperCAmelCase_						=				in_proj_weight[-256:, :]
				UpperCAmelCase_						=				in_proj_bias[-256:]
				# read in weights + bias of input projection layer of cross-attention
				UpperCAmelCase_						=				state_dict.pop(
				    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
				UpperCAmelCase_						=				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
				UpperCAmelCase_						=				in_proj_weight_cross_attn[:256, :]
				UpperCAmelCase_						=				in_proj_bias_cross_attn[:256]
				UpperCAmelCase_						=				in_proj_weight_cross_attn[256:512, :]
				UpperCAmelCase_						=				in_proj_bias_cross_attn[256:512]
				UpperCAmelCase_						=				in_proj_weight_cross_attn[-256:, :]
				UpperCAmelCase_						=				in_proj_bias_cross_attn[-256:]







def snake_case__  (			)		->							Optional[int]:
		UpperCAmelCase_						=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		UpperCAmelCase_						=				Image.open(requests.get(_lowercase			,     stream=_lowercase ).raw )

		return im







@torch.no_grad()
def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=False )		->							List[str]:
		UpperCAmelCase_						=				get_detr_config(_lowercase )

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

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

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

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

		UpperCAmelCase_						=				detr(_lowercase )
		UpperCAmelCase_						=				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__":
		SCREAMING_SNAKE_CASE     				=						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     				=						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_tokenizers_available


SCREAMING_SNAKE_CASE     				=						{"tokenization_herbert": ["HerbertTokenizer"]}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["HerbertTokenizerFast"]


if TYPE_CHECKING:
			from .tokenization_herbert import HerbertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_herbert_fast import HerbertTokenizerFast

else:
			import sys

			SCREAMING_SNAKE_CASE     				=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

