






'''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()
__lowerCAmelCase									=   logging.get_logger(__name__)





def 		_UpperCAmelCase (				__A				: List[str]   ):
				# initialize config
				if "resnet-50" in model_name:
								a_		:	int									=   ResNetConfig.from_pretrained('''microsoft/resnet-50'''   )
				elif "resnet-101" in model_name:
								a_		:	Optional[Any]									=   ResNetConfig.from_pretrained('''microsoft/resnet-101'''   )
				else:
								raise ValueError('''Model name should include either resnet50 or resnet101'''   )

				a_		:	Optional[Any]									=   DetrConfig(use_timm_backbone=__A    ,		backbone_config=__A   )

				# set label attributes
				a_		:	Any									=   '''panoptic''' in model_name
				if is_panoptic:
								a_		:	Optional[int]									=   2_50
				else:
								a_		:	str									=   91
								a_		:	Optional[int]									=   '''huggingface/label-files'''
								a_		:	str									=   '''coco-detection-id2label.json'''
								a_		:	str									=   json.load(open(hf_hub_download(__A    ,		__A    ,		repo_type='''dataset'''   )    ,		'''r'''   )   )
								a_		:	Union[str, Any]									=   {int(__A   ): v for k, v in idalabel.items()}
								a_		:	List[Any]									=   idalabel
								a_		:	Optional[Any]									=   {v: k for k, v in idalabel.items()}

				return config, is_panoptic





def 		_UpperCAmelCase (				__A				: Any   ):
				# here we list all keys to be renamed (original name on the left, our name on the right)
				a_		:	Optional[int]									=   []

				# 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 		_UpperCAmelCase (				__A				: Optional[Any]    ,		__A				: Dict    ,		__A				: int   ):
				a_		:	Dict									=   state_dict.pop(__A   )
				a_		:	List[str]									=   val





def 		_UpperCAmelCase (				__A				: Dict    ,		__A				: List[str]=False   ):
				a_		:	Tuple									=   ''''''
				if is_panoptic:
								a_		:	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)
								a_		:	Any									=   state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'   )
								a_		:	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_		:	Dict									=   in_proj_weight[:2_56, :]
								a_		:	Optional[Any]									=   in_proj_bias[:2_56]
								a_		:	List[str]									=   in_proj_weight[2_56:5_12, :]
								a_		:	int									=   in_proj_bias[2_56:5_12]
								a_		:	List[str]									=   in_proj_weight[-2_56:, :]
								a_		:	Dict									=   in_proj_bias[-2_56:]
				# 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_		:	Optional[int]									=   state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'   )
								a_		:	int									=   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_		:	Union[str, Any]									=   in_proj_weight[:2_56, :]
								a_		:	int									=   in_proj_bias[:2_56]
								a_		:	List[str]									=   in_proj_weight[2_56:5_12, :]
								a_		:	Optional[Any]									=   in_proj_bias[2_56:5_12]
								a_		:	int									=   in_proj_weight[-2_56:, :]
								a_		:	Optional[Any]									=   in_proj_bias[-2_56:]
								# read in weights + bias of input projection layer of cross-attention
								a_		:	Optional[Any]									=   state_dict.pop(
								    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'   )
								a_		:	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
								a_		:	str									=   in_proj_weight_cross_attn[:2_56, :]
								a_		:	Tuple									=   in_proj_bias_cross_attn[:2_56]
								a_		:	Optional[int]									=   in_proj_weight_cross_attn[2_56:5_12, :]
								a_		:	Optional[Any]									=   in_proj_bias_cross_attn[2_56:5_12]
								a_		:	List[str]									=   in_proj_weight_cross_attn[-2_56:, :]
								a_		:	Optional[Any]									=   in_proj_bias_cross_attn[-2_56:]





def 		_UpperCAmelCase (				):
				a_		:	Optional[int]									=   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				a_		:	List[str]									=   Image.open(requests.get(__A    ,		stream=__A   ).raw   )

				return im





@torch.no_grad()
def 		_UpperCAmelCase (				__A				: Any    ,		__A				: List[str]=None    ,		__A				: Dict=False   ):
				a_					,      a_		:	Union[str, Any]									=   get_detr_config(__A   )

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

    # finally, create HuggingFace model and load state dict
				a_		:	str									=   DetrForSegmentation(__A   ) if is_panoptic else DetrForObjectDetection(__A   )
				model.load_state_dict(__A   )
				model.eval()

				# verify our conversion on an image
				a_		:	Union[str, Any]									=   '''coco_panoptic''' if is_panoptic else '''coco_detection'''
				a_		:	Optional[Any]									=   DetrImageProcessor(format=__A   )

				a_		:	str									=   processor(images=prepare_img()    ,		return_tensors='''pt'''   )
				a_		:	str									=   encoding['''pixel_values''']

				a_		:	List[str]									=   detr(__A   )
				a_		:	Optional[int]									=   model(__A   )

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

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












'''simple docstring'''







from __future__ import annotations

from typing import TypedDict





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					42
			snake_case__	=					42





def 		_UpperCAmelCase (				__A				: str   ):
				if not isinstance(__A    ,		__A   ):
								raise TypeError('''The parameter s type must be str.'''   )

				return [s[i:] + s[:i] for i in range(len(__A   )   )]





def 		_UpperCAmelCase (				__A				: str   ):
				if not isinstance(__A    ,		__A   ):
								raise TypeError('''The parameter s type must be str.'''   )
				if not s:
								raise ValueError('''The parameter s must not be empty.'''   )

				a_		:	str									=   all_rotations(__A   )
				rotations.sort()  # sort the list of rotations in alphabetically order
				# make a string composed of the last char of each rotation
				a_		:	BWTTransformDict									=   {
				    "bwt_string": "".join([word[-1] for word in rotations]   ),
				    "idx_original_string": rotations.index(__A   ),
				}
				return response





def 		_UpperCAmelCase (				__A				: str    ,		__A				: int   ):
				if not isinstance(__A    ,		__A   ):
								raise TypeError('''The parameter bwt_string type must be str.'''   )
				if not bwt_string:
								raise ValueError('''The parameter bwt_string must not be empty.'''   )
				try:
								a_		:	Union[str, Any]									=   int(__A   )
				except ValueError:
								raise TypeError(
								    '''The parameter idx_original_string type must be int or passive'''
								    ''' of cast to int.'''   )
				if idx_original_string < 0:
								raise ValueError('''The parameter idx_original_string must not be lower than 0.'''   )
				if idx_original_string >= len(__A   ):
								raise ValueError(
								    '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).'''   )

				a_		:	Union[str, Any]									=   [''''''] * len(__A   )
				for _ in range(len(__A   )   ):
								for i in range(len(__A   )   ):
												a_		:	int									=   bwt_string[i] + ordered_rotations[i]
								ordered_rotations.sort()
				return ordered_rotations[idx_original_string]


if __name__ == "__main__":
		__lowerCAmelCase									=   'Provide a string that I will generate its BWT transform: '
		__lowerCAmelCase									=   input(entry_msg).strip()
		__lowerCAmelCase									=   bwt_transform(s)
		print(
		    F"""Burrows Wheeler transform for string '{s}' results """
		    F"""in '{result['bwt_string']}'"""
		)
		__lowerCAmelCase									=   reverse_bwt(result['bwt_string'], result['idx_original_string'])
		print(
		    F"""Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' """
		    F"""we get original string '{original_string}'"""
		)




