

"""simple docstring"""






import argparse
import json
from collections import OrderedDict
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
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCAmelCase				      =   []
for i in range(6):
							# 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'''),
    ]
)
def 		A_	(   __UpperCamelCase     :				Tuple       ,	__UpperCamelCase     :				int       ,	__UpperCamelCase     :				List[Any]				):
		lowercase										=							state_dict.pop(__UpperCamelCase				)
		lowercase										=							val
def 		A_	(   __UpperCamelCase     :				str				):
		lowercase										=							OrderedDict()
		for key, value in state_dict.items():
				if "backbone.0.body" in key:
						lowercase										=							key.replace('''backbone.0.body'''       ,	'''backbone.conv_encoder.model'''				)
						lowercase										=							value
				else:
						lowercase										=							value

		return new_state_dict
def 		A_	(   __UpperCamelCase     :				Dict       ,	__UpperCamelCase     :				str=False				):
		lowercase										=							''''''
		if is_panoptic:
				lowercase										=							'''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)
				lowercase										=							state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
				lowercase										=							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
				lowercase										=							in_proj_weight[:2_56, :]
				lowercase										=							in_proj_bias[:2_56]
				lowercase										=							in_proj_weight[2_56:5_12, :]
				lowercase										=							in_proj_bias[2_56:5_12]
				lowercase										=							in_proj_weight[-2_56:, :]
				lowercase										=							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
				lowercase										=							state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""				)
				lowercase										=							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
				lowercase										=							in_proj_weight[:2_56, :]
				lowercase										=							in_proj_bias[:2_56]
				lowercase										=							in_proj_weight[2_56:5_12, :]
				lowercase										=							in_proj_bias[2_56:5_12]
				lowercase										=							in_proj_weight[-2_56:, :]
				lowercase										=							in_proj_bias[-2_56:]
				# read in weights + bias of input projection layer of cross-attention
				lowercase										=							state_dict.pop(
				    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""				)
				lowercase										=							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
				lowercase										=							in_proj_weight_cross_attn[:2_56, :]
				lowercase										=							in_proj_bias_cross_attn[:2_56]
				lowercase										=							in_proj_weight_cross_attn[2_56:5_12, :]
				lowercase										=							in_proj_bias_cross_attn[2_56:5_12]
				lowercase										=							in_proj_weight_cross_attn[-2_56:, :]
				lowercase										=							in_proj_bias_cross_attn[-2_56:]
def 		A_	(   ):
		lowercase										=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowercase										=							Image.open(requests.get(__UpperCamelCase       ,	stream=__UpperCamelCase				).raw				)

		return im
@torch.no_grad()
def 		A_	(   __UpperCamelCase     :				Tuple       ,	__UpperCamelCase     :				Tuple				):
		lowercase										=							DetrConfig()
		# set backbone and dilation attributes
		if "resnet101" in model_name:
				lowercase										=							'''resnet101'''
		if "dc5" in model_name:
				lowercase										=							True
		lowercase										=							'''panoptic''' in model_name
		if is_panoptic:
				lowercase										=							2_50
		else:
				lowercase										=							91
				lowercase										=							'''huggingface/label-files'''
				lowercase										=							'''coco-detection-id2label.json'''
				lowercase										=							json.load(open(hf_hub_download(__UpperCamelCase       ,	__UpperCamelCase       ,	repo_type='''dataset'''				)       ,	'''r'''				)				)
				lowercase										=							{int(__UpperCamelCase				): v for k, v in idalabel.items()}
				lowercase										=							idalabel
				lowercase										=							{v: k for k, v in idalabel.items()}

		# load image processor
		lowercase										=							'''coco_panoptic''' if is_panoptic else '''coco_detection'''
		lowercase										=							DetrImageProcessor(format=__UpperCamelCase				)

		# prepare image
		lowercase										=							prepare_img()
		lowercase										=							image_processor(images=__UpperCamelCase       ,	return_tensors='''pt'''				)
		lowercase										=							encoding['''pixel_values''']

		logger.info(f"""Converting model {model_name}..."""				)

		# load original model from torch hub
		lowercase										=							torch.hub.load('''facebookresearch/detr'''       ,	__UpperCamelCase       ,	pretrained=__UpperCamelCase				).eval()
		lowercase										=							detr.state_dict()
		# rename keys
		for src, dest in rename_keys:
				if is_panoptic:
						lowercase										=							'''detr.''' + src
				rename_key(__UpperCamelCase       ,	__UpperCamelCase       ,	__UpperCamelCase				)
		lowercase										=							rename_backbone_keys(__UpperCamelCase				)
		# query, key and value matrices need special treatment
		read_in_q_k_v(__UpperCamelCase       ,	is_panoptic=__UpperCamelCase				)
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		lowercase										=							'''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'''				)
						):
								lowercase										=							state_dict.pop(__UpperCamelCase				)
								lowercase										=							val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								lowercase										=							state_dict.pop(__UpperCamelCase				)
								lowercase										=							val
						elif key.startswith('''bbox_attention'''				) or key.startswith('''mask_head'''				):
								continue
						else:
								lowercase										=							state_dict.pop(__UpperCamelCase				)
								lowercase										=							val
				else:
						if not key.startswith('''class_labels_classifier'''				) and not key.startswith('''bbox_predictor'''				):
								lowercase										=							state_dict.pop(__UpperCamelCase				)
								lowercase										=							val
    # finally, create HuggingFace model and load state dict
		lowercase										=							DetrForSegmentation(__UpperCamelCase				) if is_panoptic else DetrForObjectDetection(__UpperCamelCase				)
		model.load_state_dict(__UpperCamelCase				)
		model.eval()
		# verify our conversion
		lowercase										=							detr(__UpperCamelCase				)
		lowercase										=							model(__UpperCamelCase				)
		assert torch.allclose(outputs.logits       ,	original_outputs['''pred_logits''']       ,	atol=1e-4				)
		assert torch.allclose(outputs.pred_boxes       ,	original_outputs['''pred_boxes''']       ,	atol=1e-4				)
		if is_panoptic:
				assert torch.allclose(outputs.pred_masks       ,	original_outputs['''pred_masks''']       ,	atol=1e-4				)

		# Save model and image processor
		logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""				)
		Path(__UpperCamelCase				).mkdir(exist_ok=__UpperCamelCase				)
		model.save_pretrained(__UpperCamelCase				)
		image_processor.save_pretrained(__UpperCamelCase				)


if __name__ == "__main__":
							__lowerCAmelCase				      =   argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_name''', default='''detr_resnet50''', type=str, 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.'''
							)
							__lowerCAmelCase				      =   parser.parse_args()
							convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


"""simple docstring"""






def 		A_	(   __UpperCamelCase     :				str       ,	__UpperCamelCase     :				str				):
		if not isinstance(__UpperCamelCase       ,	__UpperCamelCase				):
				raise TypeError('''plaintext must be a string'''				)
		if not isinstance(__UpperCamelCase       ,	__UpperCamelCase				):
				raise TypeError('''key must be a string'''				)

		if not plaintext:
				raise ValueError('''plaintext is empty'''				)
		if not key:
				raise ValueError('''key is empty'''				)

		key += plaintext
		lowercase										=							plaintext.lower()
		lowercase										=							key.lower()
		lowercase										=							0
		lowercase										=							0
		lowercase										=							''''''
		while plaintext_iterator < len(__UpperCamelCase				):
				if (
				    ord(plaintext[plaintext_iterator]				) < 97
				    or ord(plaintext[plaintext_iterator]				) > 1_22
				):
						ciphertext += plaintext[plaintext_iterator]
						plaintext_iterator += 1
				elif ord(key[key_iterator]				) < 97 or ord(key[key_iterator]				) > 1_22:
						key_iterator += 1
				else:
						ciphertext += chr(
						    (
						        (ord(plaintext[plaintext_iterator]				) - 97 + ord(key[key_iterator]				))
						        - 97
						    )
						    % 26
						    + 97				)
						key_iterator += 1
						plaintext_iterator += 1
		return ciphertext
def 		A_	(   __UpperCamelCase     :				str       ,	__UpperCamelCase     :				str				):
		if not isinstance(__UpperCamelCase       ,	__UpperCamelCase				):
				raise TypeError('''ciphertext must be a string'''				)
		if not isinstance(__UpperCamelCase       ,	__UpperCamelCase				):
				raise TypeError('''key must be a string'''				)

		if not ciphertext:
				raise ValueError('''ciphertext is empty'''				)
		if not key:
				raise ValueError('''key is empty'''				)

		lowercase										=							key.lower()
		lowercase										=							0
		lowercase										=							0
		lowercase										=							''''''
		while ciphertext_iterator < len(__UpperCamelCase				):
				if (
				    ord(ciphertext[ciphertext_iterator]				) < 97
				    or ord(ciphertext[ciphertext_iterator]				) > 1_22
				):
						plaintext += ciphertext[ciphertext_iterator]
				else:
						plaintext += chr(
						    (ord(ciphertext[ciphertext_iterator]				) - ord(key[key_iterator]				)) % 26
						    + 97				)
						key += chr(
						    (ord(ciphertext[ciphertext_iterator]				) - ord(key[key_iterator]				)) % 26
						    + 97				)
						key_iterator += 1
				ciphertext_iterator += 1
		return plaintext


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							__lowerCAmelCase				      =   int(input('''Type 1 to encrypt or 2 to decrypt:'''))
							if operation == 1:
														__lowerCAmelCase				      =   input('''Typeplaintext to be encrypted:\n''')
														__lowerCAmelCase				      =   input('''Type the key:\n''')
														print(encrypt(plaintext, key))
							elif operation == 2:
														__lowerCAmelCase				      =   input('''Type the ciphertext to be decrypted:\n''')
														__lowerCAmelCase				      =   input('''Type the key:\n''')
														print(decrypt(ciphertext, key))
							decrypt('''jsqqs avvwo''', '''coffee''')