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						:   str         =			logging.get_logger(__name__)
def  snake_case						(__lowercase						)	->	List[str]:

						'''simple docstring'''



						if "resnet-50" in model_name:
												_snake_case						:					str    =   ResNetConfig.from_pretrained("microsoft/resnet-50"						)
						elif "resnet-101" in model_name:
												_snake_case						:					Optional[int]    =   ResNetConfig.from_pretrained("microsoft/resnet-101"						)
						else:
												raise ValueError("Model name should include either resnet50 or resnet101"						)

						_snake_case						:					int    =   DetrConfig(use_timm_backbone=__lowercase ,      backbone_config=__lowercase						)

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

						return config, is_panoptic
def  snake_case						(__lowercase						)	->	Optional[int]:

						'''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  snake_case						(__lowercase ,      __lowercase ,      __lowercase						)	->	Tuple:

						'''simple docstring'''



						_snake_case						:					str    =   state_dict.pop(__lowercase						)
						_snake_case						:					Tuple    =   val
def  snake_case						(__lowercase ,      __lowercase=False						)	->	List[str]:

						'''simple docstring'''



						_snake_case						:					Optional[int]    =   ""
						if is_panoptic:
												_snake_case						:					int    =   "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						:					Dict    =   state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""						)
												_snake_case						:					Optional[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[Any]    =   in_proj_weight[:256, :]
												_snake_case						:					Optional[int]    =   in_proj_bias[:256]
												_snake_case						:					Optional[int]    =   in_proj_weight[256:512, :]
												_snake_case						:					List[str]    =   in_proj_bias[256:512]
												_snake_case						:					Optional[int]    =   in_proj_weight[-256:, :]
												_snake_case						:					Optional[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
												_snake_case						:					Dict    =   state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""						)
												_snake_case						:					List[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
												_snake_case						:					Any    =   in_proj_weight[:256, :]
												_snake_case						:					Any    =   in_proj_bias[:256]
												_snake_case						:					Dict    =   in_proj_weight[256:512, :]
												_snake_case						:					List[str]    =   in_proj_bias[256:512]
												_snake_case						:					str    =   in_proj_weight[-256:, :]
												_snake_case						:					List[Any]    =   in_proj_bias[-256:]
												# read in weights + bias of input projection layer of cross-attention
												_snake_case						:					int    =   state_dict.pop(
												    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""						)
												_snake_case						:					str    =   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						:					Any    =   in_proj_weight_cross_attn[:256, :]
												_snake_case						:					List[Any]    =   in_proj_bias_cross_attn[:256]
												_snake_case						:					Tuple    =   in_proj_weight_cross_attn[256:512, :]
												_snake_case						:					List[str]    =   in_proj_bias_cross_attn[256:512]
												_snake_case						:					List[Any]    =   in_proj_weight_cross_attn[-256:, :]
												_snake_case						:					Optional[int]    =   in_proj_bias_cross_attn[-256:]
def  snake_case						()	->	int:

						'''simple docstring'''



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

						return im
@torch.no_grad()
def  snake_case						(__lowercase ,      __lowercase=None ,      __lowercase=False						)	->	Tuple:

						'''simple docstring'''



						_snake_case    ,_snake_case						:					Optional[Any]    =   get_detr_config(__lowercase						)

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

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

						# verify our conversion on an image
						_snake_case						:					Tuple    =   "coco_panoptic" if is_panoptic else "coco_detection"
						_snake_case						:					int    =   DetrImageProcessor(format=__lowercase						)

						_snake_case						:					Dict    =   processor(images=prepare_img() ,      return_tensors="pt"						)
						_snake_case						:					Tuple    =   encoding["pixel_values"]

						_snake_case						:					Optional[int]    =   detr(__lowercase						)
						_snake_case						:					Union[str, 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__":
				__SCREAMING_SNAKE_CASE						:   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.')
				__SCREAMING_SNAKE_CASE						:   str         =			parser.parse_args()
				convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

__SCREAMING_SNAKE_CASE						:   str         =			(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__SCREAMING_SNAKE_CASE						:   list[int]         =			[ord(letter) for letter in string.ascii_lowercase]
__SCREAMING_SNAKE_CASE						:   set[int]         =			{ord(char) for char in VALID_CHARS}

__SCREAMING_SNAKE_CASE						:   list[str]         =			["the", "be", "to", "of", "and", "in", "that", "have"]
def  snake_case						(__lowercase ,      __lowercase						)	->	str | None:

						'''simple docstring'''



						_snake_case						:					str    =   ""
						_snake_case						:					int
						_snake_case						:					int
						_snake_case						:					int

						for keychar, cipherchar in zip(cycle(__lowercase						) ,      __lowercase						):
												_snake_case						:					str    =   cipherchar ^ keychar
												if decodedchar not in VALID_INTS:
																		return None
												decoded += chr(__lowercase						)

						return decoded
def  snake_case						(__lowercase						)	->	list[str]:

						'''simple docstring'''



						_snake_case						:					list[str]    =   []
						for key in product(__lowercase ,      repeat=3						):
												_snake_case						:					Union[str, Any]    =   try_key(__lowercase ,      __lowercase						)
												if encoded is not None:
																		possibles.append(__lowercase						)
						return possibles
def  snake_case						(__lowercase ,      __lowercase						)	->	list[str]:

						'''simple docstring'''



						return [possible for possible in possibles if common_word in possible.lower()]
def  snake_case						(__lowercase = "p059_cipher.txt"						)	->	int:

						'''simple docstring'''



						_snake_case						:					list[int]
						_snake_case						:					list[str]
						_snake_case						:					str
						_snake_case						:					str
						_snake_case						:					str    =   Path(__lowercase						).parent.joinpath(__lowercase						).read_text(encoding="utf-8"						)

						_snake_case						:					Dict    =   [int(__lowercase						) for number in data.strip().split(","						)]

						_snake_case						:					Tuple    =   filter_valid_chars(__lowercase						)
						for common_word in COMMON_WORDS:
												_snake_case						:					Optional[int]    =   filter_common_word(__lowercase ,      __lowercase						)
												if len(__lowercase						) == 1:
																		break

						_snake_case						:					int    =   possibles[0]
						return sum(ord(__lowercase						) for char in decoded_text						)


if __name__ == "__main__":
				print(F'''{solution() = }''')