




"""simple docstring"""

import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed



class  _snake_case				(							A__       ):

				'''simple docstring'''




				def __init__(							self      :  Dict					,							snake_case      :  Optional[Any]					,							snake_case      :  Dict					,							snake_case      :  Union[str, Any]=False					,							snake_case      :  List[Any]=1_024					,							snake_case      :  Dict=1_024					,							snake_case      :  Optional[int]=3.6					,							snake_case      :  Optional[int]=False					,							):
								UpperCAmelCase_					:List[Any]      			=      tokenizer
								UpperCAmelCase_					:List[Any]      			=      tokenizer.bos_token_id
								UpperCAmelCase_					:Optional[Any]      			=      dataset
								UpperCAmelCase_					:Any      			=      seq_length
								UpperCAmelCase_					:int      			=      0
								UpperCAmelCase_					:Dict      			=      infinite
								UpperCAmelCase_					:Optional[int]      			=      0
								UpperCAmelCase_					:Union[str, Any]      			=      tokenized

								if self.tokenized:
												UpperCAmelCase_					:str      			=      seq_length * num_of_sequences
												UpperCAmelCase_					:List[str]      			=      '''input_ids'''
								else:
												UpperCAmelCase_					:str      			=      seq_length * chars_per_token * num_of_sequences
												UpperCAmelCase_					:Any      			=      '''content'''





				def __iter__(							self      :  Any     ):
								UpperCAmelCase_					:Optional[Any]      			=      iter(self.dataset     )
								UpperCAmelCase_					:Optional[Any]      			=      True
								while more_examples:
												UpperCAmelCase_					:Tuple      			=      [], 0
												while True:
																if buffer_len >= self.max_buffer_size:
																				break
																try:
																				buffer.append(next(snake_case     )[self.content_field]     )
																				buffer_len += len(buffer[-1]     )
																except StopIteration:
																				if self.infinite:
																								UpperCAmelCase_					:List[str]      			=      iter(self.dataset     )
																								self.epoch += 1
																								logger.info(f'Dataset epoch: {self.epoch}'     )
																				else:
																								UpperCAmelCase_					:str      			=      False
																								break
												if self.tokenized:
																UpperCAmelCase_					:List[Any]      			=      buffer
												else:
																UpperCAmelCase_					:Tuple      			=      self.tokenizer(snake_case					,							truncation=snake_case     )['''input_ids''']
												UpperCAmelCase_					:int      			=      []
												for tokenized_input in tokenized_inputs:
																all_token_ids.extend(tokenized_input + [self.concat_token_id]     )
												for i in range(0					,							len(snake_case     )					,							self.seq_length     ):
																UpperCAmelCase_					:Tuple      			=      all_token_ids[i : i + self.seq_length]
																if len(snake_case     ) == self.seq_length:
																				self.current_size += 1
																				yield torch.tensor(snake_case     )





				def 				snake_case_ (							self      :  Any					,							snake_case      :  Dict=1_000     ):
								return ShufflerIterDataPipe(self					,							buffer_size=snake_case     )



def 				a (						__snake_case     :				Any  ):



				'''simple docstring'''





				UpperCAmelCase_					:Union[str, Any]      			=      args.model_ckpt.split('''/'''  )[-1]
				UpperCAmelCase_					:List[str]      			=      logging.getLogger(__name__  )
				UpperCAmelCase_					:Union[str, Any]      			=      Path(args.save_dir  ) / '''log/'''
				log_dir.mkdir(exist_ok=__snake_case  )
				UpperCAmelCase_					:Optional[Any]      			=      f'debug_{accelerator.process_index}.log'
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',    datefmt='''%m/%d/%Y %H:%M:%S''',    level=logging.INFO,    handlers=[logging.FileHandler(log_dir / filename  ), logging.StreamHandler()],    )
				if accelerator.is_main_process:  # we only want to setup logging once
								accelerator.init_trackers(__snake_case,    vars(__snake_case  )  )
								UpperCAmelCase_					:List[Any]      			=      accelerator.trackers[0].run.name
								logger.setLevel(logging.INFO  )
								datasets.utils.logging.set_verbosity_info()
								transformers.utils.logging.set_verbosity_info()
				else:
								UpperCAmelCase_					:Any      			=      ''''''
								logger.setLevel(logging.ERROR  )
								datasets.utils.logging.set_verbosity_error()
								transformers.utils.logging.set_verbosity_error()
				return logger, run_name



def 				a (						__snake_case     :				int  ):



				'''simple docstring'''





				UpperCAmelCase_					:str      			=      {'''streaming''': True}
				UpperCAmelCase_					:Union[str, Any]      			=      load_dataset(args.dataset_name_train,    split='''train''',    **__snake_case  )
				UpperCAmelCase_					:Any      			=      train_data.shuffle(buffer_size=args.shuffle_buffer,    seed=args.seed  )
				UpperCAmelCase_					:Tuple      			=      load_dataset(args.dataset_name_valid,    split='''train''',    **__snake_case  )
				UpperCAmelCase_					:List[Any]      			=      ConstantLengthDataset(
				    __snake_case,    __snake_case,    infinite=__snake_case,    seq_length=args.seq_length,    tokenized=args.tokenized  )
				UpperCAmelCase_					:Dict      			=      ConstantLengthDataset(
				    __snake_case,    __snake_case,    infinite=__snake_case,    seq_length=args.seq_length,    tokenized=args.tokenized  )
				UpperCAmelCase_					:Optional[int]      			=      train_dataset.shuffle(buffer_size=args.shuffle_buffer  )
				UpperCAmelCase_					:Tuple      			=      DataLoader(__snake_case,    batch_size=args.train_batch_size,    shuffle=__snake_case  )
				UpperCAmelCase_					:str      			=      DataLoader(__snake_case,    batch_size=args.valid_batch_size  )
				return train_dataloader, eval_dataloader



def 				a (						__snake_case     :				Optional[int],    __snake_case     :				Optional[Any],    __snake_case     :				Dict=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  ):



				'''simple docstring'''





				UpperCAmelCase_					:Any      			=      [], []
				for n, p in model.named_parameters():
								if any(nd in n for nd in no_decay  ):
												params_without_wd.append(__snake_case  )
								else:
												params_with_wd.append(__snake_case  )
				return [
				    {"params": params_with_wd, "weight_decay": args.weight_decay},
				    {"params": params_without_wd, "weight_decay": 0.0},
				]



def 				a (						__snake_case     :				Tuple,    __snake_case     :				Tuple  ):



				'''simple docstring'''





				logger.info(f'Step {step}: {metrics}'  )
				if accelerator.is_main_process:
								accelerator.log(__snake_case,    __snake_case  )



def 				a (						__snake_case     :				List[Any],    __snake_case     :				List[str],    __snake_case     :				Optional[int]  ):



				'''simple docstring'''





				UpperCAmelCase_					:List[str]      			=      accelerator.unwrap_model(__snake_case  ).config
				UpperCAmelCase_					:Optional[int]      			=      4 if args.gradient_checkpointing else 3
				UpperCAmelCase_					:Union[str, Any]      			=      args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
				UpperCAmelCase_					:str      			=      24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
				UpperCAmelCase_					:Any      			=      factor * (
				    1.0
				    + (args.seq_length / (6.0 * config_model.n_embd))
				    + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
				)
				UpperCAmelCase_					:str      			=      flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
				return tflops



def 				a (						__snake_case     :				Optional[int]  ):



				'''simple docstring'''





				model.eval()
				UpperCAmelCase_					:str      			=      []
				for step, batch in enumerate(__snake_case  ):
								with torch.no_grad():
												UpperCAmelCase_					:Union[str, Any]      			=      model(__snake_case,    labels=__snake_case  )
								UpperCAmelCase_					:Optional[Any]      			=      outputs.loss.repeat(args.valid_batch_size  )
								losses.append(accelerator.gather(__snake_case  )  )
								if args.max_eval_steps > 0 and step >= args.max_eval_steps:
												break
				UpperCAmelCase_					:Optional[int]      			=      torch.cat(__snake_case  )
				UpperCAmelCase_					:Tuple      			=      losses[: eval_dataloader.dataset.current_size].mean()
				try:
								UpperCAmelCase_					:Dict      			=      torch.exp(__snake_case  )
				except OverflowError:
								UpperCAmelCase_					:Union[str, Any]      			=      float('''inf'''  )
				return loss.item(), perplexity.item()


# Settings
__lowerCamelCase								=						HfArgumentParser(TrainingArguments)
__lowerCamelCase								=						parser.parse_args()

# Accelerator
__lowerCamelCase								=						ProjectConfiguration(project_dir=args.save_dir, logging_dir="log")
__lowerCamelCase								=						Accelerator(log_with=["wandb", "tensorboard"], project_config=config)
__lowerCamelCase								=						{str(k): str(v) for k, v in accelerator.state.__dict__.items()}

__lowerCamelCase								=						Namespace(**vars(args), **acc_state)
__lowerCamelCase								=						accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
						__lowerCamelCase								=						Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
__lowerCamelCase				,     __lowerCamelCase								=						setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
						hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
__lowerCamelCase								=						AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
						model.gradient_checkpointing_enable()
__lowerCamelCase								=						AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
__lowerCamelCase				,     __lowerCamelCase								=						create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
__lowerCamelCase								=						AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__lowerCamelCase								=						get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)



def 				a (						):



				'''simple docstring'''





				return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase								=						accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# load in the weights and states from a previous save
if args.resume_from_checkpoint:
						if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
												accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}''')
												accelerator.load_state(args.resume_from_checkpoint)
												__lowerCamelCase								=						os.path.basename(args.resume_from_checkpoint)
						else:
												# Get the most recent checkpoint
												__lowerCamelCase								=						[f.name for f in os.scandir(args.save_dir) if f.is_dir() and "step" in str(f)]
												dirs.sort(key=os.path.getctime)
												__lowerCamelCase								=						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract the step of the checkpoint to continue from there
						__lowerCamelCase								=						os.path.splitext(path)[0]
						__lowerCamelCase								=						int(training_difference.replace("step_", ""))

# Train model
model.train()
__lowerCamelCase								=						0
__lowerCamelCase								=						time.time()
__lowerCamelCase								=						0
for step, batch in enumerate(train_dataloader, start=1):
						if args.resume_from_checkpoint and step < resume_step:
												continue  # we need to skip steps until we reach the resumed step
						__lowerCamelCase								=						model(batch, labels=batch, use_cache=False).loss
						__lowerCamelCase								=						accelerator.gather(loss.repeat(args.train_batch_size)).mean()
						loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
						log_metrics(step, {"samples": step * samples_per_step, "loss_per_step/train": loss.item()})
						__lowerCamelCase								=						loss / args.gradient_accumulation_steps
						if step % args.gradient_accumulation_steps != 0:
												# Prevent backward from doing gradient all_reduce in every step
												if accelerator.distributed_type == DistributedType.MULTI_GPU:
																		with model.no_sync():
																								accelerator.backward(loss)
												else:
																		accelerator.backward(loss)
						else:
												__lowerCamelCase								=						get_lr()
												accelerator.backward(loss)
												accelerator.clip_grad_norm_(model.parameters(), 1.0)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()
												__lowerCamelCase								=						time.time() - t_start
												__lowerCamelCase								=						compute_tflops(elapsed_time, accelerator, args)
												log_metrics(
												    step,
												    {
												        "steps": completed_steps,
												        "loss/train": loss_tracking,
												        "lr": lr,
												        "tflops": tflops,
												        "time_per_iteration": elapsed_time,
												    },
												)
												__lowerCamelCase								=						time.time()
												__lowerCamelCase								=						0
												completed_steps += 1
						if step % args.save_checkpoint_steps == 0:
												logger.info("Evaluating and saving model checkpoint")
												__lowerCamelCase				,     __lowerCamelCase								=						evaluate(args)
												log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
												accelerator.wait_for_everyone()
												__lowerCamelCase								=						os.path.join(args.save_dir, f'''step_{step}''')
												accelerator.save_state(save_dir)
												if accelerator.is_main_process:
																		hf_repo.push_to_hub(commit_message=f'''step {step}''')
												model.train()
						if completed_steps >= args.max_train_steps:
												break

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
__lowerCamelCase				,     __lowerCamelCase								=						evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
__lowerCamelCase								=						accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
__lowerCamelCase								=						os.path.join(args.save_dir, f'''step_{step}''')
accelerator.save_state(save_dir)
if accelerator.is_main_process:
						hf_repo.push_to_hub(commit_message="final model")








"""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 (						__snake_case     :				Optional[Any],    __snake_case     :				Any,    __snake_case     :				Tuple  ):



				'''simple docstring'''





				UpperCAmelCase_					:Optional[Any]      			=      state_dict.pop(__snake_case  )
				UpperCAmelCase_					:List[str]      			=      val



def 				a (						__snake_case     :				int  ):



				'''simple docstring'''





				UpperCAmelCase_					:int      			=      OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												UpperCAmelCase_					:List[Any]      			=      key.replace('''backbone.0.body''',    '''backbone.conv_encoder.model'''  )
												UpperCAmelCase_					:Union[str, Any]      			=      value
								else:
												UpperCAmelCase_					:Any      			=      value

				return new_state_dict



def 				a (						__snake_case     :				Tuple,    __snake_case     :				Optional[Any]=False  ):



				'''simple docstring'''





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



def 				a (						):



				'''simple docstring'''





				UpperCAmelCase_					:Tuple      			=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				UpperCAmelCase_					:List[str]      			=      Image.open(requests.get(__snake_case,    stream=__snake_case  ).raw  )

				return im



@torch.no_grad()
def 				a (						__snake_case     :				Union[str, Any],    __snake_case     :				List[Any]  ):



				'''simple docstring'''





				UpperCAmelCase_					:Optional[int]      			=      DetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								UpperCAmelCase_					:Optional[Any]      			=      '''resnet101'''
				if "dc5" in model_name:
								UpperCAmelCase_					:Optional[int]      			=      True
				UpperCAmelCase_					:Dict      			=      '''panoptic''' in model_name
				if is_panoptic:
								UpperCAmelCase_					:Optional[int]      			=      250
				else:
								UpperCAmelCase_					:Dict      			=      91
								UpperCAmelCase_					:Union[str, Any]      			=      '''huggingface/label-files'''
								UpperCAmelCase_					:str      			=      '''coco-detection-id2label.json'''
								UpperCAmelCase_					:Dict      			=      json.load(open(hf_hub_download(__snake_case,    __snake_case,    repo_type='''dataset'''  ),    '''r'''  )  )
								UpperCAmelCase_					:Optional[Any]      			=      {int(__snake_case  ): v for k, v in idalabel.items()}
								UpperCAmelCase_					:str      			=      idalabel
								UpperCAmelCase_					:Tuple      			=      {v: k for k, v in idalabel.items()}

				# load image processor
				UpperCAmelCase_					:Dict      			=      '''coco_panoptic''' if is_panoptic else '''coco_detection'''
				UpperCAmelCase_					:Optional[Any]      			=      DetrImageProcessor(format=__snake_case  )

				# prepare image
				UpperCAmelCase_					:Dict      			=      prepare_img()
				UpperCAmelCase_					:Union[str, Any]      			=      image_processor(images=__snake_case,    return_tensors='''pt'''  )
				UpperCAmelCase_					:Optional[Any]      			=      encoding['''pixel_values''']

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

				# load original model from torch hub
				UpperCAmelCase_					:int      			=      torch.hub.load('''facebookresearch/detr''',    __snake_case,    pretrained=__snake_case  ).eval()
				UpperCAmelCase_					:List[Any]      			=      detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												UpperCAmelCase_					:Optional[int]      			=      '''detr.''' + src
								rename_key(__snake_case,    __snake_case,    __snake_case  )
				UpperCAmelCase_					:int      			=      rename_backbone_keys(__snake_case  )
				# query, key and value matrices need special treatment
				read_in_q_k_v(__snake_case,    is_panoptic=__snake_case  )
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				UpperCAmelCase_					: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'''  )
												):
																UpperCAmelCase_					:List[str]      			=      state_dict.pop(__snake_case  )
																UpperCAmelCase_					:List[Any]      			=      val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																UpperCAmelCase_					:int      			=      state_dict.pop(__snake_case  )
																UpperCAmelCase_					:Union[str, Any]      			=      val
												elif key.startswith('''bbox_attention'''  ) or key.startswith('''mask_head'''  ):
																continue
												else:
																UpperCAmelCase_					:Tuple      			=      state_dict.pop(__snake_case  )
																UpperCAmelCase_					:Dict      			=      val
								else:
												if not key.startswith('''class_labels_classifier'''  ) and not key.startswith('''bbox_predictor'''  ):
																UpperCAmelCase_					:Any      			=      state_dict.pop(__snake_case  )
																UpperCAmelCase_					:Any      			=      val
    # finally, create HuggingFace model and load state dict
				UpperCAmelCase_					:Optional[int]      			=      DetrForSegmentation(__snake_case  ) if is_panoptic else DetrForObjectDetection(__snake_case  )
				model.load_state_dict(__snake_case  )
				model.eval()
				# verify our conversion
				UpperCAmelCase_					:List[Any]      			=      detr(__snake_case  )
				UpperCAmelCase_					:Optional[int]      			=      model(__snake_case  )
				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(__snake_case  ).mkdir(exist_ok=__snake_case  )
				model.save_pretrained(__snake_case  )
				image_processor.save_pretrained(__snake_case  )


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)


