




"""simple docstring"""

import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
						import torch

__lowerCamelCase								=						logging.get_logger(__name__)



def 				a (						):



				'''simple docstring'''





				return importlib.util.find_spec('''deepspeed'''  ) is not None


if is_accelerate_available() and is_deepspeed_available():
						from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
						# Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
						# Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
						from builtins import object as DeepSpeedConfig



class  _snake_case				(							A__       ):

				'''simple docstring'''




				def __init__(							self      :  Dict					,							snake_case      :  List[Any]     ):
								# set global weakref object
								set_hf_deepspeed_config(self     )
								dep_version_check('''accelerate'''     )
								dep_version_check('''deepspeed'''     )
								super().__init__(snake_case     )



class  _snake_case				(							A__       ):

				'''simple docstring'''




				def __init__(							self      :  Any					,							snake_case      :  Dict     ):
								super().__init__(snake_case     )
								UpperCAmelCase_					:str      			=      None
								UpperCAmelCase_					:List[Any]      			=      []





				def 				snake_case_ (							self      :  List[str]     ):
								if self._dtype is None:
												raise ValueError('''trainer_config_process() wasn\'t called yet to tell dtype'''     )
								return self._dtype





				def 				snake_case_ (							self      :  Union[str, Any]					,							snake_case      :  Tuple     ):
								UpperCAmelCase_					:Any      			=      self.get_value(snake_case     )
								if val is None:
												return False
								else:
												return val == "auto"





				def 				snake_case_ (							self      :  List[Any]					,							snake_case      :  Dict					,							snake_case      :  Dict					,							snake_case      :  int=None					,							snake_case      :  List[Any]=True     ):
								UpperCAmelCase_			,UpperCAmelCase_					:Optional[int]      			=      self.find_config_node(snake_case     )
								if config is None:
												return

								if config.get(snake_case     ) == "auto":
												UpperCAmelCase_					:List[str]      			=      hf_val
												return

								if not must_match:
												return

								UpperCAmelCase_					:Union[str, Any]      			=      config.get(snake_case     )
								if ds_val is not None and ds_val != hf_val:
												self.mismatches.append(f'- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}'     )

				UpperCamelCase__		     =partialmethod(A__							,						must_match=A__       )





				def 				snake_case_ (							self      :  Optional[Any]					,							snake_case      :  Optional[int]     ):
								UpperCAmelCase_					:Tuple      			=      args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
								self.fill_match(
								    '''train_micro_batch_size_per_gpu'''					,							args.per_device_train_batch_size					,							'''per_device_train_batch_size'''     )
								self.fill_match('''gradient_accumulation_steps'''					,							args.gradient_accumulation_steps					,							'''gradient_accumulation_steps'''     )
								self.fill_match('''train_batch_size'''					,							snake_case					,							'''train_batch_size (calculated)'''     )
								self.fill_match('''gradient_clipping'''					,							args.max_grad_norm					,							'''max_grad_norm'''     )

								self.fill_match('''optimizer.params.lr'''					,							args.learning_rate					,							'''learning_rate'''     )
								self.fill_match('''optimizer.params.betas'''					,							[args.adam_betaa, args.adam_betaa]					,							'''adam_beta1+adam_beta2'''     )
								self.fill_match('''optimizer.params.eps'''					,							args.adam_epsilon					,							'''adam_epsilon'''     )
								self.fill_match('''optimizer.params.weight_decay'''					,							args.weight_decay					,							'''weight_decay'''     )

								self.fill_only('''scheduler.params.warmup_min_lr'''					,							0     )  # not a trainer arg
								self.fill_match('''scheduler.params.warmup_max_lr'''					,							args.learning_rate					,							'''learning_rate'''     )
								# total_num_steps - will get set in trainer_config_finalize

								# fp16
								if args.fpaa or args.fpaa_full_eval:
												UpperCAmelCase_					:str      			=      '''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
								else:
												UpperCAmelCase_					:Any      			=      None

								if args.save_on_each_node:
												# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
												UpperCAmelCase_					:List[Any]      			=      self.config.get('''checkpoint'''					,							{}     )
												UpperCAmelCase_					:Union[str, Any]      			=      args.save_on_each_node

								# amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
								# any here unless the user did the work
								self.fill_match(
								    '''fp16.enabled'''					,							((args.fpaa or args.fpaa_full_eval) and fpaa_backend == '''amp''')					,							'''fp16|fp16_full_eval+fp16_backend(amp)'''					,							)

								# apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
								# ZeRO features
								self.fill_match('''amp.enabled'''					,							fpaa_backend == '''apex'''					,							'''fp16+fp16_backend(apex)'''     )
								self.fill_match('''amp.opt_level'''					,							args.fpaa_opt_level					,							'''fp16_opt_level'''     )

								self.fill_match('''bf16.enabled'''					,							(args.bfaa or args.bfaa_full_eval)					,							'''bf16|bf16_full_eval'''     )

								# deepspeed's default mode is fp16 unless there is a config that says differently
								if self.is_true('''bf16.enabled'''     ):
												UpperCAmelCase_					:Dict      			=      torch.bfloataa
								elif self.is_false('''fp16.enabled'''     ):
												UpperCAmelCase_					:Union[str, Any]      			=      torch.floataa
								else:
												UpperCAmelCase_					:Dict      			=      torch.floataa





				def 				snake_case_ (							self      :  Tuple					,							snake_case      :  Dict					,							snake_case      :  Optional[int]					,							snake_case      :  Optional[int]     ):
								UpperCAmelCase_					:Tuple      			=      [
								    '''zero_optimization.reduce_bucket_size''',
								    '''zero_optimization.stage3_prefetch_bucket_size''',
								    '''zero_optimization.stage3_param_persistence_threshold''',
								]
								UpperCAmelCase_					:Tuple      			=      [x for x in hidden_size_based_keys if self.is_auto(snake_case     )]

								if len(snake_case     ) > 0:
												if hasattr(model.config					,							'''hidden_size'''     ):
																UpperCAmelCase_					:Union[str, Any]      			=      model.config.hidden_size
												elif hasattr(model.config					,							'''hidden_sizes'''     ):
																# if there are many hidden sizes pick the largest one
																UpperCAmelCase_					:Union[str, Any]      			=      max(model.config.hidden_sizes     )
												else:
																raise ValueError(
																    '''The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '''
																    '''therefore it\'s not possible to automatically fill out the following `auto` entries '''
																    f'in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing '
																    '''`auto` values for these keys with an integer value of your choice.'''     )

												self.fill_only('''zero_optimization.reduce_bucket_size'''					,							hidden_size * hidden_size     )
												if self.is_zeroa():
																# automatically assign the optimal config values based on model config
																self.fill_only('''zero_optimization.stage3_prefetch_bucket_size'''					,							0.9 * hidden_size * hidden_size     )
																self.fill_only('''zero_optimization.stage3_param_persistence_threshold'''					,							10 * hidden_size     )

        # scheduler
								self.fill_match('''scheduler.params.total_num_steps'''					,							snake_case					,							'''num_training_steps (calculated)'''     )
								self.fill_match('''scheduler.params.warmup_num_steps'''					,							args.get_warmup_steps(snake_case     )					,							'''warmup_steps'''     )

								if len(self.mismatches     ) > 0:
												UpperCAmelCase_					:Dict      			=      '''\n'''.join(self.mismatches     )
												raise ValueError(
												    '''Please correct the following DeepSpeed config values that mismatch TrainingArguments'''
												    f' values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to \'auto\'.'     )


__lowerCamelCase								=						None



def 				a (						__snake_case     :				Union[str, Any]  ):



				'''simple docstring'''





				global _hf_deepspeed_config_weak_ref
				# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
				UpperCAmelCase_					:Tuple      			=      weakref.ref(__snake_case  )



def 				a (						):



				'''simple docstring'''





				global _hf_deepspeed_config_weak_ref
				UpperCAmelCase_					:int      			=      None



def 				a (						):



				'''simple docstring'''





				if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
								return _hf_deepspeed_config_weak_ref().is_zeroa()
				else:
								return False



def 				a (						):



				'''simple docstring'''





				if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
								return _hf_deepspeed_config_weak_ref().config
				else:
								return None



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



				'''simple docstring'''





				from accelerate.utils import DummyOptim, DummyScheduler

				UpperCAmelCase_					:List[Any]      			=      hf_deepspeed_config.config

				# Optimizer + Scheduler
				# Currently supported combos:
				# 1. DS scheduler + DS optimizer: Yes
				# 2. HF scheduler + HF optimizer: Yes
				# 3. DS scheduler + HF optimizer: Yes
				# 4. HF scheduler + DS optimizer: No
				#
				# Unless Offload is enabled in which case it's:
				# 1. DS scheduler + DS optimizer: Yes
				# 2. HF scheduler + HF optimizer: Mostly*
				# 3. DS scheduler + HF optimizer: Mostly*
				# 4. HF scheduler + DS optimizer: No
				#
				# Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

				UpperCAmelCase_					:Optional[Any]      			=      None
				if "optimizer" in config:
								if args.adafactor:
												raise ValueError(
												    '''--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '''
												    '''Only one optimizer can be configured.'''  )
								UpperCAmelCase_					:Union[str, Any]      			=      DummyOptim(params=__snake_case  )
				else:
								if hf_deepspeed_config.is_offload():
												logger.info(
												    '''Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'''
												    ''' custom optimizer has both CPU and GPU implementation (except LAMB)'''  )

								# ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
								# But trainer uses AdamW by default.
								UpperCAmelCase_					:str      			=      trainer.create_optimizer()
								# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
								UpperCAmelCase_					:Optional[Any]      			=      True

				UpperCAmelCase_					:Tuple      			=      None
				if "scheduler" in config:
								UpperCAmelCase_					:Optional[int]      			=      DummyScheduler(__snake_case  )
				else:
								if isinstance(__snake_case,    __snake_case  ):
												raise ValueError(
												    '''Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '''
												    '''Please configure a scheduler in the DeepSpeed config.'''  )
								UpperCAmelCase_					:Dict      			=      trainer.create_scheduler(num_training_steps=__snake_case,    optimizer=__snake_case  )

				return optimizer, lr_scheduler



def 				a (						__snake_case     :				Union[str, Any],    __snake_case     :				Optional[int],    __snake_case     :				Union[str, Any]=False  ):



				'''simple docstring'''





				from deepspeed.utils import logger as ds_logger

				UpperCAmelCase_					:Union[str, Any]      			=      trainer.model
				UpperCAmelCase_					:Optional[int]      			=      trainer.args

				UpperCAmelCase_					:List[str]      			=      trainer.accelerator.state.deepspeed_plugin.hf_ds_config

				# resume config update - some bits like `model` and `num_training_steps` only become available during train
				hf_deepspeed_config.trainer_config_finalize(__snake_case,    __snake_case,    __snake_case  )

				# set the Deepspeed log level consistent with the Trainer
				ds_logger.setLevel(args.get_process_log_level()  )

				if inference:
								# only Z3 makes sense for the inference
								if not hf_deepspeed_config.is_zeroa():
												raise ValueError('''ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'''  )

								# in case the training config is re-used for inference
								hf_deepspeed_config.del_config_sub_tree('''optimizer'''  )
								hf_deepspeed_config.del_config_sub_tree('''lr_scheduler'''  )
								UpperCAmelCase_			,UpperCAmelCase_					:List[str]      			=      None, None
								UpperCAmelCase_					:Tuple      			=      None
				else:
								UpperCAmelCase_					:str      			=      None  # important for when deepspeed_init is used as re-init
								UpperCAmelCase_					:Union[str, Any]      			=      list(filter(lambda __snake_case  : p.requires_grad,    model.parameters()  )  )
								UpperCAmelCase_			,UpperCAmelCase_					:Tuple      			=      deepspeed_optim_sched(
								    __snake_case,    __snake_case,    __snake_case,    __snake_case,    __snake_case  )

				# keep for quick debug:
				# from pprint import pprint; pprint(config)

				return optimizer, lr_scheduler





def 				a (						__snake_case     :				Optional[int],    __snake_case     :				int  ):



				'''simple docstring'''





				import glob

				UpperCAmelCase_					:List[Any]      			=      sorted(glob.glob(f'{checkpoint_path}/global_step*'  )  )

				if len(__snake_case  ) > 0:
								logger.info(f'Attempting to resume from {checkpoint_path}'  )
								# this magically updates self.optimizer and self.lr_scheduler
								UpperCAmelCase_			,UpperCAmelCase_					:Union[str, Any]      			=      deepspeed_engine.load_checkpoint(
								    __snake_case,    load_optimizer_states=__snake_case,    load_lr_scheduler_states=__snake_case  )
								if load_path is None:
												raise ValueError(f'[deepspeed] failed to resume from checkpoint {checkpoint_path}'  )
				else:
								raise ValueError(f'Can\'t find a valid checkpoint at {checkpoint_path}'  )








"""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)


