'''simple docstring'''

import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

lowercase_  		=			logging.getLogger()
lowercase_  		=			torch.cuda.is_available()
lowercase_  		=			{
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 42,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1e-0_8,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 12,
    '''max_target_length''': 12,
    '''val_max_target_length''': 12,
    '''test_max_target_length''': 12,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}



def 				UpperCamelCase__      (      a__      ,   a__   ):


						'''simple docstring'''






						_lowerCAmelCase					    ='\n'.join(a__   )
						Path(a__   ).open('w'   ).writelines(a__   )


lowercase_  		=			[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowercase_  		=			['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowercase_  		=			'''patrickvonplaten/t5-tiny-random'''
lowercase_  		=			'''sshleifer/t5-tinier-random'''
lowercase_  		=			'''sshleifer/bart-tiny-random'''
lowercase_  		=			'''sshleifer/tiny-mbart'''
lowercase_  		=			'''sshleifer/tiny-marian-en-de'''
lowercase_  		=			'''stas/tiny-wmt19-en-de'''


lowercase_  		=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks



def 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






						for split in ["train", "val", "test"]:
												_dump_articles(os.path.join(a__      ,   F'''{split}.source'''   )      ,   a__   )
												_dump_articles(os.path.join(a__      ,   F'''{split}.target'''   )      ,   a__   )
						return tmp_dir
class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""






		@classmethod
		def 		UpperCamelCase__							( cls					)			->  str:
								logging.disable(logging.CRITICAL					)  # remove noisy download output from tracebacks
								return cls






		@slow
		@require_torch_gpu
		def 		UpperCamelCase__							( self					)			->  Union[str, Any]:
								_lowerCAmelCase					    =list_models()
								_lowerCAmelCase					    ='sshleifer'
								_lowerCAmelCase					    =[x.modelId for x in model_list if x.modelId.startswith(__A					)]
								_lowerCAmelCase					    =['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
								_lowerCAmelCase					    =[]
								for m in model_ids:
														if m in allowed_to_be_broken:
																				continue
														try:
																				AutoConfig.from_pretrained(__A					)
														except Exception:
																				failures.append(__A					)
								assert not failures, F'''The following models could not be loaded through AutoConfig: {failures}'''






		def 		UpperCamelCase__							( self					)			->  Dict:
								_lowerCAmelCase					    ={'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
								self._test_distiller_cli(__A					)






		def 		UpperCamelCase__							( self					)			->  Dict:
								_lowerCAmelCase					    ={
								    'student_encoder_layers': 2,
								    'student_decoder_layers': 1,
								    'max_epochs': 4,
								    'val_check_interval': 0.25,
								    'alpha_hid': 2.0,
								    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
								}
								_lowerCAmelCase					    =self._test_distiller_cli(__A		,    check_contents=__A					)

								_lowerCAmelCase					    =list(Path(model.output_dir					).glob('*.ckpt'					)					)
								self.assertEqual(1		,    len(__A					)					)
								_lowerCAmelCase					    =list(Path(model.output_dir					).glob('**/*.bin'					)					)
								self.assertEqual(len(__A					)		,    2					)
								_lowerCAmelCase					    =lmap(str.strip		,    Path(model.hparams.data_dir					).joinpath('test.source'					).open().readlines()					)
								_lowerCAmelCase					    =tempfile.mktemp()  # XXX: not being cleaned up
								generate_summaries_or_translations(__A		,    __A		,    str(model.output_dir / 'best_tfmr'					)					)
								self.assertTrue(Path(__A					).exists()					)

								_lowerCAmelCase					    =self.get_auto_remove_tmp_dir()
								convert_pl_to_hf(ckpts[0]		,    transformer_ckpts[0].parent		,    __A					)
								assert os.path.exists(os.path.join(__A		,    'pytorch_model.bin'					)					)






		def 		UpperCamelCase__							( self					)			->  int:
								_lowerCAmelCase					    =AutoModelForSeqaSeqLM.from_pretrained(__A					)
								_lowerCAmelCase    ,					_lowerCAmelCase					    =model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
								_lowerCAmelCase					    =torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]		,    dtype=torch.long		,    device=model.device					)
								_lowerCAmelCase					    =target_ids[:, :-1].contiguous()  # Why this line?
								_lowerCAmelCase					    =target_ids[:, 1:].clone()  # why clone?
								_lowerCAmelCase					    =model(
								    __A		,    attention_mask=__A		,    decoder_input_ids=__A		,    labels=__A		,    use_cache=__A					).loss

								_lowerCAmelCase					    =model(__A		,    attention_mask=__A		,    decoder_input_ids=__A		,    use_cache=__A					).logits

								_lowerCAmelCase					    =nn.functional.log_softmax(__A		,    dim=-1					)
								_lowerCAmelCase    ,					_lowerCAmelCase					    =label_smoothed_nll_loss(
								    __A		,    __A		,    0.1		,    ignore_index=model.config.pad_token_id					)
								with self.assertRaises(__A					):
														# TODO: understand why this breaks
														self.assertEqual(__A		,    __A					)






		def 		UpperCamelCase__							( self					)			->  Optional[int]:
								_lowerCAmelCase					    ={
								    'student_encoder_layers': 2,
								    'student_decoder_layers': 1,
								    'num_train_epochs': 4,
								    'val_check_interval': 0.25,
								    'alpha_hid': 2.0,
								    'task': 'translation',
								    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
								    'tokenizer_name': MBART_TINY,
								    'teacher': MBART_TINY,
								    'src_lang': 'en_XX',
								    'tgt_lang': 'ro_RO',
								}
								_lowerCAmelCase					    =self._test_distiller_cli(__A		,    check_contents=__A					)
								assert model.model.config.model_type == "mbart"

								_lowerCAmelCase					    =list(Path(model.output_dir					).glob('*.ckpt'					)					)
								self.assertEqual(1		,    len(__A					)					)
								_lowerCAmelCase					    =list(Path(model.output_dir					).glob('**/*.bin'					)					)
								_lowerCAmelCase					    =list(Path(model.output_dir					).glob('best_tfmr/*'					)					)
								assert len(__A					) > 2
								self.assertEqual(len(__A					)		,    2					)






		def 		UpperCamelCase__							( self					)			->  Optional[int]:
								_lowerCAmelCase					    ={
								    'student_encoder_layers': 1,
								    'student_decoder_layers': 1,
								    'alpha_hid': 2.0,
								    'teacher': T5_TINY,
								    'model_name_or_path': T5_TINY,
								    'tokenizer_name': T5_TINY,
								}
								self._test_distiller_cli(__A					)






		def 		UpperCamelCase__							( self					)			->  Tuple:
								_lowerCAmelCase					    ={
								    'teacher': T5_TINY,
								    'student': T5_TINIER,
								    'model_name_or_path': T5_TINIER,
								    'tokenizer_name': T5_TINIER,
								}
								self._test_distiller_cli(__A					)






		def 		UpperCamelCase__							( self		,    __A		,    __A=True					)			->  Union[str, Any]:
								_lowerCAmelCase					    ={
								    'label_smoothing': 0.0,
								    'early_stopping_patience': -1,
								    'train_batch_size': 1,
								    'eval_batch_size': 2,
								    'max_epochs': 2,
								    'alpha_mlm': 0.2,
								    'alpha_ce': 0.8,
								    'do_predict': True,
								    'model_name_or_path': 'sshleifer/tinier_bart',
								    'teacher': CHEAP_ARGS['model_name_or_path'],
								    'val_check_interval': 0.5,
								}
								default_updates.update(__A					)
								_lowerCAmelCase					    =CHEAP_ARGS.copy()
								_lowerCAmelCase					    =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
								_lowerCAmelCase					    =self.get_auto_remove_tmp_dir()

								args_d.update(data_dir=__A		,    output_dir=__A		,    **__A					)
								_lowerCAmelCase					    =distill_main(argparse.Namespace(**__A					)					)
								if not check_contents:
														return model
								_lowerCAmelCase					    =os.listdir(__A					)
								_lowerCAmelCase					    ={os.path.basename(__A					) for p in contents}
								_lowerCAmelCase					    =[p for p in contents if p.endswith('ckpt'					)]
								assert len(__A					) > 0

								self.assertIn('test_generations.txt'		,    __A					)
								self.assertIn('test_results.txt'		,    __A					)

								_lowerCAmelCase					    =load_json(model.metrics_save_path					)
								_lowerCAmelCase					    =metrics['val'][-1]
								self.assertGreaterEqual(last_step_stats['val_avg_gen_time']		,    0.01					)
								self.assertGreaterEqual(1.0		,    last_step_stats['val_avg_gen_time']					)
								self.assertIsInstance(last_step_stats[F'''val_avg_{model.val_metric}''']		,    __A					)
								_lowerCAmelCase					    =int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1					)
								self.assertEqual(len(metrics['val']					)		,    __A					)
								self.assertEqual(len(metrics['test']					)		,    1					)
								return model

class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""






		@parameterized.expand(
		    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]		,    )
		def 		UpperCamelCase__							( self		,    __A					)			->  List[str]:
								_lowerCAmelCase					    =CHEAP_ARGS.copy()
								_lowerCAmelCase					    ='translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
								_lowerCAmelCase					    =0.1 if task == 'translation' else 0

								_lowerCAmelCase					    =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
								_lowerCAmelCase					    =self.get_auto_remove_tmp_dir()
								args_d.update(
								    data_dir=__A		,    model_name_or_path=__A		,    tokenizer_name=__A		,    train_batch_size=2		,    eval_batch_size=2		,    output_dir=__A		,    do_predict=__A		,    task=__A		,    src_lang='en_XX'		,    tgt_lang='ro_RO'		,    freeze_encoder=__A		,    freeze_embeds=__A		,    )
								assert "n_train" in args_d
								_lowerCAmelCase					    =argparse.Namespace(**__A					)
								_lowerCAmelCase					    =main(__A					)

								_lowerCAmelCase					    =module.model.get_input_embeddings()
								assert not input_embeds.weight.requires_grad
								if model == T5_TINY:
														_lowerCAmelCase					    =module.model.lm_head
														assert not lm_head.weight.requires_grad
														assert (lm_head.weight == input_embeds.weight).all().item()
								elif model == FSMT_TINY:
														_lowerCAmelCase					    =module.model.model
														_lowerCAmelCase					    =fsmt.decoder.embed_positions
														assert not embed_pos.weight.requires_grad
														assert not fsmt.decoder.embed_tokens.weight.requires_grad
														# check that embeds are not the same
														assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
								else:
														_lowerCAmelCase					    =module.model.model
														_lowerCAmelCase					    =bart.decoder.embed_positions
														assert not embed_pos.weight.requires_grad
														assert not bart.shared.weight.requires_grad
														# check that embeds are the same
														assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
														assert bart.decoder.embed_tokens == bart.shared

								_lowerCAmelCase					    =load_json(module.output_dir / 'text_batch.json'					)
								assert isinstance(__A		,    __A					)
								assert len(__A					) >= 4






		def 		UpperCamelCase__							( self					)			->  Union[str, Any]:
								_lowerCAmelCase					    =CHEAP_ARGS.copy()

								_lowerCAmelCase					    ='summarization'
								_lowerCAmelCase					    =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)

								args_d.update(
								    data_dir=__A		,    tokenizer_name=__A		,    train_batch_size=2		,    eval_batch_size=2		,    do_predict=__A		,    task=__A		,    src_lang='en_XX'		,    tgt_lang='ro_RO'		,    freeze_encoder=__A		,    freeze_embeds=__A		,    )

								# test models whose config includes the extra_model_args
								_lowerCAmelCase					    =BART_TINY
								_lowerCAmelCase					    =self.get_auto_remove_tmp_dir()
								_lowerCAmelCase					    =args_d.copy()
								args_da.update(
								    model_name_or_path=__A		,    output_dir=__A		,    )
								_lowerCAmelCase					    =('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
								for p in extra_model_params:
														_lowerCAmelCase					    =0.5
								_lowerCAmelCase					    =argparse.Namespace(**__A					)
								_lowerCAmelCase					    =main(__A					)
								for p in extra_model_params:
														assert getattr(model.config		,    __A					) == 0.5, F'''failed to override the model config for param {p}'''

								# test models whose config doesn't include the extra_model_args
								_lowerCAmelCase					    =T5_TINY
								_lowerCAmelCase					    =self.get_auto_remove_tmp_dir()
								_lowerCAmelCase					    =args_d.copy()
								args_da.update(
								    model_name_or_path=__A		,    output_dir=__A		,    )
								_lowerCAmelCase					    ='encoder_layerdrop'
								_lowerCAmelCase					    =0.5
								_lowerCAmelCase					    =argparse.Namespace(**__A					)
								with pytest.raises(__A					) as excinfo:
														_lowerCAmelCase					    =main(__A					)
								assert str(excinfo.value					) == F'''model config doesn\'t have a `{unsupported_param}` attribute'''






		def 		UpperCamelCase__							( self					)			->  Optional[Any]:
								_lowerCAmelCase					    =CHEAP_ARGS.copy()

								_lowerCAmelCase					    ='summarization'
								_lowerCAmelCase					    =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)

								_lowerCAmelCase					    =BART_TINY
								_lowerCAmelCase					    =self.get_auto_remove_tmp_dir()

								args_d.update(
								    data_dir=__A		,    model_name_or_path=__A		,    output_dir=__A		,    tokenizer_name=__A		,    train_batch_size=2		,    eval_batch_size=2		,    do_predict=__A		,    task=__A		,    src_lang='en_XX'		,    tgt_lang='ro_RO'		,    freeze_encoder=__A		,    freeze_embeds=__A		,    )

								# emulate finetune.py
								_lowerCAmelCase					    =argparse.ArgumentParser()
								_lowerCAmelCase					    =pl.Trainer.add_argparse_args(__A					)
								_lowerCAmelCase					    =SummarizationModule.add_model_specific_args(__A		,    os.getcwd()					)
								_lowerCAmelCase					    ={'--help': True}

								# --help test
								with pytest.raises(__A					) as excinfo:
														with CaptureStdout() as cs:
																				_lowerCAmelCase					    =parser.parse_args(__A					)
														assert False, "--help is expected to sys.exit"
								assert excinfo.type == SystemExit
								_lowerCAmelCase					    =lightning_base.arg_to_scheduler_metavar
								assert expected in cs.out, "--help is expected to list the supported schedulers"

								# --lr_scheduler=non_existing_scheduler test
								_lowerCAmelCase					    ='non_existing_scheduler'
								_lowerCAmelCase					    ={F'''--lr_scheduler={unsupported_param}'''}
								with pytest.raises(__A					) as excinfo:
														with CaptureStderr() as cs:
																				_lowerCAmelCase					    =parser.parse_args(__A					)
														assert False, "invalid argument is expected to sys.exit"
								assert excinfo.type == SystemExit
								_lowerCAmelCase					    =F'''invalid choice: \'{unsupported_param}\''''
								assert expected in cs.err, F'''should have bailed on invalid choice of scheduler {unsupported_param}'''

								# --lr_scheduler=existing_scheduler test
								_lowerCAmelCase					    ='cosine'
								_lowerCAmelCase					    =args_d.copy()
								_lowerCAmelCase					    =supported_param
								_lowerCAmelCase					    =argparse.Namespace(**__A					)
								_lowerCAmelCase					    =main(__A					)
								assert (
								    getattr(model.hparams		,    'lr_scheduler'					) == supported_param
								), F'''lr_scheduler={supported_param} shouldn\'t fail'''






'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


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



def 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






						if "backbone.0.body" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('backbone.0.body'      ,   'backbone.conv_encoder.model'   )
						if "transformer" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('transformer.'      ,   ''   )
						if "norm1" in orig_key:
												if "encoder" in orig_key:
																		_lowerCAmelCase					    =orig_key.replace('norm1'      ,   'self_attn_layer_norm'   )
												else:
																		_lowerCAmelCase					    =orig_key.replace('norm1'      ,   'encoder_attn_layer_norm'   )
						if "norm2" in orig_key:
												if "encoder" in orig_key:
																		_lowerCAmelCase					    =orig_key.replace('norm2'      ,   'final_layer_norm'   )
												else:
																		_lowerCAmelCase					    =orig_key.replace('norm2'      ,   'self_attn_layer_norm'   )
						if "norm3" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('norm3'      ,   'final_layer_norm'   )
						if "linear1" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('linear1'      ,   'fc1'   )
						if "linear2" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('linear2'      ,   'fc2'   )
						if "query_embed" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('query_embed'      ,   'query_position_embeddings'   )
						if "cross_attn" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('cross_attn'      ,   'encoder_attn'   )

						return orig_key



def 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






						for i in range(6   ):
												# read in weights + bias of input projection layer of self-attention
												_lowerCAmelCase					    =state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''   )
												_lowerCAmelCase					    =state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_bias'''   )
												# next, add query, keys and values (in that order) to the state dict
												_lowerCAmelCase					    =in_proj_weight[:2_5_6, :]
												_lowerCAmelCase					    =in_proj_bias[:2_5_6]
												_lowerCAmelCase					    =in_proj_weight[2_5_6:5_1_2, :]
												_lowerCAmelCase					    =in_proj_bias[2_5_6:5_1_2]
												_lowerCAmelCase					    =in_proj_weight[-2_5_6:, :]
												_lowerCAmelCase					    =in_proj_bias[-2_5_6:]



def 				UpperCamelCase__      (      ):


						'''simple docstring'''






						_lowerCAmelCase					    ='http://images.cocodataset.org/val2017/000000039769.jpg'
						_lowerCAmelCase					    =Image.open(requests.get(a__      ,   stream=a__   ).raw   )

						return im



@torch.no_grad()
def 				UpperCamelCase__      (      a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   ):


						'''simple docstring'''






						_lowerCAmelCase					    =DeformableDetrConfig()
						# set config attributes
						if single_scale:
												_lowerCAmelCase					    =1
						_lowerCAmelCase					    =dilation
						_lowerCAmelCase					    =with_box_refine
						_lowerCAmelCase					    =two_stage
						# set labels
						_lowerCAmelCase					    =9_1
						_lowerCAmelCase					    ='huggingface/label-files'
						_lowerCAmelCase					    ='coco-detection-id2label.json'
						_lowerCAmelCase					    =json.load(open(cached_download(hf_hub_url(a__      ,   a__      ,   repo_type='dataset'   )   )      ,   'r'   )   )
						_lowerCAmelCase					    ={int(a__   ): v for k, v in idalabel.items()}
						_lowerCAmelCase					    =idalabel
						_lowerCAmelCase					    ={v: k for k, v in idalabel.items()}

						# load image processor
						_lowerCAmelCase					    =DeformableDetrImageProcessor(format='coco_detection'   )

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

						logger.info('Converting model...'   )

						# load original state dict
						_lowerCAmelCase					    =torch.load(a__      ,   map_location='cpu'   )['model']
						# rename keys
						for key in state_dict.copy().keys():
												_lowerCAmelCase					    =state_dict.pop(a__   )
												_lowerCAmelCase					    =val
						# query, key and value matrices need special treatment
						read_in_q_k_v(a__   )
						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
						_lowerCAmelCase					    ='model.'
						for key in state_dict.copy().keys():
												if not key.startswith('class_embed'   ) and not key.startswith('bbox_embed'   ):
																		_lowerCAmelCase					    =state_dict.pop(a__   )
																		_lowerCAmelCase					    =val
    # finally, create HuggingFace model and load state dict
						_lowerCAmelCase					    =DeformableDetrForObjectDetection(a__   )
						model.load_state_dict(a__   )
						model.eval()

						_lowerCAmelCase					    ='cuda' if torch.cuda.is_available() else 'cpu'
						model.to(a__   )
						# verify our conversion
						_lowerCAmelCase					    =model(pixel_values.to(a__   )   )

						_lowerCAmelCase					    =torch.tensor(
						    [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]   )
						_lowerCAmelCase					    =torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]   )

						if single_scale:
												_lowerCAmelCase					    =torch.tensor(
												    [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]   )
												_lowerCAmelCase					    =torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]   )

						if single_scale and dilation:
												_lowerCAmelCase					    =torch.tensor(
												    [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]   )
												_lowerCAmelCase					    =torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]   )

						if with_box_refine:
												_lowerCAmelCase					    =torch.tensor(
												    [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]   )
												_lowerCAmelCase					    =torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]   )

						if with_box_refine and two_stage:
												_lowerCAmelCase					    =torch.tensor(
												    [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]   )
												_lowerCAmelCase					    =torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]   )

						print('Logits:'      ,   outputs.logits[0, :3, :3]   )

						assert torch.allclose(outputs.logits[0, :3, :3]      ,   expected_logits.to(a__   )      ,   atol=1E-4   )
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,   expected_boxes.to(a__   )      ,   atol=1E-4   )

						print('Everything ok!'   )

						# Save model and image processor
						logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''   )
						Path(a__   ).mkdir(exist_ok=a__   )
						model.save_pretrained(a__   )
						image_processor.save_pretrained(a__   )

						# Push to hub
						if push_to_hub:
												_lowerCAmelCase					    ='deformable-detr'
												model_name += "-single-scale" if single_scale else ""
												model_name += "-dc5" if dilation else ""
												model_name += "-with-box-refine" if with_box_refine else ""
												model_name += "-two-stage" if two_stage else ""
												print('Pushing model to hub...'   )
												model.push_to_hub(repo_path_or_name=a__      ,   organization='nielsr'      ,   commit_message='Add model'   )


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

							parser.add_argument(
							    '''--checkpoint_path''',
							    type=str,
							    default='''/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth''',
							    help='''Path to Pytorch checkpoint (.pth file) you\'d like to convert.''',
							)
							parser.add_argument('''--single_scale''', action='''store_true''', help='''Whether to set config.num_features_levels = 1.''')
							parser.add_argument('''--dilation''', action='''store_true''', help='''Whether to set config.dilation=True.''')
							parser.add_argument('''--with_box_refine''', action='''store_true''', help='''Whether to set config.with_box_refine=True.''')
							parser.add_argument('''--two_stage''', action='''store_true''', help='''Whether to set config.two_stage=True.''')
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default=None,
							    type=str,
							    required=True,
							    help='''Path to the folder to output PyTorch model.''',
							)
							parser.add_argument(
							    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
							)
							lowercase_  		=			parser.parse_args()
							convert_deformable_detr_checkpoint(
							    args.checkpoint_path,
							    args.single_scale,
							    args.dilation,
							    args.with_box_refine,
							    args.two_stage,
							    args.pytorch_dump_folder_path,
							    args.push_to_hub,
							)





