

"""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()
lowerCamelCase__				:  Tuple													=  logging.get_logger(__name__)
def       UpperCamelCase					(			_lowerCAmelCase     :						Any )				->			List[Any]:
	if "backbone.0.body" in orig_key:
		_UpperCAmelCase    :							Union[str, Any]									=					orig_key.replace("""backbone.0.body""",  """backbone.conv_encoder.model""" )
	if "transformer" in orig_key:
		_UpperCAmelCase    :							Dict									=					orig_key.replace("""transformer.""",  """""" )
	if "norm1" in orig_key:
		if "encoder" in orig_key:
			_UpperCAmelCase    :							Any									=					orig_key.replace("""norm1""",  """self_attn_layer_norm""" )
		else:
			_UpperCAmelCase    :							int									=					orig_key.replace("""norm1""",  """encoder_attn_layer_norm""" )
	if "norm2" in orig_key:
		if "encoder" in orig_key:
			_UpperCAmelCase    :							List[Any]									=					orig_key.replace("""norm2""",  """final_layer_norm""" )
		else:
			_UpperCAmelCase    :							Union[str, Any]									=					orig_key.replace("""norm2""",  """self_attn_layer_norm""" )
	if "norm3" in orig_key:
		_UpperCAmelCase    :							Union[str, Any]									=					orig_key.replace("""norm3""",  """final_layer_norm""" )
	if "linear1" in orig_key:
		_UpperCAmelCase    :							Union[str, Any]									=					orig_key.replace("""linear1""",  """fc1""" )
	if "linear2" in orig_key:
		_UpperCAmelCase    :							int									=					orig_key.replace("""linear2""",  """fc2""" )
	if "query_embed" in orig_key:
		_UpperCAmelCase    :							Union[str, Any]									=					orig_key.replace("""query_embed""",  """query_position_embeddings""" )
	if "cross_attn" in orig_key:
		_UpperCAmelCase    :							List[str]									=					orig_key.replace("""cross_attn""",  """encoder_attn""" )

	return orig_key
def       UpperCamelCase					(			_lowerCAmelCase     :						Dict )				->			Tuple:
	# transformer decoder self-attention layers
	for i in range(6 ):
		# read in weights + bias of input projection layer of self-attention
		_UpperCAmelCase    :							int									=					state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight''' )
		_UpperCAmelCase    :							Optional[int]									=					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
		_UpperCAmelCase    :							List[str]									=					in_proj_weight[:256, :]
		_UpperCAmelCase    :							Union[str, Any]									=					in_proj_bias[:256]
		_UpperCAmelCase    :							int									=					in_proj_weight[256:512, :]
		_UpperCAmelCase    :							Tuple									=					in_proj_bias[256:512]
		_UpperCAmelCase    :							Optional[int]									=					in_proj_weight[-256:, :]
		_UpperCAmelCase    :							str									=					in_proj_bias[-256:]
def       UpperCamelCase					(			)				->			Union[str, Any]:
	_UpperCAmelCase    :							List[str]									=					"http://images.cocodataset.org/val2017/000000039769.jpg"
	_UpperCAmelCase    :							List[Any]									=					Image.open(requests.get(__lowerCAmelCase,  stream=__lowerCAmelCase ).raw )

	return im
@torch.no_grad()
def       UpperCamelCase					(			_lowerCAmelCase     :						Optional[Any],  _lowerCAmelCase     :						int,  _lowerCAmelCase     :						Optional[int],  _lowerCAmelCase     :						Union[str, Any],  _lowerCAmelCase     :						Dict,  _lowerCAmelCase     :						List[str],  _lowerCAmelCase     :						Optional[int],  )				->			Optional[Any]:
	_UpperCAmelCase    :							List[Any]									=					DeformableDetrConfig()
	# set config attributes
	if single_scale:
		_UpperCAmelCase    :							Tuple									=					1
	_UpperCAmelCase    :							List[Any]									=					dilation
	_UpperCAmelCase    :							List[Any]									=					with_box_refine
	_UpperCAmelCase    :							str									=					two_stage
	# set labels
	_UpperCAmelCase    :							List[Any]									=					91
	_UpperCAmelCase    :							Dict									=					"huggingface/label-files"
	_UpperCAmelCase    :							Any									=					"coco-detection-id2label.json"
	_UpperCAmelCase    :							Optional[int]									=					json.load(open(cached_download(hf_hub_url(__lowerCAmelCase,  __lowerCAmelCase,  repo_type="""dataset""" ) ),  """r""" ) )
	_UpperCAmelCase    :							List[Any]									=					{int(__lowerCAmelCase ): v for k, v in idalabel.items()}
	_UpperCAmelCase    :							Any									=					idalabel
	_UpperCAmelCase    :							int									=					{v: k for k, v in idalabel.items()}

	# load image processor
	_UpperCAmelCase    :							Any									=					DeformableDetrImageProcessor(format="""coco_detection""" )

	# prepare image
	_UpperCAmelCase    :							Tuple									=					prepare_img()
	_UpperCAmelCase    :							Optional[Any]									=					image_processor(images=__lowerCAmelCase,  return_tensors="""pt""" )
	_UpperCAmelCase    :							str									=					encoding["pixel_values"]

	logger.info("""Converting model...""" )

	# load original state dict
	_UpperCAmelCase    :							Any									=					torch.load(__lowerCAmelCase,  map_location="""cpu""" )["model"]
	# rename keys
	for key in state_dict.copy().keys():
		_UpperCAmelCase    :							Any									=					state_dict.pop(__lowerCAmelCase )
		_UpperCAmelCase    :							List[Any]									=					val
	# query, key and value matrices need special treatment
	read_in_q_k_v(__lowerCAmelCase )
	# 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									=					"model."
	for key in state_dict.copy().keys():
		if not key.startswith("""class_embed""" ) and not key.startswith("""bbox_embed""" ):
			_UpperCAmelCase    :							Optional[int]									=					state_dict.pop(__lowerCAmelCase )
			_UpperCAmelCase    :							Optional[int]									=					val
    # finally, create HuggingFace model and load state dict
	_UpperCAmelCase    :							Optional[int]									=					DeformableDetrForObjectDetection(__lowerCAmelCase )
	model.load_state_dict(__lowerCAmelCase )
	model.eval()

	_UpperCAmelCase    :							str									=					"cuda" if torch.cuda.is_available() else "cpu"
	model.to(__lowerCAmelCase )
	# verify our conversion
	_UpperCAmelCase    :							Tuple									=					model(pixel_values.to(__lowerCAmelCase ) )

	_UpperCAmelCase    :							List[str]									=					torch.tensor(
	    [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]] )
	_UpperCAmelCase    :							int									=					torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]] )

	if single_scale:
		_UpperCAmelCase    :							Union[str, Any]									=					torch.tensor(
		    [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]] )
		_UpperCAmelCase    :							Dict									=					torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]] )

	if single_scale and dilation:
		_UpperCAmelCase    :							Any									=					torch.tensor(
		    [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]] )
		_UpperCAmelCase    :							Optional[int]									=					torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]] )

	if with_box_refine:
		_UpperCAmelCase    :							Any									=					torch.tensor(
		    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]] )
		_UpperCAmelCase    :							Tuple									=					torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]] )

	if with_box_refine and two_stage:
		_UpperCAmelCase    :							str									=					torch.tensor(
		    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]] )
		_UpperCAmelCase    :							Optional[int]									=					torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]] )

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

	assert torch.allclose(outputs.logits[0, :3, :3],  expected_logits.to(__lowerCAmelCase ),  atol=1E-4 )
	assert torch.allclose(outputs.pred_boxes[0, :3, :3],  expected_boxes.to(__lowerCAmelCase ),  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(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase )
	model.save_pretrained(__lowerCAmelCase )
	image_processor.save_pretrained(__lowerCAmelCase )

	# Push to hub
	if push_to_hub:
		_UpperCAmelCase    :							List[str]									=					"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=__lowerCAmelCase,  organization="""nielsr""",  commit_message="""Add model""" )


if __name__ == "__main__":
						lowerCamelCase__				:  Optional[int]													=  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.'''
						)
						lowerCamelCase__				:  Dict													=  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,
						)








"""simple docstring"""






import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


lowerCamelCase__				:  Any													=  torch.cuda.is_available()
lowerCamelCase__				:  Optional[Any]													=  [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowerCamelCase__				:  List[str]													=  ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowerCamelCase__				:  int													=  {
    '''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-08,
    '''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					(			_lowerCAmelCase     :						Path,  _lowerCAmelCase     :						list )				->			str:
	_UpperCAmelCase    :							Dict									=					"""\n""".join(_lowerCAmelCase )
	Path(_lowerCAmelCase ).open("""w""" ).writelines(_lowerCAmelCase )
def       UpperCamelCase					(			_lowerCAmelCase     :						Optional[int] )				->			Optional[Any]:
	for split in ["train", "val", "test"]:
		_dump_articles(os.path.join(_lowerCAmelCase,  f'''{split}.source''' ),  _lowerCAmelCase )
		_dump_articles(os.path.join(_lowerCAmelCase,  f'''{split}.target''' ),  _lowerCAmelCase )
	return tmp_dir






class     _UpperCAmelCase			(		__a):
			@classmethod
			def 							__snake_case (		cls     )    ->    Optional[int]:






				'''simple docstring'''







				return cls
			@require_torch_multi_gpu
			def 							__snake_case (		self     )    ->    List[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							List[str]									=					{
				    """no_teacher""": True,
				    """freeze_encoder""": True,
				    """gpus""": 2,
				    """overwrite_output_dir""": True,
				    """sortish_sampler""": True,
				}
				self._test_distiller_cli_fork(_A      ,		check_contents=_A     )
			def 							__snake_case (		self      ,		_A      ,		_A=True     )    ->    int:






				'''simple docstring'''







				_UpperCAmelCase    :							Dict									=					{
				    """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     )
				_UpperCAmelCase    :							dict									=					CHEAP_ARGS.copy()
				_UpperCAmelCase    :							List[Any]									=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
				_UpperCAmelCase    :							Optional[Any]									=					self.get_auto_remove_tmp_dir()
				args_d.update(data_dir=_A      ,		output_dir=_A      ,		**_A     )

				def convert(_A      ,		_A     ):
					if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
						return ""
					if v is False or v is None:
						return ""
					if v is True:  # or len(str(v))==0:
						return f'''--{k}'''
					return f'''--{k}={v}'''

				_UpperCAmelCase    :							List[Any]									=					[x for x in (convert(_A      ,		_A     ) for k, v in args_d.items()) if len(_A     )]
				_UpperCAmelCase    :							Union[str, Any]									=					[sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
				execute_subprocess_async(_A      ,		env=self.get_env()     )

				_UpperCAmelCase    :							Union[str, Any]									=					os.listdir(_A     )
				_UpperCAmelCase    :							Dict									=					{os.path.basename(_A     ) for p in contents}
				_UpperCAmelCase    :							str									=					[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     )

				# get the following from the module, (we don't have access to `model` here)
				_UpperCAmelCase    :							List[str]									=					os.path.join(_A      ,		"""metrics.json"""     )
				_UpperCAmelCase    :							List[Any]									=					"""rouge2"""

				_UpperCAmelCase    :							List[Any]									=					load_json(_A     )
				# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
				print(_A     )
				_UpperCAmelCase    :							str									=					metrics["""val"""][-1]
				self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]      ,		0.01     )
				self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']      ,		_A     )
				self.assertEqual(len(metrics["""test"""]     )      ,		1     )
				_UpperCAmelCase    :							Optional[int]									=					int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1     )
				self.assertEqual(len(metrics["""val"""]     )      ,		_A     )





