
'''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()
__A          =    logging.get_logger(__name__)





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

						return orig_key





def  _A		(							lowercase__	):
						# transformer decoder self-attention layers
						for i in range(6	):
												# read in weights + bias of input projection layer of self-attention
												lowercase__							 =       state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''	)
												lowercase__							 =       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
												lowercase__							 =       in_proj_weight[:256, :]
												lowercase__							 =       in_proj_bias[:256]
												lowercase__							 =       in_proj_weight[256:512, :]
												lowercase__							 =       in_proj_bias[256:512]
												lowercase__							 =       in_proj_weight[-256:, :]
												lowercase__							 =       in_proj_bias[-256:]





def  _A		(							):
						lowercase__							 =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
						lowercase__							 =       Image.open(requests.get(lowercase__		,		stream=lowercase__	).raw	)

						return im





@torch.no_grad()
def  _A		(							lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		):
						lowercase__							 =       DeformableDetrConfig()
						# set config attributes
						if single_scale:
												lowercase__							 =       1
						lowercase__							 =       dilation
						lowercase__							 =       with_box_refine
						lowercase__							 =       two_stage
						# set labels
						lowercase__							 =       91
						lowercase__							 =       """huggingface/label-files"""
						lowercase__							 =       """coco-detection-id2label.json"""
						lowercase__							 =       json.load(open(cached_download(hf_hub_url(lowercase__		,		lowercase__		,		repo_type="""dataset"""	)	)		,		"""r"""	)	)
						lowercase__							 =       {int(lowercase__	): v for k, v in idalabel.items()}
						lowercase__							 =       idalabel
						lowercase__							 =       {v: k for k, v in idalabel.items()}

						# load image processor
						lowercase__							 =       DeformableDetrImageProcessor(format="""coco_detection"""	)

						# prepare image
						lowercase__							 =       prepare_img()
						lowercase__							 =       image_processor(images=lowercase__		,		return_tensors="""pt"""	)
						lowercase__							 =       encoding["""pixel_values"""]

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

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

						lowercase__							 =       """cuda""" if torch.cuda.is_available() else """cpu"""
						model.to(lowercase__	)
						# verify our conversion
						lowercase__							 =       model(pixel_values.to(lowercase__	)	)

						lowercase__							 =       torch.tensor(
						    [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-1_0.5_6_3_4, -5.3_3_7_9, -7.5_1_1_6]]	)
						lowercase__							 =       torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]	)

						if single_scale:
												lowercase__							 =       torch.tensor(
												    [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-1_0.0_6_6_5, -5.8_4_7_0, -7.7_0_0_3]]	)
												lowercase__							 =       torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]	)

						if single_scale and dilation:
												lowercase__							 =       torch.tensor(
												    [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-1_0.1_1_7_8, -4.5_2_7_5, -6.2_6_7_1]]	)
												lowercase__							 =       torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]	)

						if with_box_refine:
												lowercase__							 =       torch.tensor(
												    [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]	)
												lowercase__							 =       torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]	)

						if with_box_refine and two_stage:
												lowercase__							 =       torch.tensor(
												    [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]	)
												lowercase__							 =       torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]	)

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

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

						# Push to hub
						if push_to_hub:
												lowercase__							 =       """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=lowercase__		,		organization="""nielsr"""		,		commit_message="""Add model"""	)


if __name__ == "__main__":
				__A          =    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."
				)
				__A          =    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


__A          =    torch.cuda.is_available()
__A          =    [" Sam ate lunch today.", "Sams lunch ingredients."]
__A          =    ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
__A          =    {
    "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  _A		(							lowercase__		,		lowercase__	):
						lowercase__							 =       """\n""".join(lowercase__	)
						Path(lowercase__	).open("""w"""	).writelines(lowercase__	)





def  _A		(							lowercase__	):
						for split in ["train", "val", "test"]:
												_dump_articles(os.path.join(lowercase__		,		f'''{split}.source'''	)		,		lowercase__	)
												_dump_articles(os.path.join(lowercase__		,		f'''{split}.target'''	)		,		lowercase__	)
						return tmp_dir







class 			A							(       __UpperCAmelCase				):



			@classmethod
			def A__ (					cls       )			->		Any:




									'''simple docstring'''



									return cls



			@require_torch_multi_gpu
			def A__ (					self       )			->		List[Any]:




									'''simple docstring'''



									lowercase__							 =       {
									    """no_teacher""": True,
									    """freeze_encoder""": True,
									    """gpus""": 2,
									    """overwrite_output_dir""": True,
									    """sortish_sampler""": True,
									}
									self._test_distiller_cli_fork(lowerCamelCase__       ,     check_contents=lowerCamelCase__       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__=True       )			->		Dict:




									'''simple docstring'''



									lowercase__							 =       {
									    """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(lowerCamelCase__       )
									lowercase__							 =       CHEAP_ARGS.copy()
									lowercase__							 =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
									lowercase__							 =       self.get_auto_remove_tmp_dir()
									args_d.update(data_dir=lowerCamelCase__       ,     output_dir=lowerCamelCase__       ,     **lowerCamelCase__       )

									def convert(lowerCamelCase__       ,     lowerCamelCase__       ):
															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}'''

									lowercase__							 =       [x for x in (convert(lowerCamelCase__       ,     lowerCamelCase__       ) for k, v in args_d.items()) if len(lowerCamelCase__       )]
									lowercase__							 =       [sys.executable, F'''{self.test_file_dir}/distillation.py'''] + cli_args
									execute_subprocess_async(lowerCamelCase__       ,     env=self.get_env()       )

									lowercase__							 =       os.listdir(lowerCamelCase__       )
									lowercase__							 =       {os.path.basename(lowerCamelCase__       ) for p in contents}
									lowercase__							 =       [p for p in contents if p.endswith("""ckpt"""       )]
									assert len(lowerCamelCase__       ) > 0

									self.assertIn("""test_generations.txt"""       ,     lowerCamelCase__       )
									self.assertIn("""test_results.txt"""       ,     lowerCamelCase__       )

									# get the following from the module, (we don't have access to `model` here)
									lowercase__							 =       os.path.join(lowerCamelCase__       ,     """metrics.json"""       )
									lowercase__							 =       """rouge2"""

									lowercase__							 =       load_json(lowerCamelCase__       )
									# {'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(lowerCamelCase__       )
									lowercase__							 =       metrics["""val"""][-1]
									self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]       ,     0.01       )
									self.assertIsInstance(last_step_stats[F'''val_avg_{val_metric}''']       ,     lowerCamelCase__       )
									self.assertEqual(len(metrics["""test"""]       )       ,     1       )
									lowercase__							 =       int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1       )
									self.assertEqual(len(metrics["""val"""]       )       ,     lowerCamelCase__       )






