





"""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_          =	logging.get_logger(__name__)



def   __lowerCamelCase     (							a_  :	int							)    ->   Union[str, Any]:
						if "backbone.0.body" in orig_key:
												__SCREAMING_SNAKE_CASE     :str	       =						orig_key.replace('''backbone.0.body'''				,     '''backbone.conv_encoder.model'''							)
						if "transformer" in orig_key:
												__SCREAMING_SNAKE_CASE     :Tuple	       =						orig_key.replace('''transformer.'''				,     ''''''							)
						if "norm1" in orig_key:
												if "encoder" in orig_key:
																		__SCREAMING_SNAKE_CASE     :Any	       =						orig_key.replace('''norm1'''				,     '''self_attn_layer_norm'''							)
												else:
																		__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						orig_key.replace('''norm1'''				,     '''encoder_attn_layer_norm'''							)
						if "norm2" in orig_key:
												if "encoder" in orig_key:
																		__SCREAMING_SNAKE_CASE     :Any	       =						orig_key.replace('''norm2'''				,     '''final_layer_norm'''							)
												else:
																		__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						orig_key.replace('''norm2'''				,     '''self_attn_layer_norm'''							)
						if "norm3" in orig_key:
												__SCREAMING_SNAKE_CASE     :Dict	       =						orig_key.replace('''norm3'''				,     '''final_layer_norm'''							)
						if "linear1" in orig_key:
												__SCREAMING_SNAKE_CASE     :str	       =						orig_key.replace('''linear1'''				,     '''fc1'''							)
						if "linear2" in orig_key:
												__SCREAMING_SNAKE_CASE     :str	       =						orig_key.replace('''linear2'''				,     '''fc2'''							)
						if "query_embed" in orig_key:
												__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						orig_key.replace('''query_embed'''				,     '''query_position_embeddings'''							)
						if "cross_attn" in orig_key:
												__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						orig_key.replace('''cross_attn'''				,     '''encoder_attn'''							)

						return orig_key



def   __lowerCamelCase     (							a_  :	int							)    ->   str:
						# transformer decoder self-attention layers
						for i in range(6							):
												# read in weights + bias of input projection layer of self-attention
												__SCREAMING_SNAKE_CASE     :List[str]	       =						state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''							)
												__SCREAMING_SNAKE_CASE     :Tuple	       =						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
												__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						in_proj_weight[:2_56, :]
												__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						in_proj_bias[:2_56]
												__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						in_proj_weight[2_56:5_12, :]
												__SCREAMING_SNAKE_CASE     :str	       =						in_proj_bias[2_56:5_12]
												__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						in_proj_weight[-2_56:, :]
												__SCREAMING_SNAKE_CASE     :Optional[int]	       =						in_proj_bias[-2_56:]



def   __lowerCamelCase     (							)    ->   Dict:
						__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						"http://images.cocodataset.org/val2017/000000039769.jpg"
						__SCREAMING_SNAKE_CASE     :Any	       =						Image.open(requests.get(a_				,     stream=a_							).raw							)

						return im



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

						# load image processor
						__SCREAMING_SNAKE_CASE     :Optional[int]	       =						DeformableDetrImageProcessor(format='''coco_detection'''							)

						# prepare image
						__SCREAMING_SNAKE_CASE     :List[str]	       =						prepare_img()
						__SCREAMING_SNAKE_CASE     :Tuple	       =						image_processor(images=a_				,     return_tensors='''pt'''							)
						__SCREAMING_SNAKE_CASE     :int	       =						encoding["pixel_values"]

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

						# load original state dict
						__SCREAMING_SNAKE_CASE     :Optional[int]	       =						torch.load(a_				,     map_location='''cpu'''							)["model"]
						# rename keys
						for key in state_dict.copy().keys():
												__SCREAMING_SNAKE_CASE     :Dict	       =						state_dict.pop(a_							)
												__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						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
						__SCREAMING_SNAKE_CASE     :List[str]	       =						"model."
						for key in state_dict.copy().keys():
												if not key.startswith('''class_embed'''							) and not key.startswith('''bbox_embed'''							):
																		__SCREAMING_SNAKE_CASE     :Optional[int]	       =						state_dict.pop(a_							)
																		__SCREAMING_SNAKE_CASE     :Any	       =						val
    # finally, create HuggingFace model and load state dict
						__SCREAMING_SNAKE_CASE     :Optional[int]	       =						DeformableDetrForObjectDetection(a_							)
						model.load_state_dict(a_							)
						model.eval()

						__SCREAMING_SNAKE_CASE     :Dict	       =						"cuda" if torch.cuda.is_available() else "cpu"
						model.to(a_							)
						# verify our conversion
						__SCREAMING_SNAKE_CASE     :int	       =						model(pixel_values.to(a_							)							)

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

						if single_scale:
												__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						torch.tensor(
												    [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]							)
												__SCREAMING_SNAKE_CASE     :Tuple	       =						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:
												__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						torch.tensor(
												    [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]							)
												__SCREAMING_SNAKE_CASE     :Dict	       =						torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]							)

						if with_box_refine:
												__SCREAMING_SNAKE_CASE     :Any	       =						torch.tensor(
												    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]							)
												__SCREAMING_SNAKE_CASE     :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:
												__SCREAMING_SNAKE_CASE     :str	       =						torch.tensor(
												    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]							)
												__SCREAMING_SNAKE_CASE     :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(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:
												__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						"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__":
		lowerCamelCase_          =	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_          =	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 argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowerCamelCase_          =	logging.getLogger(__name__)



def   __lowerCamelCase     (							)    ->   Optional[Any]:
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						argparse.ArgumentParser(description='''PyTorch Transformer Language Model'''							)
      parser.add_argument('''--model_name'''				,     type=a_				,     default='''transfo-xl-wt103'''				,     help='''pretrained model name'''							)
      parser.add_argument(
          '''--split'''				,     type=a_				,     default='''test'''				,     choices=['''all''', '''valid''', '''test''']				,     help='''which split to evaluate'''							)
      parser.add_argument('''--batch_size'''				,     type=a_				,     default=10				,     help='''batch size'''							)
      parser.add_argument('''--tgt_len'''				,     type=a_				,     default=1_28				,     help='''number of tokens to predict'''							)
      parser.add_argument('''--ext_len'''				,     type=a_				,     default=0				,     help='''length of the extended context'''							)
      parser.add_argument('''--mem_len'''				,     type=a_				,     default=16_00				,     help='''length of the retained previous heads'''							)
      parser.add_argument('''--clamp_len'''				,     type=a_				,     default=10_00				,     help='''max positional embedding index'''							)
      parser.add_argument('''--no_cuda'''				,     action='''store_true'''				,     help='''Do not use CUDA even though CUA is available'''							)
      parser.add_argument('''--work_dir'''				,     type=a_				,     required=a_				,     help='''path to the work_dir'''							)
      parser.add_argument('''--no_log'''				,     action='''store_true'''				,     help='''do not log the eval result'''							)
      parser.add_argument('''--same_length'''				,     action='''store_true'''				,     help='''set same length attention with masking'''							)
      parser.add_argument('''--server_ip'''				,     type=a_				,     default=''''''				,     help='''Can be used for distant debugging.'''							)
      parser.add_argument('''--server_port'''				,     type=a_				,     default=''''''				,     help='''Can be used for distant debugging.'''							)
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						parser.parse_args()
      assert args.ext_len >= 0, "extended context length must be non-negative"

      if args.server_ip and args.server_port:
            # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
            import ptvsd

            print('''Waiting for debugger attach'''							)
            ptvsd.enable_attach(address=(args.server_ip, args.server_port)				,     redirect_output=a_							)
            ptvsd.wait_for_attach()

      __SCREAMING_SNAKE_CASE     :Tuple	       =						torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''							)
      logger.info('''device: {}'''.format(a_							)							)

      # Load a pre-processed dataset
      # You can also build the corpus yourself using TransfoXLCorpus methods
      # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
      # and tokenizing the dataset
      # The pre-processed corpus is a convertion (using the conversion script )
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						TransfoXLCorpus.from_pretrained(args.model_name							)

      __SCREAMING_SNAKE_CASE     :Tuple	       =						corpus.get_iterator('''valid'''				,     args.batch_size				,     args.tgt_len				,     device=a_				,     ext_len=args.ext_len							)
      __SCREAMING_SNAKE_CASE     :Tuple	       =						corpus.get_iterator('''test'''				,     args.batch_size				,     args.tgt_len				,     device=a_				,     ext_len=args.ext_len							)

      # Load a pre-trained model
      __SCREAMING_SNAKE_CASE     :int	       =						TransfoXLLMHeadModel.from_pretrained(args.model_name							)
      model.to(a_							)

      logger.info(
          '''Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'''.format(
              args.batch_size				,     args.tgt_len				,     args.ext_len				,     args.mem_len				,     args.clamp_len							)							)

      model.reset_memory_length(args.mem_len							)
      if args.clamp_len > 0:
            __SCREAMING_SNAKE_CASE     :Dict	       =						args.clamp_len
      if args.same_length:
            __SCREAMING_SNAKE_CASE     :int	       =						True

      ###############################################################################
      # Evaluation code
      ###############################################################################
      def evaluate(a_  :	Optional[int]							):
            # Turn on evaluation mode which disables dropout.
            model.eval()
            __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						0, 0.0
            __SCREAMING_SNAKE_CASE     :Tuple	       =						time.time()
            with torch.no_grad():
                  __SCREAMING_SNAKE_CASE     :Any	       =						None
                  for idx, (data, target, seq_len) in enumerate(a_							):
                        __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						model(a_				,     lm_labels=a_				,     mems=a_							)
                        __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :List[str]	       =						ret
                        __SCREAMING_SNAKE_CASE     :Tuple	       =						loss.mean()
                        total_loss += seq_len * loss.item()
                        total_len += seq_len
                  __SCREAMING_SNAKE_CASE     :List[str]	       =						time.time() - start_time
            logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(a_				,     10_00 * total_time / (idx + 1)							)							)
            return total_loss / total_len

      # Run on test data.
      if args.split == "all":
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						evaluate(a_							)
            __SCREAMING_SNAKE_CASE     :Any	       =						evaluate(a_							)
      elif args.split == "valid":
            __SCREAMING_SNAKE_CASE     :Tuple	       =						evaluate(a_							)
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						None
      elif args.split == "test":
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						evaluate(a_							)
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						None

      def format_log(a_  :	int				,     a_  :	List[Any]							):
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						'''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(a_				,     a_				,     math.exp(a_							)							)
            return log_str

      __SCREAMING_SNAKE_CASE     :Tuple	       =						''''''
      if valid_loss is not None:
            log_str += format_log(a_				,     '''valid'''							)
      if test_loss is not None:
            log_str += format_log(a_				,     '''test'''							)

      logger.info('''=''' * 1_00							)
      logger.info(a_							)
      logger.info('''=''' * 1_00							)


if __name__ == "__main__":
  main()