





import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer
class SCREAMING_SNAKE_CASE_    (       tf.keras.layers.Layer       ):






						def __init__(   self   :   List[Any]		, _A   :   List		, _A   :   bool		, _A   :   int = None		, _A   :   int = None		, _A   :   int = None		, _A   :   str = "longest"		, _A   :   bool = True		, _A   :   int = 512		, _A   :   int = None		, _A   :   bool = True		, _A   :   bool = True		, _A   :   bool = True		, **_A   :   Any		, )				->     Tuple:







							"""simple docstring"""


							super().__init__()
							if use_fast_bert_tokenizer:
								snake_case_		:      Any           =						FastBertTokenizer(
								    _A		, token_out_type=tf.intaa		, lower_case_nfd_strip_accents=_A		, **_A						)
							else:
								snake_case_		:      int           =						tf.lookup.StaticVocabularyTable(
								    tf.lookup.KeyValueTensorInitializer(
								        keys=_A		, key_dtype=tf.string		, values=tf.range(tf.size(_A		, out_type=tf.intaa						)		, dtype=tf.intaa						)		, value_dtype=tf.intaa		, )		, num_oov_buckets=1		, )
								snake_case_		:      Any           =						BertTokenizerLayer(
								    _A		, token_out_type=tf.intaa		, lower_case=_A		, **_A						)

							snake_case_		:      Optional[int]           =						vocab_list
							snake_case_		:      int           =						do_lower_case
							snake_case_		:      Optional[Any]           =						cls_token_id or vocab_list.index('[CLS]'						)
							snake_case_		:      Any           =						sep_token_id or vocab_list.index('[SEP]'						)
							snake_case_		:      Union[str, Any]           =						pad_token_id or vocab_list.index('[PAD]'						)
							snake_case_		:      List[Any]           =						ShrinkLongestTrimmer(max_length - 3		, axis=1						)  # Allow room for special tokens
							snake_case_		:      Tuple           =						max_length
							snake_case_		:      Optional[int]           =						padding
							snake_case_		:      List[Any]           =						truncation
							snake_case_		:      int           =						pad_to_multiple_of
							snake_case_		:      Optional[int]           =						return_token_type_ids
							snake_case_		:      str           =						return_attention_mask






						@classmethod
						def UpperCAmelCase_					(   cls   :   Any		, _A   :   "PreTrainedTokenizerBase"		, **_A   :   str						)				->     List[str]:  # noqa: F821







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						kwargs.pop('do_lower_case'		, _A						)
							snake_case_		:      Tuple           =						tokenizer.do_lower_case if do_lower_case is None else do_lower_case
							snake_case_		:      List[str]           =						kwargs.pop('cls_token_id'		, _A						)
							snake_case_		:      Any           =						tokenizer.cls_token_id if cls_token_id is None else cls_token_id
							snake_case_		:      Tuple           =						kwargs.pop('sep_token_id'		, _A						)
							snake_case_		:      Tuple           =						tokenizer.sep_token_id if sep_token_id is None else sep_token_id
							snake_case_		:      List[str]           =						kwargs.pop('pad_token_id'		, _A						)
							snake_case_		:      List[str]           =						tokenizer.pad_token_id if pad_token_id is None else pad_token_id

							snake_case_		:      Union[str, Any]           =						tokenizer.get_vocab()
							snake_case_		:      Optional[int]           =						sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]		, key=lambda _A						: x[1]						)
							snake_case_		:      Optional[int]           =						[entry[0] for entry in vocab]
							return cls(
							    vocab_list=_A		, do_lower_case=_A		, cls_token_id=_A		, sep_token_id=_A		, pad_token_id=_A		, **_A		, )






						@classmethod
						def UpperCAmelCase_					(   cls   :   Union[str, Any]		, _A   :   Union[str, os.PathLike]		, *_A   :   int		, **_A   :   List[str]						)				->     Tuple:







							"""simple docstring"""


							try:
								snake_case_		:      Any           =						BertTokenizer.from_pretrained(_A		, *_A		, **_A						)
							except:  # noqa: E722
								from .tokenization_bert_fast import BertTokenizerFast

								snake_case_		:      int           =						BertTokenizerFast.from_pretrained(_A		, *_A		, **_A						)
							return cls.from_tokenizer(_A		, **_A						)






						def UpperCAmelCase_					(   self   :   List[str]		, _A   :   List[str]						)				->     str:







							"""simple docstring"""


							if self.do_lower_case:
								snake_case_		:      Optional[int]           =						case_fold_utfa(_A						)
							snake_case_		:      Tuple           =						self.tf_tokenizer.tokenize(_A						)
							return tokens.merge_dims(1		, -1						)






						def UpperCAmelCase_					(   self   :   Optional[int]		, _A   :   Optional[int]		, _A   :   Optional[Any]=None		, _A   :   Optional[Any]=None		, _A   :   List[str]=None		, _A   :   List[Any]=None		, _A   :   Any=None		, _A   :   Optional[int]=None		, _A   :   int=None		, )				->     Optional[int]:







							"""simple docstring"""


							if padding is None:
								snake_case_		:      Union[str, Any]           =						self.padding
							if padding not in ("longest", "max_length"):
								raise ValueError('Padding must be either \'longest\' or \'max_length\'!'						)
							if max_length is not None and text_pair is not None:
								# Because we have to instantiate a Trimmer to do it properly
								raise ValueError('max_length cannot be overridden at call time when truncating paired texts!'						)
							if max_length is None:
								snake_case_		:      Optional[Any]           =						self.max_length
							if truncation is None:
								snake_case_		:      str           =						self.truncation
							if pad_to_multiple_of is None:
								snake_case_		:      Tuple           =						self.pad_to_multiple_of
							if return_token_type_ids is None:
								snake_case_		:      List[Any]           =						self.return_token_type_ids
							if return_attention_mask is None:
								snake_case_		:      Dict           =						self.return_attention_mask
							if not isinstance(_A		, tf.Tensor						):
								snake_case_		:      Optional[Any]           =						tf.convert_to_tensor(_A						)
							if text_pair is not None and not isinstance(_A		, tf.Tensor						):
								snake_case_		:      List[Any]           =						tf.convert_to_tensor(_A						)
							if text_pair is not None:
								if text.shape.rank > 1:
									raise ValueError('text argument should not be multidimensional when a text pair is supplied!'						)
								if text_pair.shape.rank > 1:
									raise ValueError('text_pair should not be multidimensional!'						)
							if text.shape.rank == 2:
								snake_case_    ,snake_case_		:      Dict           =						text[:, 0], text[:, 1]
							snake_case_		:      str           =						self.unpaired_tokenize(_A						)
							if text_pair is None:  # Unpaired text
								if truncation:
									snake_case_		:      List[str]           =						text[:, : max_length - 2]  # Allow room for special tokens
								snake_case_    ,snake_case_		:      Dict           =						combine_segments(
								    (text,)		, start_of_sequence_id=self.cls_token_id		, end_of_segment_id=self.sep_token_id						)
							else:  # Paired text
								snake_case_		:      Tuple           =						self.unpaired_tokenize(_A						)
								if truncation:
									snake_case_    ,snake_case_		:      List[Any]           =						self.paired_trimmer.trim([text, text_pair]						)
								snake_case_    ,snake_case_		:      Optional[Any]           =						combine_segments(
								    (text, text_pair)		, start_of_sequence_id=self.cls_token_id		, end_of_segment_id=self.sep_token_id						)
							if padding == "longest":
								snake_case_		:      List[Any]           =						input_ids.bounding_shape(axis=1						)
								if pad_to_multiple_of is not None:
									# No ceiling division in tensorflow, so we negate floordiv instead
									snake_case_		:      Any           =						pad_to_multiple_of * (-tf.math.floordiv(-pad_length		, _A						))
							else:
								snake_case_		:      Optional[Any]           =						max_length

							snake_case_    ,snake_case_		:      Tuple           =						pad_model_inputs(_A		, max_seq_length=_A		, pad_value=self.pad_token_id						)
							snake_case_		:      str           =						{'input_ids': input_ids}
							if return_attention_mask:
								snake_case_		:      Any           =						attention_mask
							if return_token_type_ids:
								snake_case_    ,snake_case_		:      Dict           =						pad_model_inputs(
								    _A		, max_seq_length=_A		, pad_value=self.pad_token_id						)
								snake_case_		:      Union[str, Any]           =						token_type_ids
							return output






						def UpperCAmelCase_					(   self   :   Any						)				->     Any:







							"""simple docstring"""


							return {
							    "vocab_list": self.vocab_list,
							    "do_lower_case": self.do_lower_case,
							    "cls_token_id": self.cls_token_id,
							    "sep_token_id": self.sep_token_id,
							    "pad_token_id": self.pad_token_id,
							}




"""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__			:						Optional[int]							=			logging.get_logger(__name__)





def 					__UpperCamelCase (     a__:			str      )					->      List[str]:




					"""simple docstring"""



					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 					__UpperCamelCase (     a__:			Union[str, Any]      )					->      Tuple:




					"""simple docstring"""



					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[:2_56, :]
										__lowercase        =      in_proj_bias[:2_56]
										__lowercase        =      in_proj_weight[2_56:5_12, :]
										__lowercase        =      in_proj_bias[2_56:5_12]
										__lowercase        =      in_proj_weight[-2_56:, :]
										__lowercase        =      in_proj_bias[-2_56:]





def 					__UpperCamelCase (     )					->      str:




					"""simple docstring"""



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

					return im





@torch.no_grad()
def 					__UpperCamelCase (     a__:			List[Any]			,						a__:			Union[str, Any]			,						a__:			Optional[int]			,						a__:			Optional[int]			,						a__:			List[Any]			,						a__:			Optional[Any]			,						a__:			str			,						)					->      List[str]:




					"""simple docstring"""



					__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(a__			,						a__			,						repo_type='''dataset'''      )      )			,						'''r'''      )      )
					__lowercase        =      {int(a__      ): 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=a__			,						return_tensors='''pt'''      )
					__lowercase        =      encoding['''pixel_values''']

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

					# load original state dict
					__lowercase        =      torch.load(a__			,						map_location='''cpu'''      )['''model''']
					# rename keys
					for key in state_dict.copy().keys():
										__lowercase        =      state_dict.pop(a__      )
										__lowercase        =      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
					__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(a__      )
															__lowercase        =      val
    # finally, create HuggingFace model and load state dict
					__lowercase        =      DeformableDetrForObjectDetection(a__      )
					model.load_state_dict(a__      )
					model.eval()

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

					__lowercase        =      torch.tensor(
					    [[-9.66_45, -4.34_49, -5.87_05], [-9.70_35, -3.85_04, -5.07_24], [-10.56_34, -5.33_79, -7.51_16]]      )
					__lowercase        =      torch.tensor([[0.86_93, 0.22_89, 0.24_92], [0.31_50, 0.54_89, 0.58_45], [0.55_63, 0.75_80, 0.85_18]]      )

					if single_scale:
										__lowercase        =      torch.tensor(
										    [[-9.90_51, -4.25_41, -6.48_52], [-9.69_47, -4.08_54, -6.80_33], [-10.06_65, -5.84_70, -7.70_03]]      )
										__lowercase        =      torch.tensor([[0.72_92, 0.49_91, 0.55_32], [0.79_59, 0.24_26, 0.42_36], [0.75_82, 0.35_18, 0.44_51]]      )

					if single_scale and dilation:
										__lowercase        =      torch.tensor(
										    [[-8.96_52, -4.10_74, -5.66_35], [-9.05_96, -4.94_47, -6.60_75], [-10.11_78, -4.52_75, -6.26_71]]      )
										__lowercase        =      torch.tensor([[0.76_65, 0.41_30, 0.47_69], [0.83_64, 0.18_41, 0.33_91], [0.62_61, 0.38_95, 0.79_78]]      )

					if with_box_refine:
										__lowercase        =      torch.tensor(
										    [[-8.88_95, -5.41_87, -6.81_53], [-8.47_06, -6.16_68, -7.61_84], [-9.00_42, -5.53_59, -6.91_41]]      )
										__lowercase        =      torch.tensor([[0.78_28, 0.22_08, 0.43_23], [0.08_92, 0.59_96, 0.13_19], [0.55_24, 0.63_89, 0.89_14]]      )

					if with_box_refine and two_stage:
										__lowercase        =      torch.tensor(
										    [[-6.71_08, -4.32_13, -6.37_77], [-8.90_14, -6.17_99, -6.72_40], [-6.93_15, -4.47_35, -6.22_98]]      )
										__lowercase        =      torch.tensor([[0.25_83, 0.54_99, 0.46_83], [0.76_52, 0.90_68, 0.48_82], [0.54_90, 0.27_63, 0.05_64]]      )

					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:
										__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=a__			,						organization='''nielsr'''			,						commit_message='''Add model'''      )


if __name__ == "__main__":
							a__			:						str							=			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__			:						Union[str, Any]							=			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,
							)


