
"""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 						UpperCAmelCase   (						a_     ):





		'''simple docstring'''


		if "backbone.0.body" in orig_key:
				lowerCamelCase	:     Any            =       orig_key.replace('backbone.0.body',		'backbone.conv_encoder.model'     )
		if "transformer" in orig_key:
				lowerCamelCase	:     List[Any]            =       orig_key.replace('transformer.',		''     )
		if "norm1" in orig_key:
				if "encoder" in orig_key:
						lowerCamelCase	:     Optional[int]            =       orig_key.replace('norm1',		'self_attn_layer_norm'     )
				else:
						lowerCamelCase	:     Union[str, Any]            =       orig_key.replace('norm1',		'encoder_attn_layer_norm'     )
		if "norm2" in orig_key:
				if "encoder" in orig_key:
						lowerCamelCase	:     Tuple            =       orig_key.replace('norm2',		'final_layer_norm'     )
				else:
						lowerCamelCase	:     int            =       orig_key.replace('norm2',		'self_attn_layer_norm'     )
		if "norm3" in orig_key:
				lowerCamelCase	:     Any            =       orig_key.replace('norm3',		'final_layer_norm'     )
		if "linear1" in orig_key:
				lowerCamelCase	:     Any            =       orig_key.replace('linear1',		'fc1'     )
		if "linear2" in orig_key:
				lowerCamelCase	:     Any            =       orig_key.replace('linear2',		'fc2'     )
		if "query_embed" in orig_key:
				lowerCamelCase	:     List[str]            =       orig_key.replace('query_embed',		'query_position_embeddings'     )
		if "cross_attn" in orig_key:
				lowerCamelCase	:     Any            =       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	:     List[str]            =       state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight"""     )
				lowerCamelCase	:     Optional[Any]            =       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	:     Optional[Any]            =       in_proj_weight[:256, :]
				lowerCamelCase	:     List[Any]            =       in_proj_bias[:256]
				lowerCamelCase	:     Any            =       in_proj_weight[256:512, :]
				lowerCamelCase	:     Any            =       in_proj_bias[256:512]
				lowerCamelCase	:     Any            =       in_proj_weight[-256:, :]
				lowerCamelCase	:     Optional[Any]            =       in_proj_bias[-256:]
def 						UpperCAmelCase   (						):





		'''simple docstring'''


		lowerCamelCase	:     List[Any]            =       'http://images.cocodataset.org/val2017/000000039769.jpg'
		lowerCamelCase	:     Tuple            =       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	:     List[str]            =       DeformableDetrConfig()
		# set config attributes
		if single_scale:
				lowerCamelCase	:     Optional[int]            =       1
		lowerCamelCase	:     Union[str, Any]            =       dilation
		lowerCamelCase	:     Any            =       with_box_refine
		lowerCamelCase	:     Optional[int]            =       two_stage
		# set labels
		lowerCamelCase	:     Optional[int]            =       91
		lowerCamelCase	:     Optional[int]            =       'huggingface/label-files'
		lowerCamelCase	:     Optional[Any]            =       'coco-detection-id2label.json'
		lowerCamelCase	:     int            =       json.load(open(cached_download(hf_hub_url(a_,		a_,		repo_type='dataset'     )     ),		'r'     )     )
		lowerCamelCase	:     Dict            =       {int(a_     ): v for k, v in idalabel.items()}
		lowerCamelCase	:     Any            =       idalabel
		lowerCamelCase	:     str            =       {v: k for k, v in idalabel.items()}

		# load image processor
		lowerCamelCase	:     Tuple            =       DeformableDetrImageProcessor(format='coco_detection'     )

		# prepare image
		lowerCamelCase	:     int            =       prepare_img()
		lowerCamelCase	:     Optional[Any]            =       image_processor(images=a_,		return_tensors='pt'     )
		lowerCamelCase	:     Optional[int]            =       encoding['pixel_values']

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

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

		lowerCamelCase	:     Union[str, Any]            =       'cuda' if torch.cuda.is_available() else 'cpu'
		model.to(a_     )
		# verify our conversion
		lowerCamelCase	:     Tuple            =       model(pixel_values.to(a_     )     )

		lowerCamelCase	:     str            =       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]]     )
		lowerCamelCase	:     List[str]            =       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:
				lowerCamelCase	:     Optional[Any]            =       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]]     )
				lowerCamelCase	:     Union[str, Any]            =       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:
				lowerCamelCase	:     Dict            =       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]]     )
				lowerCamelCase	:     Optional[int]            =       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:
				lowerCamelCase	:     Dict            =       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]]     )
				lowerCamelCase	:     List[str]            =       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:
				lowerCamelCase	:     str            =       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]]     )
				lowerCamelCase	:     str            =       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(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	:     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=a_,		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
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ... import AutoTokenizer
from ...utils import logging


_A				=					'block_records.npy'


_A				=					logging.get_logger(__name__)
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		import tensorflow.compat.va as tf

		lowerCamelCase	:     Union[str, Any]            =       tf.data.TFRecordDataset(a_,		buffer_size=512 * 1024 * 1024     )
		lowerCamelCase	:     Union[str, Any]            =       blocks_dataset.batch(a_,		drop_remainder=a_     )
		lowerCamelCase	:     Union[str, Any]            =       next(blocks_dataset.take(1     ).as_numpy_iterator()     )

		return np_record






class 			_lowercase  :




	def __init__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_=2						,					UpperCAmelCase_=1000						,					UpperCAmelCase_=100						,					UpperCAmelCase_=100000						,					)						->				List[Any]:
			from scann.scann_ops.py.scann_ops_pybind import builder as Builder

			lowerCamelCase	:     int            =       Builder(db=UpperCAmelCase_						,					num_neighbors=UpperCAmelCase_						,					distance_measure='dot_product'   )
			lowerCamelCase	:     Optional[int]            =       builder.tree(
			    num_leaves=UpperCAmelCase_						,					num_leaves_to_search=UpperCAmelCase_						,					training_sample_size=UpperCAmelCase_   )
			lowerCamelCase	:     Union[str, Any]            =       builder.score_ah(dimensions_per_block=UpperCAmelCase_   )

			lowerCamelCase	:     List[Any]            =       builder.build()




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				Optional[int]:
			lowerCamelCase      ,       lowerCamelCase	:     List[Any]            =       self.searcher.search_batched(question_projection.detach().cpu()   )
			return retrieved_block_ids.astype('int64'   )






class 			_lowercase  :




	def __init__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				Optional[Any]:
			super().__init__()
			lowerCamelCase	:     Tuple            =       block_records
			lowerCamelCase	:     Any            =       tokenizer




	def __call__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_=None						,					UpperCAmelCase_="pt"   )						->				Tuple:
			lowerCamelCase	:     List[str]            =       np.take(self.block_records						,					indices=UpperCAmelCase_						,					axis=0   )

			lowerCamelCase	:     Any            =       self.tokenizer.decode(question_input_ids[0]						,					skip_special_tokens=UpperCAmelCase_   )

			lowerCamelCase	:     int            =       []
			lowerCamelCase	:     Optional[int]            =       []
			for retrieved_block in retrieved_blocks:
					text.append(UpperCAmelCase_   )
					text_pair.append(retrieved_block.decode()   )

			lowerCamelCase	:     Union[str, Any]            =       self.tokenizer(
			    UpperCAmelCase_						,					UpperCAmelCase_						,					padding=UpperCAmelCase_						,					truncation=UpperCAmelCase_						,					return_special_tokens_mask=UpperCAmelCase_						,					max_length=UpperCAmelCase_   )
			lowerCamelCase	:     str            =       concat_inputs.convert_to_tensors(UpperCAmelCase_   )

			if answer_ids is not None:
					return self.block_has_answer(UpperCAmelCase_						,					UpperCAmelCase_   ) + (concat_inputs_tensors,)
			else:
					return (None, None, None, concat_inputs_tensors)




	@classmethod
	def      _UpperCamelCase       (					cls						,					UpperCAmelCase_						,					*UpperCAmelCase_						,					**UpperCAmelCase_   )						->				Dict:
			if os.path.isdir(UpperCAmelCase_   ):
					lowerCamelCase	:     str            =       os.path.join(UpperCAmelCase_						,					_REALM_BLOCK_RECORDS_FILENAME   )
			else:
					lowerCamelCase	:     Tuple            =       hf_hub_download(
					    repo_id=UpperCAmelCase_						,					filename=_REALM_BLOCK_RECORDS_FILENAME						,					**UpperCAmelCase_   )
			lowerCamelCase	:     Dict            =       np.load(UpperCAmelCase_						,					allow_pickle=UpperCAmelCase_   )

			lowerCamelCase	:     Union[str, Any]            =       AutoTokenizer.from_pretrained(UpperCAmelCase_						,					*UpperCAmelCase_						,					**UpperCAmelCase_   )

			return cls(UpperCAmelCase_						,					UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				Union[str, Any]:
			# save block records
			np.save(os.path.join(UpperCAmelCase_						,					_REALM_BLOCK_RECORDS_FILENAME   )						,					self.block_records   )
			# save tokenizer
			self.tokenizer.save_pretrained(UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				Tuple:
			lowerCamelCase	:     List[Any]            =       []
			lowerCamelCase	:     Any            =       []
			lowerCamelCase	:     int            =       []
			lowerCamelCase	:     Union[str, Any]            =       0

			for input_id in concat_inputs.input_ids:
					lowerCamelCase	:     Optional[Any]            =       input_id.tolist()
					# Check answers between two [SEP] tokens
					lowerCamelCase	:     Tuple            =       input_id_list.index(self.tokenizer.sep_token_id   )
					lowerCamelCase	:     Any            =       first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id   )

					start_pos.append([]   )
					end_pos.append([]   )
					for answer in answer_ids:
							for idx in range(first_sep_idx + 1						,					UpperCAmelCase_   ):
									if answer[0] == input_id_list[idx]:
											if input_id_list[idx : idx + len(UpperCAmelCase_   )] == answer:
													start_pos[-1].append(UpperCAmelCase_   )
													end_pos[-1].append(idx + len(UpperCAmelCase_   ) - 1   )

					if len(start_pos[-1]   ) == 0:
							has_answers.append(UpperCAmelCase_   )
					else:
							has_answers.append(UpperCAmelCase_   )
							if len(start_pos[-1]   ) > max_answers:
									lowerCamelCase	:     List[str]            =       len(start_pos[-1]   )

        # Pad -1 to max_answers
			for start_pos_, end_pos_ in zip(UpperCAmelCase_						,					UpperCAmelCase_   ):
					if len(start_pos_   ) < max_answers:
							lowerCamelCase	:     Optional[int]            =       [-1] * (max_answers - len(start_pos_   ))
							start_pos_ += padded
							end_pos_ += padded
			return has_answers, start_pos, end_pos






