



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		( _UpperCAmelCase				:   str		):







		'''simple docstring'''





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

		return orig_key

def 			a		( _UpperCAmelCase				:   int		):







		'''simple docstring'''





		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[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
				__UpperCAmelCase       :	Optional[int]						=      in_proj_weight[:2_56, :]
				__UpperCAmelCase       :	Optional[int]						=      in_proj_bias[:2_56]
				__UpperCAmelCase       :	Optional[Any]						=      in_proj_weight[2_56:5_12, :]
				__UpperCAmelCase       :	str						=      in_proj_bias[2_56:5_12]
				__UpperCAmelCase       :	Dict						=      in_proj_weight[-2_56:, :]
				__UpperCAmelCase       :	str						=      in_proj_bias[-2_56:]

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	Optional[Any]						=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		__UpperCAmelCase       :	Any						=      Image.open(requests.get(_UpperCAmelCase					,  stream=_UpperCAmelCase		).raw		)

		return im

@torch.no_grad()
def 			a		( _UpperCAmelCase				:   int					,  _UpperCAmelCase				:   Dict					,  _UpperCAmelCase				:   Optional[int]					,  _UpperCAmelCase				:   str					,  _UpperCAmelCase				:   List[Any]					,  _UpperCAmelCase				:   List[Any]					,  _UpperCAmelCase				:   Optional[Any]					,  ):







		'''simple docstring'''





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

		# load image processor
		__UpperCAmelCase       :	List[str]						=      DeformableDetrImageProcessor(format='''coco_detection'''		)

		# prepare image
		__UpperCAmelCase       :	List[str]						=      prepare_img()
		__UpperCAmelCase       :	Optional[Any]						=      image_processor(images=_UpperCAmelCase					,  return_tensors='''pt'''		)
		__UpperCAmelCase       :	int						=      encoding['''pixel_values''']

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

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

		__UpperCAmelCase       :	int						=      '''cuda''' if torch.cuda.is_available() else '''cpu'''
		model.to(_UpperCAmelCase		)
		# verify our conversion
		__UpperCAmelCase       :	Optional[Any]						=      model(pixel_values.to(_UpperCAmelCase		)		)

		__UpperCAmelCase       :	Optional[int]						=      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]]		)
		__UpperCAmelCase       :	Tuple						=      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:
				__UpperCAmelCase       :	str						=      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]]		)
				__UpperCAmelCase       :	int						=      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:
				__UpperCAmelCase       :	List[str]						=      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]]		)
				__UpperCAmelCase       :	List[Any]						=      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:
				__UpperCAmelCase       :	str						=      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]]		)
				__UpperCAmelCase       :	int						=      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:
				__UpperCAmelCase       :	List[str]						=      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]]		)
				__UpperCAmelCase       :	Any						=      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(_UpperCAmelCase		)					,  atol=1e-4		)
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,  expected_boxes.to(_UpperCAmelCase		)					,  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(_UpperCAmelCase		).mkdir(exist_ok=_UpperCAmelCase		)
		model.save_pretrained(_UpperCAmelCase		)
		image_processor.save_pretrained(_UpperCAmelCase		)

		# Push to hub
		if push_to_hub:
				__UpperCAmelCase       :	int						=      '''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=_UpperCAmelCase					,  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,
				)










import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						UpperCAmelCase__     (     __UpperCamelCase		,unittest.TestCase  ):




			'''simple docstring'''






			UpperCamelCase     		=			HerbertTokenizer
			UpperCamelCase     		=			HerbertTokenizerFast
			UpperCamelCase     		=			True




			def 					snake_case__	(  self				:					Tuple					):
					'''simple docstring'''





					super().setUp()

					# Use a simpler test file without japanese/chinese characters
					with open(F'{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'			,			encoding='''utf-8'''					) as f_data:
							__UpperCAmelCase       :	List[str]						=      f_data.read().replace('''\n\n'''			,			'''\n'''					).strip()

					__UpperCAmelCase       :	Dict						=      [
					    '''<s>''',
					    '''</s>''',
					    '''l''',
					    '''o''',
					    '''w''',
					    '''e''',
					    '''r''',
					    '''s''',
					    '''t''',
					    '''i''',
					    '''d''',
					    '''n''',
					    '''w</w>''',
					    '''r</w>''',
					    '''t</w>''',
					    '''lo''',
					    '''low''',
					    '''er</w>''',
					    '''low</w>''',
					    '''lowest</w>''',
					    '''newer</w>''',
					    '''wider</w>''',
					    ''',</w>''',
					    '''<unk>''',
					]
					__UpperCAmelCase       :	Tuple						=      dict(zip(a_			,			range(len(a_					)					)					)					)
					__UpperCAmelCase       :	Optional[int]						=      ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

					__UpperCAmelCase       :	str						=      os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''vocab_file''']					)
					__UpperCAmelCase       :	List[str]						=      os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''merges_file''']					)
					with open(self.vocab_file			,			'''w'''					) as fp:
							fp.write(json.dumps(a_					)					)
					with open(self.merges_file			,			'''w'''					) as fp:
							fp.write('''\n'''.join(a_					)					)




			def 					snake_case__	(  self				:					Union[str, Any]			,			a_				:					Optional[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[str]						=      '''lower newer'''
					__UpperCAmelCase       :	List[Any]						=      '''lower newer'''
					return input_text, output_text




			def 					snake_case__	(  self				:					int					):
					'''simple docstring'''





					__UpperCAmelCase       :	Any						=      self.tokenizer_class(vocab_file=self.vocab_file			,			merges_file=self.merges_file					)

					__UpperCAmelCase       :	Optional[int]						=      '''lower'''
					__UpperCAmelCase       :	Any						=      ['''low''', '''er</w>''']
					__UpperCAmelCase       :	Any						=      tokenizer.tokenize(a_					)
					self.assertListEqual(a_			,			a_					)

					__UpperCAmelCase       :	int						=      tokens + ['''<unk>''']
					__UpperCAmelCase       :	Optional[Any]						=      [16, 17, 23]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(a_					)			,			a_					)




			def 					snake_case__	(  self				:					List[Any]					):
					'''simple docstring'''





					if not self.test_rust_tokenizer:
							return

					__UpperCAmelCase       :	str						=      self.get_tokenizer()
					__UpperCAmelCase       :	Optional[int]						=      self.get_rust_tokenizer()

					__UpperCAmelCase       :	Tuple						=      '''lower,newer'''

					__UpperCAmelCase       :	Optional[int]						=      tokenizer.tokenize(a_					)
					__UpperCAmelCase       :	Tuple						=      rust_tokenizer.tokenize(a_					)
					self.assertListEqual(a_			,			a_					)

					__UpperCAmelCase       :	Optional[Any]						=      tokenizer.encode(a_			,			add_special_tokens=a_					)
					__UpperCAmelCase       :	Union[str, Any]						=      rust_tokenizer.encode(a_			,			add_special_tokens=a_					)
					self.assertListEqual(a_			,			a_					)

					__UpperCAmelCase       :	Tuple						=      self.get_rust_tokenizer()
					__UpperCAmelCase       :	Union[str, Any]						=      tokenizer.encode(a_					)
					__UpperCAmelCase       :	str						=      rust_tokenizer.encode(a_					)
					self.assertListEqual(a_			,			a_					)




			@slow
			def 					snake_case__	(  self				:					Tuple					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[Any]						=      self.tokenizer_class.from_pretrained('''allegro/herbert-base-cased'''					)

					__UpperCAmelCase       :	Tuple						=      tokenizer.encode('''konstruowanie sekwencji'''			,			add_special_tokens=a_					)
					__UpperCAmelCase       :	int						=      tokenizer.encode('''konstruowanie wielu sekwencji'''			,			add_special_tokens=a_					)

					__UpperCAmelCase       :	Union[str, Any]						=      tokenizer.build_inputs_with_special_tokens(a_					)
					__UpperCAmelCase       :	Any						=      tokenizer.build_inputs_with_special_tokens(a_			,			a_					)

					assert encoded_sentence == [0] + text + [2]
					assert encoded_pair == [0] + text + [2] + text_a + [2]





