




"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a_        =       logging.get_logger(__name__)







def      __UpperCAmelCase     ( __UpperCamelCase	):
			__lowercase			:  int								=       YolosConfig()

			# size of the architecture
			if "yolos_ti" in yolos_name:
						__lowercase			:  str								=       1_92
						__lowercase			:  Tuple								=       7_68
						__lowercase			:  str								=       12
						__lowercase			:  str								=       3
						__lowercase			:  Optional[Any]								=       [8_00, 13_33]
						__lowercase			:  Optional[Any]								=       False
			elif yolos_name == "yolos_s_dWr":
						__lowercase			:  Any								=       3_30
						__lowercase			:  List[str]								=       14
						__lowercase			:  Tuple								=       6
						__lowercase			:  Any								=       13_20
			elif "yolos_s" in yolos_name:
						__lowercase			:  Tuple								=       3_84
						__lowercase			:  Any								=       15_36
						__lowercase			:  List[str]								=       12
						__lowercase			:  Dict								=       6
			elif "yolos_b" in yolos_name:
						__lowercase			:  int								=       [8_00, 13_44]

			__lowercase			:  Any								=       91
			__lowercase			:  str								=       '''huggingface/label-files'''
			__lowercase			:  str								=       '''coco-detection-id2label.json'''
			__lowercase			:  Optional[Any]								=       json.load(open(hf_hub_download(__UpperCamelCase	,				__UpperCamelCase	,				repo_type='''dataset'''	)	,				'''r'''	)	)
			__lowercase			:  List[Any]								=       {int(__UpperCamelCase	): v for k, v in idalabel.items()}
			__lowercase			:  str								=       idalabel
			__lowercase			:  Union[str, Any]								=       {v: k for k, v in idalabel.items()}

			return config







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = False	):
			for i in range(config.num_hidden_layers	):
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						__lowercase			:  str								=       state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""	)
						__lowercase			:  int								=       state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""	)
						# next, add query, keys and values (in that order) to the state dict
						__lowercase			:  Tuple								=       in_proj_weight[: config.hidden_size, :]
						__lowercase			:  Any								=       in_proj_bias[: config.hidden_size]
						__lowercase			:  Tuple								=       in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						__lowercase			:  Optional[int]								=       in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						__lowercase			:  Any								=       in_proj_weight[-config.hidden_size :, :]
						__lowercase			:  List[str]								=       in_proj_bias[-config.hidden_size :]







def      __UpperCAmelCase     ( __UpperCamelCase	):
			if "backbone" in name:
						__lowercase			:  List[str]								=       name.replace('''backbone'''	,				'''vit'''	)
			if "cls_token" in name:
						__lowercase			:  Tuple								=       name.replace('''cls_token'''	,				'''embeddings.cls_token'''	)
			if "det_token" in name:
						__lowercase			:  Optional[Any]								=       name.replace('''det_token'''	,				'''embeddings.detection_tokens'''	)
			if "mid_pos_embed" in name:
						__lowercase			:  List[str]								=       name.replace('''mid_pos_embed'''	,				'''encoder.mid_position_embeddings'''	)
			if "pos_embed" in name:
						__lowercase			:  Dict								=       name.replace('''pos_embed'''	,				'''embeddings.position_embeddings'''	)
			if "patch_embed.proj" in name:
						__lowercase			:  Optional[int]								=       name.replace('''patch_embed.proj'''	,				'''embeddings.patch_embeddings.projection'''	)
			if "blocks" in name:
						__lowercase			:  Tuple								=       name.replace('''blocks'''	,				'''encoder.layer'''	)
			if "attn.proj" in name:
						__lowercase			:  Optional[Any]								=       name.replace('''attn.proj'''	,				'''attention.output.dense'''	)
			if "attn" in name:
						__lowercase			:  Optional[int]								=       name.replace('''attn'''	,				'''attention.self'''	)
			if "norm1" in name:
						__lowercase			:  Tuple								=       name.replace('''norm1'''	,				'''layernorm_before'''	)
			if "norm2" in name:
						__lowercase			:  Dict								=       name.replace('''norm2'''	,				'''layernorm_after'''	)
			if "mlp.fc1" in name:
						__lowercase			:  Optional[Any]								=       name.replace('''mlp.fc1'''	,				'''intermediate.dense'''	)
			if "mlp.fc2" in name:
						__lowercase			:  int								=       name.replace('''mlp.fc2'''	,				'''output.dense'''	)
			if "class_embed" in name:
						__lowercase			:  Union[str, Any]								=       name.replace('''class_embed'''	,				'''class_labels_classifier'''	)
			if "bbox_embed" in name:
						__lowercase			:  Dict								=       name.replace('''bbox_embed'''	,				'''bbox_predictor'''	)
			if "vit.norm" in name:
						__lowercase			:  Optional[int]								=       name.replace('''vit.norm'''	,				'''vit.layernorm'''	)

			return name







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	):
			for key in orig_state_dict.copy().keys():
						__lowercase			:  Tuple								=       orig_state_dict.pop(__UpperCamelCase	)

						if "qkv" in key:
									__lowercase			:  List[Any]								=       key.split('''.'''	)
									__lowercase			:  Union[str, Any]								=       int(key_split[2]	)
									__lowercase			:  Union[str, Any]								=       model.vit.encoder.layer[layer_num].attention.attention.all_head_size
									if "weight" in key:
												__lowercase			:  str								=       val[:dim, :]
												__lowercase			:  int								=       val[
												    dim : dim * 2, :
												]
												__lowercase			:  Union[str, Any]								=       val[-dim:, :]
									else:
												__lowercase			:  Any								=       val[:dim]
												__lowercase			:  Optional[Any]								=       val[dim : dim * 2]
												__lowercase			:  int								=       val[-dim:]
						else:
									__lowercase			:  Tuple								=       val

			return orig_state_dict







def      __UpperCAmelCase     ( ):
			__lowercase			:  Optional[int]								=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			__lowercase			:  str								=       Image.open(requests.get(__UpperCamelCase	,				stream=__UpperCamelCase	).raw	)
			return im







@torch.no_grad()
def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = False	):
			__lowercase			:  List[Any]								=       get_yolos_config(__UpperCamelCase	)

			# load original state_dict
			__lowercase			:  Optional[int]								=       torch.load(__UpperCamelCase	,				map_location='''cpu'''	)['''model''']

			# load 🤗 model
			__lowercase			:  Any								=       YolosForObjectDetection(__UpperCamelCase	)
			model.eval()
			__lowercase			:  int								=       convert_state_dict(__UpperCamelCase	,				__UpperCamelCase	)
			model.load_state_dict(__UpperCamelCase	)

			# Check outputs on an image, prepared by YolosImageProcessor
			__lowercase			:  List[str]								=       8_00 if yolos_name != '''yolos_ti''' else 5_12
			__lowercase			:  Union[str, Any]								=       YolosImageProcessor(format='''coco_detection'''	,				size=__UpperCamelCase	)
			__lowercase			:  Dict								=       image_processor(images=prepare_img()	,				return_tensors='''pt'''	)
			__lowercase			:  str								=       model(**__UpperCamelCase	)
			__lowercase			,__lowercase			:  Dict								=       outputs.logits, outputs.pred_boxes

			__lowercase			,__lowercase			:  Dict								=       None, None
			if yolos_name == "yolos_ti":
						__lowercase			:  Optional[int]								=       torch.tensor(
						    [[-39.5_022, -11.9_820, -17.6_888], [-29.9_574, -9.9_769, -17.7_691], [-42.3_281, -20.7_200, -30.6_294]]	)
						__lowercase			:  Dict								=       torch.tensor(
						    [[0.4_021, 0.0_836, 0.7_979], [0.0_184, 0.2_609, 0.0_364], [0.1_781, 0.2_004, 0.2_095]]	)
			elif yolos_name == "yolos_s_200_pre":
						__lowercase			:  Union[str, Any]								=       torch.tensor(
						    [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]	)
						__lowercase			:  Tuple								=       torch.tensor(
						    [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]	)
			elif yolos_name == "yolos_s_300_pre":
						__lowercase			:  Union[str, Any]								=       torch.tensor(
						    [[-36.2_220, -14.4_385, -23.5_457], [-35.6_970, -14.7_583, -21.3_935], [-31.5_939, -13.6_042, -16.8_049]]	)
						__lowercase			:  List[str]								=       torch.tensor(
						    [[0.7_614, 0.2_316, 0.4_728], [0.7_168, 0.4_495, 0.3_855], [0.4_996, 0.1_466, 0.9_996]]	)
			elif yolos_name == "yolos_s_dWr":
						__lowercase			:  Dict								=       torch.tensor(
						    [[-42.8_668, -24.1_049, -41.1_690], [-34.7_456, -14.1_274, -24.9_194], [-33.7_898, -12.1_946, -25.6_495]]	)
						__lowercase			:  str								=       torch.tensor(
						    [[0.5_587, 0.2_773, 0.0_605], [0.5_004, 0.3_014, 0.9_994], [0.4_999, 0.1_548, 0.9_994]]	)
			elif yolos_name == "yolos_base":
						__lowercase			:  int								=       torch.tensor(
						    [[-40.6_064, -24.3_084, -32.6_447], [-55.1_990, -30.7_719, -35.5_877], [-51.4_311, -33.3_507, -35.6_462]]	)
						__lowercase			:  Optional[Any]								=       torch.tensor(
						    [[0.5_555, 0.2_794, 0.0_655], [0.9_049, 0.2_664, 0.1_894], [0.9_183, 0.1_984, 0.1_635]]	)
			else:
						raise ValueError(f"""Unknown yolos_name: {yolos_name}"""	)

			assert torch.allclose(logits[0, :3, :3]	,				__UpperCamelCase	,				atol=1e-4	)
			assert torch.allclose(pred_boxes[0, :3, :3]	,				__UpperCamelCase	,				atol=1e-4	)

			Path(__UpperCamelCase	).mkdir(exist_ok=__UpperCamelCase	)
			print(f"""Saving model {yolos_name} to {pytorch_dump_folder_path}"""	)
			model.save_pretrained(__UpperCamelCase	)
			print(f"""Saving image processor to {pytorch_dump_folder_path}"""	)
			image_processor.save_pretrained(__UpperCamelCase	)

			if push_to_hub:
						__lowercase			:  Any								=       {
						    '''yolos_ti''': '''yolos-tiny''',
						    '''yolos_s_200_pre''': '''yolos-small''',
						    '''yolos_s_300_pre''': '''yolos-small-300''',
						    '''yolos_s_dWr''': '''yolos-small-dwr''',
						    '''yolos_base''': '''yolos-base''',
						}

						print('''Pushing to the hub...'''	)
						__lowercase			:  Union[str, Any]								=       model_mapping[yolos_name]
						image_processor.push_to_hub(__UpperCamelCase	,				organization='''hustvl'''	)
						model.push_to_hub(__UpperCamelCase	,				organization='''hustvl'''	)


if __name__ == "__main__":
			a_        =       argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--yolos_name',
			    default='yolos_s_200_pre',
			    type=str,
			    help=(
			        'Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','
			        ' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'
			    ),
			)
			parser.add_argument(
			    '--checkpoint_path', default=None, type=str, help='Path to the original state dict (.pth file).'
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
			)
			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_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import AlignProcessor, EfficientNetImageProcessor




@require_vision
class  __lowerCamelCase  ( unittest.TestCase			):




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  tempfile.mkdtemp()

							lowerCamelCase_					=  [
							    '''[UNK]''',
							    '''[CLS]''',
							    '''[SEP]''',
							    '''[PAD]''',
							    '''[MASK]''',
							    '''want''',
							    '''##want''',
							    '''##ed''',
							    '''wa''',
							    '''un''',
							    '''runn''',
							    '''##ing''',
							    ''',''',
							    '''low''',
							    '''lowest''',
							]
							lowerCamelCase_					=  os.path.join(self.tmpdirname							,						VOCAB_FILES_NAMES['''vocab_file''']   )
							with open(self.vocab_file							,						'''w'''							,						encoding='''utf-8'''   ) as vocab_writer:
										vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]   )   )

							lowerCamelCase_					=  {
							    '''do_resize''': True,
							    '''size''': 20,
							    '''do_center_crop''': True,
							    '''crop_size''': 18,
							    '''do_normalize''': True,
							    '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
							    '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
							}
							lowerCamelCase_					=  os.path.join(self.tmpdirname							,						UpperCAmelCase   )
							with open(self.image_processor_file							,						'''w'''							,						encoding='''utf-8'''   ) as fp:
										json.dump(UpperCAmelCase							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							return BertTokenizer.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							return BertTokenizerFast.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							return EfficientNetImageProcessor.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							shutil.rmtree(self.tmpdirname   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  [np.random.randint(255							,						size=(3, 30, 400)							,						dtype=np.uinta   )]
							lowerCamelCase_					=  [Image.fromarray(np.moveaxis(UpperCAmelCase							,						0							,						-1   )   ) for x in image_inputs]
							return image_inputs




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_tokenizer()
							lowerCamelCase_					=  self.get_rust_tokenizer()
							lowerCamelCase_					=  self.get_image_processor()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )
							processor_slow.save_pretrained(self.tmpdirname   )
							lowerCamelCase_					=  AlignProcessor.from_pretrained(self.tmpdirname							,						use_fast=UpperCAmelCase   )

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )
							processor_fast.save_pretrained(self.tmpdirname   )
							lowerCamelCase_					=  AlignProcessor.from_pretrained(self.tmpdirname   )

							self.assertEqual(processor_slow.tokenizer.get_vocab()							,						tokenizer_slow.get_vocab()   )
							self.assertEqual(processor_fast.tokenizer.get_vocab()							,						tokenizer_fast.get_vocab()   )
							self.assertEqual(tokenizer_slow.get_vocab()							,						tokenizer_fast.get_vocab()   )
							self.assertIsInstance(processor_slow.tokenizer							,						UpperCAmelCase   )
							self.assertIsInstance(processor_fast.tokenizer							,						UpperCAmelCase   )

							self.assertEqual(processor_slow.image_processor.to_json_string()							,						image_processor.to_json_string()   )
							self.assertEqual(processor_fast.image_processor.to_json_string()							,						image_processor.to_json_string()   )
							self.assertIsInstance(processor_slow.image_processor							,						UpperCAmelCase   )
							self.assertIsInstance(processor_fast.image_processor							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  AlignProcessor(tokenizer=self.get_tokenizer()							,						image_processor=self.get_image_processor()   )
							processor.save_pretrained(self.tmpdirname   )

							lowerCamelCase_					=  self.get_tokenizer(bos_token='''(BOS)'''							,						eos_token='''(EOS)'''   )
							lowerCamelCase_					=  self.get_image_processor(do_normalize=UpperCAmelCase							,						padding_value=1.0   )

							lowerCamelCase_					=  AlignProcessor.from_pretrained(
							    self.tmpdirname							,						bos_token='''(BOS)'''							,						eos_token='''(EOS)'''							,						do_normalize=UpperCAmelCase							,						padding_value=1.0   )

							self.assertEqual(processor.tokenizer.get_vocab()							,						tokenizer_add_kwargs.get_vocab()   )
							self.assertIsInstance(processor.tokenizer							,						UpperCAmelCase   )

							self.assertEqual(processor.image_processor.to_json_string()							,						image_processor_add_kwargs.to_json_string()   )
							self.assertIsInstance(processor.image_processor							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  self.prepare_image_inputs()

							lowerCamelCase_					=  image_processor(UpperCAmelCase							,						return_tensors='''np'''   )
							lowerCamelCase_					=  processor(images=UpperCAmelCase							,						return_tensors='''np'''   )

							for key in input_image_proc.keys():
										self.assertAlmostEqual(input_image_proc[key].sum()							,						input_processor[key].sum()							,						delta=1e-2   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  '''lower newer'''

							lowerCamelCase_					=  processor(text=UpperCAmelCase   )

							lowerCamelCase_					=  tokenizer(UpperCAmelCase							,						padding='''max_length'''							,						max_length=64   )

							for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]							,						encoded_processor[key]   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  '''lower newer'''
							lowerCamelCase_					=  self.prepare_image_inputs()

							lowerCamelCase_					=  processor(text=UpperCAmelCase							,						images=UpperCAmelCase   )

							self.assertListEqual(list(inputs.keys()   )							,						['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']   )

							# test if it raises when no input is passed
							with pytest.raises(UpperCAmelCase   ):
										processor()




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							lowerCamelCase_					=  processor.batch_decode(UpperCAmelCase   )
							lowerCamelCase_					=  tokenizer.batch_decode(UpperCAmelCase   )

							self.assertListEqual(UpperCAmelCase							,						UpperCAmelCase   )





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  '''lower newer'''
							lowerCamelCase_					=  self.prepare_image_inputs()

							lowerCamelCase_					=  processor(text=UpperCAmelCase							,						images=UpperCAmelCase   )

							self.assertListEqual(list(inputs.keys()   )							,						processor.model_input_names   )



