



'''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   :			Any					  =    logging.get_logger(__name__)



def       __lowerCamelCase  (					_lowercase		)      ->			YolosConfig:
							UpperCAmelCase     :		Tuple							     =		YolosConfig()

							# size of the architecture
							if "yolos_ti" in yolos_name:
														UpperCAmelCase     :		Union[str, Any]							     =		1_9_2
														UpperCAmelCase     :		Any							     =		7_6_8
														UpperCAmelCase     :		int							     =		1_2
														UpperCAmelCase     :		Tuple							     =		3
														UpperCAmelCase     :		Dict							     =		[8_0_0, 1_3_3_3]
														UpperCAmelCase     :		Dict							     =		False
							elif yolos_name == "yolos_s_dWr":
														UpperCAmelCase     :		str							     =		3_3_0
														UpperCAmelCase     :		List[str]							     =		1_4
														UpperCAmelCase     :		List[Any]							     =		6
														UpperCAmelCase     :		int							     =		1_3_2_0
							elif "yolos_s" in yolos_name:
														UpperCAmelCase     :		Optional[int]							     =		3_8_4
														UpperCAmelCase     :		Tuple							     =		1_5_3_6
														UpperCAmelCase     :		Union[str, Any]							     =		1_2
														UpperCAmelCase     :		List[Any]							     =		6
							elif "yolos_b" in yolos_name:
														UpperCAmelCase     :		Tuple							     =		[8_0_0, 1_3_4_4]

							UpperCAmelCase     :		List[Any]							     =		9_1
							UpperCAmelCase     :		str							     =		"""huggingface/label-files"""
							UpperCAmelCase     :		Optional[int]							     =		"""coco-detection-id2label.json"""
							UpperCAmelCase     :		Dict							     =		json.load(open(hf_hub_download(_lowercase	,				_lowercase	,				repo_type="""dataset"""		)	,				"""r"""		)		)
							UpperCAmelCase     :		Optional[Any]							     =		{int(_lowercase		): v for k, v in idalabel.items()}
							UpperCAmelCase     :		Any							     =		idalabel
							UpperCAmelCase     :		str							     =		{v: k for k, v in idalabel.items()}

							return config



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



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

							return name



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			dict:
							for key in orig_state_dict.copy().keys():
														UpperCAmelCase     :		Dict							     =		orig_state_dict.pop(_lowercase		)

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

							return orig_state_dict



def       __lowerCamelCase  (					)      ->			torch.Tensor:
							UpperCAmelCase     :		Optional[Any]							     =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase     :		str							     =		Image.open(requests.get(_lowercase	,				stream=_lowercase		).raw		)
							return im



@torch.no_grad()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase = False		)      ->			Optional[int]:
							UpperCAmelCase     :		Dict							     =		get_yolos_config(_lowercase		)

							# load original state_dict
							UpperCAmelCase     :		int							     =		torch.load(_lowercase	,				map_location="""cpu"""		)["""model"""]

							# load 🤗 model
							UpperCAmelCase     :		int							     =		YolosForObjectDetection(_lowercase		)
							model.eval()
							UpperCAmelCase     :		List[Any]							     =		convert_state_dict(_lowercase	,				_lowercase		)
							model.load_state_dict(_lowercase		)

							# Check outputs on an image, prepared by YolosImageProcessor
							UpperCAmelCase     :		str							     =		8_0_0 if yolos_name != """yolos_ti""" else 5_1_2
							UpperCAmelCase     :		Optional[int]							     =		YolosImageProcessor(format="""coco_detection"""	,				size=_lowercase		)
							UpperCAmelCase     :		str							     =		image_processor(images=prepare_img()	,				return_tensors="""pt"""		)
							UpperCAmelCase     :		List[Any]							     =		model(**_lowercase		)
							UpperCAmelCase     ,     UpperCAmelCase     :		List[Any]							     =		outputs.logits, outputs.pred_boxes

							UpperCAmelCase     ,     UpperCAmelCase     :		Union[str, Any]							     =		None, None
							if yolos_name == "yolos_ti":
														UpperCAmelCase     :		List[Any]							     =		torch.tensor(
														    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]		)
														UpperCAmelCase     :		List[Any]							     =		torch.tensor(
														    [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]		)
							elif yolos_name == "yolos_s_200_pre":
														UpperCAmelCase     :		Any							     =		torch.tensor(
														    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]		)
														UpperCAmelCase     :		Optional[int]							     =		torch.tensor(
														    [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]		)
							elif yolos_name == "yolos_s_300_pre":
														UpperCAmelCase     :		Optional[Any]							     =		torch.tensor(
														    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]		)
														UpperCAmelCase     :		Union[str, Any]							     =		torch.tensor(
														    [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]		)
							elif yolos_name == "yolos_s_dWr":
														UpperCAmelCase     :		List[str]							     =		torch.tensor(
														    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]		)
														UpperCAmelCase     :		List[Any]							     =		torch.tensor(
														    [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]		)
							elif yolos_name == "yolos_base":
														UpperCAmelCase     :		str							     =		torch.tensor(
														    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]		)
														UpperCAmelCase     :		List[str]							     =		torch.tensor(
														    [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]		)
							else:
														raise ValueError(F'''Unknown yolos_name: {yolos_name}'''		)

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

							Path(_lowercase		).mkdir(exist_ok=_lowercase		)
							print(F'''Saving model {yolos_name} to {pytorch_dump_folder_path}'''		)
							model.save_pretrained(_lowercase		)
							print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
							image_processor.save_pretrained(_lowercase		)

							if push_to_hub:
														UpperCAmelCase     :		int							     =		{
														    """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..."""		)
														UpperCAmelCase     :		Dict							     =		model_mapping[yolos_name]
														image_processor.push_to_hub(_lowercase	,				organization="""hustvl"""		)
														model.push_to_hub(_lowercase	,				organization="""hustvl"""		)


if __name__ == "__main__":
							a   :			Optional[int]					  =    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   :			Tuple					  =    parser.parse_args()
							convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''




import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / """utils"""))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


a   :			List[str]					  =    get_tests_dir("""fixtures""")

class 			UpperCamelCase_ (							unittest.TestCase			):





	def  _lowercase(  self    )			->			int:
								# A mock response for an HTTP head request to emulate server down
								UpperCAmelCase     :		Tuple							     =		mock.Mock()
								UpperCAmelCase     :		List[str]							     =		500
								UpperCAmelCase     :		Any							     =		{}
								UpperCAmelCase     :		List[str]							     =		HTTPError
								UpperCAmelCase     :		str							     =		{}

								# Download this model to make sure it's in the cache.
								UpperCAmelCase     :		Optional[int]							     =		ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""    )
								# Under the mock environment we get a 500 error when trying to reach the model.
								with mock.patch("""requests.Session.request"""		,		return_value=A    ) as mock_head:
															UpperCAmelCase     :		Optional[int]							     =		ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""    )
															# This check we did call the fake head request
															mock_head.assert_called()





	def  _lowercase(  self    )			->			Any:
								# This test is for deprecated behavior and can be removed in v5
								UpperCAmelCase     :		Tuple							     =		ViTImageProcessor.from_pretrained(
								    """https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json"""    )






	def  _lowercase(  self    )			->			Union[str, Any]:
								with self.assertRaises(A    ):
															# config is in subfolder, the following should not work without specifying the subfolder
															UpperCAmelCase     :		Any							     =		AutoImageProcessor.from_pretrained("""hf-internal-testing/stable-diffusion-all-variants"""    )

								UpperCAmelCase     :		Union[str, Any]							     =		AutoImageProcessor.from_pretrained(
								    """hf-internal-testing/stable-diffusion-all-variants"""		,		subfolder="""feature_extractor"""    )

								self.assertIsNotNone(A    )




@is_staging_test
class 			UpperCamelCase_ (							unittest.TestCase			):





	@classmethod
	def  _lowercase(  cls    )			->			Dict:
								UpperCAmelCase     :		Tuple							     =		TOKEN
								HfFolder.save_token(A    )





	@classmethod
	def  _lowercase(  cls    )			->			List[str]:
								try:
															delete_repo(token=cls._token		,		repo_id="""test-image-processor"""    )
								except HTTPError:
															pass

								try:
															delete_repo(token=cls._token		,		repo_id="""valid_org/test-image-processor-org"""    )
								except HTTPError:
															pass

								try:
															delete_repo(token=cls._token		,		repo_id="""test-dynamic-image-processor"""    )
								except HTTPError:
															pass





	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Union[str, Any]							     =		ViTImageProcessor.from_pretrained(A    )
								image_processor.push_to_hub("""test-image-processor"""		,		use_auth_token=self._token    )

								UpperCAmelCase     :		Optional[int]							     =		ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor'''    )
								for k, v in image_processor.__dict__.items():
															self.assertEqual(A		,		getattr(A		,		A    )    )

								# Reset repo
								delete_repo(token=self._token		,		repo_id="""test-image-processor"""    )

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
															image_processor.save_pretrained(
															    A		,		repo_id="""test-image-processor"""		,		push_to_hub=A		,		use_auth_token=self._token    )

								UpperCAmelCase     :		Tuple							     =		ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor'''    )
								for k, v in image_processor.__dict__.items():
															self.assertEqual(A		,		getattr(A		,		A    )    )





	def  _lowercase(  self    )			->			List[str]:
								UpperCAmelCase     :		List[str]							     =		ViTImageProcessor.from_pretrained(A    )
								image_processor.push_to_hub("""valid_org/test-image-processor"""		,		use_auth_token=self._token    )

								UpperCAmelCase     :		Tuple							     =		ViTImageProcessor.from_pretrained("""valid_org/test-image-processor"""    )
								for k, v in image_processor.__dict__.items():
															self.assertEqual(A		,		getattr(A		,		A    )    )

								# Reset repo
								delete_repo(token=self._token		,		repo_id="""valid_org/test-image-processor"""    )

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
															image_processor.save_pretrained(
															    A		,		repo_id="""valid_org/test-image-processor-org"""		,		push_to_hub=A		,		use_auth_token=self._token    )

								UpperCAmelCase     :		int							     =		ViTImageProcessor.from_pretrained("""valid_org/test-image-processor-org"""    )
								for k, v in image_processor.__dict__.items():
															self.assertEqual(A		,		getattr(A		,		A    )    )






	def  _lowercase(  self    )			->			Optional[int]:
								CustomImageProcessor.register_for_auto_class()
								UpperCAmelCase     :		Optional[Any]							     =		CustomImageProcessor.from_pretrained(A    )

								image_processor.push_to_hub("""test-dynamic-image-processor"""		,		use_auth_token=self._token    )

								# This has added the proper auto_map field to the config
								self.assertDictEqual(
								    image_processor.auto_map		,		{"""AutoImageProcessor""": """custom_image_processing.CustomImageProcessor"""}		,		)

								UpperCAmelCase     :		Union[str, Any]							     =		AutoImageProcessor.from_pretrained(
								    f'''{USER}/test-dynamic-image-processor'''		,		trust_remote_code=A    )
								# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
								self.assertEqual(new_image_processor.__class__.__name__		,		"""CustomImageProcessor"""    )

