

'''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()
__snake_case			:     List[Any]										=      logging.get_logger(__name__)
def 						__lowerCamelCase	(       __snake_case					:		int	)	->							str:






						"""simple docstring"""





						A__					:       List[str]     =YolosConfig()

						# size of the architecture
						if "yolos_ti" in yolos_name:
												A__					:       str     =192
												A__					:       Any     =768
												A__					:       str     =12
												A__					:       Any     =3
												A__					:       Any     =[800, 1_333]
												A__					:       Optional[int]     =False
						elif yolos_name == "yolos_s_dWr":
												A__					:       Tuple     =330
												A__					:       int     =14
												A__					:       Tuple     =6
												A__					:       str     =1_320
						elif "yolos_s" in yolos_name:
												A__					:       int     =384
												A__					:       Union[str, Any]     =1_536
												A__					:       str     =12
												A__					:       Union[str, Any]     =6
						elif "yolos_b" in yolos_name:
												A__					:       List[str]     =[800, 1_344]

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

						return config
def 						__lowerCamelCase	(       __snake_case					:		Optional[Any],		__snake_case					:		Dict,		__snake_case					:		int = False	)	->							Union[str, Any]:






						"""simple docstring"""





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






						"""simple docstring"""





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

						return name
def 						__lowerCamelCase	(       __snake_case					:		int,		__snake_case					:		Optional[Any]	)	->							int:






						"""simple docstring"""





						for key in orig_state_dict.copy().keys():
												A__					:       List[Any]     =orig_state_dict.pop(a__	)

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

						return orig_state_dict
def 						__lowerCamelCase	(       )	->							Tuple:






						"""simple docstring"""





						A__					:       Tuple     ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
						A__					:       List[Any]     =Image.open(requests.get(a__,		stream=a__	).raw	)
						return im
@torch.no_grad()
def 						__lowerCamelCase	(       __snake_case					:		str,		__snake_case					:		List[str],		__snake_case					:		Dict,		__snake_case					:		str = False	)	->							Optional[int]:






						"""simple docstring"""





						A__					:       Union[str, Any]     =get_yolos_config(a__	)

						# load original state_dict
						A__					:       Union[str, Any]     =torch.load(a__,		map_location="""cpu"""	)['''model''']

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

						# Check outputs on an image, prepared by YolosImageProcessor
						A__					:       Optional[int]     =800 if yolos_name != '''yolos_ti''' else 512
						A__					:       int     =YolosImageProcessor(format="""coco_detection""",		size=a__	)
						A__					:       Optional[Any]     =image_processor(images=prepare_img(),		return_tensors="""pt"""	)
						A__					:       int     =model(**a__	)
						A__					:       Union[str, Any]     =outputs.logits, outputs.pred_boxes

						A__					:       List[Any]     =None, None
						if yolos_name == "yolos_ti":
												A__					:       str     =torch.tensor(
												    [[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]]	)
												A__					:       Tuple     =torch.tensor(
												    [[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]]	)
						elif yolos_name == "yolos_s_200_pre":
												A__					:       Optional[Any]     =torch.tensor(
												    [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]	)
												A__					:       Any     =torch.tensor(
												    [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]	)
						elif yolos_name == "yolos_s_300_pre":
												A__					:       Optional[Any]     =torch.tensor(
												    [[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]]	)
												A__					:       int     =torch.tensor(
												    [[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]]	)
						elif yolos_name == "yolos_s_dWr":
												A__					:       Optional[Any]     =torch.tensor(
												    [[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]]	)
												A__					:       Union[str, Any]     =torch.tensor(
												    [[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]]	)
						elif yolos_name == "yolos_base":
												A__					:       Optional[Any]     =torch.tensor(
												    [[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]]	)
												A__					:       str     =torch.tensor(
												    [[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]]	)
						else:
												raise ValueError(f"Unknown yolos_name: {yolos_name}"	)

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

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

						if push_to_hub:
												A__					:       Optional[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..."""	)
												A__					:       Dict     =model_mapping[yolos_name]
												image_processor.push_to_hub(a__,		organization="""hustvl"""	)
												model.push_to_hub(a__,		organization="""hustvl"""	)


if __name__ == "__main__":
		__snake_case			:     Dict										=      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.'
		)

		__snake_case			:     Dict										=      parser.parse_args()
		convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''



import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
		import jax
		import jax.numpy as jnp



@slow
@require_flax
class lowerCamelCase			(  unittest.TestCase						):



			'''simple docstring'''






			def lowercase__						(				self	:						Optional[Any]		,      lowerCAmelCase_	:						Union[str, Any]		,      lowerCAmelCase_	:						List[str]			)      ->		List[str]:





									'''simple docstring'''
									return f"gaussian_noise_s={seed}_shape={'_'.join([str(lowerCAmelCase_			) for s in shape]			)}.npy"


			def lowercase__						(				self	:						Optional[Any]			)      ->		Dict:





									'''simple docstring'''
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()


			def lowercase__						(				self	:						Tuple		,      lowerCAmelCase_	:						str=0		,      lowerCAmelCase_	:						List[str]=(4, 4, 64, 64)		,      lowerCAmelCase_	:						Optional[Any]=False			)      ->		str:





									'''simple docstring'''
									A__					:       Union[str, Any]     =jnp.bfloataa if fpaa else jnp.floataa
									A__					:       Any     =jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase_		,      lowerCAmelCase_			)			)		,      dtype=lowerCAmelCase_			)
									return image


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						Optional[int]=False		,      lowerCAmelCase_	:						Dict="CompVis/stable-diffusion-v1-4"			)      ->		List[Any]:





									'''simple docstring'''
									A__					:       Any     =jnp.bfloataa if fpaa else jnp.floataa
									A__					:       int     ="""bf16""" if fpaa else None

									A__   ,						A__					:       Any     =FlaxUNetaDConditionModel.from_pretrained(
									    lowerCAmelCase_		,      subfolder="""unet"""		,      dtype=lowerCAmelCase_		,      revision=lowerCAmelCase_			)
									return model, params


			def lowercase__						(				self	:						Optional[Any]		,      lowerCAmelCase_	:						int=0		,      lowerCAmelCase_	:						Optional[Any]=(4, 77, 7_68)		,      lowerCAmelCase_	:						List[str]=False			)      ->		Tuple:





									'''simple docstring'''
									A__					:       Union[str, Any]     =jnp.bfloataa if fpaa else jnp.floataa
									A__					:       Optional[int]     =jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase_		,      lowerCAmelCase_			)			)		,      dtype=lowerCAmelCase_			)
									return hidden_states


			@parameterized.expand(
			    [
			        # fmt: off
			        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
			        [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
			        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
			        [3, 10_00, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
			        # fmt: on
			    ]			)
			def lowercase__						(				self	:						Any		,      lowerCAmelCase_	:						Tuple		,      lowerCAmelCase_	:						Tuple		,      lowerCAmelCase_	:						str			)      ->		int:





									'''simple docstring'''
									A__   ,						A__					:       Tuple     =self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""		,      fpaa=lowerCAmelCase_			)
									A__					:       List[Any]     =self.get_latents(lowerCAmelCase_		,      fpaa=lowerCAmelCase_			)
									A__					:       Optional[int]     =self.get_encoder_hidden_states(lowerCAmelCase_		,      fpaa=lowerCAmelCase_			)

									A__					:       Union[str, Any]     =model.apply(
									    {"""params""": params}		,      lowerCAmelCase_		,      jnp.array(lowerCAmelCase_		,      dtype=jnp.intaa			)		,      encoder_hidden_states=lowerCAmelCase_		,      ).sample

									assert sample.shape == latents.shape

									A__					:       Optional[int]     =jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())			)		,      dtype=jnp.floataa			)
									A__					:       Tuple     =jnp.array(lowerCAmelCase_		,      dtype=jnp.floataa			)

									# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
									assert jnp.allclose(lowerCAmelCase_		,      lowerCAmelCase_		,      atol=1e-2			)



			@parameterized.expand(
			    [
			        # fmt: off
			        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
			        [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
			        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
			        [3, 10_00, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
			        # fmt: on
			    ]			)
			def lowercase__						(				self	:						Dict		,      lowerCAmelCase_	:						str		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						Optional[int]			)      ->		List[str]:





									'''simple docstring'''
									A__   ,						A__					:       List[str]     =self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""		,      fpaa=lowerCAmelCase_			)
									A__					:       Union[str, Any]     =self.get_latents(lowerCAmelCase_		,      shape=(4, 4, 96, 96)		,      fpaa=lowerCAmelCase_			)
									A__					:       Dict     =self.get_encoder_hidden_states(lowerCAmelCase_		,      shape=(4, 77, 10_24)		,      fpaa=lowerCAmelCase_			)

									A__					:       Optional[Any]     =model.apply(
									    {"""params""": params}		,      lowerCAmelCase_		,      jnp.array(lowerCAmelCase_		,      dtype=jnp.intaa			)		,      encoder_hidden_states=lowerCAmelCase_		,      ).sample

									assert sample.shape == latents.shape

									A__					:       Optional[Any]     =jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())			)		,      dtype=jnp.floataa			)
									A__					:       List[Any]     =jnp.array(lowerCAmelCase_		,      dtype=jnp.floataa			)

									# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
									assert jnp.allclose(lowerCAmelCase_		,      lowerCAmelCase_		,      atol=1e-2			)

