
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()
_UpperCAmelCase :			Optional[Any]              =	logging.get_logger(__name__)


def 				A	( lowercase			)       ->  YolosConfig:
		'''simple docstring'''

		UpperCamelCase          =					YolosConfig()

		# size of the architecture
		if "yolos_ti" in yolos_name:
				UpperCamelCase          =					192
				UpperCamelCase          =					768
				UpperCamelCase          =					12
				UpperCamelCase          =					3
				UpperCamelCase          =					[800, 1_333]
				UpperCamelCase          =					False
		elif yolos_name == "yolos_s_dWr":
				UpperCamelCase          =					330
				UpperCamelCase          =					14
				UpperCamelCase          =					6
				UpperCamelCase          =					1_320
		elif "yolos_s" in yolos_name:
				UpperCamelCase          =					384
				UpperCamelCase          =					1_536
				UpperCamelCase          =					12
				UpperCamelCase          =					6
		elif "yolos_b" in yolos_name:
				UpperCamelCase          =					[800, 1_344]

		UpperCamelCase          =					91
		UpperCamelCase          =					'huggingface/label-files'
		UpperCamelCase          =					'coco-detection-id2label.json'
		UpperCamelCase          =					json.load(open(hf_hub_download(lowercase   ,	lowercase   ,	repo_type='dataset'			)   ,	'r'			)			)
		UpperCamelCase          =					{int(lowercase			): v for k, v in idalabel.items()}
		UpperCamelCase          =					idalabel
		UpperCamelCase          =					{v: k for k, v in idalabel.items()}

		return config


def 				A	( lowercase   ,	lowercase   ,	lowercase = False			)       ->  int:
		'''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)
				UpperCamelCase          =					state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''			)
				UpperCamelCase          =					state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''			)
				# next, add query, keys and values (in that order) to the state dict
				UpperCamelCase          =					in_proj_weight[: config.hidden_size, :]
				UpperCamelCase          =					in_proj_bias[: config.hidden_size]
				UpperCamelCase          =					in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				UpperCamelCase          =					in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				UpperCamelCase          =					in_proj_weight[-config.hidden_size :, :]
				UpperCamelCase          =					in_proj_bias[-config.hidden_size :]


def 				A	( lowercase			)       ->  str:
		'''simple docstring'''

		if "backbone" in name:
				UpperCamelCase          =					name.replace('backbone'   ,	'vit'			)
		if "cls_token" in name:
				UpperCamelCase          =					name.replace('cls_token'   ,	'embeddings.cls_token'			)
		if "det_token" in name:
				UpperCamelCase          =					name.replace('det_token'   ,	'embeddings.detection_tokens'			)
		if "mid_pos_embed" in name:
				UpperCamelCase          =					name.replace('mid_pos_embed'   ,	'encoder.mid_position_embeddings'			)
		if "pos_embed" in name:
				UpperCamelCase          =					name.replace('pos_embed'   ,	'embeddings.position_embeddings'			)
		if "patch_embed.proj" in name:
				UpperCamelCase          =					name.replace('patch_embed.proj'   ,	'embeddings.patch_embeddings.projection'			)
		if "blocks" in name:
				UpperCamelCase          =					name.replace('blocks'   ,	'encoder.layer'			)
		if "attn.proj" in name:
				UpperCamelCase          =					name.replace('attn.proj'   ,	'attention.output.dense'			)
		if "attn" in name:
				UpperCamelCase          =					name.replace('attn'   ,	'attention.self'			)
		if "norm1" in name:
				UpperCamelCase          =					name.replace('norm1'   ,	'layernorm_before'			)
		if "norm2" in name:
				UpperCamelCase          =					name.replace('norm2'   ,	'layernorm_after'			)
		if "mlp.fc1" in name:
				UpperCamelCase          =					name.replace('mlp.fc1'   ,	'intermediate.dense'			)
		if "mlp.fc2" in name:
				UpperCamelCase          =					name.replace('mlp.fc2'   ,	'output.dense'			)
		if "class_embed" in name:
				UpperCamelCase          =					name.replace('class_embed'   ,	'class_labels_classifier'			)
		if "bbox_embed" in name:
				UpperCamelCase          =					name.replace('bbox_embed'   ,	'bbox_predictor'			)
		if "vit.norm" in name:
				UpperCamelCase          =					name.replace('vit.norm'   ,	'vit.layernorm'			)

		return name


def 				A	( lowercase   ,	lowercase			)       ->  dict:
		'''simple docstring'''

		for key in orig_state_dict.copy().keys():
				UpperCamelCase          =					orig_state_dict.pop(lowercase			)

				if "qkv" in key:
						UpperCamelCase          =					key.split('.'			)
						UpperCamelCase          =					int(key_split[2]			)
						UpperCamelCase          =					model.vit.encoder.layer[layer_num].attention.attention.all_head_size
						if "weight" in key:
								UpperCamelCase          =					val[:dim, :]
								UpperCamelCase          =					val[
								    dim : dim * 2, :
								]
								UpperCamelCase          =					val[-dim:, :]
						else:
								UpperCamelCase          =					val[:dim]
								UpperCamelCase          =					val[dim : dim * 2]
								UpperCamelCase          =					val[-dim:]
				else:
						UpperCamelCase          =					val

		return orig_state_dict


def 				A	( )       ->  torch.Tensor:
		'''simple docstring'''

		UpperCamelCase          =					'http://images.cocodataset.org/val2017/000000039769.jpg'
		UpperCamelCase          =					Image.open(requests.get(lowercase   ,	stream=lowercase			).raw			)
		return im


@torch.no_grad()
def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase = False			)       ->  Any:
		'''simple docstring'''

		UpperCamelCase          =					get_yolos_config(lowercase			)

		# load original state_dict
		UpperCamelCase          =					torch.load(lowercase   ,	map_location='cpu'			)['model']

		# load 🤗 model
		UpperCamelCase          =					YolosForObjectDetection(lowercase			)
		model.eval()
		UpperCamelCase          =					convert_state_dict(lowercase   ,	lowercase			)
		model.load_state_dict(lowercase			)

		# Check outputs on an image, prepared by YolosImageProcessor
		UpperCamelCase          =					800 if yolos_name != 'yolos_ti' else 512
		UpperCamelCase          =					YolosImageProcessor(format='coco_detection'   ,	size=lowercase			)
		UpperCamelCase          =					image_processor(images=prepare_img()   ,	return_tensors='pt'			)
		UpperCamelCase          =					model(**lowercase			)
		UpperCamelCase     ,  UpperCamelCase          =					outputs.logits, outputs.pred_boxes

		UpperCamelCase     ,  UpperCamelCase          =					None, None
		if yolos_name == "yolos_ti":
				UpperCamelCase          =					torch.tensor(
				    [[-3_9.5_0_2_2, -1_1.9_8_2_0, -1_7.6_8_8_8], [-2_9.9_5_7_4, -9.9_7_6_9, -1_7.7_6_9_1], [-4_2.3_2_8_1, -2_0.7_2_0_0, -3_0.6_2_9_4]]			)
				UpperCamelCase          =					torch.tensor(
				    [[0.4_0_2_1, 0.0_8_3_6, 0.7_9_7_9], [0.0_1_8_4, 0.2_6_0_9, 0.0_3_6_4], [0.1_7_8_1, 0.2_0_0_4, 0.2_0_9_5]]			)
		elif yolos_name == "yolos_s_200_pre":
				UpperCamelCase          =					torch.tensor(
				    [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]			)
				UpperCamelCase          =					torch.tensor(
				    [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]			)
		elif yolos_name == "yolos_s_300_pre":
				UpperCamelCase          =					torch.tensor(
				    [[-3_6.2_2_2_0, -1_4.4_3_8_5, -2_3.5_4_5_7], [-3_5.6_9_7_0, -1_4.7_5_8_3, -2_1.3_9_3_5], [-3_1.5_9_3_9, -1_3.6_0_4_2, -1_6.8_0_4_9]]			)
				UpperCamelCase          =					torch.tensor(
				    [[0.7_6_1_4, 0.2_3_1_6, 0.4_7_2_8], [0.7_1_6_8, 0.4_4_9_5, 0.3_8_5_5], [0.4_9_9_6, 0.1_4_6_6, 0.9_9_9_6]]			)
		elif yolos_name == "yolos_s_dWr":
				UpperCamelCase          =					torch.tensor(
				    [[-4_2.8_6_6_8, -2_4.1_0_4_9, -4_1.1_6_9_0], [-3_4.7_4_5_6, -1_4.1_2_7_4, -2_4.9_1_9_4], [-3_3.7_8_9_8, -1_2.1_9_4_6, -2_5.6_4_9_5]]			)
				UpperCamelCase          =					torch.tensor(
				    [[0.5_5_8_7, 0.2_7_7_3, 0.0_6_0_5], [0.5_0_0_4, 0.3_0_1_4, 0.9_9_9_4], [0.4_9_9_9, 0.1_5_4_8, 0.9_9_9_4]]			)
		elif yolos_name == "yolos_base":
				UpperCamelCase          =					torch.tensor(
				    [[-4_0.6_0_6_4, -2_4.3_0_8_4, -3_2.6_4_4_7], [-5_5.1_9_9_0, -3_0.7_7_1_9, -3_5.5_8_7_7], [-5_1.4_3_1_1, -3_3.3_5_0_7, -3_5.6_4_6_2]]			)
				UpperCamelCase          =					torch.tensor(
				    [[0.5_5_5_5, 0.2_7_9_4, 0.0_6_5_5], [0.9_0_4_9, 0.2_6_6_4, 0.1_8_9_4], [0.9_1_8_3, 0.1_9_8_4, 0.1_6_3_5]]			)
		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          =					{
				    '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          =					model_mapping[yolos_name]
				image_processor.push_to_hub(lowercase   ,	organization='hustvl'			)
				model.push_to_hub(lowercase   ,	organization='hustvl'			)


if __name__ == "__main__":
					_UpperCAmelCase :			Union[str, Any]              =	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."
					)

					_UpperCAmelCase :			Union[str, Any]              =	parser.parse_args()
					convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	List[Any]     					=    KandinskyVaaControlnetImgaImgPipeline
		__lowercase	:	Optional[Any]     					=    ["image_embeds", "negative_image_embeds", "image", "hint"]
		__lowercase	:	Any     					=    ["image_embeds", "negative_image_embeds", "image", "hint"]
		__lowercase	:	Union[str, Any]     					=    [
		    "generator",
		    "height",
		    "width",
		    "strength",
		    "guidance_scale",
		    "num_inference_steps",
		    "return_dict",
		    "guidance_scale",
		    "num_images_per_prompt",
		    "output_type",
		    "return_dict",
		]
		__lowercase	:	Optional[int]     					=    False



		@property
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				return 32



		@property
		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				return 32



		@property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return self.time_input_dim



		@property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return self.time_input_dim * 4



		@property
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				return 100



		@property
		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				torch.manual_seed(0	)

				UpperCamelCase          =					{
				    'in_channels': 8,
				    # Out channels is double in channels because predicts mean and variance
				    'out_channels': 8,
				    'addition_embed_type': 'image_hint',
				    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
				    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
				    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
				    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
				    'layers_per_block': 1,
				    'encoder_hid_dim': self.text_embedder_hidden_size,
				    'encoder_hid_dim_type': 'image_proj',
				    'cross_attention_dim': self.cross_attention_dim,
				    'attention_head_dim': 4,
				    'resnet_time_scale_shift': 'scale_shift',
				    'class_embed_type': None,
				}

				UpperCamelCase          =					UNetaDConditionModel(**A_	)
				return model



		@property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return {
				    "block_out_channels": [32, 32, 64, 64],
				    "down_block_types": [
				        "DownEncoderBlock2D",
				        "DownEncoderBlock2D",
				        "DownEncoderBlock2D",
				        "AttnDownEncoderBlock2D",
				    ],
				    "in_channels": 3,
				    "latent_channels": 4,
				    "layers_per_block": 1,
				    "norm_num_groups": 8,
				    "norm_type": "spatial",
				    "num_vq_embeddings": 12,
				    "out_channels": 3,
				    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
				    "vq_embed_dim": 4,
				}



		@property
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				torch.manual_seed(0	)
				UpperCamelCase          =					VQModel(**self.dummy_movq_kwargs	)
				return model



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.dummy_unet
				UpperCamelCase          =					self.dummy_movq

				UpperCamelCase          =					{
				    'num_train_timesteps': 1_000,
				    'beta_schedule': 'linear',
				    'beta_start': 0.0_0085,
				    'beta_end': 0.012,
				    'clip_sample': False,
				    'set_alpha_to_one': False,
				    'steps_offset': 0,
				    'prediction_type': 'epsilon',
				    'thresholding': False,
				}

				UpperCamelCase          =					DDIMScheduler(**A_	)

				UpperCamelCase          =					{
				    'unet': unet,
				    'scheduler': scheduler,
				    'movq': movq,
				}

				return components



		def __UpperCamelCase			(			self    ,				A_    ,				A_=0	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					floats_tensor((1, self.text_embedder_hidden_size)    ,				rng=random.Random(A_	)	).to(A_	)
				UpperCamelCase          =					floats_tensor((1, self.text_embedder_hidden_size)    ,				rng=random.Random(seed + 1	)	).to(
				    A_	)
				# create init_image
				UpperCamelCase          =					floats_tensor((1, 3, 64, 64)    ,				rng=random.Random(A_	)	).to(A_	)
				UpperCamelCase          =					image.cpu().permute(0    ,				2    ,				3    ,				1	)[0]
				UpperCamelCase          =					Image.fromarray(np.uinta(A_	)	).convert('RGB'	).resize((256, 256)	)
				# create hint
				UpperCamelCase          =					floats_tensor((1, 3, 64, 64)    ,				rng=random.Random(A_	)	).to(A_	)

				if str(A_	).startswith('mps'	):
						UpperCamelCase          =					torch.manual_seed(A_	)
				else:
						UpperCamelCase          =					torch.Generator(device=A_	).manual_seed(A_	)
				UpperCamelCase          =					{
				    'image': init_image,
				    'image_embeds': image_embeds,
				    'negative_image_embeds': negative_image_embeds,
				    'hint': hint,
				    'generator': generator,
				    'height': 64,
				    'width': 64,
				    'num_inference_steps': 10,
				    'guidance_scale': 7.0,
				    'strength': 0.2,
				    'output_type': 'np',
				}
				return inputs



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					'cpu'

				UpperCamelCase          =					self.get_dummy_components()

				UpperCamelCase          =					self.pipeline_class(**A_	)
				UpperCamelCase          =					pipe.to(A_	)

				pipe.set_progress_bar_config(disable=A_	)

				UpperCamelCase          =					pipe(**self.get_dummy_inputs(A_	)	)
				UpperCamelCase          =					output.images

				UpperCamelCase          =					pipe(
				    **self.get_dummy_inputs(A_	)    ,				return_dict=A_    ,				)[0]

				UpperCamelCase          =					image[0, -3:, -3:, -1]
				UpperCamelCase          =					image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)

				UpperCamelCase          =					np.array(
				    [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]	)
				assert (
				    np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
				), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
				assert (
				    np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2
				), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'	)

				UpperCamelCase          =					load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'	)
				UpperCamelCase          =					init_image.resize((512, 512)	)

				UpperCamelCase          =					load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/kandinskyv22/hint_image_cat.png'	)
				UpperCamelCase          =					torch.from_numpy(np.array(A_	)	).float() / 255.0
				UpperCamelCase          =					hint.permute(2    ,				0    ,				1	).unsqueeze(0	)

				UpperCamelCase          =					'A robot, 4k photo'

				UpperCamelCase          =					KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-2-prior'    ,				torch_dtype=torch.floataa	)
				pipe_prior.to(A_	)

				UpperCamelCase          =					KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-2-controlnet-depth'    ,				torch_dtype=torch.floataa	)
				UpperCamelCase          =					pipeline.to(A_	)

				pipeline.set_progress_bar_config(disable=A_	)

				UpperCamelCase          =					torch.Generator(device='cpu'	).manual_seed(0	)

				UpperCamelCase     ,  UpperCamelCase          =					pipe_prior(
				    A_    ,				image=A_    ,				strength=0.85    ,				generator=A_    ,				negative_prompt=''    ,				).to_tuple()

				UpperCamelCase          =					pipeline(
				    image=A_    ,				image_embeds=A_    ,				negative_image_embeds=A_    ,				hint=A_    ,				generator=A_    ,				num_inference_steps=100    ,				height=512    ,				width=512    ,				strength=0.5    ,				output_type='np'    ,				)

				UpperCamelCase          =					output.images[0]

				assert image.shape == (512, 512, 3)

				assert_mean_pixel_difference(A_    ,				A_	)
