

'''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()
lowerCamelCase :      List[Any]					 =     logging.get_logger(__name__)


def        _lowerCAmelCase					(						_UpperCamelCase						:     str				)      ->			YolosConfig:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =YolosConfig()

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

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

     return config


def        _lowerCAmelCase					(						_UpperCamelCase						:     dict							, _UpperCamelCase						:     YolosConfig							, _UpperCamelCase						:     bool = 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)
          _SCREAMING_SNAKE_CASE							    =state_dict.pop(f"blocks.{i}.attn.qkv.weight"				)
          _SCREAMING_SNAKE_CASE							    =state_dict.pop(f"blocks.{i}.attn.qkv.bias"				)
          # next, add query, keys and values (in that order) to the state dict
          _SCREAMING_SNAKE_CASE							    =in_proj_weight[: config.hidden_size, :]
          _SCREAMING_SNAKE_CASE							    =in_proj_bias[: config.hidden_size]
          _SCREAMING_SNAKE_CASE							    =in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          _SCREAMING_SNAKE_CASE							    =in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          _SCREAMING_SNAKE_CASE							    =in_proj_weight[-config.hidden_size :, :]
          _SCREAMING_SNAKE_CASE							    =in_proj_bias[-config.hidden_size :]


def        _lowerCAmelCase					(						_UpperCamelCase						:     str				)      ->			str:
     """simple docstring"""




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

     return name


def        _lowerCAmelCase					(						_UpperCamelCase						:     dict							, _UpperCamelCase						:     YolosForObjectDetection				)      ->			dict:
     """simple docstring"""




     for key in orig_state_dict.copy().keys():
          _SCREAMING_SNAKE_CASE							    =orig_state_dict.pop(_UpperCamelCase				)

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

     return orig_state_dict


def        _lowerCAmelCase					(						)      ->			torch.Tensor:
     """simple docstring"""




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


@torch.no_grad()
def        _lowerCAmelCase					(						_UpperCamelCase						:     str							, _UpperCamelCase						:     str							, _UpperCamelCase						:     str							, _UpperCamelCase						:     bool = False				)      ->			Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =get_yolos_config(_UpperCamelCase				)

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

     # load 🤗 model
     _SCREAMING_SNAKE_CASE							    =YolosForObjectDetection(_UpperCamelCase				)
     model.eval()
     _SCREAMING_SNAKE_CASE							    =convert_state_dict(_UpperCamelCase							, _UpperCamelCase				)
     model.load_state_dict(_UpperCamelCase				)

     # Check outputs on an image, prepared by YolosImageProcessor
     _SCREAMING_SNAKE_CASE							    =8_00 if yolos_name != 'yolos_ti' else 5_12
     _SCREAMING_SNAKE_CASE							    =YolosImageProcessor(format='coco_detection'							, size=_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =image_processor(images=prepare_img()							, return_tensors='pt'				)
     _SCREAMING_SNAKE_CASE							    =model(**_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =outputs.logits, outputs.pred_boxes

     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =None, None
     if yolos_name == "yolos_ti":
          _SCREAMING_SNAKE_CASE							    =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]]				)
          _SCREAMING_SNAKE_CASE							    =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":
          _SCREAMING_SNAKE_CASE							    =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]]				)
          _SCREAMING_SNAKE_CASE							    =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":
          _SCREAMING_SNAKE_CASE							    =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]]				)
          _SCREAMING_SNAKE_CASE							    =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":
          _SCREAMING_SNAKE_CASE							    =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]]				)
          _SCREAMING_SNAKE_CASE							    =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":
          _SCREAMING_SNAKE_CASE							    =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]]				)
          _SCREAMING_SNAKE_CASE							    =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]							, _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:
          _SCREAMING_SNAKE_CASE							    ={
              '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...'				)
          _SCREAMING_SNAKE_CASE							    =model_mapping[yolos_name]
          image_processor.push_to_hub(_UpperCamelCase							, organization='hustvl'				)
          model.push_to_hub(_UpperCamelCase							, organization='hustvl'				)


if __name__ == "__main__":
     lowerCamelCase :      Optional[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."
     )

     lowerCamelCase :      int					 =     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 random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class 				snake_case       ( __UpperCAmelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = ShapEImgaImgPipeline
					snake_case__				    = ["image"]
					snake_case__				    = ["image"]
					snake_case__				    = [
					    "num_images_per_prompt",
					    "num_inference_steps",
					    "generator",
					    "latents",
					    "guidance_scale",
					    "frame_size",
					    "output_type",
					    "return_dict",
					]
					snake_case__				    = False







					@property
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									return 32







					@property
					def 		__lowerCAmelCase						(				self   :	str			):
									return 32







					@property
					def 		__lowerCAmelCase						(				self   :	int			):
									return self.time_input_dim * 4







					@property
					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									return 8







					@property
					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									torch.manual_seed(0			)
									UpperCAmelCase__             =							CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size    ,image_size=64    ,projection_dim=self.text_embedder_hidden_size    ,intermediate_size=37    ,num_attention_heads=4    ,num_channels=3    ,num_hidden_layers=5    ,patch_size=1    ,)

									UpperCAmelCase__             =							CLIPVisionModel(lowerCamelCase__			)
									return model







					@property
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							CLIPImageProcessor(
									    crop_size=224    ,do_center_crop=lowerCamelCase__    ,do_normalize=lowerCamelCase__    ,do_resize=lowerCamelCase__    ,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]    ,resample=3    ,size=224    ,)

									return image_processor







					@property
					def 		__lowerCAmelCase						(				self   :	str			):
									torch.manual_seed(0			)

									UpperCAmelCase__             =							{
									    'num_attention_heads': 2,
									    'attention_head_dim': 16,
									    'embedding_dim': self.time_input_dim,
									    'num_embeddings': 32,
									    'embedding_proj_dim': self.text_embedder_hidden_size,
									    'time_embed_dim': self.time_embed_dim,
									    'num_layers': 1,
									    'clip_embed_dim': self.time_input_dim * 2,
									    'additional_embeddings': 0,
									    'time_embed_act_fn': 'gelu',
									    'norm_in_type': 'layer',
									    'embedding_proj_norm_type': 'layer',
									    'encoder_hid_proj_type': None,
									    'added_emb_type': None,
									}

									UpperCAmelCase__             =							PriorTransformer(**lowerCamelCase__			)
									return model







					@property
					def 		__lowerCAmelCase						(				self   :	Tuple			):
									torch.manual_seed(0			)

									UpperCAmelCase__             =							{
									    'param_shapes': (
									        (self.renderer_dim, 93),
									        (self.renderer_dim, 8),
									        (self.renderer_dim, 8),
									        (self.renderer_dim, 8),
									    ),
									    'd_latent': self.time_input_dim,
									    'd_hidden': self.renderer_dim,
									    'n_output': 12,
									    'background': (
									        0.1,
									        0.1,
									        0.1,
									    ),
									}
									UpperCAmelCase__             =							ShapERenderer(**lowerCamelCase__			)
									return model







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							self.dummy_prior
									UpperCAmelCase__             =							self.dummy_image_encoder
									UpperCAmelCase__             =							self.dummy_image_processor
									UpperCAmelCase__             =							self.dummy_renderer

									UpperCAmelCase__             =							HeunDiscreteScheduler(
									    beta_schedule='exp'    ,num_train_timesteps=1_024    ,prediction_type='sample'    ,use_karras_sigmas=lowerCamelCase__    ,clip_sample=lowerCamelCase__    ,clip_sample_range=1.0    ,)
									UpperCAmelCase__             =							{
									    'prior': prior,
									    'image_encoder': image_encoder,
									    'image_processor': image_processor,
									    'renderer': renderer,
									    'scheduler': scheduler,
									}

									return components







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	Any    ,lowerCamelCase__   :	str=0			):
									UpperCAmelCase__             =							floats_tensor((1, 3, 64, 64)    ,rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)

									if str(lowerCamelCase__			).startswith('mps'			):
													UpperCAmelCase__             =							torch.manual_seed(lowerCamelCase__			)
									else:
													UpperCAmelCase__             =							torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
									UpperCAmelCase__             =							{
									    'image': input_image,
									    'generator': generator,
									    'num_inference_steps': 1,
									    'frame_size': 32,
									    'output_type': 'np',
									}
									return inputs







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									UpperCAmelCase__             =							'cpu'

									UpperCAmelCase__             =							self.get_dummy_components()

									UpperCAmelCase__             =							self.pipeline_class(**lowerCamelCase__			)
									UpperCAmelCase__             =							pipe.to(lowerCamelCase__			)

									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							pipe(**self.get_dummy_inputs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							output.images[0]
									UpperCAmelCase__             =							image[0, -3:, -3:, -1]

									assert image.shape == (20, 32, 32, 3)

									UpperCAmelCase__             =							np.array(
									    [
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									    ]			)

									assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
									self._test_inference_batch_consistent(batch_sizes=[1, 2]			)







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							torch_device == 'cpu'
									UpperCAmelCase__             =							True
									self._test_inference_batch_single_identical(
									    batch_size=2    ,test_max_difference=lowerCamelCase__    ,relax_max_difference=lowerCamelCase__    ,)







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							self.get_dummy_components()
									UpperCAmelCase__             =							self.pipeline_class(**lowerCamelCase__			)
									UpperCAmelCase__             =							pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							1
									UpperCAmelCase__             =							2

									UpperCAmelCase__             =							self.get_dummy_inputs(lowerCamelCase__			)

									for key in inputs.keys():
													if key in self.batch_params:
																	UpperCAmelCase__             =							batch_size * [inputs[key]]

									UpperCAmelCase__             =							pipe(**lowerCamelCase__    ,num_images_per_prompt=lowerCamelCase__			)[0]

									assert images.shape[0] == batch_size * num_images_per_prompt






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




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	int			):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png'			)
									UpperCAmelCase__             =							load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/shap_e/test_shap_e_img2img_out.npy'			)
									UpperCAmelCase__             =							ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img'			)
									UpperCAmelCase__             =							pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							torch.Generator(device=lowerCamelCase__			).manual_seed(0			)

									UpperCAmelCase__             =							pipe(
									    lowerCamelCase__    ,generator=lowerCamelCase__    ,guidance_scale=3.0    ,num_inference_steps=64    ,frame_size=64    ,output_type='np'    ,).images[0]

									assert images.shape == (20, 64, 64, 3)

									assert_mean_pixel_difference(lowerCamelCase__    ,lowerCamelCase__			)



