



'''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[str]       = logging.get_logger(__name__)



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Dict     =					YolosConfig()

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

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

			return config



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__ = False   ):
			'''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_     :						Union[str, Any]     =					state_dict.pop(f'blocks.{i}.attn.qkv.bias'   )
						# next, add query, keys and values (in that order) to the state dict
						A_     :						Any     =					in_proj_weight[: config.hidden_size, :]
						A_     :						List[Any]     =					in_proj_bias[: config.hidden_size]
						A_     :						str     =					in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						A_     :						List[Any]     =					in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						A_     :						List[Any]     =					in_proj_weight[-config.hidden_size :, :]
						A_     :						Any     =					in_proj_bias[-config.hidden_size :]



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





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

			return name



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			for key in orig_state_dict.copy().keys():
						A_     :						Optional[Any]     =					orig_state_dict.pop(lowerCamelCase__   )

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

			return orig_state_dict



def      a      (			):
			'''simple docstring'''





			A_     :						Dict     =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			A_     :						Dict     =					Image.open(requests.get(lowerCamelCase__						,				stream=lowerCamelCase__   ).raw   )
			return im



@torch.no_grad()
def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__ = False   ):
			'''simple docstring'''





			A_     :						Any     =					get_yolos_config(lowerCamelCase__   )

			# load original state_dict
			A_     :						Any     =					torch.load(lowerCamelCase__						,				map_location="""cpu"""   )["""model"""]

			# load 🤗 model
			A_     :						Dict     =					YolosForObjectDetection(lowerCamelCase__   )
			model.eval()
			A_     :						Any     =					convert_state_dict(lowerCamelCase__						,				lowerCamelCase__   )
			model.load_state_dict(lowerCamelCase__   )

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

			A_,   A_     :						Tuple     =					None, None
			if yolos_name == "yolos_ti":
						A_     :						Dict     =					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]]   )
						A_     :						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":
						A_     :						Optional[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]]   )
						A_     :						int     =					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":
						A_     :						List[str]     =					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]]   )
						A_     :						Union[str, Any]     =					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":
						A_     :						Tuple     =					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]]   )
						A_     :						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":
						A_     :						Dict     =					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]]   )
						A_     :						Union[str, 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]						,				lowerCamelCase__						,				atol=1E-4   )
			assert torch.allclose(pred_boxes[0, :3, :3]						,				lowerCamelCase__						,				atol=1E-4   )

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

			if push_to_hub:
						A_     :						Optional[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..."""   )
						A_     :						int     =					model_mapping[yolos_name]
						image_processor.push_to_hub(lowerCamelCase__						,				organization="""hustvl"""   )
						model.push_to_hub(lowerCamelCase__						,				organization="""hustvl"""   )


if __name__ == "__main__":
						lowerCamelCase							: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.'''
						)

						lowerCamelCase							:List[str]       = 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

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import 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      _lowerCAmelCase							(      __UpperCAmelCase				,      unittest.TestCase			):
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = ShapEPipeline
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = ['prompt']
	__SCREAMING_SNAKE_CASE       :		str		      = ['prompt']
	__SCREAMING_SNAKE_CASE       :		Dict		      = [
	    'num_images_per_prompt',
	    'num_inference_steps',
	    'generator',
	    'latents',
	    'guidance_scale',
	    'frame_size',
	    'output_type',
	    'return_dict',
	]
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = False


	@property
	def    _a     (self							):
				return 32


	@property
	def    _a     (self							):
				return 32


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


	@property
	def    _a     (self							):
				return 8


	@property
	def    _a     (self							):
				A_     :						Dict     =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
				return tokenizer


	@property
	def    _a     (self							):
				torch.manual_seed(0							)
				A_     :						Any     =					CLIPTextConfig(
				    bos_token_id=0	,				eos_token_id=2	,				hidden_size=self.text_embedder_hidden_size	,				projection_dim=self.text_embedder_hidden_size	,				intermediate_size=37	,				layer_norm_eps=1E-05	,				num_attention_heads=4	,				num_hidden_layers=5	,				pad_token_id=1	,				vocab_size=1000	,				)
				return CLIPTextModelWithProjection(lowercase							)


	@property
	def    _a     (self							):
				torch.manual_seed(0							)

				A_     :						Any     =					{
				    """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""",
				    """encoder_hid_proj_type""": None,
				    """added_emb_type""": None,
				}

				A_     :						List[str]     =					PriorTransformer(**lowercase							)
				return model


	@property
	def    _a     (self							):
				torch.manual_seed(0							)

				A_     :						str     =					{
				    """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,
				    ),
				}
				A_     :						Dict     =					ShapERenderer(**lowercase							)
				return model


	def    _a     (self							):
				A_     :						Optional[int]     =					self.dummy_prior
				A_     :						Optional[int]     =					self.dummy_text_encoder
				A_     :						int     =					self.dummy_tokenizer
				A_     :						Dict     =					self.dummy_renderer

				A_     :						Tuple     =					HeunDiscreteScheduler(
				    beta_schedule="""exp"""	,				num_train_timesteps=1024	,				prediction_type="""sample"""	,				use_karras_sigmas=lowercase	,				clip_sample=lowercase	,				clip_sample_range=1.0	,				)
				A_     :						Union[str, Any]     =					{
				    """prior""": prior,
				    """text_encoder""": text_encoder,
				    """tokenizer""": tokenizer,
				    """renderer""": renderer,
				    """scheduler""": scheduler,
				}

				return components


	def    _a     (self	,				lowercase	,				lowercase=0							):
				if str(lowercase							).startswith("""mps"""							):
							A_     :						Any     =					torch.manual_seed(lowercase							)
				else:
							A_     :						str     =					torch.Generator(device=lowercase							).manual_seed(lowercase							)
				A_     :						List[str]     =					{
				    """prompt""": """horse""",
				    """generator""": generator,
				    """num_inference_steps""": 1,
				    """frame_size""": 32,
				    """output_type""": """np""",
				}
				return inputs


	def    _a     (self							):
				A_     :						str     =					"""cpu"""

				A_     :						Union[str, Any]     =					self.get_dummy_components()

				A_     :						Optional[int]     =					self.pipeline_class(**lowercase							)
				A_     :						str     =					pipe.to(lowercase							)

				pipe.set_progress_bar_config(disable=lowercase							)

				A_     :						Dict     =					pipe(**self.get_dummy_inputs(lowercase							)							)
				A_     :						Dict     =					output.images[0]
				A_     :						int     =					image[0, -3:, -3:, -1]

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

				A_     :						Tuple     =					np.array(
				    [
				        0.00_03_92_16,
				        0.00_03_92_16,
				        0.00_03_92_16,
				        0.00_03_92_16,
				        0.00_03_92_16,
				        0.00_03_92_16,
				        0.00_03_92_16,
				        0.00_03_92_16,
				        0.00_03_92_16,
				    ]							)

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


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


	def    _a     (self							):
				A_     :						Tuple     =					torch_device == """cpu"""
				A_     :						Union[str, Any]     =					True

				self._test_inference_batch_single_identical(
				    batch_size=2	,				test_max_difference=lowercase	,				relax_max_difference=lowercase	,				)


	def    _a     (self							):
				A_     :						List[Any]     =					self.get_dummy_components()
				A_     :						Any     =					self.pipeline_class(**lowercase							)
				A_     :						Any     =					pipe.to(lowercase							)
				pipe.set_progress_bar_config(disable=lowercase							)

				A_     :						int     =					1
				A_     :						Union[str, Any]     =					2

				A_     :						Dict     =					self.get_dummy_inputs(lowercase							)

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

				A_     :						List[Any]     =					pipe(**lowercase	,				num_images_per_prompt=lowercase							)[0]

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







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


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


	def    _a     (self							):
				A_     :						Union[str, Any]     =					load_numpy(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
				    """/shap_e/test_shap_e_np_out.npy"""							)
				A_     :						Tuple     =					ShapEPipeline.from_pretrained("""openai/shap-e"""							)
				A_     :						int     =					pipe.to(lowercase							)
				pipe.set_progress_bar_config(disable=lowercase							)

				A_     :						int     =					torch.Generator(device=lowercase							).manual_seed(0							)

				A_     :						List[str]     =					pipe(
				    """a shark"""	,				generator=lowercase	,				guidance_scale=15.0	,				num_inference_steps=64	,				frame_size=64	,				output_type="""np"""	,				).images[0]

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

				assert_mean_pixel_difference(lowercase	,				lowercase							)