

import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


A_	:			int  	=      {
    'iou_prediction_head.layers.0': 'iou_prediction_head.proj_in',
    'iou_prediction_head.layers.1': 'iou_prediction_head.layers.0',
    'iou_prediction_head.layers.2': 'iou_prediction_head.proj_out',
    'mask_decoder.output_upscaling.0': 'mask_decoder.upscale_conv1',
    'mask_decoder.output_upscaling.1': 'mask_decoder.upscale_layer_norm',
    'mask_decoder.output_upscaling.3': 'mask_decoder.upscale_conv2',
    'mask_downscaling.0': 'mask_embed.conv1',
    'mask_downscaling.1': 'mask_embed.layer_norm1',
    'mask_downscaling.3': 'mask_embed.conv2',
    'mask_downscaling.4': 'mask_embed.layer_norm2',
    'mask_downscaling.6': 'mask_embed.conv3',
    'point_embeddings': 'point_embed',
    'pe_layer.positional_encoding_gaussian_matrix': 'shared_embedding.positional_embedding',
    'image_encoder': 'vision_encoder',
    'neck.0': 'neck.conv1',
    'neck.1': 'neck.layer_norm1',
    'neck.2': 'neck.conv2',
    'neck.3': 'neck.layer_norm2',
    'patch_embed.proj': 'patch_embed.projection',
    '.norm': '.layer_norm',
    'blocks': 'layers',
}

def 						UpperCamelCase (lowercase_:			List[str] )					->				str:
	A__							:   Union[str, Any]					       =		{}
	state_dict.pop("""pixel_mean"""   ,				lowercase_ )
	state_dict.pop("""pixel_std"""   ,				lowercase_ )

	A__							:   List[Any]					       =		r""".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"""

	for key, value in state_dict.items():
		for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
			if key_to_modify in key:
				A__							:   Union[str, Any]					       =		key.replace(lowercase_   ,				lowercase_ )

		if re.match(lowercase_   ,				lowercase_ ):
			A__							:   List[Any]					       =		int(re.match(lowercase_   ,				lowercase_ ).group(2 ) )
			if layer_nb == 0:
				A__							:   List[str]					       =		key.replace("""layers.0"""   ,				"""proj_in""" )
			elif layer_nb == 1:
				A__							:   Union[str, Any]					       =		key.replace("""layers.1"""   ,				"""layers.0""" )
			elif layer_nb == 2:
				A__							:   Optional[int]					       =		key.replace("""layers.2"""   ,				"""proj_out""" )

		A__							:   Any					       =		value

	A__							:   Union[str, Any]					       =		model_state_dict[
	    """prompt_encoder.shared_embedding.positional_embedding"""
	]

	return model_state_dict

def 						UpperCamelCase (lowercase_:			Union[str, Any]   ,				lowercase_:			int   ,				lowercase_:			Any   ,				lowercase_:			Optional[int]="ybelkada/segment-anything" )					->				Dict:
	A__							:   List[Any]					       =		hf_hub_download(lowercase_   ,				f"""checkpoints/{model_name}.pth""" )

	if "sam_vit_b" in model_name:
		A__							:   Dict					       =		SamConfig()
	elif "sam_vit_l" in model_name:
		A__							:   List[Any]					       =		SamVisionConfig(
		    hidden_size=1024   ,				num_hidden_layers=24   ,				num_attention_heads=16   ,				global_attn_indexes=[5, 11, 17, 23]   ,				)

		A__							:   Union[str, Any]					       =		SamConfig(
		    vision_config=lowercase_   ,				)
	elif "sam_vit_h" in model_name:
		A__							:   Union[str, Any]					       =		SamVisionConfig(
		    hidden_size=1280   ,				num_hidden_layers=32   ,				num_attention_heads=16   ,				global_attn_indexes=[7, 15, 23, 31]   ,				)

		A__							:   Any					       =		SamConfig(
		    vision_config=lowercase_   ,				)

	A__							:   Dict					       =		torch.load(lowercase_   ,				map_location="""cpu""" )
	A__							:   Dict					       =		replace_keys(lowercase_ )

	A__							:   Any					       =		SamImageProcessor()

	A__							:   Any					       =		SamProcessor(image_processor=lowercase_ )
	A__							:   int					       =		SamModel(lowercase_ )

	hf_model.load_state_dict(lowercase_ )
	A__							:   Optional[int]					       =		hf_model.to("""cuda""" )

	A__							:   Union[str, Any]					       =		"""https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"""
	A__							:   int					       =		Image.open(requests.get(lowercase_   ,				stream=lowercase_ ).raw ).convert("""RGB""" )

	A__							:   Union[str, Any]					       =		[[[400, 650]]]
	A__							:   int					       =		[[1]]

	A__							:   Dict					       =		processor(images=np.array(lowercase_ )   ,				return_tensors="""pt""" ).to("""cuda""" )

	with torch.no_grad():
		A__							:   int					       =		hf_model(**lowercase_ )
	A__							:   Dict					       =		output.iou_scores.squeeze()

	if model_name == "sam_vit_h_4b8939":
		assert scores[-1].item() == 0.579_8902_5115_9668

		A__							:   Dict					       =		processor(
		    images=np.array(lowercase_ )   ,				input_points=lowercase_   ,				input_labels=lowercase_   ,				return_tensors="""pt""" ).to("""cuda""" )

		with torch.no_grad():
			A__							:   Union[str, Any]					       =		hf_model(**lowercase_ )
		A__							:   int					       =		output.iou_scores.squeeze()

		assert scores[-1].item() == 0.9712_6030_9219_3604

		A__							:   Tuple					       =		((75, 275, 1725, 850),)

		A__							:   Any					       =		processor(images=np.array(lowercase_ )   ,				input_boxes=lowercase_   ,				return_tensors="""pt""" ).to("""cuda""" )

		with torch.no_grad():
			A__							:   int					       =		hf_model(**lowercase_ )
		A__							:   List[Any]					       =		output.iou_scores.squeeze()

		assert scores[-1].item() == 0.8686_0156_0592_6514

		# Test with 2 points and 1 image.
		A__							:   Dict					       =		[[[400, 650], [800, 650]]]
		A__							:   str					       =		[[1, 1]]

		A__							:   Tuple					       =		processor(
		    images=np.array(lowercase_ )   ,				input_points=lowercase_   ,				input_labels=lowercase_   ,				return_tensors="""pt""" ).to("""cuda""" )

		with torch.no_grad():
			A__							:   Optional[int]					       =		hf_model(**lowercase_ )
		A__							:   int					       =		output.iou_scores.squeeze()

		assert scores[-1].item() == 0.9936_0477_9243_4692


if __name__ == "__main__":
	A_	:			Union[str, Any]  	=      argparse.ArgumentParser()
	A_	:			Optional[Any]  	=      ['sam_vit_b_01ec64', 'sam_vit_h_4b8939', 'sam_vit_l_0b3195']
	parser.add_argument(
	    '--model_name',
	    default='sam_vit_h_4b8939',
	    choices=choices,
	    type=str,
	    help='Path to hf config.json of model to convert',
	)
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument(
	    '--push_to_hub',
	    action='store_true',
	    help='Whether to push the model and processor to the hub after converting',
	)
	parser.add_argument(
	    '--model_hub_id',
	    default='ybelkada/segment-anything',
	    choices=choices,
	    type=str,
	    help='Path to hf config.json of model to convert',
	)

	A_	:			Dict  	=      parser.parse_args()

	convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    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    _a						(__magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Any  							=	KandinskyVaaImgaImgPipeline
					UpperCAmelCase__:       Optional[Any]  							=	['''image_embeds''', '''negative_image_embeds''', '''image''']
					UpperCAmelCase__:       str  							=	[
					    '''image_embeds''',
					    '''negative_image_embeds''',
					    '''image''',
					]
					UpperCAmelCase__:       int  							=	[
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''strength''',
					    '''guidance_scale''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					UpperCAmelCase__:       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






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






					@property
					def   __A      (			self      ):
						return 100






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

						A__							:   Dict					       =		{
						    """in_channels""": 4,
						    # Out channels is double in channels because predicts mean and variance
						    """out_channels""": 8,
						    """addition_embed_type""": """image""",
						    """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,
						}

						A__							:   List[str]					       =		UNetaDConditionModel(**A__      )
						return model






					@property
					def   __A      (			self      ):
						return {
						    "block_out_channels": [32, 64],
						    "down_block_types": ["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",
						    ],
						    "vq_embed_dim": 4,
						}






					@property
					def   __A      (			self      ):
						torch.manual_seed(0      )
						A__							:   Tuple					       =		VQModel(**self.dummy_movq_kwargs      )
						return model






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		self.dummy_unet
						A__							:   Dict					       =		self.dummy_movq

						A__							:   List[Any]					       =		{
						    """num_train_timesteps""": 1000,
						    """beta_schedule""": """linear""",
						    """beta_start""": 0.0_0_0_8_5,
						    """beta_end""": 0.0_1_2,
						    """clip_sample""": False,
						    """set_alpha_to_one""": False,
						    """steps_offset""": 0,
						    """prediction_type""": """epsilon""",
						    """thresholding""": False,
						}

						A__							:   List[str]					       =		DDIMScheduler(**A__      )

						A__							:   List[str]					       =		{
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """movq""": movq,
						}

						return components






					def   __A      (			self   ,	A__   ,	A__=0      ):
						A__							:   List[str]					       =		floats_tensor((1, self.text_embedder_hidden_size)   ,	rng=random.Random(A__      )      ).to(A__      )
						A__							:   Any					       =		floats_tensor((1, self.text_embedder_hidden_size)   ,	rng=random.Random(seed + 1      )      ).to(
						    A__      )
						# create init_image
						A__							:   Dict					       =		floats_tensor((1, 3, 64, 64)   ,	rng=random.Random(A__      )      ).to(A__      )
						A__							:   Optional[int]					       =		image.cpu().permute(0   ,	2   ,	3   ,	1      )[0]
						A__							:   Dict					       =		Image.fromarray(np.uinta(A__      )      ).convert("""RGB"""      ).resize((256, 256)      )

						if str(A__      ).startswith("""mps"""      ):
							A__							:   Any					       =		torch.manual_seed(A__      )
						else:
							A__							:   List[Any]					       =		torch.Generator(device=A__      ).manual_seed(A__      )
						A__							:   Optional[int]					       =		{
						    """image""": init_image,
						    """image_embeds""": image_embeds,
						    """negative_image_embeds""": negative_image_embeds,
						    """generator""": generator,
						    """height""": 64,
						    """width""": 64,
						    """num_inference_steps""": 10,
						    """guidance_scale""": 7.0,
						    """strength""": 0.2,
						    """output_type""": """np""",
						}
						return inputs






					def   __A      (			self      ):
						A__							:   str					       =		"""cpu"""

						A__							:   Any					       =		self.get_dummy_components()

						A__							:   Union[str, Any]					       =		self.pipeline_class(**A__      )
						A__							:   List[str]					       =		pipe.to(A__      )

						pipe.set_progress_bar_config(disable=A__      )

						A__							:   Dict					       =		pipe(**self.get_dummy_inputs(A__      )      )
						A__							:   Any					       =		output.images

						A__							:   List[str]					       =		pipe(
						    **self.get_dummy_inputs(A__      )   ,	return_dict=A__   ,	)[0]

						A__							:   Optional[int]					       =		image[0, -3:, -3:, -1]
						A__							:   str					       =		image_from_tuple[0, -3:, -3:, -1]

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

						A__							:   str					       =		np.array(
						    [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3]      )
						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    _a						(unittest.TestCase    ):


					'''simple docstring'''






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






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/kandinskyv22/kandinskyv22_img2img_frog.npy"""      )

						A__							:   List[Any]					       =		load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""      )
						A__							:   str					       =		"""A red cartoon frog, 4k"""

						A__							:   int					       =		KandinskyVaaPriorPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-2-prior"""   ,	torch_dtype=torch.floataa      )
						pipe_prior.to(A__      )

						A__							:   List[Any]					       =		KandinskyVaaImgaImgPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-2-decoder"""   ,	torch_dtype=torch.floataa      )
						A__							:   List[str]					       =		pipeline.to(A__      )

						pipeline.set_progress_bar_config(disable=A__      )

						A__							:   Tuple					       =		torch.Generator(device="""cpu"""      ).manual_seed(0      )
						A__       ,       A__							:   Optional[Any]					       =		pipe_prior(
						    A__   ,	generator=A__   ,	num_inference_steps=5   ,	negative_prompt=""""""   ,	).to_tuple()

						A__							:   str					       =		pipeline(
						    image=A__   ,	image_embeds=A__   ,	negative_image_embeds=A__   ,	generator=A__   ,	num_inference_steps=100   ,	height=768   ,	width=768   ,	strength=0.2   ,	output_type="""np"""   ,	)

						A__							:   Optional[int]					       =		output.images[0]

						assert image.shape == (768, 768, 3)

						assert_mean_pixel_difference(A__   ,	A__      )



