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    (   UpperCAmelCase_   :    Any  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							List[Any]						=			{}
	state_dict.pop('pixel_mean'      ,							UpperCAmelCase_  )
	state_dict.pop('pixel_std'      ,							UpperCAmelCase_  )

	__snake_case      :							int						=			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:
				__snake_case      :							Optional[int]						=			key.replace(UpperCAmelCase_      ,							UpperCAmelCase_  )

		if re.match(UpperCAmelCase_      ,							UpperCAmelCase_  ):
			__snake_case      :							str						=			int(re.match(UpperCAmelCase_      ,							UpperCAmelCase_  ).group(2  )  )
			if layer_nb == 0:
				__snake_case      :							Union[str, Any]						=			key.replace('layers.0'      ,							'proj_in'  )
			elif layer_nb == 1:
				__snake_case      :							List[Any]						=			key.replace('layers.1'      ,							'layers.0'  )
			elif layer_nb == 2:
				__snake_case      :							Union[str, Any]						=			key.replace('layers.2'      ,							'proj_out'  )

		__snake_case      :							Any						=			value

	__snake_case      :							Optional[Any]						=			model_state_dict[
	    'prompt_encoder.shared_embedding.positional_embedding'
	]

	return model_state_dict
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Any      ,							UpperCAmelCase_   :    List[str]      ,							UpperCAmelCase_   :    List[str]      ,							UpperCAmelCase_   :    int="ybelkada/segment-anything"  )							->      Optional[int]:

	'''simple docstring'''





	__snake_case      :							Union[str, Any]						=			hf_hub_download(UpperCAmelCase_      ,							F"checkpoints/{model_name}.pth"  )

	if "sam_vit_b" in model_name:
		__snake_case      :							Optional[int]						=			SamConfig()
	elif "sam_vit_l" in model_name:
		__snake_case      :							Dict						=			SamVisionConfig(
		    hidden_size=10_24      ,							num_hidden_layers=24      ,							num_attention_heads=16      ,							global_attn_indexes=[5, 11, 17, 23]      ,							)

		__snake_case      :							Any						=			SamConfig(
		    vision_config=UpperCAmelCase_      ,							)
	elif "sam_vit_h" in model_name:
		__snake_case      :							List[Any]						=			SamVisionConfig(
		    hidden_size=12_80      ,							num_hidden_layers=32      ,							num_attention_heads=16      ,							global_attn_indexes=[7, 15, 23, 31]      ,							)

		__snake_case      :							int						=			SamConfig(
		    vision_config=UpperCAmelCase_      ,							)

	__snake_case      :							Optional[Any]						=			torch.load(UpperCAmelCase_      ,							map_location='cpu'  )
	__snake_case      :							List[Any]						=			replace_keys(UpperCAmelCase_  )

	__snake_case      :							Union[str, Any]						=			SamImageProcessor()

	__snake_case      :							Optional[int]						=			SamProcessor(image_processor=UpperCAmelCase_  )
	__snake_case      :							List[str]						=			SamModel(UpperCAmelCase_  )

	hf_model.load_state_dict(UpperCAmelCase_  )
	__snake_case      :							Tuple						=			hf_model.to('cuda'  )

	__snake_case      :							Optional[int]						=			'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'
	__snake_case      :							Optional[int]						=			Image.open(requests.get(UpperCAmelCase_      ,							stream=UpperCAmelCase_  ).raw  ).convert('RGB'  )

	__snake_case      :							Optional[int]						=			[[[4_00, 6_50]]]
	__snake_case      :							Tuple						=			[[1]]

	__snake_case      :							Dict						=			processor(images=np.array(UpperCAmelCase_  )      ,							return_tensors='pt'  ).to('cuda'  )

	with torch.no_grad():
		__snake_case      :							Dict						=			hf_model(**UpperCAmelCase_  )
	__snake_case      :							Optional[int]						=			output.iou_scores.squeeze()

	if model_name == "sam_vit_h_4b8939":
		assert scores[-1].item() == 0.579_890_251_159_668

		__snake_case      :							Any						=			processor(
		    images=np.array(UpperCAmelCase_  )      ,							input_points=UpperCAmelCase_      ,							input_labels=UpperCAmelCase_      ,							return_tensors='pt'  ).to('cuda'  )

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

		assert scores[-1].item() == 0.9_712_603_092_193_604

		__snake_case      :							Optional[Any]						=			((75, 2_75, 17_25, 8_50),)

		__snake_case      :							Dict						=			processor(images=np.array(UpperCAmelCase_  )      ,							input_boxes=UpperCAmelCase_      ,							return_tensors='pt'  ).to('cuda'  )

		with torch.no_grad():
			__snake_case      :							Optional[int]						=			hf_model(**UpperCAmelCase_  )
		__snake_case      :							str						=			output.iou_scores.squeeze()

		assert scores[-1].item() == 0.8_686_015_605_926_514

		# Test with 2 points and 1 image.
		__snake_case      :							List[Any]						=			[[[4_00, 6_50], [8_00, 6_50]]]
		__snake_case      :							List[str]						=			[[1, 1]]

		__snake_case      :							Optional[int]						=			processor(
		    images=np.array(UpperCAmelCase_  )      ,							input_points=UpperCAmelCase_      ,							input_labels=UpperCAmelCase_      ,							return_tensors='pt'  ).to('cuda'  )

		with torch.no_grad():
			__snake_case      :							Dict						=			hf_model(**UpperCAmelCase_  )
		__snake_case      :							List[str]						=			output.iou_scores.squeeze()

		assert scores[-1].item() == 0.9_936_047_792_434_692


if __name__ == "__main__":
	_a					:					List[Any]=			argparse.ArgumentParser()
	_a					:					Tuple=			["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					:					str=			parser.parse_args()

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






"""simple docstring"""







import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_a					:					List[str]=			False

class 		UpperCamelCase      (   unittest.TestCase						):



				def  _lowercase					(self    :				List[str])   ->     List[Any]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



				@property
				def  _lowercase					(self    :				Optional[Any])   ->     Union[str, Any]:
					return 12



				@property
				def  _lowercase					(self    :				Dict)   ->     Union[str, Any]:
					return 12



				@property
				def  _lowercase					(self    :				int)   ->     Tuple:
					return 32



				@property
				def  _lowercase					(self    :				Optional[int])   ->     Dict:
					torch.manual_seed(0)
					__snake_case      :							Any						=			VQModel(
					    block_out_channels=[32, 64]     ,  in_channels=3     ,  out_channels=3     ,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']     ,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']     ,  latent_channels=3     ,  num_vq_embeddings=self.num_embed     ,  vq_embed_dim=3     ,  )
					return model



				@property
				def  _lowercase					(self    :				List[Any])   ->     Optional[int]:
					__snake_case      :							Dict						=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
					return tokenizer



				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[int]:
					torch.manual_seed(0)
					__snake_case      :							Any						=			CLIPTextConfig(
					    bos_token_id=0     ,  eos_token_id=2     ,  hidden_size=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=10_00     ,  )
					return CLIPTextModel(_A)



				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     Dict:
					torch.manual_seed(0)

					__snake_case      :							Any						=			12
					__snake_case      :							int						=			12

					__snake_case      :							List[Any]						=			{
					    'attention_bias': True,
					    'cross_attention_dim': 32,
					    'attention_head_dim': height * width,
					    'num_attention_heads': 1,
					    'num_vector_embeds': self.num_embed,
					    'num_embeds_ada_norm': self.num_embeds_ada_norm,
					    'norm_num_groups': 32,
					    'sample_size': width,
					    'activation_fn': 'geglu-approximate',
					}

					__snake_case      :							Union[str, Any]						=			TransformeraDModel(**_A)
					return model



				def  _lowercase					(self    :				Union[str, Any])   ->     Dict:
					__snake_case      :							Tuple						=			'cpu'

					__snake_case      :							List[str]						=			self.dummy_vqvae
					__snake_case      :							str						=			self.dummy_text_encoder
					__snake_case      :							Optional[Any]						=			self.dummy_tokenizer
					__snake_case      :							Dict						=			self.dummy_transformer
					__snake_case      :							Optional[int]						=			VQDiffusionScheduler(self.num_embed)
					__snake_case      :							List[Any]						=			LearnedClassifierFreeSamplingEmbeddings(learnable=_A)

					__snake_case      :							List[Any]						=			VQDiffusionPipeline(
					    vqvae=_A     ,  text_encoder=_A     ,  tokenizer=_A     ,  transformer=_A     ,  scheduler=_A     ,  learned_classifier_free_sampling_embeddings=_A     ,  )
					__snake_case      :							List[Any]						=			pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case      :							Optional[Any]						=			'teddy bear playing in the pool'

					__snake_case      :							str						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Union[str, Any]						=			pipe([prompt]     ,  generator=_A     ,  num_inference_steps=2     ,  output_type='np')
					__snake_case      :							Optional[int]						=			output.images

					__snake_case      :							int						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Tuple						=			pipe(
					    [prompt]     ,  generator=_A     ,  output_type='np'     ,  return_dict=_A     ,  num_inference_steps=2)[0]

					__snake_case      :							str						=			image[0, -3:, -3:, -1]
					__snake_case      :							Union[str, Any]						=			image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 24, 24, 3)

					__snake_case      :							str						=			np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992])

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






				def  _lowercase					(self    :				Tuple)   ->     Optional[int]:
					__snake_case      :							Optional[Any]						=			'cpu'

					__snake_case      :							Optional[int]						=			self.dummy_vqvae
					__snake_case      :							List[str]						=			self.dummy_text_encoder
					__snake_case      :							Optional[int]						=			self.dummy_tokenizer
					__snake_case      :							Optional[Any]						=			self.dummy_transformer
					__snake_case      :							Union[str, Any]						=			VQDiffusionScheduler(self.num_embed)
					__snake_case      :							Optional[int]						=			LearnedClassifierFreeSamplingEmbeddings(
					    learnable=_A     ,  hidden_size=self.text_embedder_hidden_size     ,  length=tokenizer.model_max_length)

					__snake_case      :							Union[str, Any]						=			VQDiffusionPipeline(
					    vqvae=_A     ,  text_encoder=_A     ,  tokenizer=_A     ,  transformer=_A     ,  scheduler=_A     ,  learned_classifier_free_sampling_embeddings=_A     ,  )
					__snake_case      :							Union[str, Any]						=			pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case      :							Union[str, Any]						=			'teddy bear playing in the pool'

					__snake_case      :							Optional[int]						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Tuple						=			pipe([prompt]     ,  generator=_A     ,  num_inference_steps=2     ,  output_type='np')
					__snake_case      :							Optional[Any]						=			output.images

					__snake_case      :							str						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Dict						=			pipe(
					    [prompt]     ,  generator=_A     ,  output_type='np'     ,  return_dict=_A     ,  num_inference_steps=2)[0]

					__snake_case      :							int						=			image[0, -3:, -3:, -1]
					__snake_case      :							Tuple						=			image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 24, 24, 3)

					__snake_case      :							Optional[Any]						=			np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988])

					assert np.abs(image_slice.flatten() - expected_slice).max() < 2.0
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2







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



				def  _lowercase					(self    :				Any)   ->     Union[str, Any]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






				def  _lowercase					(self    :				Tuple)   ->     Optional[int]:
					__snake_case      :							List[Any]						=			load_numpy(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					    '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy')

					__snake_case      :							Union[str, Any]						=			VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq')
					__snake_case      :							Tuple						=			pipeline.to(_A)
					pipeline.set_progress_bar_config(disable=_A)

					# requires GPU generator for gumbel softmax
					# don't use GPU generator in tests though
					__snake_case      :							Optional[int]						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							int						=			pipeline(
					    'teddy bear playing in the pool'     ,  num_images_per_prompt=1     ,  generator=_A     ,  output_type='np'     ,  )

					__snake_case      :							int						=			output.images[0]

					assert image.shape == (2_56, 2_56, 3)
					assert np.abs(expected_image - image).max() < 2.0





