




"""simple docstring"""





import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


A				=							logging.get_logger(__name__)  # pylint: disable=invalid-name

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Any:


			"""simple docstring"""
			warnings.warn(
			    "The preprocess method is deprecated and will be removed in a future version. Please"
			    " use VaeImageProcessor.preprocess instead"					,							UpperCamelCase					,							)
			if isinstance(UpperCamelCase					,							torch.Tensor				):
						return image
			elif isinstance(UpperCamelCase					,							PIL.Image.Image				):
						__UpperCAmelCase						:			int												=       [image]

			if isinstance(image[0]					,							PIL.Image.Image				):
						__UpperCAmelCase						,  __UpperCAmelCase						:			Optional[Any]												=       image[0].size
						__UpperCAmelCase						,  __UpperCAmelCase						:			Optional[int]												=       (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

						__UpperCAmelCase						:			str												=       [np.array(i.resize((w, h)					,							resample=PIL_INTERPOLATION["lanczos"]				)				)[None, :] for i in image]
						__UpperCAmelCase						:			Optional[int]												=       np.concatenate(UpperCamelCase					,							axis=0				)
						__UpperCAmelCase						:			int												=       np.array(UpperCamelCase				).astype(np.floataa				) / 255.0
						__UpperCAmelCase						:			Optional[int]												=       image.transpose(0					,							3					,							1					,							2				)
						__UpperCAmelCase						:			Tuple												=       2.0 * image - 1.0
						__UpperCAmelCase						:			List[str]												=       torch.from_numpy(UpperCamelCase				)
			elif isinstance(image[0]					,							torch.Tensor				):
						__UpperCAmelCase						:			Dict												=       torch.cat(UpperCamelCase					,							dim=0				)
			return image

def        _UpperCamelCase     (      UpperCamelCase				)      ->      List[Any]:


			"""simple docstring"""
			if isinstance(UpperCamelCase					,							torch.Tensor				):
						return mask
			elif isinstance(UpperCamelCase					,							PIL.Image.Image				):
						__UpperCAmelCase						:			str												=       [mask]

			if isinstance(mask[0]					,							PIL.Image.Image				):
						__UpperCAmelCase						,  __UpperCAmelCase						:			Dict												=       mask[0].size
						__UpperCAmelCase						,  __UpperCAmelCase						:			Tuple												=       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						__UpperCAmelCase						:			Union[str, Any]												=       [np.array(m.convert("L"				).resize((w, h)					,							resample=PIL_INTERPOLATION["nearest"]				)				)[None, :] for m in mask]
						__UpperCAmelCase						:			Optional[Any]												=       np.concatenate(UpperCamelCase					,							axis=0				)
						__UpperCAmelCase						:			Dict												=       mask.astype(np.floataa				) / 255.0
						__UpperCAmelCase						:			int												=       0
						__UpperCAmelCase						:			Dict												=       1
						__UpperCAmelCase						:			List[str]												=       torch.from_numpy(UpperCamelCase				)
			elif isinstance(mask[0]					,							torch.Tensor				):
						__UpperCAmelCase						:			List[str]												=       torch.cat(UpperCamelCase					,							dim=0				)
			return mask





class   a__					(						__magic_name__ ):
			lowercase_		=						42
			lowercase_		=						42


			def __init__(   self   :     List[Any]   ,	UpperCamelCase_   :     Dict   ,	UpperCamelCase_   :     List[str]):


						"""simple docstring"""




						super().__init__()
						self.register_modules(unet=UpperCamelCase_   ,	scheduler=UpperCamelCase_)


			@torch.no_grad()
			def __call__(   self   :     int   ,	UpperCamelCase_   :     Union[torch.Tensor, PIL.Image.Image]   ,	UpperCamelCase_   :     Union[torch.Tensor, PIL.Image.Image]   ,	UpperCamelCase_   :     int = 250   ,	UpperCamelCase_   :     float = 0.0   ,	UpperCamelCase_   :     int = 10   ,	UpperCamelCase_   :     int = 10   ,	UpperCamelCase_   :     Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,	UpperCamelCase_   :     Optional[str] = "pil"   ,	UpperCamelCase_   :     bool = True   ,	):


						"""simple docstring"""




						__UpperCAmelCase						:			Dict												=       image

						__UpperCAmelCase						:			Any												=       _preprocess_image(UpperCamelCase_)
						__UpperCAmelCase						:			str												=       original_image.to(device=self.device   ,	dtype=self.unet.dtype)
						__UpperCAmelCase						:			List[str]												=       _preprocess_mask(UpperCamelCase_)
						__UpperCAmelCase						:			List[Any]												=       mask_image.to(device=self.device   ,	dtype=self.unet.dtype)

						__UpperCAmelCase						:			Dict												=       original_image.shape[0]

						# sample gaussian noise to begin the loop
						if isinstance(UpperCamelCase_   ,	UpperCamelCase_) and len(UpperCamelCase_) != batch_size:
									raise ValueError(
									    F"You have passed a list of generators of length {len(UpperCamelCase_)}, but requested an effective batch"
									    F" size of {batch_size}. Make sure the batch size matches the length of the generators.")

						__UpperCAmelCase						:			Tuple												=       original_image.shape
						__UpperCAmelCase						:			int												=       randn_tensor(UpperCamelCase_   ,	generator=UpperCamelCase_   ,	device=self.device   ,	dtype=self.unet.dtype)

						# set step values
						self.scheduler.set_timesteps(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	self.device)
						__UpperCAmelCase						:			Any												=       eta

						__UpperCAmelCase						:			List[Any]												=       self.scheduler.timesteps[0] + 1
						__UpperCAmelCase						:			Optional[Any]												=       generator[0] if isinstance(UpperCamelCase_   ,	UpperCamelCase_) else generator
						for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
									if t < t_last:
												# predict the noise residual
												__UpperCAmelCase						:			Optional[Any]												=       self.unet(UpperCamelCase_   ,	UpperCamelCase_).sample
												# compute previous image: x_t -> x_t-1
												__UpperCAmelCase						:			int												=       self.scheduler.step(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_).prev_sample

									else:
												# compute the reverse: x_t-1 -> x_t
												__UpperCAmelCase						:			Union[str, Any]												=       self.scheduler.undo_step(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_)
									__UpperCAmelCase						:			Tuple												=       t

						__UpperCAmelCase						:			str												=       (image / 2 + 0.5).clamp(0   ,	1)
						__UpperCAmelCase						:			Dict												=       image.cpu().permute(0   ,	2   ,	3   ,	1).numpy()
						if output_type == "pil":
									__UpperCAmelCase						:			Tuple												=       self.numpy_to_pil(UpperCamelCase_)

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=UpperCamelCase_)









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,
)


SCREAMING_SNAKE_CASE_   :      str			=   {
    '''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  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		__lowercase											=     {}
		state_dict.pop('pixel_mean'						,   snake_case							)
		state_dict.pop('pixel_std'						,   snake_case							)

		__lowercase											=     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:
								__lowercase											=     key.replace(snake_case						,   snake_case							)

				if re.match(snake_case						,   snake_case							):
						__lowercase											=     int(re.match(snake_case						,   snake_case							).group(2							)							)
						if layer_nb == 0:
								__lowercase											=     key.replace('layers.0'						,   'proj_in'							)
						elif layer_nb == 1:
								__lowercase											=     key.replace('layers.1'						,   'layers.0'							)
						elif layer_nb == 2:
								__lowercase											=     key.replace('layers.2'						,   'proj_out'							)

				__lowercase											=     value

		__lowercase											=     model_state_dict[
		    'prompt_encoder.shared_embedding.positional_embedding'
		]

		return model_state_dict



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case="ybelkada/segment-anything"							)		->    int:
		__lowercase											=     hf_hub_download(snake_case						,   F"checkpoints/{model_name}.pth"							)

		if "sam_vit_b" in model_name:
				__lowercase											=     SamConfig()
		elif "sam_vit_l" in model_name:
				__lowercase											=     SamVisionConfig(
				    hidden_size=1_024						,   num_hidden_layers=24						,   num_attention_heads=16						,   global_attn_indexes=[5, 11, 17, 23]						,   )

				__lowercase											=     SamConfig(
				    vision_config=snake_case						,   )
		elif "sam_vit_h" in model_name:
				__lowercase											=     SamVisionConfig(
				    hidden_size=1_280						,   num_hidden_layers=32						,   num_attention_heads=16						,   global_attn_indexes=[7, 15, 23, 31]						,   )

				__lowercase											=     SamConfig(
				    vision_config=snake_case						,   )

		__lowercase											=     torch.load(snake_case						,   map_location='cpu'							)
		__lowercase											=     replace_keys(snake_case							)

		__lowercase											=     SamImageProcessor()

		__lowercase											=     SamProcessor(image_processor=snake_case							)
		__lowercase											=     SamModel(snake_case							)

		hf_model.load_state_dict(snake_case							)
		__lowercase											=     hf_model.to('cuda'							)

		__lowercase											=     'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'
		__lowercase											=     Image.open(requests.get(snake_case						,   stream=snake_case							).raw							).convert('RGB'							)

		__lowercase											=     [[[400, 650]]]
		__lowercase											=     [[1]]

		__lowercase											=     processor(images=np.array(snake_case							)						,   return_tensors='pt'							).to('cuda'							)

		with torch.no_grad():
				__lowercase											=     hf_model(**snake_case							)
		__lowercase											=     output.iou_scores.squeeze()

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

				__lowercase											=     processor(
				    images=np.array(snake_case							)						,   input_points=snake_case						,   input_labels=snake_case						,   return_tensors='pt'							).to('cuda'							)

				with torch.no_grad():
						__lowercase											=     hf_model(**snake_case							)
				__lowercase											=     output.iou_scores.squeeze()

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

				__lowercase											=     ((75, 275, 1_725, 850),)

				__lowercase											=     processor(images=np.array(snake_case							)						,   input_boxes=snake_case						,   return_tensors='pt'							).to('cuda'							)

				with torch.no_grad():
						__lowercase											=     hf_model(**snake_case							)
				__lowercase											=     output.iou_scores.squeeze()

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

				# Test with 2 points and 1 image.
				__lowercase											=     [[[400, 650], [800, 650]]]
				__lowercase											=     [[1, 1]]

				__lowercase											=     processor(
				    images=np.array(snake_case							)						,   input_points=snake_case						,   input_labels=snake_case						,   return_tensors='pt'							).to('cuda'							)

				with torch.no_grad():
						__lowercase											=     hf_model(**snake_case							)
				__lowercase											=     output.iou_scores.squeeze()

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


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   argparse.ArgumentParser()
		SCREAMING_SNAKE_CASE_   :      Union[str, 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''',
		)

		SCREAMING_SNAKE_CASE_   :      List[Any]			=   parser.parse_args()

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




