





import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import BridgeTowerImageProcessor

class 		snake_case_   (							unittest.TestCase						):







	'''simple docstring'''




	def __init__(						self							:     Optional[Any]					,      __lowerCamelCase							:     Union[str, Any]					,      __lowerCamelCase							:     bool = True					,      __lowerCamelCase							:     Dict[str, int] = None					,      __lowerCamelCase							:     int = 32					,      __lowerCamelCase							:     bool = True					,      __lowerCamelCase							:     Union[int, float] = 1 / 255					,      __lowerCamelCase							:     bool = True					,      __lowerCamelCase							:     bool = True					,      __lowerCamelCase							:     Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073]					,      __lowerCamelCase							:     Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711]					,      __lowerCamelCase							:     bool = True					,      __lowerCamelCase							:     Optional[int]=7					,      __lowerCamelCase							:     Tuple=30					,      __lowerCamelCase							:     str=400					,      __lowerCamelCase							:     int=3					,      )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     parent
			__lowercase											=     do_resize
			__lowercase											=     size if size is not None else {'shortest_edge': 288}
			__lowercase											=     size_divisor
			__lowercase											=     do_rescale
			__lowercase											=     rescale_factor
			__lowercase											=     do_normalize
			__lowercase											=     do_center_crop
			__lowercase											=     image_mean
			__lowercase											=     image_std
			__lowercase											=     do_pad
			__lowercase											=     batch_size
			__lowercase											=     num_channels
			__lowercase											=     min_resolution
			__lowercase											=     max_resolution




	def    UpperCAmelCase			(						self							:     Union[str, Any]   )       -> Tuple:

			'''simple docstring'''




			return {
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			    "do_normalize": self.do_normalize,
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "size_divisor": self.size_divisor,
			}






	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     Union[str, Any]					,      __lowerCamelCase							:     Union[str, Any]=False   )       -> List[Any]:

			'''simple docstring'''




			if not batched:
					__lowercase											=     self.size['shortest_edge']
					__lowercase											=     image_inputs[0]
					if isinstance(__lowerCamelCase					,      Image.Image   ):
							__lowercase						,      __lowercase											=     image.size
					else:
							__lowercase						,      __lowercase											=     image.shape[1], image.shape[2]
					__lowercase											=     size / min(__lowerCamelCase					,      __lowerCamelCase   )
					if h < w:
							__lowercase						,      __lowercase											=     size, scale * w
					else:
							__lowercase						,      __lowercase											=     scale * h, size

					__lowercase											=     int((1_333 / 800) * size   )
					if max(__lowerCamelCase					,      __lowerCamelCase   ) > max_size:
							__lowercase											=     max_size / max(__lowerCamelCase					,      __lowerCamelCase   )
							__lowercase											=     newh * scale
							__lowercase											=     neww * scale

					__lowercase						,      __lowercase											=     int(newh + 0.5   ), int(neww + 0.5   )
					__lowercase						,      __lowercase											=     (
					    newh // self.size_divisor * self.size_divisor,
					    neww // self.size_divisor * self.size_divisor,
					)

			else:
					__lowercase											=     []
					for image in image_inputs:
							__lowercase						,      __lowercase											=     self.get_expected_values([image]   )
							expected_values.append((expected_height, expected_width)   )
					__lowercase											=     max(__lowerCamelCase					,      key=lambda __lowerCamelCase   : item[0]   )[0]
					__lowercase											=     max(__lowerCamelCase					,      key=lambda __lowerCamelCase   : item[1]   )[1]

			return expected_height, expected_width





@require_torch
@require_vision
class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		BridgeTowerImageProcessor if is_vision_available() else None




	def    UpperCAmelCase			(						self							:     List[str]   )       -> Any:

			'''simple docstring'''




			__lowercase											=     BridgeTowerImageProcessingTester(self   )




	@property
	def    UpperCAmelCase			(						self							:     List[str]   )       -> Union[str, Any]:

			'''simple docstring'''




			return self.image_processor_tester.prepare_image_processor_dict()




	def    UpperCAmelCase			(						self							:     int   )       -> Any:

			'''simple docstring'''




			__lowercase											=     self.image_processing_class(**self.image_processor_dict   )
			self.assertTrue(hasattr(__lowerCamelCase					,      'image_mean'   )   )
			self.assertTrue(hasattr(__lowerCamelCase					,      'image_std'   )   )
			self.assertTrue(hasattr(__lowerCamelCase					,      'do_normalize'   )   )
			self.assertTrue(hasattr(__lowerCamelCase					,      'do_resize'   )   )
			self.assertTrue(hasattr(__lowerCamelCase					,      'size'   )   )
			self.assertTrue(hasattr(__lowerCamelCase					,      'size_divisor'   )   )




	def    UpperCAmelCase			(						self							:     str   )       -> Union[str, Any]:

			'''simple docstring'''




			pass




	def    UpperCAmelCase			(						self							:     Union[str, Any]   )       -> Tuple:

			'''simple docstring'''




			__lowercase											=     self.image_processing_class(**self.image_processor_dict   )
			# create random PIL images
			__lowercase											=     prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase   )
			for image in image_inputs:
					self.assertIsInstance(__lowerCamelCase					,      Image.Image   )

			# Test not batched input
			__lowercase											=     image_processing(image_inputs[0]					,      return_tensors='pt'   ).pixel_values

			__lowercase						,      __lowercase											=     self.image_processor_tester.get_expected_values(__lowerCamelCase   )
			self.assertEqual(
			    encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

			# Test batched
			__lowercase											=     image_processing(__lowerCamelCase					,      return_tensors='pt'   ).pixel_values

			__lowercase						,      __lowercase											=     self.image_processor_tester.get_expected_values(__lowerCamelCase					,      batched=__lowerCamelCase   )
			self.assertEqual(
			    encoded_images.shape					,      (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    )					,      )




	def    UpperCAmelCase			(						self							:     Optional[Any]   )       -> List[str]:

			'''simple docstring'''




			__lowercase											=     self.image_processing_class(**self.image_processor_dict   )
			# create random numpy tensors
			__lowercase											=     prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase					,      numpify=__lowerCamelCase   )
			for image in image_inputs:
					self.assertIsInstance(__lowerCamelCase					,      np.ndarray   )

			# Test not batched input
			__lowercase											=     image_processing(image_inputs[0]					,      return_tensors='pt'   ).pixel_values

			__lowercase						,      __lowercase											=     self.image_processor_tester.get_expected_values(__lowerCamelCase   )
			self.assertEqual(
			    encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

			# Test batched
			__lowercase											=     image_processing(__lowerCamelCase					,      return_tensors='pt'   ).pixel_values

			__lowercase						,      __lowercase											=     self.image_processor_tester.get_expected_values(__lowerCamelCase					,      batched=__lowerCamelCase   )
			self.assertEqual(
			    encoded_images.shape					,      (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    )					,      )






	def    UpperCAmelCase			(						self							:     List[str]   )       -> int:

			'''simple docstring'''




			__lowercase											=     self.image_processing_class(**self.image_processor_dict   )
			# create random PyTorch tensors
			__lowercase											=     prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase					,      torchify=__lowerCamelCase   )
			for image in image_inputs:
					self.assertIsInstance(__lowerCamelCase					,      torch.Tensor   )

			# Test not batched input
			__lowercase											=     image_processing(image_inputs[0]					,      return_tensors='pt'   ).pixel_values

			__lowercase						,      __lowercase											=     self.image_processor_tester.get_expected_values(__lowerCamelCase   )
			self.assertEqual(
			    encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

			# Test batched
			__lowercase											=     image_processing(__lowerCamelCase					,      return_tensors='pt'   ).pixel_values

			__lowercase						,      __lowercase											=     self.image_processor_tester.get_expected_values(__lowerCamelCase					,      batched=__lowerCamelCase   )
			self.assertEqual(
			    encoded_images.shape					,      (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    )					,      )











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)




