
'''simple docstring'''
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,
)


lowerCAmelCase					:    Tuple           = {
    '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 		A_(     A						:		List[str]):
							UpperCamelCase   			=     {}
							state_dict.pop('pixel_mean'		,     A)
							state_dict.pop('pixel_std'		,     A)

							UpperCamelCase   			=     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:
																												UpperCamelCase   			=     key.replace(A		,     A)

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

														UpperCamelCase   			=     value

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

							return model_state_dict







def 		A_(     A						:		Dict		,     A						:		List[Any]		,     A						:		List[Any]		,     A						:		List[str]="ybelkada/segment-anything"):
							UpperCamelCase   			=     hf_hub_download(A		,     f'''checkpoints/{model_name}.pth''')

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

														UpperCamelCase   			=     SamConfig(
														    vision_config=A		,     )
							elif "sam_vit_h" in model_name:
														UpperCamelCase   			=     SamVisionConfig(
														    hidden_size=1280		,     num_hidden_layers=32		,     num_attention_heads=16		,     global_attn_indexes=[7, 15, 23, 31]		,     )

														UpperCamelCase   			=     SamConfig(
														    vision_config=A		,     )

							UpperCamelCase   			=     torch.load(A		,     map_location='cpu')
							UpperCamelCase   			=     replace_keys(A)

							UpperCamelCase   			=     SamImageProcessor()

							UpperCamelCase   			=     SamProcessor(image_processor=A)
							UpperCamelCase   			=     SamModel(A)

							hf_model.load_state_dict(A)
							UpperCamelCase   			=     hf_model.to('cuda')

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

							UpperCamelCase   			=     [[[400, 650]]]
							UpperCamelCase   			=     [[1]]

							UpperCamelCase   			=     processor(images=np.array(A)		,     return_tensors='pt').to('cuda')

							with torch.no_grad():
														UpperCamelCase   			=     hf_model(**A)
							UpperCamelCase   			=     output.iou_scores.squeeze()

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

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

														with torch.no_grad():
																					UpperCamelCase   			=     hf_model(**A)
														UpperCamelCase   			=     output.iou_scores.squeeze()

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

														UpperCamelCase   			=     ((75, 275, 1725, 850),)

														UpperCamelCase   			=     processor(images=np.array(A)		,     input_boxes=A		,     return_tensors='pt').to('cuda')

														with torch.no_grad():
																					UpperCamelCase   			=     hf_model(**A)
														UpperCamelCase   			=     output.iou_scores.squeeze()

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

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

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

														with torch.no_grad():
																					UpperCamelCase   			=     hf_model(**A)
														UpperCamelCase   			=     output.iou_scores.squeeze()

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


if __name__ == "__main__":
						lowerCAmelCase					:    Tuple           = argparse.ArgumentParser()
						lowerCAmelCase					:    List[str]           = ['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',
						)

						lowerCAmelCase					:    Dict           = 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 shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast






@require_vision
class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			def   snake_case__	(      self		)      ->	Any:
				A__											=					tempfile.mkdtemp()

				A__											=					BlipImageProcessor()
				A__											=					GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"		)

				A__											=					BlipaProcessor(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				processor.save_pretrained(self.tmpdirname		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Dict:
				return AutoProcessor.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		).tokenizer


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Optional[int]:
				return AutoProcessor.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		).image_processor


			def   snake_case__	(      self		)      ->	str:
				shutil.rmtree(self.tmpdirname		)


			def   snake_case__	(      self		)      ->	List[str]:
				A__											=					[np.random.randint(255						,	size=(3, 30, 400)						,	dtype=np.uinta		)]

				A__											=					[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__						,	0						,	-1		)		) for x in image_inputs]

				return image_inputs


			def   snake_case__	(      self		)      ->	str:
				A__											=					BlipaProcessor(tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor()		)
				processor.save_pretrained(self.tmpdirname		)

				A__											=					self.get_tokenizer(bos_token="(BOS)"						,	eos_token="(EOS)"		)
				A__											=					self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__						,	padding_value=1.0		)

				A__											=					BlipaProcessor.from_pretrained(
				    self.tmpdirname						,	bos_token="(BOS)"						,	eos_token="(EOS)"						,	do_normalize=SCREAMING_SNAKE_CASE__						,	padding_value=1.0		)

				self.assertEqual(processor.tokenizer.get_vocab()						,	tokenizer_add_kwargs.get_vocab()		)
				self.assertIsInstance(processor.tokenizer						,	SCREAMING_SNAKE_CASE__		)

				self.assertEqual(processor.image_processor.to_json_string()						,	image_processor_add_kwargs.to_json_string()		)
				self.assertIsInstance(processor.image_processor						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	int:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					self.prepare_image_inputs()

				A__											=					image_processor(SCREAMING_SNAKE_CASE__						,	return_tensors="np"		)
				A__											=					processor(images=SCREAMING_SNAKE_CASE__						,	return_tensors="np"		)

				for key in input_feat_extract.keys():
					self.assertAlmostEqual(input_feat_extract[key].sum()						,	input_processor[key].sum()						,	delta=1e-2		)


			def   snake_case__	(      self		)      ->	Optional[int]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"

				A__											=					processor(text=SCREAMING_SNAKE_CASE__		)

				A__											=					tokenizer(SCREAMING_SNAKE_CASE__						,	return_token_type_ids=SCREAMING_SNAKE_CASE__		)

				for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]						,	encoded_processor[key]		)


			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"
				A__											=					self.prepare_image_inputs()

				A__											=					processor(text=SCREAMING_SNAKE_CASE__						,	images=SCREAMING_SNAKE_CASE__		)

				self.assertListEqual(list(inputs.keys()		)						,	["pixel_values", "input_ids", "attention_mask"]		)

				# test if it raises when no input is passed
				with pytest.raises(SCREAMING_SNAKE_CASE__		):
					processor()


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				A__											=					processor.batch_decode(SCREAMING_SNAKE_CASE__		)
				A__											=					tokenizer.batch_decode(SCREAMING_SNAKE_CASE__		)

				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)







			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"
				A__											=					self.prepare_image_inputs()

				A__											=					processor(text=SCREAMING_SNAKE_CASE__						,	images=SCREAMING_SNAKE_CASE__		)

				# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
				self.assertListEqual(list(inputs.keys()		)						,	["pixel_values", "input_ids", "attention_mask"]		)




