


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,
)


_UpperCAmelCase	:     Optional[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 	lowerCAmelCase       (						lowerCAmelCase_       )->       Tuple:
				lowerCAmelCase_       :					str			 = {}
				state_dict.pop('''pixel_mean'''       ,      _lowercase       )
				state_dict.pop('''pixel_std'''       ,      _lowercase       )

				lowerCAmelCase_       :					Tuple			 = 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:
																lowerCAmelCase_       :					Union[str, Any]			 = key.replace(_lowercase       ,      _lowercase       )

								if re.match(_lowercase       ,      _lowercase       ):
												lowerCAmelCase_       :					Dict			 = int(re.match(_lowercase       ,      _lowercase       ).group(2       )       )
												if layer_nb == 0:
																lowerCAmelCase_       :					Tuple			 = key.replace('''layers.0'''       ,      '''proj_in'''       )
												elif layer_nb == 1:
																lowerCAmelCase_       :					Union[str, Any]			 = key.replace('''layers.1'''       ,      '''layers.0'''       )
												elif layer_nb == 2:
																lowerCAmelCase_       :					Tuple			 = key.replace('''layers.2'''       ,      '''proj_out'''       )

								lowerCAmelCase_       :					List[str]			 = value

				lowerCAmelCase_       :					List[str]			 = model_state_dict[
				    """prompt_encoder.shared_embedding.positional_embedding"""
				]

				return model_state_dict



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_="ybelkada/segment-anything"       )->       Optional[Any]:
				lowerCAmelCase_       :					Dict			 = hf_hub_download(_lowercase       ,      f"""checkpoints/{model_name}.pth"""       )

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

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

								lowerCAmelCase_       :					Optional[int]			 = SamConfig(
								    vision_config=_lowercase       ,      )

				lowerCAmelCase_       :					Union[str, Any]			 = torch.load(_lowercase       ,      map_location='''cpu'''       )
				lowerCAmelCase_       :					Union[str, Any]			 = replace_keys(_lowercase       )

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

				lowerCAmelCase_       :					Union[str, Any]			 = SamProcessor(image_processor=_lowercase       )
				lowerCAmelCase_       :					Optional[int]			 = SamModel(_lowercase       )

				hf_model.load_state_dict(_lowercase       )
				lowerCAmelCase_       :					Dict			 = hf_model.to('''cuda'''       )

				lowerCAmelCase_       :					Optional[Any]			 = """https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"""
				lowerCAmelCase_       :					List[Any]			 = Image.open(requests.get(_lowercase       ,      stream=_lowercase       ).raw       ).convert('''RGB'''       )

				lowerCAmelCase_       :					List[Any]			 = [[[400, 650]]]
				lowerCAmelCase_       :					Any			 = [[1]]

				lowerCAmelCase_       :					Optional[Any]			 = processor(images=np.array(_lowercase       )       ,      return_tensors='''pt'''       ).to('''cuda'''       )

				with torch.no_grad():
								lowerCAmelCase_       :					Dict			 = hf_model(**_lowercase       )
				lowerCAmelCase_       :					str			 = output.iou_scores.squeeze()

				if model_name == "sam_vit_h_4b8939":
								assert scores[-1].item() == 0.579890251159668

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

								with torch.no_grad():
												lowerCAmelCase_       :					List[str]			 = hf_model(**_lowercase       )
								lowerCAmelCase_       :					Optional[Any]			 = output.iou_scores.squeeze()

								assert scores[-1].item() == 0.9712603092193604

								lowerCAmelCase_       :					Optional[int]			 = ((75, 275, 1_725, 850),)

								lowerCAmelCase_       :					Optional[int]			 = processor(images=np.array(_lowercase       )       ,      input_boxes=_lowercase       ,      return_tensors='''pt'''       ).to('''cuda'''       )

								with torch.no_grad():
												lowerCAmelCase_       :					int			 = hf_model(**_lowercase       )
								lowerCAmelCase_       :					List[str]			 = output.iou_scores.squeeze()

								assert scores[-1].item() == 0.8686015605926514

								# Test with 2 points and 1 image.
								lowerCAmelCase_       :					Any			 = [[[400, 650], [800, 650]]]
								lowerCAmelCase_       :					Optional[int]			 = [[1, 1]]

								lowerCAmelCase_       :					List[str]			 = processor(
								    images=np.array(_lowercase       )       ,      input_points=_lowercase       ,      input_labels=_lowercase       ,      return_tensors='''pt'''       ).to('''cuda'''       )

								with torch.no_grad():
												lowerCAmelCase_       :					Optional[int]			 = hf_model(**_lowercase       )
								lowerCAmelCase_       :					Optional[Any]			 = output.iou_scores.squeeze()

								assert scores[-1].item() == 0.9936047792434692


if __name__ == "__main__":
						_UpperCAmelCase	:     Optional[int]   =argparse.ArgumentParser()
						_UpperCAmelCase	:     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""",
						)

						_UpperCAmelCase	:     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 contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
							from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
							from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
							from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


a   :			str					  =    DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

a   :			int					  =    """main"""
# Default branch name
a   :			Any					  =    """f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"""
# One particular commit (not the top of `main`)
a   :			str					  =    """aaaaaaa"""
# This commit does not exist, so we should 404.

a   :			int					  =    """d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"""
# Sha-1 of config.json on the top of `main`, for checking purposes
a   :			Any					  =    """4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"""



@contextlib.contextmanager
def       __lowerCamelCase  (					)      ->			List[str]:
							print("""Welcome!"""		)
							yield
							print("""Bye!"""		)



@contextlib.contextmanager
def       __lowerCamelCase  (					)      ->			Optional[int]:
							print("""Bonjour!"""		)
							yield
							print("""Au revoir!"""		)

class 			UpperCamelCase_ (							unittest.TestCase			):





	def  _lowercase(  self    )			->			List[Any]:
								# If the spec is missing, importlib would not be able to import the module dynamically.
								assert transformers.__spec__ is not None
								assert importlib.util.find_spec("""transformers"""    ) is not None




class 			UpperCamelCase_ (							unittest.TestCase			):





	@unittest.mock.patch("""sys.stdout"""		,		new_callable=io.StringIO    )
	def  _lowercase(  self		,		A    )			->			Tuple:
								with ContextManagers([]    ):
															print("""Transformers are awesome!"""    )
								# The print statement adds a new line at the end of the output
								self.assertEqual(mock_stdout.getvalue()		,		"""Transformers are awesome!\n"""    )





	@unittest.mock.patch("""sys.stdout"""		,		new_callable=io.StringIO    )
	def  _lowercase(  self		,		A    )			->			Dict:
								with ContextManagers([context_en()]    ):
															print("""Transformers are awesome!"""    )
								# The output should be wrapped with an English welcome and goodbye
								self.assertEqual(mock_stdout.getvalue()		,		"""Welcome!\nTransformers are awesome!\nBye!\n"""    )





	@unittest.mock.patch("""sys.stdout"""		,		new_callable=io.StringIO    )
	def  _lowercase(  self		,		A    )			->			Union[str, Any]:
								with ContextManagers([context_fr(), context_en()]    ):
															print("""Transformers are awesome!"""    )
								# The output should be wrapped with an English and French welcome and goodbye
								self.assertEqual(mock_stdout.getvalue()		,		"""Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n"""    )





	@require_torch
	def  _lowercase(  self    )			->			Optional[int]:
								self.assertEqual(find_labels(A    )		,		["""labels"""]    )
								self.assertEqual(find_labels(A    )		,		["""labels""", """next_sentence_label"""]    )
								self.assertEqual(find_labels(A    )		,		["""start_positions""", """end_positions"""]    )

								class 			UpperCamelCase_ (							__magic_name__			):
									pass




								self.assertEqual(find_labels(A    )		,		["""labels"""]    )





	@require_tf
	def  _lowercase(  self    )			->			int:
								self.assertEqual(find_labels(A    )		,		["""labels"""]    )
								self.assertEqual(find_labels(A    )		,		["""labels""", """next_sentence_label"""]    )
								self.assertEqual(find_labels(A    )		,		["""start_positions""", """end_positions"""]    )

								class 			UpperCamelCase_ (							__magic_name__			):
									pass




								self.assertEqual(find_labels(A    )		,		["""labels"""]    )






	@require_flax
	def  _lowercase(  self    )			->			Any:
								# Flax models don't have labels
								self.assertEqual(find_labels(A    )		,		[]    )
								self.assertEqual(find_labels(A    )		,		[]    )
								self.assertEqual(find_labels(A    )		,		[]    )

								class 			UpperCamelCase_ (							__magic_name__			):
									pass




								self.assertEqual(find_labels(A    )		,		[]    )

