





import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
		import torch

		SCREAMING_SNAKE_CASE_   :      str			=   True
except ImportError:
		SCREAMING_SNAKE_CASE_   :      int			=   False


try:
		from torch.hub import _get_torch_home

		SCREAMING_SNAKE_CASE_   :      Tuple			=   _get_torch_home()
except ImportError:
		SCREAMING_SNAKE_CASE_   :      Dict			=   os.path.expanduser(
		    os.getenv('''TORCH_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''torch'''))
		)

SCREAMING_SNAKE_CASE_   :      List[Any]			=   os.path.join(torch_cache_home, '''transformers''')

SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   '''https://cdn.huggingface.co'''
SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   '''https://s3.amazonaws.com/models.huggingface.co/bert'''
SCREAMING_SNAKE_CASE_   :      Dict			=   '''/'''.join(str(Path(__file__).resolve()).split('''/''')[:-1])
SCREAMING_SNAKE_CASE_   :      Dict			=   os.path.join(PATH, '''config.yaml''')
SCREAMING_SNAKE_CASE_   :      str			=   os.path.join(PATH, '''attributes.txt''')
SCREAMING_SNAKE_CASE_   :      List[Any]			=   os.path.join(PATH, '''objects.txt''')
SCREAMING_SNAKE_CASE_   :      str			=   os.getenv('''PYTORCH_PRETRAINED_BERT_CACHE''', default_cache_path)
SCREAMING_SNAKE_CASE_   :      Dict			=   os.getenv('''PYTORCH_TRANSFORMERS_CACHE''', PYTORCH_PRETRAINED_BERT_CACHE)
SCREAMING_SNAKE_CASE_   :      List[str]			=   os.getenv('''TRANSFORMERS_CACHE''', PYTORCH_TRANSFORMERS_CACHE)
SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   '''pytorch_model.bin'''
SCREAMING_SNAKE_CASE_   :      Any			=   '''config.yaml'''



def  SCREAMING_SNAKE_CASE				(      snake_case=OBJECTS						,   snake_case=ATTRIBUTES							)		->    Optional[int]:
		__lowercase											=     []
		with open(snake_case							) as f:
				for object in f.readlines():
						vg_classes.append(object.split(','							)[0].lower().strip()							)

		__lowercase											=     []
		with open(snake_case							) as f:
				for object in f.readlines():
						vg_attrs.append(object.split(','							)[0].lower().strip()							)
		return vg_classes, vg_attrs



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Any:
		__lowercase											=     OrderedDict()
		with open(snake_case						,   'rb'							) as f:
				__lowercase											=     pkl.load(snake_case							)['model']
		for k in copy.deepcopy(list(ckp.keys()							)							):
				__lowercase											=     ckp.pop(snake_case							)
				if isinstance(snake_case						,   np.ndarray							):
						__lowercase											=     torch.tensor(snake_case							)
				else:
						assert isinstance(snake_case						,   torch.tensor							), type(snake_case							)
				__lowercase											=     v
		return r

class 		snake_case_   :







	'''simple docstring'''
	__UpperCamelCase   =		{}




	def __init__(						self							:     List[str]					,      __lowerCamelCase							:     dict					,      __lowerCamelCase							:     str = "root"					,      __lowerCamelCase							:     Optional[int]=0   )       -> int:

			'''simple docstring'''




			__lowercase											=     name
			__lowercase											=     level
			__lowercase											=     {}
			for k, v in dictionary.items():
					if v is None:
							raise ValueError()
					__lowercase											=     copy.deepcopy(__lowerCamelCase   )
					__lowercase											=     copy.deepcopy(__lowerCamelCase   )
					if isinstance(__lowerCamelCase					,      __lowerCamelCase   ):
							__lowercase											=     Config(__lowerCamelCase					,      name=__lowerCamelCase					,      level=level + 1   )
					__lowercase											=     v
					setattr(self					,      __lowerCamelCase					,      __lowerCamelCase   )

			__lowercase											=     d




	def __repr__(						self							:     Dict   )       -> List[str]:

			'''simple docstring'''




			return str(list((self._pointer.keys())   )   )




	def __setattr__(						self							:     int					,      __lowerCamelCase							:     Any					,      __lowerCamelCase							:     Union[str, Any]   )       -> Any:

			'''simple docstring'''




			__lowercase											=     val
			__lowercase											=     val
			__lowercase											=     key.split('.'   )
			__lowercase											=     len(__lowerCamelCase   ) - 1
			__lowercase											=     self._pointer
			if len(__lowerCamelCase   ) > 1:
					for i, l in enumerate(__lowerCamelCase   ):
							if hasattr(self					,      __lowerCamelCase   ) and isinstance(getattr(self					,      __lowerCamelCase   )					,      __lowerCamelCase   ):
									setattr(getattr(self					,      __lowerCamelCase   )					,      '.'.join(levels[i:]   )					,      __lowerCamelCase   )
							if l == last_level:
									__lowercase											=     val
							else:
									__lowercase											=     pointer[l]




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

			'''simple docstring'''




			return self._pointer




	def    UpperCAmelCase			(						self							:     Optional[Any]					,      __lowerCamelCase							:     str					,      __lowerCamelCase							:     Optional[Any]   )       -> Optional[int]:

			'''simple docstring'''




			with open(F"{file_name}"					,      'w'   ) as stream:
					dump(__lowerCamelCase					,      __lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Tuple					,      __lowerCamelCase							:     List[str]					,      __lowerCamelCase							:     Optional[int]   )       -> str:

			'''simple docstring'''




			with open(F"{file_name}"					,      'w'   ) as stream:
					json.dump(__lowerCamelCase					,      __lowerCamelCase   )




	@staticmethod
	def    UpperCAmelCase			(						__lowerCamelCase							:     Optional[int]   )       -> Any:

			'''simple docstring'''




			with open(__lowerCamelCase   ) as stream:
					__lowercase											=     load(__lowerCamelCase					,      Loader=__lowerCamelCase   )
			return data




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

			'''simple docstring'''




			__lowercase											=     '    '
			if self._name != "root":
					__lowercase											=     F"{t * (self._level-1)}{self._name}:\n"
			else:
					__lowercase											=     ''
			__lowercase											=     self._level
			for i, (k, v) in enumerate(self._pointer.items()   ):
					if isinstance(__lowerCamelCase					,      __lowerCamelCase   ):
							r += F"{t * (self._level)}{v}\n"
							self._level += 1
					else:
							r += F"{t * (self._level)}{k}: {v} ({type(__lowerCamelCase   ).__name__})\n"
					__lowercase											=     level
			return r[:-1]




	@classmethod
	def    UpperCAmelCase			(						cls							:     Optional[int]					,      __lowerCamelCase							:     str					,      **__lowerCamelCase							:     List[Any]   )       -> List[Any]:

			'''simple docstring'''




			__lowercase						,      __lowercase											=     cls.get_config_dict(__lowerCamelCase					,      **__lowerCamelCase   )
			return cls(__lowerCamelCase   )






	@classmethod
	def    UpperCAmelCase			(						cls							:     Tuple					,      __lowerCamelCase							:     str					,      **__lowerCamelCase							:     Tuple   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     kwargs.pop('cache_dir'					,      __lowerCamelCase   )
			__lowercase											=     kwargs.pop('force_download'					,      __lowerCamelCase   )
			__lowercase											=     kwargs.pop('resume_download'					,      __lowerCamelCase   )
			__lowercase											=     kwargs.pop('proxies'					,      __lowerCamelCase   )
			__lowercase											=     kwargs.pop('local_files_only'					,      __lowerCamelCase   )

			if os.path.isdir(__lowerCamelCase   ):
					__lowercase											=     os.path.join(__lowerCamelCase					,      __lowerCamelCase   )
			elif os.path.isfile(__lowerCamelCase   ) or is_remote_url(__lowerCamelCase   ):
					__lowercase											=     pretrained_model_name_or_path
			else:
					__lowercase											=     hf_bucket_url(__lowerCamelCase					,      filename=__lowerCamelCase					,      use_cdn=__lowerCamelCase   )

			try:
					# Load from URL or cache if already cached
					__lowercase											=     cached_path(
					    __lowerCamelCase					,      cache_dir=__lowerCamelCase					,      force_download=__lowerCamelCase					,      proxies=__lowerCamelCase					,      resume_download=__lowerCamelCase					,      local_files_only=__lowerCamelCase					,      )
					# Load config dict
					if resolved_config_file is None:
							raise EnvironmentError

					__lowercase											=     Config.load_yaml(__lowerCamelCase   )

			except EnvironmentError:
					__lowercase											=     'Can\'t load config for'
					raise EnvironmentError(__lowerCamelCase   )

			if resolved_config_file == config_file:
					print('loading configuration file from path'   )
			else:
					print('loading configuration file cache'   )

			return Config.load_yaml(__lowerCamelCase   ), kwargs





def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		__lowercase											=     torch.load('dump.pt'						,   map_location=in_tensor.device							)
		__lowercase											=     in_tensor.numpy()
		__lowercase											=     out_tensor.numpy()[0]
		print(na.shape						,   na[0, 0, :5]							)
		print(na.shape						,   na[0, 0, :5]							)
		assert np.allclose(snake_case						,   snake_case						,   rtol=0.01						,   atol=0.1							), (
		    F"{sum([1 for x in np.isclose(snake_case						,   snake_case						,   rtol=0.01						,   atol=0.1							).flatten() if x is False]							)/len(na.flatten()							)*100:.4f} %"
		    " element-wise mismatch"
		)
		raise Exception('tensors are all good'							)

		# Hugging face functions below



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Any:
		__lowercase											=     urlparse(snake_case							)
		return parsed.scheme in ("http", "https")



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case=True							)		->    str:
		__lowercase											=     CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
		__lowercase											=     '/' not in model_id
		if legacy_format:
				return F"{endpoint}/{model_id}-{filename}"
		else:
				return F"{endpoint}/{model_id}/{filename}"



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case=None						,   snake_case=0						,   snake_case=None						,   )		->    List[str]:
		__lowercase											=     'python/{}'.format(sys.version.split()[0]							)
		if _torch_available:
				ua += "; torch/{}".format(torch.__version__							)
		if isinstance(snake_case						,   snake_case							):
				ua += "; " + "; ".join('{}/{}'.format(snake_case						,   snake_case							) for k, v in user_agent.items()							)
		elif isinstance(snake_case						,   snake_case							):
				ua += "; " + user_agent
		__lowercase											=     {'user-agent': ua}
		if resume_size > 0:
				__lowercase											=     'bytes=%d-' % (resume_size,)
		__lowercase											=     requests.get(snake_case						,   stream=snake_case						,   proxies=snake_case						,   headers=snake_case							)
		if response.status_code == 416:  # Range not satisfiable
				return
		__lowercase											=     response.headers.get('Content-Length'							)
		__lowercase											=     resume_size + int(snake_case							) if content_length is not None else None
		__lowercase											=     tqdm(
		    unit='B'						,   unit_scale=snake_case						,   total=snake_case						,   initial=snake_case						,   desc='Downloading'						,   )
		for chunk in response.iter_content(chunk_size=1_024							):
				if chunk:  # filter out keep-alive new chunks
						progress.update(len(snake_case							)							)
						temp_file.write(snake_case							)
		progress.close()



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=None						,   snake_case=False						,   snake_case=None						,   snake_case=10						,   snake_case=False						,   snake_case=None						,   snake_case=False						,   )		->    str:
		if cache_dir is None:
				__lowercase											=     TRANSFORMERS_CACHE
		if isinstance(snake_case						,   snake_case							):
				__lowercase											=     str(snake_case							)

		os.makedirs(snake_case						,   exist_ok=snake_case							)

		__lowercase											=     None
		if not local_files_only:
				try:
						__lowercase											=     requests.head(snake_case						,   allow_redirects=snake_case						,   proxies=snake_case						,   timeout=snake_case							)
						if response.status_code == 200:
								__lowercase											=     response.headers.get('ETag'							)
				except (EnvironmentError, requests.exceptions.Timeout):
						# etag is already None
						pass

		__lowercase											=     url_to_filename(snake_case						,   snake_case							)

		# get cache path to put the file
		__lowercase											=     os.path.join(snake_case						,   snake_case							)

		# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
		# try to get the last downloaded one
		if etag is None:
				if os.path.exists(snake_case							):
						return cache_path
				else:
						__lowercase											=     [
						    file
						    for file in fnmatch.filter(os.listdir(snake_case							)						,   filename + '.*'							)
						    if not file.endswith('.json'							) and not file.endswith('.lock'							)
						]
						if len(snake_case							) > 0:
								return os.path.join(snake_case						,   matching_files[-1]							)
						else:
								# If files cannot be found and local_files_only=True,
								# the models might've been found if local_files_only=False
								# Notify the user about that
								if local_files_only:
										raise ValueError(
										    'Cannot find the requested files in the cached path and outgoing traffic has been'
										    ' disabled. To enable model look-ups and downloads online, set \'local_files_only\''
										    ' to False.'							)
								return None

    # From now on, etag is not None.
		if os.path.exists(snake_case							) and not force_download:
				return cache_path

		# Prevent parallel downloads of the same file with a lock.
		__lowercase											=     cache_path + '.lock'
		with FileLock(snake_case							):
				# If the download just completed while the lock was activated.
				if os.path.exists(snake_case							) and not force_download:
						# Even if returning early like here, the lock will be released.
						return cache_path

				if resume_download:
						__lowercase											=     cache_path + '.incomplete'

						@contextmanager
						def _resumable_file_manager():
								with open(snake_case						,   'a+b'							) as f:
										yield f

						__lowercase											=     _resumable_file_manager
						if os.path.exists(snake_case							):
								__lowercase											=     os.stat(snake_case							).st_size
						else:
								__lowercase											=     0
				else:
						__lowercase											=     partial(tempfile.NamedTemporaryFile						,   dir=snake_case						,   delete=snake_case							)
						__lowercase											=     0

				# Download to temporary file, then copy to cache dir once finished.
				# Otherwise you get corrupt cache entries if the download gets interrupted.
				with temp_file_manager() as temp_file:
						print(
						    '%s not found in cache or force_download set to True, downloading to %s'						,   snake_case						,   temp_file.name						,   )

						http_get(
						    snake_case						,   snake_case						,   proxies=snake_case						,   resume_size=snake_case						,   user_agent=snake_case						,   )

				os.replace(temp_file.name						,   snake_case							)

				__lowercase											=     {'url': url, 'etag': etag}
				__lowercase											=     cache_path + '.json'
				with open(snake_case						,   'w'							) as meta_file:
						json.dump(snake_case						,   snake_case							)

		return cache_path



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=None							)		->    str:
		__lowercase											=     url.encode('utf-8'							)
		__lowercase											=     shaaaa(snake_case							)
		__lowercase											=     url_hash.hexdigest()

		if etag:
				__lowercase											=     etag.encode('utf-8'							)
				__lowercase											=     shaaaa(snake_case							)
				filename += "." + etag_hash.hexdigest()

		if url.endswith('.h5'							):
				filename += ".h5"

		return filename



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=None						,   snake_case=False						,   snake_case=None						,   snake_case=False						,   snake_case=None						,   snake_case=False						,   snake_case=False						,   snake_case=False						,   )		->    Optional[Any]:
		if cache_dir is None:
				__lowercase											=     TRANSFORMERS_CACHE
		if isinstance(snake_case						,   snake_case							):
				__lowercase											=     str(snake_case							)
		if isinstance(snake_case						,   snake_case							):
				__lowercase											=     str(snake_case							)

		if is_remote_url(snake_case							):
				# URL, so get it from the cache (downloading if necessary)
				__lowercase											=     get_from_cache(
				    snake_case						,   cache_dir=snake_case						,   force_download=snake_case						,   proxies=snake_case						,   resume_download=snake_case						,   user_agent=snake_case						,   local_files_only=snake_case						,   )
		elif os.path.exists(snake_case							):
				# File, and it exists.
				__lowercase											=     url_or_filename
		elif urlparse(snake_case							).scheme == "":
				# File, but it doesn't exist.
				raise EnvironmentError('file {} not found'.format(snake_case							)							)
		else:
				# Something unknown
				raise ValueError('unable to parse {} as a URL or as a local path'.format(snake_case							)							)

		if extract_compressed_file:
				if not is_zipfile(snake_case							) and not tarfile.is_tarfile(snake_case							):
						return output_path

				# Path where we extract compressed archives
				# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
				__lowercase						,      __lowercase											=     os.path.split(snake_case							)
				__lowercase											=     output_file.replace('.'						,   '-'							) + '-extracted'
				__lowercase											=     os.path.join(snake_case						,   snake_case							)

				if os.path.isdir(snake_case							) and os.listdir(snake_case							) and not force_extract:
						return output_path_extracted

				# Prevent parallel extractions
				__lowercase											=     output_path + '.lock'
				with FileLock(snake_case							):
						shutil.rmtree(snake_case						,   ignore_errors=snake_case							)
						os.makedirs(snake_case							)
						if is_zipfile(snake_case							):
								with ZipFile(snake_case						,   'r'							) as zip_file:
										zip_file.extractall(snake_case							)
										zip_file.close()
						elif tarfile.is_tarfile(snake_case							):
								__lowercase											=     tarfile.open(snake_case							)
								tar_file.extractall(snake_case							)
								tar_file.close()
						else:
								raise EnvironmentError('Archive format of {} could not be identified'.format(snake_case							)							)

				return output_path_extracted

		return output_path



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=","							)		->    List[Any]:
		assert isinstance(snake_case						,   snake_case							)
		if os.path.isfile(snake_case							):
				with open(snake_case							) as f:
						__lowercase											=     eval(f.read()							)
		else:
				__lowercase											=     requests.get(snake_case							)
				try:
						__lowercase											=     requests.json()
				except Exception:
						__lowercase											=     req.content.decode()
						assert data is not None, "could not connect"
						try:
								__lowercase											=     eval(snake_case							)
						except Exception:
								__lowercase											=     data.split('\n'							)
				req.close()
		return data



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		__lowercase											=     requests.get(snake_case							)
		__lowercase											=     np.array(Image.open(BytesIO(response.content							)							)							)
		return img



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Optional[Any]:
		__lowercase											=     url.split('/'							)[-1]
		if fn not in os.listdir(os.getcwd()							):
				wget.download(snake_case							)
		with open(snake_case						,   'rb'							) as stream:
				__lowercase											=     pkl.load(snake_case							)
		__lowercase											=     weights.pop('model'							)
		__lowercase											=     {}
		for k, v in model.items():
				__lowercase											=     torch.from_numpy(snake_case							)
				if "running_var" in k:
						__lowercase											=     torch.tensor([0]							)
						__lowercase											=     k.replace('running_var'						,   'num_batches_tracked'							)
						__lowercase											=     zero
		return new



def  SCREAMING_SNAKE_CASE				(      )		->    Optional[int]:
		print(F"{os.path.abspath(os.path.join(snake_case						,   os.pardir							)							)}/demo.ipynb"							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case="RGB"							)		->    int:
		assert isinstance(snake_case						,   snake_case							)
		if os.path.isfile(snake_case							):
				__lowercase											=     cva.imread(snake_case							)
		else:
				__lowercase											=     get_image_from_url(snake_case							)
				assert img is not None, F"could not connect to: {im}"
		__lowercase											=     cva.cvtColor(snake_case						,   cva.COLOR_BGR2RGB							)
		if input_format == "RGB":
				__lowercase											=     img[:, :, ::-1]
		return img



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=1							)		->    Tuple:
		return (images[i : i + batch] for i in range(0						,   len(snake_case							)						,   snake_case							))











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)




