'''simple docstring'''

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

		UpperCamelCase__     :	Union[str, Any]				=			True
except ImportError:
		UpperCamelCase__     :	int				=			False


try:
		from torch.hub import _get_torch_home

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

UpperCamelCase__     :	Dict				=			os.path.join(torch_cache_home, 'transformers')

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







def __UpperCamelCase(   _A					:				Tuple=OBJECTS  ,			_A					:				Optional[int]=ATTRIBUTES    ):
			'''simple docstring'''
			UpperCAmelCase__							:				Tuple   				=  []
			with open(__snake_case    ) as f:
						for object in f.readlines():
									vg_classes.append(object.split(''','''    )[0].lower().strip()    )

			UpperCAmelCase__							:				List[str]   				=  []
			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 __UpperCamelCase(   _A					:				List[str]    ):
			'''simple docstring'''
			UpperCAmelCase__							:				List[str]   				=  OrderedDict()
			with open(__snake_case  ,			'''rb'''    ) as f:
						UpperCAmelCase__							:				int   				=  pkl.load(__snake_case    )['''model''']
			for k in copy.deepcopy(list(ckp.keys()    )    ):
						UpperCAmelCase__							:				Optional[int]   				=  ckp.pop(__snake_case    )
						if isinstance(__snake_case  ,			np.ndarray    ):
									UpperCAmelCase__							:				Dict   				=  torch.tensor(__snake_case    )
						else:
									assert isinstance(__snake_case  ,			torch.tensor    ), type(__snake_case    )
						UpperCAmelCase__							:				str   				=  v
			return r


class 				_lowercase	:
						'''simple docstring'''


						UpperCAmelCase_      :      Optional[int]      							=   {}




						def __init__(				self					,lowerCamelCase_					,lowerCamelCase_ = "root"					,lowerCamelCase_=0							)			->			int:



									'''simple docstring'''



									UpperCAmelCase__							:				Optional[Any]   				=  name
									UpperCAmelCase__							:				Optional[int]   				=  level
									UpperCAmelCase__							:				Dict   				=  {}
									for k, v in dictionary.items():
												if v is None:
															raise ValueError()
												UpperCAmelCase__							:				Tuple   				=  copy.deepcopy(__lowerCAmelCase							)
												UpperCAmelCase__							:				Optional[int]   				=  copy.deepcopy(__lowerCAmelCase							)
												if isinstance(__lowerCAmelCase					,__lowerCAmelCase							):
															UpperCAmelCase__							:				int   				=  Config(__lowerCAmelCase					,name=__lowerCAmelCase					,level=level + 1							)
												UpperCAmelCase__							:				str   				=  v
												setattr(self					,__lowerCAmelCase					,__lowerCAmelCase							)

									UpperCAmelCase__							:				int   				=  d




						def __repr__(				self							)			->			Tuple:



									'''simple docstring'''



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




						def __setattr__(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			Optional[int]:



									'''simple docstring'''



									UpperCAmelCase__							:				Union[str, Any]   				=  val
									UpperCAmelCase__							:				Union[str, Any]   				=  val
									UpperCAmelCase__							:				Union[str, Any]   				=  key.split('''.'''							)
									UpperCAmelCase__							:				int   				=  len(__lowerCAmelCase							) - 1
									UpperCAmelCase__							:				int   				=  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:
																		UpperCAmelCase__							:				List[str]   				=  val
															else:
																		UpperCAmelCase__							:				int   				=  pointer[l]




						def 		lowerCAmelCase__					(				self							)			->			str:



									'''simple docstring'''



									return self._pointer




						def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			List[Any]:



									'''simple docstring'''



									with open(f'''{file_name}'''					,'''w'''							) as stream:
												dump(__lowerCAmelCase					,__lowerCAmelCase							)




						def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			Any:



									'''simple docstring'''



									with open(f'''{file_name}'''					,'''w'''							) as stream:
												json.dump(__lowerCAmelCase					,__lowerCAmelCase							)




						@staticmethod
						def 		lowerCAmelCase__					(				lowerCamelCase_							)			->			Union[str, Any]:



									'''simple docstring'''



									with open(__lowerCAmelCase							) as stream:
												UpperCAmelCase__							:				str   				=  load(__lowerCAmelCase					,Loader=__lowerCAmelCase							)
									return data




						def __str__(				self							)			->			Tuple:



									'''simple docstring'''



									UpperCAmelCase__							:				str   				=  '''    '''
									if self._name != "root":
												UpperCAmelCase__							:				str   				=  f'''{t * (self._level-1)}{self._name}:\n'''
									else:
												UpperCAmelCase__							:				str   				=  ''''''
									UpperCAmelCase__							:				List[Any]   				=  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'''
												UpperCAmelCase__							:				Union[str, Any]   				=  level
									return r[:-1]




						@classmethod
						def 		lowerCAmelCase__					(				cls					,lowerCamelCase_					,**lowerCamelCase_							)			->			Tuple:



									'''simple docstring'''



									UpperCAmelCase__				,      UpperCAmelCase__							:				Tuple   				=  cls.get_config_dict(__lowerCAmelCase					,**__lowerCAmelCase							)
									return cls(__lowerCAmelCase							)




						@classmethod
						def 		lowerCAmelCase__					(				cls					,lowerCamelCase_					,**lowerCamelCase_							)			->			Any:



									'''simple docstring'''



									UpperCAmelCase__							:				str   				=  kwargs.pop('''cache_dir'''					,__lowerCAmelCase							)
									UpperCAmelCase__							:				Dict   				=  kwargs.pop('''force_download'''					,__lowerCAmelCase							)
									UpperCAmelCase__							:				List[str]   				=  kwargs.pop('''resume_download'''					,__lowerCAmelCase							)
									UpperCAmelCase__							:				List[Any]   				=  kwargs.pop('''proxies'''					,__lowerCAmelCase							)
									UpperCAmelCase__							:				Optional[Any]   				=  kwargs.pop('''local_files_only'''					,__lowerCAmelCase							)

									if os.path.isdir(__lowerCAmelCase							):
												UpperCAmelCase__							:				Any   				=  os.path.join(__lowerCAmelCase					,__lowerCAmelCase							)
									elif os.path.isfile(__lowerCAmelCase							) or is_remote_url(__lowerCAmelCase							):
												UpperCAmelCase__							:				Optional[Any]   				=  pretrained_model_name_or_path
									else:
												UpperCAmelCase__							:				int   				=  hf_bucket_url(__lowerCAmelCase					,filename=__lowerCAmelCase					,use_cdn=__lowerCAmelCase							)

									try:
												# Load from URL or cache if already cached
												UpperCAmelCase__							:				List[Any]   				=  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

												UpperCAmelCase__							:				int   				=  Config.load_yaml(__lowerCAmelCase							)

									except EnvironmentError:
												UpperCAmelCase__							:				List[Any]   				=  '''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 __UpperCamelCase(   _A					:				Optional[Any]    ):
			'''simple docstring'''
			UpperCAmelCase__							:				Optional[Any]   				=  torch.load('''dump.pt'''  ,			map_location=in_tensor.device    )
			UpperCAmelCase__							:				Optional[Any]   				=  in_tensor.numpy()
			UpperCAmelCase__							:				int   				=  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=0.0_1  ,			atol=0.1    ), (
			    F'''{sum([1 for x in np.isclose(__snake_case  ,			__snake_case  ,			rtol=0.0_1  ,			atol=0.1    ).flatten() if x is False]    )/len(na.flatten()    )*1_00:.4f} %'''
			    " element-wise mismatch"
			)
			raise Exception('''tensors are all good'''    )

			# Hugging face functions below







def __UpperCamelCase(   _A					:				Tuple    ):
			'''simple docstring'''
			UpperCAmelCase__							:				int   				=  urlparse(__snake_case    )
			return parsed.scheme in ("http", "https")







def __UpperCamelCase(   _A					:				Any  ,			_A					:				Dict  ,			_A					:				Dict=True    ):
			'''simple docstring'''
			UpperCAmelCase__							:				Optional[Any]   				=  CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
			UpperCAmelCase__							:				List[str]   				=  '''/''' not in model_id
			if legacy_format:
						return F'''{endpoint}/{model_id}-{filename}'''
			else:
						return F'''{endpoint}/{model_id}/{filename}'''







def __UpperCamelCase(   _A					:				Optional[int]  ,			_A					:				Optional[int]  ,			_A					:				int=None  ,			_A					:				Optional[int]=0  ,			_A					:				Any=None  ,			):
			'''simple docstring'''
			UpperCAmelCase__							:				Optional[Any]   				=  '''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
			UpperCAmelCase__							:				Union[str, Any]   				=  {'''user-agent''': ua}
			if resume_size > 0:
						UpperCAmelCase__							:				Optional[Any]   				=  '''bytes=%d-''' % (resume_size,)
			UpperCAmelCase__							:				Tuple   				=  requests.get(__snake_case  ,			stream=__snake_case  ,			proxies=__snake_case  ,			headers=__snake_case    )
			if response.status_code == 4_16:  # Range not satisfiable
						return
			UpperCAmelCase__							:				Union[str, Any]   				=  response.headers.get('''Content-Length'''    )
			UpperCAmelCase__							:				str   				=  resume_size + int(__snake_case    ) if content_length is not None else None
			UpperCAmelCase__							:				Union[str, Any]   				=  tqdm(
			    unit='''B'''  ,			unit_scale=__snake_case  ,			total=__snake_case  ,			initial=__snake_case  ,			desc='''Downloading'''  ,			)
			for chunk in response.iter_content(chunk_size=10_24    ):
						if chunk:  # filter out keep-alive new chunks
									progress.update(len(__snake_case    )    )
									temp_file.write(__snake_case    )
			progress.close()







def __UpperCamelCase(   _A					:				List[Any]  ,			_A					:				Dict=None  ,			_A					:				Union[str, Any]=False  ,			_A					:				List[str]=None  ,			_A					:				Union[str, Any]=10  ,			_A					:				Optional[Any]=False  ,			_A					:				str=None  ,			_A					:				Optional[Any]=False  ,			):
			'''simple docstring'''







			if cache_dir is None:
						UpperCAmelCase__							:				Optional[int]   				=  TRANSFORMERS_CACHE
			if isinstance(__snake_case  ,			__snake_case    ):
						UpperCAmelCase__							:				List[str]   				=  str(__snake_case    )

			os.makedirs(__snake_case  ,			exist_ok=__snake_case    )

			UpperCAmelCase__							:				Optional[int]   				=  None
			if not local_files_only:
						try:
									UpperCAmelCase__							:				Optional[Any]   				=  requests.head(__snake_case  ,			allow_redirects=__snake_case  ,			proxies=__snake_case  ,			timeout=__snake_case    )
									if response.status_code == 2_00:
												UpperCAmelCase__							:				List[Any]   				=  response.headers.get('''ETag'''    )
						except (EnvironmentError, requests.exceptions.Timeout):
									# etag is already None
									pass

			UpperCAmelCase__							:				List[Any]   				=  url_to_filename(__snake_case  ,			__snake_case    )

			# get cache path to put the file
			UpperCAmelCase__							:				Optional[int]   				=  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:
									UpperCAmelCase__							:				Optional[Any]   				=  [
									    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.
			UpperCAmelCase__							:				Optional[int]   				=  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:
									UpperCAmelCase__							:				Dict   				=  cache_path + '''.incomplete'''

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

									UpperCAmelCase__							:				Optional[int]   				=  _resumable_file_manager
									if os.path.exists(__snake_case    ):
												UpperCAmelCase__							:				Any   				=  os.stat(__snake_case    ).st_size
									else:
												UpperCAmelCase__							:				Optional[int]   				=  0
						else:
									UpperCAmelCase__							:				Union[str, Any]   				=  partial(tempfile.NamedTemporaryFile  ,			dir=__snake_case  ,			delete=__snake_case    )
									UpperCAmelCase__							:				Optional[Any]   				=  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    )

						UpperCAmelCase__							:				Optional[Any]   				=  {'''url''': url, '''etag''': etag}
						UpperCAmelCase__							:				Any   				=  cache_path + '''.json'''
						with open(__snake_case  ,			'''w'''    ) as meta_file:
									json.dump(__snake_case  ,			__snake_case    )

			return cache_path







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				int=None    ):
			'''simple docstring'''
			UpperCAmelCase__							:				Union[str, Any]   				=  url.encode('''utf-8'''    )
			UpperCAmelCase__							:				Dict   				=  shaaaa(__snake_case    )
			UpperCAmelCase__							:				Union[str, Any]   				=  url_hash.hexdigest()

			if etag:
						UpperCAmelCase__							:				List[Any]   				=  etag.encode('''utf-8'''    )
						UpperCAmelCase__							:				Optional[Any]   				=  shaaaa(__snake_case    )
						filename += "." + etag_hash.hexdigest()

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

			return filename







def __UpperCamelCase(   _A					:				Optional[Any]  ,			_A					:				Tuple=None  ,			_A					:				List[Any]=False  ,			_A					:				Tuple=None  ,			_A					:				Any=False  ,			_A					:				List[str]=None  ,			_A					:				int=False  ,			_A					:				Optional[Any]=False  ,			_A					:				Optional[Any]=False  ,			):
			'''simple docstring'''







			if cache_dir is None:
						UpperCAmelCase__							:				Optional[Any]   				=  TRANSFORMERS_CACHE
			if isinstance(__snake_case  ,			__snake_case    ):
						UpperCAmelCase__							:				Optional[int]   				=  str(__snake_case    )
			if isinstance(__snake_case  ,			__snake_case    ):
						UpperCAmelCase__							:				Tuple   				=  str(__snake_case    )

			if is_remote_url(__snake_case    ):
						# URL, so get it from the cache (downloading if necessary)
						UpperCAmelCase__							:				Dict   				=  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.
						UpperCAmelCase__							:				Optional[int]   				=  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/"
						UpperCAmelCase__				,      UpperCAmelCase__							:				Dict   				=  os.path.split(__snake_case    )
						UpperCAmelCase__							:				int   				=  output_file.replace('''.'''  ,			'''-'''    ) + '''-extracted'''
						UpperCAmelCase__							:				List[Any]   				=  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
						UpperCAmelCase__							:				List[str]   				=  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    ):
												UpperCAmelCase__							:				List[str]   				=  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 __UpperCamelCase(   _A					:				Optional[int]  ,			_A					:				Optional[int]=","    ):
			'''simple docstring'''
			assert isinstance(__snake_case  ,			__snake_case    )
			if os.path.isfile(__snake_case    ):
						with open(__snake_case    ) as f:
									UpperCAmelCase__							:				str   				=  eval(f.read()    )
			else:
						UpperCAmelCase__							:				List[Any]   				=  requests.get(__snake_case    )
						try:
									UpperCAmelCase__							:				List[Any]   				=  requests.json()
						except Exception:
									UpperCAmelCase__							:				Optional[int]   				=  req.content.decode()
									assert data is not None, "could not connect"
									try:
												UpperCAmelCase__							:				int   				=  eval(__snake_case    )
									except Exception:
												UpperCAmelCase__							:				str   				=  data.split('''\n'''    )
						req.close()
			return data







def __UpperCamelCase(   _A					:				Union[str, Any]    ):
			'''simple docstring'''
			UpperCAmelCase__							:				str   				=  requests.get(__snake_case    )
			UpperCAmelCase__							:				Tuple   				=  np.array(Image.open(BytesIO(response.content    )    )    )
			return img







def __UpperCamelCase(   _A					:				Tuple    ):
			'''simple docstring'''
			UpperCAmelCase__							:				Dict   				=  url.split('''/'''    )[-1]
			if fn not in os.listdir(os.getcwd()    ):
						wget.download(__snake_case    )
			with open(__snake_case  ,			'''rb'''    ) as stream:
						UpperCAmelCase__							:				Optional[Any]   				=  pkl.load(__snake_case    )
			UpperCAmelCase__							:				Tuple   				=  weights.pop('''model'''    )
			UpperCAmelCase__							:				List[Any]   				=  {}
			for k, v in model.items():
						UpperCAmelCase__							:				List[str]   				=  torch.from_numpy(__snake_case    )
						if "running_var" in k:
									UpperCAmelCase__							:				List[Any]   				=  torch.tensor([0]    )
									UpperCAmelCase__							:				List[str]   				=  k.replace('''running_var'''  ,			'''num_batches_tracked'''    )
									UpperCAmelCase__							:				Tuple   				=  zero
			return new







def __UpperCamelCase(   ):
			'''simple docstring'''
			print(F'''{os.path.abspath(os.path.join(__snake_case  ,			os.pardir    )    )}/demo.ipynb'''    )







def __UpperCamelCase(   _A					:				int  ,			_A					:				Tuple="RGB"    ):
			'''simple docstring'''
			assert isinstance(__snake_case  ,			__snake_case    )
			if os.path.isfile(__snake_case    ):
						UpperCAmelCase__							:				List[str]   				=  cva.imread(__snake_case    )
			else:
						UpperCAmelCase__							:				List[Any]   				=  get_image_from_url(__snake_case    )
						assert img is not None, F'''could not connect to: {im}'''
			UpperCAmelCase__							:				Dict   				=  cva.cvtColor(__snake_case  ,			cva.COLOR_BGR2RGB    )
			if input_format == "RGB":
						UpperCAmelCase__							:				List[str]   				=  img[:, :, ::-1]
			return img







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				Optional[int]=1    ):
			'''simple docstring'''
			return (images[i : i + batch] for i in range(0  ,			len(__snake_case    )  ,			__snake_case    ))





'''simple docstring'''

import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__     :	Optional[Any]				=			logging.get_logger(__name__)

UpperCamelCase__     :	Tuple				=			{
    'b0': efficientnet.EfficientNetBa,
    'b1': efficientnet.EfficientNetBa,
    'b2': efficientnet.EfficientNetBa,
    'b3': efficientnet.EfficientNetBa,
    'b4': efficientnet.EfficientNetBa,
    'b5': efficientnet.EfficientNetBa,
    'b6': efficientnet.EfficientNetBa,
    'b7': efficientnet.EfficientNetBa,
}

UpperCamelCase__     :	int				=			{
    'b0': {
        'hidden_dim': 1_280,
        'width_coef': 1.0,
        'depth_coef': 1.0,
        'image_size': 224,
        'dropout_rate': 0.2,
        'dw_padding': [],
    },
    'b1': {
        'hidden_dim': 1_280,
        'width_coef': 1.0,
        'depth_coef': 1.1,
        'image_size': 240,
        'dropout_rate': 0.2,
        'dw_padding': [16],
    },
    'b2': {
        'hidden_dim': 1_408,
        'width_coef': 1.1,
        'depth_coef': 1.2,
        'image_size': 260,
        'dropout_rate': 0.3,
        'dw_padding': [5, 8, 16],
    },
    'b3': {
        'hidden_dim': 1_536,
        'width_coef': 1.2,
        'depth_coef': 1.4,
        'image_size': 300,
        'dropout_rate': 0.3,
        'dw_padding': [5, 18],
    },
    'b4': {
        'hidden_dim': 1_792,
        'width_coef': 1.4,
        'depth_coef': 1.8,
        'image_size': 380,
        'dropout_rate': 0.4,
        'dw_padding': [6],
    },
    'b5': {
        'hidden_dim': 2_048,
        'width_coef': 1.6,
        'depth_coef': 2.2,
        'image_size': 456,
        'dropout_rate': 0.4,
        'dw_padding': [13, 27],
    },
    'b6': {
        'hidden_dim': 2_304,
        'width_coef': 1.8,
        'depth_coef': 2.6,
        'image_size': 528,
        'dropout_rate': 0.5,
        'dw_padding': [31],
    },
    'b7': {
        'hidden_dim': 2_560,
        'width_coef': 2.0,
        'depth_coef': 3.1,
        'image_size': 600,
        'dropout_rate': 0.5,
        'dw_padding': [18],
    },
}







def __UpperCamelCase(   _A					:				List[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  EfficientNetConfig()
   UpperCAmelCase__							:				int   				=  CONFIG_MAP[model_name]['''hidden_dim''']
   UpperCAmelCase__							:				Optional[Any]   				=  CONFIG_MAP[model_name]['''width_coef''']
   UpperCAmelCase__							:				int   				=  CONFIG_MAP[model_name]['''depth_coef''']
   UpperCAmelCase__							:				Optional[int]   				=  CONFIG_MAP[model_name]['''image_size''']
   UpperCAmelCase__							:				int   				=  CONFIG_MAP[model_name]['''dropout_rate''']
   UpperCAmelCase__							:				Any   				=  CONFIG_MAP[model_name]['''dw_padding''']

   UpperCAmelCase__							:				Tuple   				=  '''huggingface/label-files'''
   UpperCAmelCase__							:				Dict   				=  '''imagenet-1k-id2label.json'''
   UpperCAmelCase__							:				List[Any]   				=  10_00
   UpperCAmelCase__							:				Union[str, Any]   				=  json.load(open(hf_hub_download(_A  ,			_A  ,			repo_type='''dataset'''    )  ,			'''r'''    )    )
   UpperCAmelCase__							:				Union[str, Any]   				=  {int(_A    ): v for k, v in idalabel.items()}

   UpperCAmelCase__							:				Dict   				=  idalabel
   UpperCAmelCase__							:				Tuple   				=  {v: k for k, v in idalabel.items()}
   return config







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   UpperCAmelCase__							:				List[str]   				=  Image.open(requests.get(_A  ,			stream=_A    ).raw    )
   return im







def __UpperCamelCase(   _A					:				Tuple    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Tuple   				=  CONFIG_MAP[model_name]['''image_size''']
   UpperCAmelCase__							:				Tuple   				=  EfficientNetImageProcessor(
       size={'''height''': size, '''width''': size}  ,			image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]  ,			image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3]  ,			do_center_crop=_A  ,			)
   return preprocessor







def __UpperCamelCase(   _A					:				str    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  [v.split('''_'''    )[0].split('''block'''    )[1] for v in original_param_names if v.startswith('''block'''    )]
   UpperCAmelCase__							:				List[str]   				=  sorted(set(_A    )    )
   UpperCAmelCase__							:				Optional[Any]   				=  len(_A    )
   UpperCAmelCase__							:				int   				=  {b: str(_A    ) for b, i in zip(_A  ,			range(_A    )    )}

   UpperCAmelCase__							:				int   				=  []
   rename_keys.append(('''stem_conv/kernel:0''', '''embeddings.convolution.weight''')    )
   rename_keys.append(('''stem_bn/gamma:0''', '''embeddings.batchnorm.weight''')    )
   rename_keys.append(('''stem_bn/beta:0''', '''embeddings.batchnorm.bias''')    )
   rename_keys.append(('''stem_bn/moving_mean:0''', '''embeddings.batchnorm.running_mean''')    )
   rename_keys.append(('''stem_bn/moving_variance:0''', '''embeddings.batchnorm.running_var''')    )

   for b in block_names:
      UpperCAmelCase__							:				Tuple   				=  block_name_mapping[b]
      rename_keys.append((F'''block{b}_expand_conv/kernel:0''', F'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''')    )
      rename_keys.append((F'''block{b}_expand_bn/gamma:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''')    )
      rename_keys.append((F'''block{b}_expand_bn/beta:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''')    )
      rename_keys.append(
          (F'''block{b}_expand_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''')    )
      rename_keys.append(
          (F'''block{b}_expand_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''')    )
      rename_keys.append(
          (F'''block{b}_dwconv/depthwise_kernel:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''')    )
      rename_keys.append((F'''block{b}_bn/gamma:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''')    )
      rename_keys.append((F'''block{b}_bn/beta:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''')    )
      rename_keys.append(
          (F'''block{b}_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''')    )
      rename_keys.append(
          (F'''block{b}_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''')    )

      rename_keys.append((F'''block{b}_se_reduce/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''')    )
      rename_keys.append((F'''block{b}_se_reduce/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''')    )
      rename_keys.append((F'''block{b}_se_expand/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''')    )
      rename_keys.append((F'''block{b}_se_expand/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''')    )
      rename_keys.append(
          (F'''block{b}_project_conv/kernel:0''', F'''encoder.blocks.{hf_b}.projection.project_conv.weight''')    )
      rename_keys.append((F'''block{b}_project_bn/gamma:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.weight''')    )
      rename_keys.append((F'''block{b}_project_bn/beta:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.bias''')    )
      rename_keys.append(
          (F'''block{b}_project_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''')    )
      rename_keys.append(
          (F'''block{b}_project_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_var''')    )

   rename_keys.append(('''top_conv/kernel:0''', '''encoder.top_conv.weight''')    )
   rename_keys.append(('''top_bn/gamma:0''', '''encoder.top_bn.weight''')    )
   rename_keys.append(('''top_bn/beta:0''', '''encoder.top_bn.bias''')    )
   rename_keys.append(('''top_bn/moving_mean:0''', '''encoder.top_bn.running_mean''')    )
   rename_keys.append(('''top_bn/moving_variance:0''', '''encoder.top_bn.running_var''')    )

   UpperCAmelCase__							:				List[str]   				=  {}
   for item in rename_keys:
      if item[0] in original_param_names:
         UpperCAmelCase__							:				str   				=  '''efficientnet.''' + item[1]

   UpperCAmelCase__							:				List[str]   				=  '''classifier.weight'''
   UpperCAmelCase__							:				Union[str, Any]   				=  '''classifier.bias'''
   return key_mapping







def __UpperCamelCase(   _A					:				Optional[Any]  ,			_A					:				List[Any]  ,			_A					:				Optional[Any]    ):
   '''simple docstring'''
   for key, value in tf_params.items():
      if "normalization" in key:
         continue

      UpperCAmelCase__							:				Tuple   				=  key_mapping[key]
      if "_conv" in key and "kernel" in key:
         UpperCAmelCase__							:				List[str]   				=  torch.from_numpy(_A    ).permute(3  ,			2  ,			0  ,			1    )
      elif "depthwise_kernel" in key:
         UpperCAmelCase__							:				Dict   				=  torch.from_numpy(_A    ).permute(2  ,			3  ,			0  ,			1    )
      elif "kernel" in key:
         UpperCAmelCase__							:				Optional[int]   				=  torch.from_numpy(np.transpose(_A    )    )
      else:
         UpperCAmelCase__							:				str   				=  torch.from_numpy(_A    )

      # Replace HF parameters with original TF model parameters
      assert hf_params[hf_key].shape == new_hf_value.shape
      hf_params[hf_key].copy_(_A    )







@torch.no_grad()
def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				str  ,			_A					:				Union[str, Any]  ,			_A					:				List[str]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  model_classes[model_name](
       include_top=_A  ,			weights='''imagenet'''  ,			input_tensor=_A  ,			input_shape=_A  ,			pooling=_A  ,			classes=10_00  ,			classifier_activation='''softmax'''  ,			)

   UpperCAmelCase__							:				Dict   				=  original_model.trainable_variables
   UpperCAmelCase__							:				Optional[Any]   				=  original_model.non_trainable_variables
   UpperCAmelCase__							:				List[str]   				=  {param.name: param.numpy() for param in tf_params}
   for param in tf_non_train_params:
      UpperCAmelCase__							:				Union[str, Any]   				=  param.numpy()
   UpperCAmelCase__							:				Optional[Any]   				=  list(tf_params.keys()    )

   # Load HuggingFace model
   UpperCAmelCase__							:				Union[str, Any]   				=  get_efficientnet_config(_A    )
   UpperCAmelCase__							:				Any   				=  EfficientNetForImageClassification(_A    ).eval()
   UpperCAmelCase__							:				Tuple   				=  hf_model.state_dict()

   # Create src-to-dst parameter name mapping dictionary
   print('''Converting parameters...'''    )
   UpperCAmelCase__							:				Tuple   				=  rename_keys(_A    )
   replace_params(_A  ,			_A  ,			_A    )

   # Initialize preprocessor and preprocess input image
   UpperCAmelCase__							:				List[Any]   				=  convert_image_processor(_A    )
   UpperCAmelCase__							:				List[Any]   				=  preprocessor(images=prepare_img()  ,			return_tensors='''pt'''    )

   # HF model inference
   hf_model.eval()
   with torch.no_grad():
      UpperCAmelCase__							:				Optional[Any]   				=  hf_model(**_A    )
   UpperCAmelCase__							:				Union[str, Any]   				=  outputs.logits.detach().numpy()

   # Original model inference
   UpperCAmelCase__							:				str   				=  False
   UpperCAmelCase__							:				int   				=  CONFIG_MAP[model_name]['''image_size''']
   UpperCAmelCase__							:				List[str]   				=  prepare_img().resize((image_size, image_size)  ,			resample=PIL.Image.NEAREST    )
   UpperCAmelCase__							:				List[Any]   				=  image.img_to_array(_A    )
   UpperCAmelCase__							:				List[Any]   				=  np.expand_dims(_A  ,			axis=0    )
   UpperCAmelCase__							:				List[Any]   				=  original_model.predict(_A    )

   # Check whether original and HF model outputs match  -> np.allclose
   assert np.allclose(_A  ,			_A  ,			atol=1e-3    ), "The predicted logits are not the same."
   print('''Model outputs match!'''    )

   if save_model:
      # Create folder to save model
      if not os.path.isdir(_A    ):
         os.mkdir(_A    )
      # Save converted model and image processor
      hf_model.save_pretrained(_A    )
      preprocessor.save_pretrained(_A    )

   if push_to_hub:
      # Push model and image processor to hub
      print(F'''Pushing converted {model_name} to the hub...'''    )
      UpperCAmelCase__							:				Any   				=  F'''efficientnet-{model_name}'''
      preprocessor.push_to_hub(_A    )
      hf_model.push_to_hub(_A    )


if __name__ == "__main__":
  UpperCamelCase__     :	int				=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--model_name',
      default='b0',
      type=str,
      help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default='hf_model',
      type=str,
      help='Path to the output PyTorch model directory.',
  )
  parser.add_argument('--save_model', action='store_true', help='Save model to local')
  parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')

  UpperCamelCase__     :	Union[str, Any]				=			parser.parse_args()
  convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)




