

'''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

						lowerCAmelCase_     =      True
except ImportError:
						lowerCAmelCase_     =      False


try:
						from torch.hub import _get_torch_home

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

lowerCAmelCase_     =      os.path.join(torch_cache_home, "transformers")

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

def      __magic_name__							(			A=OBJECTS       , A=ATTRIBUTES				)      ->		Union[str, Any]:
	snake_case							=					[]
	with open(A				) as f:
		for object in f.readlines():
			vg_classes.append(object.split(','				)[0].lower().strip()				)

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

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







class lowerCamelCase   :
				snake_case_							     = {}

				def __init__(       self,						lowercase_,						lowercase_ = "root",						lowercase_=0 )    ->							List[Any]:
					snake_case							=					name
					snake_case							=					level
					snake_case							=					{}
					for k, v in dictionary.items():
						if v is None:
							raise ValueError()
						snake_case							=					copy.deepcopy(lowercase_ )
						snake_case							=					copy.deepcopy(lowercase_ )
						if isinstance(lowercase_,						lowercase_ ):
							snake_case							=					Config(lowercase_,						name=lowercase_,						level=level + 1 )
						snake_case							=					v
						setattr(self,						lowercase_,						lowercase_ )

					snake_case							=					d

				def __repr__(       self )    ->							Optional[int]:
					return str(list((self._pointer.keys()) ) )

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

				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					return self._pointer

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ )    ->							str:
					with open(F'''{file_name}''',						'w' ) as stream:
						dump(lowercase_,						lowercase_ )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ )    ->							Dict:
					with open(F'''{file_name}''',						'w' ) as stream:
						json.dump(lowercase_,						lowercase_ )

				@staticmethod
				def 					_lowerCamelCase    (       lowercase_ )    ->							List[Any]:
					with open(lowercase_ ) as stream:
						snake_case							=					load(lowercase_,						Loader=lowercase_ )
					return data

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

				@classmethod
				def 					_lowerCamelCase    (       cls,						lowercase_,						**lowercase_ )    ->							List[str]:
					snake_case			,			snake_case							=					cls.get_config_dict(lowercase_,						**lowercase_ )
					return cls(lowercase_ )






				@classmethod
				def 					_lowerCamelCase    (       cls,						lowercase_,						**lowercase_ )    ->							List[Any]:
					snake_case							=					kwargs.pop('cache_dir',						lowercase_ )
					snake_case							=					kwargs.pop('force_download',						lowercase_ )
					snake_case							=					kwargs.pop('resume_download',						lowercase_ )
					snake_case							=					kwargs.pop('proxies',						lowercase_ )
					snake_case							=					kwargs.pop('local_files_only',						lowercase_ )

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

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

						snake_case							=					Config.load_yaml(lowercase_ )

					except EnvironmentError:
						snake_case							=					'Can\'t load config for'
						raise EnvironmentError(lowercase_ )

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

					return Config.load_yaml(lowercase_ ), kwargs

def      __magic_name__							(			A				)      ->		str:
	snake_case							=					torch.load('dump.pt'       , map_location=in_tensor.device				)
	snake_case							=					in_tensor.numpy()
	snake_case							=					out_tensor.numpy()[0]
	print(na.shape       , na[0, 0, :5]				)
	print(na.shape       , na[0, 0, :5]				)
	assert np.allclose(A       , A       , rtol=0.01       , atol=0.1				), (
	    F'''{sum([1 for x in np.isclose(A       , A       , rtol=0.01       , atol=0.1				).flatten() if x is False]				)/len(na.flatten()				)*1_0_0:.4f} %'''
	    " element-wise mismatch"
	)
	raise Exception('tensors are all good'				)

	# Hugging face functions below

def      __magic_name__							(			A				)      ->		List[Any]:
	snake_case							=					urlparse(A				)
	return parsed.scheme in ("http", "https")

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

def      __magic_name__							(			A       , A       , A=None       , A=0       , A=None       , )      ->		List[Any]:
	snake_case							=					'python/{}'.format(sys.version.split()[0]				)
	if _torch_available:
		ua += "; torch/{}".format(torch.__version__				)
	if isinstance(A       , A				):
		ua += "; " + "; ".join('{}/{}'.format(A       , A				) for k, v in user_agent.items()				)
	elif isinstance(A       , A				):
		ua += "; " + user_agent
	snake_case							=					{'user-agent': ua}
	if resume_size > 0:
		snake_case							=					'bytes=%d-' % (resume_size,)
	snake_case							=					requests.get(A       , stream=A       , proxies=A       , headers=A				)
	if response.status_code == 4_1_6:  # Range not satisfiable
		return
	snake_case							=					response.headers.get('Content-Length'				)
	snake_case							=					resume_size + int(A				) if content_length is not None else None
	snake_case							=					tqdm(
	    unit='B'       , unit_scale=A       , total=A       , initial=A       , desc='Downloading'       , )
	for chunk in response.iter_content(chunk_size=1_0_2_4				):
		if chunk:  # filter out keep-alive new chunks
			progress.update(len(A				)				)
			temp_file.write(A				)
	progress.close()

def      __magic_name__							(			A       , A=None       , A=False       , A=None       , A=1_0       , A=False       , A=None       , A=False       , )      ->		Dict:
	if cache_dir is None:
		snake_case							=					TRANSFORMERS_CACHE
	if isinstance(A       , A				):
		snake_case							=					str(A				)

	os.makedirs(A       , exist_ok=A				)

	snake_case							=					None
	if not local_files_only:
		try:
			snake_case							=					requests.head(A       , allow_redirects=A       , proxies=A       , timeout=A				)
			if response.status_code == 2_0_0:
				snake_case							=					response.headers.get('ETag'				)
		except (EnvironmentError, requests.exceptions.Timeout):
			# etag is already None
			pass

	snake_case							=					url_to_filename(A       , A				)

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

	# 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(A				):
			return cache_path
		else:
			snake_case							=					[
			    file
			    for file in fnmatch.filter(os.listdir(A				)       , filename + '.*'				)
			    if not file.endswith('.json'				) and not file.endswith('.lock'				)
			]
			if len(A				) > 0:
				return os.path.join(A       , 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(A				) and not force_download:
		return cache_path

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

		if resume_download:
			snake_case							=					cache_path + '.incomplete'

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

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

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

		os.replace(temp_file.name       , A				)

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

	return cache_path

def      __magic_name__							(			A       , A=None				)      ->		Any:
	snake_case							=					url.encode('utf-8'				)
	snake_case							=					shaaaa(A				)
	snake_case							=					url_hash.hexdigest()

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

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

	return filename

def      __magic_name__							(			A       , A=None       , A=False       , A=None       , A=False       , A=None       , A=False       , A=False       , A=False       , )      ->		Any:
	if cache_dir is None:
		snake_case							=					TRANSFORMERS_CACHE
	if isinstance(A       , A				):
		snake_case							=					str(A				)
	if isinstance(A       , A				):
		snake_case							=					str(A				)

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

	if extract_compressed_file:
		if not is_zipfile(A				) and not tarfile.is_tarfile(A				):
			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/"
		snake_case			,			snake_case							=					os.path.split(A				)
		snake_case							=					output_file.replace('.'       , '-'				) + '-extracted'
		snake_case							=					os.path.join(A       , A				)

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

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

		return output_path_extracted

	return output_path

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

def      __magic_name__							(			A				)      ->		Any:
	snake_case							=					requests.get(A				)
	snake_case							=					np.array(Image.open(BytesIO(response.content				)				)				)
	return img

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

def      __magic_name__							(			)      ->		Any:
	print(F'''{os.path.abspath(os.path.join(A       , os.pardir				)				)}/demo.ipynb'''				)

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


def      __magic_name__							(			A       , A=1				)      ->		Any:
	return (images[i : i + batch] for i in range(0       , len(A				)       , A				))








'''simple docstring'''

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def      __magic_name__							(			A				)      ->		str:
	snake_case							=					args.serialization_dir
	snake_case							=					args.pruning_method
	snake_case							=					args.threshold

	snake_case							=					torch.load(os.path.join(A       , 'pytorch_model.bin'				)       , map_location='cpu'				)

	snake_case							=					0  # Number of remaining (not pruned) params in the encoder
	snake_case							=					0  # Number of params in the encoder

	print('name'.ljust(6_0       , ' '				)       , 'Remaining Weights %'       , 'Remaining Weight'				)
	for name, param in st.items():
		if "encoder" not in name:
			continue

		if "mask_scores" in name:
			if pruning_method == "topK":
				snake_case							=					TopKBinarizer.apply(A       , A				).sum().item()
			elif pruning_method == "sigmoied_threshold":
				snake_case							=					ThresholdBinarizer.apply(A       , A       , A				).sum().item()
			elif pruning_method == "l0":
				snake_case			,			snake_case							=					-0.1, 1.1
				snake_case							=					torch.sigmoid(A				)
				snake_case							=					s * (r - l) + l
				snake_case							=					s_bar.clamp(min=0.0       , max=1.0				)
				snake_case							=					(mask > 0.0).sum().item()
			else:
				raise ValueError('Unknown pruning method'				)
			remaining_count += mask_ones
			print(name.ljust(6_0       , ' '				)       , str(round(1_0_0 * mask_ones / param.numel()       , 3				)				).ljust(2_0       , ' '				)       , str(A				)				)
		else:
			encoder_count += param.numel()
			if "bias" in name or "LayerNorm" in name:
				remaining_count += param.numel()

	print(''				)
	print('Remaining Weights (global) %: '       , 1_0_0 * remaining_count / encoder_count				)


if __name__ == "__main__":
						lowerCAmelCase_     =      argparse.ArgumentParser()

						parser.add_argument(
						    "--pruning_method",
						    choices=["l0", "topK", "sigmoied_threshold"],
						    type=str,
						    required=True,
						    help=(
						        "Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"
						        " pruning)"
						    ),
						)
						parser.add_argument(
						    "--threshold",
						    type=float,
						    required=False,
						    help=(
						        "For `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
						        "For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
						        "Not needed for `l0`"
						    ),
						)
						parser.add_argument(
						    "--serialization_dir",
						    type=str,
						    required=True,
						    help="Folder containing the model that was previously fine-pruned",
						)

						lowerCAmelCase_     =      parser.parse_args()

						main(args)





