
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

	snake_case_      :					Tuple				      =     True
except ImportError:
	snake_case_      :					Tuple				      =     False


try:
	from torch.hub import _get_torch_home

	snake_case_      :					Optional[Any]				      =     _get_torch_home()
except ImportError:
	snake_case_      :					Dict				      =     os.path.expanduser(
	    os.getenv('TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch'))
	)

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

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



def      __UpperCAmelCase    (  snake_case_							:		Optional[Any]=OBJECTS				,					snake_case_							:		Tuple=ATTRIBUTES				):



	'''simple docstring'''



	UpperCAmelCase:					Union[str, Any] 						=     []
	with open(snake_case_				) as f:
		for object in f.readlines():
			vg_classes.append(object.split(","				)[0].lower().strip()				)

	UpperCAmelCase:					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    (  snake_case_							:		int				):



	'''simple docstring'''



	UpperCAmelCase:					Any 						=     OrderedDict()
	with open(snake_case_				,					"rb"				) as f:
		UpperCAmelCase:					Optional[Any] 						=     pkl.load(snake_case_				)["model"]
	for k in copy.deepcopy(list(ckp.keys()				)				):
		UpperCAmelCase:					List[str] 						=     ckp.pop(snake_case_				)
		if isinstance(snake_case_				,					np.ndarray				):
			UpperCAmelCase:					Any 						=     torch.tensor(snake_case_				)
		else:
			assert isinstance(snake_case_				,					torch.tensor				), type(snake_case_				)
		UpperCAmelCase:					Optional[Any] 						=     v
	return r





class 		__lowerCamelCase     :
							lowerCamelCase__:				Optional[Any]      =							{}



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






								"""simple docstring"""



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

								UpperCAmelCase:					int 						=     d



							def __repr__(     self							)						->     Any:






								"""simple docstring"""



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



							def __setattr__(     self	,				__snake_case	,				__snake_case							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					int 						=     val
								UpperCAmelCase:					Optional[Any] 						=     val
								UpperCAmelCase:					List[Any] 						=     key.split("."							)
								UpperCAmelCase:					List[str] 						=     len(__snake_case							) - 1
								UpperCAmelCase:					List[Any] 						=     self._pointer
								if len(__snake_case							) > 1:
									for i, l in enumerate(__snake_case							):
										if hasattr(self	,				__snake_case							) and isinstance(getattr(self	,				__snake_case							)	,				__snake_case							):
											setattr(getattr(self	,				__snake_case							)	,				".".join(levels[i:]							)	,				__snake_case							)
										if l == last_level:
											UpperCAmelCase:					Dict 						=     val
										else:
											UpperCAmelCase:					List[Any] 						=     pointer[l]



							def     A__ (     self							)						->     str:






								"""simple docstring"""



								return self._pointer



							def     A__ (     self	,				__snake_case	,				__snake_case							)						->     List[Any]:






								"""simple docstring"""



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



							def     A__ (     self	,				__snake_case	,				__snake_case							)						->     List[Any]:






								"""simple docstring"""



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



							@staticmethod
							def     A__ (     __snake_case							)						->     Optional[int]:






								"""simple docstring"""



								with open(__snake_case							) as stream:
									UpperCAmelCase:					Union[str, Any] 						=     load(__snake_case	,				Loader=__snake_case							)
								return data



							def __str__(     self							)						->     Dict:






								"""simple docstring"""



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



							@classmethod
							def     A__ (     cls	,				__snake_case	,				**__snake_case							)						->     int:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Union[str, Any] 						=     cls.get_config_dict(__snake_case	,				**__snake_case							)
								return cls(__snake_case							)




							@classmethod
							def     A__ (     cls	,				__snake_case	,				**__snake_case							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					List[Any] 						=     kwargs.pop("cache_dir"	,				__snake_case							)
								UpperCAmelCase:					List[Any] 						=     kwargs.pop("force_download"	,				__snake_case							)
								UpperCAmelCase:					List[Any] 						=     kwargs.pop("resume_download"	,				__snake_case							)
								UpperCAmelCase:					str 						=     kwargs.pop("proxies"	,				__snake_case							)
								UpperCAmelCase:					str 						=     kwargs.pop("local_files_only"	,				__snake_case							)

								if os.path.isdir(__snake_case							):
									UpperCAmelCase:					Union[str, Any] 						=     os.path.join(__snake_case	,				__snake_case							)
								elif os.path.isfile(__snake_case							) or is_remote_url(__snake_case							):
									UpperCAmelCase:					Optional[Any] 						=     pretrained_model_name_or_path
								else:
									UpperCAmelCase:					List[Any] 						=     hf_bucket_url(__snake_case	,				filename=__snake_case	,				use_cdn=__snake_case							)

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

									UpperCAmelCase:					int 						=     Config.load_yaml(__snake_case							)

								except EnvironmentError:
									UpperCAmelCase:					Optional[Any] 						=     "Can't load config for"
									raise EnvironmentError(__snake_case							)

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

								return Config.load_yaml(__snake_case							), kwargs



def      __UpperCAmelCase    (  snake_case_							:		List[str]				):



	'''simple docstring'''



	UpperCAmelCase:					int 						=     torch.load("dump.pt"				,					map_location=in_tensor.device				)
	UpperCAmelCase:					List[str] 						=     in_tensor.numpy()
	UpperCAmelCase:					Dict 						=     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()				)*1_0_0:.4f} %'
	    " element-wise mismatch"
	)
	raise Exception("tensors are all good"				)

	# Hugging face functions below



def      __UpperCAmelCase    (  snake_case_							:		Optional[int]				):



	'''simple docstring'''



	UpperCAmelCase:					Dict 						=     urlparse(snake_case_				)
	return parsed.scheme in ("http", "https")



def      __UpperCAmelCase    (  snake_case_							:		str				,					snake_case_							:		str				,					snake_case_							:		Dict=True				):



	'''simple docstring'''



	UpperCAmelCase:					Optional[int] 						=     CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
	UpperCAmelCase:					Union[str, Any] 						=     "/" not in model_id
	if legacy_format:
		return F'{endpoint}/{model_id}-{filename}'
	else:
		return F'{endpoint}/{model_id}/{filename}'



def      __UpperCAmelCase    (  snake_case_							:		Optional[Any]				,					snake_case_							:		int				,					snake_case_							:		Dict=None				,					snake_case_							:		Tuple=0				,					snake_case_							:		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:					List[Any] 						=     {"user-agent": ua}
	if resume_size > 0:
		UpperCAmelCase:					List[Any] 						=     "bytes=%d-" % (resume_size,)
	UpperCAmelCase:					Dict 						=     requests.get(snake_case_				,					stream=snake_case_				,					proxies=snake_case_				,					headers=snake_case_				)
	if response.status_code == 4_1_6:  # Range not satisfiable
		return
	UpperCAmelCase:					Union[str, Any] 						=     response.headers.get("Content-Length"				)
	UpperCAmelCase:					List[str] 						=     resume_size + int(snake_case_				) if content_length is not None else None
	UpperCAmelCase:					List[str] 						=     tqdm(
	    unit="B"				,					unit_scale=snake_case_				,					total=snake_case_				,					initial=snake_case_				,					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(snake_case_				)				)
			temp_file.write(snake_case_				)
	progress.close()



def      __UpperCAmelCase    (  snake_case_							:		Optional[Any]				,					snake_case_							:		Tuple=None				,					snake_case_							:		List[str]=False				,					snake_case_							:		Tuple=None				,					snake_case_							:		Tuple=1_0				,					snake_case_							:		Tuple=False				,					snake_case_							:		int=None				,					snake_case_							:		Optional[Any]=False				,					):



	'''simple docstring'''



	if cache_dir is None:
		UpperCAmelCase:					Dict 						=     TRANSFORMERS_CACHE
	if isinstance(snake_case_				,					snake_case_				):
		UpperCAmelCase:					int 						=     str(snake_case_				)

	os.makedirs(snake_case_				,					exist_ok=snake_case_				)

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

	UpperCAmelCase:					Optional[int] 						=     url_to_filename(snake_case_				,					snake_case_				)

	# get cache path to put the file
	UpperCAmelCase:					Any 						=     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:					List[Any] 						=     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:					List[str] 						=     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:					Optional[int] 						=     os.stat(snake_case_				).st_size
			else:
				UpperCAmelCase:					Dict 						=     0
		else:
			UpperCAmelCase:					Tuple 						=     partial(tempfile.NamedTemporaryFile				,					dir=snake_case_				,					delete=snake_case_				)
			UpperCAmelCase:					str 						=     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:					List[str] 						=     {"url": url, "etag": etag}
		UpperCAmelCase:					Union[str, Any] 						=     cache_path + ".json"
		with open(snake_case_				,					"w"				) as meta_file:
			json.dump(snake_case_				,					snake_case_				)

	return cache_path



def      __UpperCAmelCase    (  snake_case_							:		Union[str, Any]				,					snake_case_							:		Union[str, Any]=None				):



	'''simple docstring'''



	UpperCAmelCase:					List[Any] 						=     url.encode("utf-8"				)
	UpperCAmelCase:					Optional[int] 						=     shaaaa(snake_case_				)
	UpperCAmelCase:					Optional[Any] 						=     url_hash.hexdigest()

	if etag:
		UpperCAmelCase:					List[str] 						=     etag.encode("utf-8"				)
		UpperCAmelCase:					List[str] 						=     shaaaa(snake_case_				)
		filename += "." + etag_hash.hexdigest()

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

	return filename



def      __UpperCAmelCase    (  snake_case_							:		Union[str, Any]				,					snake_case_							:		Union[str, Any]=None				,					snake_case_							:		Tuple=False				,					snake_case_							:		Optional[Any]=None				,					snake_case_							:		str=False				,					snake_case_							:		List[str]=None				,					snake_case_							:		Any=False				,					snake_case_							:		Tuple=False				,					snake_case_							:		List[str]=False				,					):



	'''simple docstring'''



	if cache_dir is None:
		UpperCAmelCase:					Tuple 						=     TRANSFORMERS_CACHE
	if isinstance(snake_case_				,					snake_case_				):
		UpperCAmelCase:					Tuple 						=     str(snake_case_				)
	if isinstance(snake_case_				,					snake_case_				):
		UpperCAmelCase:					Optional[int] 						=     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:					str 						=     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:					List[Any] 						=     os.path.split(snake_case_				)
		UpperCAmelCase:					Union[str, Any] 						=     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:					Any 						=     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:					Tuple 						=     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    (  snake_case_							:		Optional[int]				,					snake_case_							:		Union[str, Any]=","				):



	'''simple docstring'''



	assert isinstance(snake_case_				,					snake_case_				)
	if os.path.isfile(snake_case_				):
		with open(snake_case_				) as f:
			UpperCAmelCase:					Dict 						=     eval(f.read()				)
	else:
		UpperCAmelCase:					List[Any] 						=     requests.get(snake_case_				)
		try:
			UpperCAmelCase:					Optional[int] 						=     requests.json()
		except Exception:
			UpperCAmelCase:					Optional[Any] 						=     req.content.decode()
			assert data is not None, "could not connect"
			try:
				UpperCAmelCase:					Tuple 						=     eval(snake_case_				)
			except Exception:
				UpperCAmelCase:					Dict 						=     data.split("\n"				)
		req.close()
	return data



def      __UpperCAmelCase    (  snake_case_							:		Optional[Any]				):



	'''simple docstring'''



	UpperCAmelCase:					List[str] 						=     requests.get(snake_case_				)
	UpperCAmelCase:					Union[str, Any] 						=     np.array(Image.open(BytesIO(response.content				)				)				)
	return img



def      __UpperCAmelCase    (  snake_case_							:		Tuple				):



	'''simple docstring'''



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



def      __UpperCAmelCase    (  ):



	'''simple docstring'''



	print(F'{os.path.abspath(os.path.join(snake_case_				,					os.pardir				)				)}/demo.ipynb'				)



def      __UpperCAmelCase    (  snake_case_							:		List[str]				,					snake_case_							:		Optional[Any]="RGB"				):



	'''simple docstring'''



	assert isinstance(snake_case_				,					snake_case_				)
	if os.path.isfile(snake_case_				):
		UpperCAmelCase:					Dict 						=     cva.imread(snake_case_				)
	else:
		UpperCAmelCase:					str 						=     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:					str 						=     img[:, :, ::-1]
	return img



def      __UpperCAmelCase    (  snake_case_							:		str				,					snake_case_							:		List[str]=1				):



	'''simple docstring'''



	return (images[i : i + batch] for i in range(0				,					len(snake_case_				)				,					snake_case_				))


import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
	from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
	raise ImportError('You need to install the version of fairseq from the Mega repo!')





class 		__lowerCamelCase     (   nn.Module		):



							def __init__(     self	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Dict:






								"""simple docstring"""



								super().__init__()
								UpperCAmelCase:					int 						=     mega_args
								UpperCAmelCase:					Union[str, Any] 						=     nn.Embedding(__snake_case	,				self.mega_args.encoder_embed_dim							)
								UpperCAmelCase:					List[str] 						=     nn.ModuleList([MegaEncoderLayer(self.mega_args							) for _ in range(__snake_case							)]							)
								UpperCAmelCase:					List[str] 						=     depth




							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case=True	,				__snake_case=0							)						->     List[str]:






								"""simple docstring"""



								if batch_first:
									UpperCAmelCase:					int 						=     input_ids.T

								# to make things more confusing, Mega expects the attention mask to
								# be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
								# which is the opposite of what HF returns
								if ignore_mask_value == 0:
									UpperCAmelCase:					Union[str, Any] 						=     1 - attention_mask

								# get token embeddings from IDs
								UpperCAmelCase:					Dict 						=     self.embedding_layer(__snake_case							)

								# pass through the Mega layers
								# input is (time, batch, encoder dim) and output is the same
								for encoder in self.encoders:
									UpperCAmelCase:					Any 						=     encoder(__snake_case	,				__snake_case							)

								# return according to the shape specified
								if batch_first:
									# (T, B, H) --> (B, T, H)
									return torch.transpose(__snake_case	,				0	,				1							)
								else:
									return embeds





class 		__lowerCamelCase     (   nn.Module		):



							def __init__(     self	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								super().__init__()
								UpperCAmelCase:					int 						=     MegaLM(__snake_case	,				__snake_case	,				__snake_case							)
								UpperCAmelCase:					Tuple 						=     nn.Linear(mega_args.encoder_embed_dim	,				__snake_case							)
								UpperCAmelCase:					List[str] 						=     nn.Dropout(p=0.1							)




							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case=True	,				__snake_case=0							)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     self.mega(__snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)
								return self.mlm_head(self.dropout(__snake_case							)							)



def      __UpperCAmelCase    (  snake_case_							:		Optional[Any]				,					snake_case_							:		Dict				,					snake_case_							:		Tuple				):



	'''simple docstring'''



	with open(os.path.join(snake_case_				,					"model_args.pkl"				)				,					"rb"				) as f:
		UpperCAmelCase:					Union[str, Any] 						=     pkl.load(snake_case_				)

	# load the original encoder
	UpperCAmelCase:					Tuple 						=     OriginalMegaForMaskedLM(**snake_case_				).eval()

	# load its weights
	print(
	    "Original Mega encoder:"				,					original_mlm.mega.load_state_dict(
	        torch.load(os.path.join(snake_case_				,					"encoder_weights.pt"				)				,					map_location="cpu"				)				)				,					)
	print(
	    "Original Mega MLM layer:"				,					original_mlm.mlm_head.load_state_dict(
	        torch.load(os.path.join(snake_case_				,					"mlm_head_weights.pt"				)				,					map_location="cpu"				)				)				,					)

	# create a new config from the old one
	UpperCAmelCase:					int 						=     MegaConfig(
	    num_hidden_layers=mega_original_args["depth"]				,					vocab_size=mega_original_args["vocab_size"]				,					hidden_size=mega_original_args["mega_args"].encoder_embed_dim				,					shared_representation_size=mega_original_args["mega_args"].encoder_z_dim				,					intermediate_size=mega_original_args["mega_args"].encoder_hidden_dim				,					ema_projection_size=mega_original_args["mega_args"].encoder_n_dim				,					dropout_prob=mega_original_args["mega_args"].dropout				,					attention_probs_dropout_prob=mega_original_args["mega_args"].attention_dropout				,					hidden_dropout_prob=mega_original_args["mega_args"].hidden_dropout				,					activation=mega_original_args["mega_args"].activation_fn				,					attention_activation=mega_original_args["mega_args"].attention_activation_fn				,					bidirectional=mega_original_args["mega_args"].bidirectional				,					use_chunking=mega_original_args["mega_args"].encoder_chunk_size > 0				,					chunk_size=mega_original_args["mega_args"].encoder_chunk_size				,					truncation=mega_original_args["mega_args"].truncation_length				,					normalization_type=mega_original_args["mega_args"].normalization_type				,					normalize_before_mega=snake_case_				,					norm_affine=snake_case_				,					use_feature_dropout=mega_original_args["mega_args"].feature_dropout				,					relative_positional_bias=mega_original_args["mega_args"].rel_pos_bias				,					max_positions=mega_original_args["mega_args"].max_source_positions				,					nffn_hidden_size=mega_original_args["mega_args"].encoder_ffn_embed_dim				,					normalize_before_ffn=mega_original_args["mega_args"].normalize_before				,					nffn_activation_dropout_prob=0.0				,					add_token_type_embeddings=snake_case_				,					add_lm_hidden_dense_layer=snake_case_				,					)

	UpperCAmelCase:					Union[str, Any] 						=     MegaForMaskedLM(snake_case_				).eval()

	# the originl checkpoint just uses nn.Embedding for the word embeddings
	# we use a wrapper module for embeddings to add support for positional embeddings
	UpperCAmelCase:					str 						=     original_mlm.mega.embedding_layer.weight

	# modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
	# ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
	# also renaming previously confusing parameter names
	UpperCAmelCase:					List[str] 						=     original_mlm.mega.encoders.state_dict()
	UpperCAmelCase:					Optional[Any] 						=     {}
	for module_name in original_state_dict.keys():
		UpperCAmelCase:					Optional[Any] 						=     None
		# have to handle gamma, beta, and alpha differently due to their use
		# in multiple modules within the original repository;
		# beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
		# the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
		if "beta" in module_name:
			# EMA sub-layers were always called "move" in the original repo
			if "move.beta" in module_name:
				UpperCAmelCase:					Any 						=     module_name.replace("move.beta"				,					"ema_gate.ema_expansion_matrix"				)
			elif "mega_layer.beta" in module_name:
				UpperCAmelCase:					Optional[int] 						=     module_name.replace("beta"				,					"qk_bias"				)
			else:
				UpperCAmelCase:					List[str] 						=     module_name.replace("beta"				,					"b_param"				)
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
		elif "gamma" in module_name:
			if "move.gamma" in module_name:
				UpperCAmelCase:					Union[str, Any] 						=     module_name.replace("move.gamma"				,					"ema_gate.kernel_projection_matrix"				)
			elif "mega_layer.gamma" in module_name:
				UpperCAmelCase:					Any 						=     module_name.replace("gamma"				,					"qk_weight"				)
			else:
				UpperCAmelCase:					List[Any] 						=     module_name.replace("gamma"				,					"g_param"				)
        # alpha is used in EMA and positional bias; renaming to improve readability
		elif "move.alpha" in module_name:
			UpperCAmelCase:					Tuple 						=     module_name.replace("move.alpha"				,					"ema_gate.decay_factor"				)
		# delta is only used in EMA; renaming to improve readability
		elif "move.delta" in module_name:
			UpperCAmelCase:					Any 						=     module_name.replace("move.delta"				,					"ema_gate.damping_factor"				)
		# omega is only used in EMA; renaming to improve readability
		elif "omega" in module_name:
			UpperCAmelCase:					Optional[int] 						=     module_name.replace("move.omega"				,					"ema_gate.residual_weight"				)

		if new_module_name:
			UpperCAmelCase:					List[Any] 						=     new_module_name

	if len(snake_case_				) != 0:
		print(F'Renaming these keys: {updated_keys.keys()}'				)
	else:
		print("No need to rename state dict entries"				)
	for old, new in updated_keys.items():
		UpperCAmelCase:					str 						=     original_state_dict.pop(snake_case_				)

	# now attempt to load the state dictionary with updated names
	# note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
	print("HF Mega encoder:"				,					hf_mlm.mega.layers.load_state_dict(snake_case_				)				)

	# load the MLM head weights directly
	print(
	    "HF Mega MLM layer:"				,					hf_mlm.mlm_head.load_state_dict(
	        torch.load(os.path.join(snake_case_				,					"mlm_head_weights.pt"				)				,					map_location="cpu"				)				)				,					)

	# test on a randomly generated input sequence
	UpperCAmelCase:					Optional[int] 						=     torch.randint(0				,					hf_config.vocab_size				,					size=(4, 2_5_6)				)
	UpperCAmelCase:					int 						=     torch.ones_like(snake_case_				)
	# mask a few tokens to make sure masking is applied appropriately :)
	UpperCAmelCase:					int 						=     0

	# run forward passes
	UpperCAmelCase:					Optional[int] 						=     original_mlm(snake_case_				,					snake_case_				,					batch_first=snake_case_				,					ignore_mask_value=0				)
	UpperCAmelCase:					Optional[int] 						=     hf_mlm(snake_case_				,					snake_case_				)[0]

	# print shapes and diff
	print(F'original output {original_output.shape}'				)
	print(F'hf output {hf_output.shape}'				)
	print(F'max diff: {(original_output - hf_output).max()}'				)  # 0.0
	UpperCAmelCase:					Tuple 						=     torch.allclose(snake_case_				,					snake_case_				,					atol=1e-3				)

	if success:
		print("Yay!"				)
		hf_mlm.save_pretrained(snake_case_				)
	else:
		raise RuntimeError(F'Something\'s broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}'				)

	if includes_tokenizer:
		print("Transferring tokenizer"				)
		UpperCAmelCase:					List[str] 						=     AutoTokenizer.from_pretrained(snake_case_				)
		tokenizer.save_pretrained(snake_case_				)


if __name__ == "__main__":
	snake_case_      :					str				      =     argparse.ArgumentParser()

	parser.add_argument(
	    '--pretrained_checkpoint_path',
	    default=None,
	    type=str,
	    required=True,
	    help='Point to the directory containing your model weights using the official Mega repo',
	)

	parser.add_argument(
	    '--output_path', default=None, type=str, required=True, help='Location to save the Hugging Face version'
	)

	parser.add_argument(
	    '--includes_tokenizer',
	    action='store_true',
	    help='Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo',
	)

	snake_case_      :					str				      =     parser.parse_args()

	convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)
