
'''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

						lowercase__		    =	True
except ImportError:
						lowercase__		    =	False


try:
						from torch.hub import _get_torch_home

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

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

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




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__=OBJECTS      ,    SCREAMING_SNAKE_CASE__=ATTRIBUTES       )   ->				Any:


		'''simple docstring'''


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

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




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				Any:


		'''simple docstring'''


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






class      snake_case__    :







		"""simple docstring"""

		lowerCamelCase							=		{}



		def __init__(	self							:       List[str]   ,     UpperCamelCase__							:       dict   ,     UpperCamelCase__							:       str = "root"   ,     UpperCamelCase__							:       List[Any]=0	)		->       Tuple:






				"""simple docstring"""







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

				snake_case   :    Optional[int]			       =		d



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






				"""simple docstring"""







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



		def __setattr__(	self							:       List[str]   ,     UpperCamelCase__							:       List[Any]   ,     UpperCamelCase__							:       Dict	)		->       int:






				"""simple docstring"""







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



		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       Dict:






				"""simple docstring"""







				return self._pointer



		def 	lowerCAmelCase							(	self							:       str   ,     UpperCamelCase__							:       Dict   ,     UpperCamelCase__							:       Union[str, Any]	)		->       Tuple:






				"""simple docstring"""







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



		def 	lowerCAmelCase							(	self							:       Optional[Any]   ,     UpperCamelCase__							:       Optional[int]   ,     UpperCamelCase__							:       Optional[Any]	)		->       Any:






				"""simple docstring"""







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



		@staticmethod
		def 	lowerCAmelCase							(	UpperCamelCase__							:       int	)		->       Optional[int]:






				"""simple docstring"""







				with open(UpperCamelCase__	) as stream:
						snake_case   :    Optional[int]			       =		load(UpperCamelCase__   ,     Loader=UpperCamelCase__	)
				return data



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






				"""simple docstring"""







				snake_case   :    List[str]			       =		'''    '''
				if self._name != "root":
						snake_case   :    Union[str, Any]			       =		f'{t * (self._level-1)}{self._name}:\n'
				else:
						snake_case   :    Any			       =		''''''
				snake_case   :    int			       =		self._level
				for i, (k, v) in enumerate(self._pointer.items()	):
						if isinstance(UpperCamelCase__   ,     UpperCamelCase__	):
								r += f'{t * (self._level)}{v}\n'
								self._level += 1
						else:
								r += f'{t * (self._level)}{k}: {v} ({type(UpperCamelCase__	).__name__})\n'
						snake_case   :    Dict			       =		level
				return r[:-1]



		@classmethod
		def 	lowerCAmelCase							(	cls							:       int   ,     UpperCamelCase__							:       str   ,     **UpperCamelCase__							:       Optional[Any]	)		->       Optional[Any]:






				"""simple docstring"""







				snake_case       ,snake_case   :    Optional[Any]			       =		cls.get_config_dict(UpperCamelCase__   ,     **UpperCamelCase__	)
				return cls(UpperCamelCase__	)



		@classmethod
		def 	lowerCAmelCase							(	cls							:       List[str]   ,     UpperCamelCase__							:       str   ,     **UpperCamelCase__							:       List[str]	)		->       int:






				"""simple docstring"""







				snake_case   :    List[str]			       =		kwargs.pop('''cache_dir'''   ,     UpperCamelCase__	)
				snake_case   :    Union[str, Any]			       =		kwargs.pop('''force_download'''   ,     UpperCamelCase__	)
				snake_case   :    Optional[Any]			       =		kwargs.pop('''resume_download'''   ,     UpperCamelCase__	)
				snake_case   :    str			       =		kwargs.pop('''proxies'''   ,     UpperCamelCase__	)
				snake_case   :    Union[str, Any]			       =		kwargs.pop('''local_files_only'''   ,     UpperCamelCase__	)

				if os.path.isdir(UpperCamelCase__	):
						snake_case   :    Dict			       =		os.path.join(UpperCamelCase__   ,     UpperCamelCase__	)
				elif os.path.isfile(UpperCamelCase__	) or is_remote_url(UpperCamelCase__	):
						snake_case   :    Tuple			       =		pretrained_model_name_or_path
				else:
						snake_case   :    List[str]			       =		hf_bucket_url(UpperCamelCase__   ,     filename=UpperCamelCase__   ,     use_cdn=UpperCamelCase__	)

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

						snake_case   :    str			       =		Config.load_yaml(UpperCamelCase__	)

				except EnvironmentError:
						snake_case   :    List[Any]			       =		'''Can\'t load config for'''
						raise EnvironmentError(UpperCamelCase__	)

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

				return Config.load_yaml(UpperCamelCase__	), kwargs







def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				Dict:


		'''simple docstring'''


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

		# Hugging face functions below




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				List[str]:


		'''simple docstring'''


		snake_case   :    Any			       =		urlparse(SCREAMING_SNAKE_CASE__       )
		return parsed.scheme in ("http", "https")




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__=True       )   ->				str:


		'''simple docstring'''


		snake_case   :    Dict			       =		CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
		snake_case   :    Dict			       =		'''/''' not in model_id
		if legacy_format:
				return F'{endpoint}/{model_id}-{filename}'
		else:
				return F'{endpoint}/{model_id}/{filename}'




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__=None      ,    SCREAMING_SNAKE_CASE__=0      ,    SCREAMING_SNAKE_CASE__=None      ,    )   ->				Tuple:


		'''simple docstring'''


		snake_case   :    Tuple			       =		'''python/{}'''.format(sys.version.split()[0]       )
		if _torch_available:
				ua += "; torch/{}".format(torch.__version__       )
		if isinstance(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       ):
				ua += "; " + "; ".join('''{}/{}'''.format(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       ) for k, v in user_agent.items()       )
		elif isinstance(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       ):
				ua += "; " + user_agent
		snake_case   :    Optional[int]			       =		{'''user-agent''': ua}
		if resume_size > 0:
				snake_case   :    Union[str, Any]			       =		'''bytes=%d-''' % (resume_size,)
		snake_case   :    str			       =		requests.get(SCREAMING_SNAKE_CASE__      ,    stream=SCREAMING_SNAKE_CASE__      ,    proxies=SCREAMING_SNAKE_CASE__      ,    headers=SCREAMING_SNAKE_CASE__       )
		if response.status_code == 416:  # Range not satisfiable
				return
		snake_case   :    Any			       =		response.headers.get('''Content-Length'''       )
		snake_case   :    Any			       =		resume_size + int(SCREAMING_SNAKE_CASE__       ) if content_length is not None else None
		snake_case   :    List[Any]			       =		tqdm(
		    unit='''B'''      ,    unit_scale=SCREAMING_SNAKE_CASE__      ,    total=SCREAMING_SNAKE_CASE__      ,    initial=SCREAMING_SNAKE_CASE__      ,    desc='''Downloading'''      ,    )
		for chunk in response.iter_content(chunk_size=1024       ):
				if chunk:  # filter out keep-alive new chunks
						progress.update(len(SCREAMING_SNAKE_CASE__       )       )
						temp_file.write(SCREAMING_SNAKE_CASE__       )
		progress.close()




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__=None      ,    SCREAMING_SNAKE_CASE__=False      ,    SCREAMING_SNAKE_CASE__=None      ,    SCREAMING_SNAKE_CASE__=10      ,    SCREAMING_SNAKE_CASE__=False      ,    SCREAMING_SNAKE_CASE__=None      ,    SCREAMING_SNAKE_CASE__=False      ,    )   ->				Tuple:


		'''simple docstring'''


		if cache_dir is None:
				snake_case   :    str			       =		TRANSFORMERS_CACHE
		if isinstance(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       ):
				snake_case   :    List[str]			       =		str(SCREAMING_SNAKE_CASE__       )

		os.makedirs(SCREAMING_SNAKE_CASE__      ,    exist_ok=SCREAMING_SNAKE_CASE__       )

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

		snake_case   :    Any			       =		url_to_filename(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )

		# get cache path to put the file
		snake_case   :    List[str]			       =		os.path.join(SCREAMING_SNAKE_CASE__      ,    SCREAMING_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(SCREAMING_SNAKE_CASE__       ):
						return cache_path
				else:
						snake_case   :    Optional[int]			       =		[
						    file
						    for file in fnmatch.filter(os.listdir(SCREAMING_SNAKE_CASE__       )      ,    filename + '''.*'''       )
						    if not file.endswith('''.json'''       ) and not file.endswith('''.lock'''       )
						]
						if len(SCREAMING_SNAKE_CASE__       ) > 0:
								return os.path.join(SCREAMING_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(SCREAMING_SNAKE_CASE__       ) and not force_download:
				return cache_path

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

				if resume_download:
						snake_case   :    List[Any]			       =		cache_path + '''.incomplete'''

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

						snake_case   :    int			       =		_resumable_file_manager
						if os.path.exists(SCREAMING_SNAKE_CASE__       ):
								snake_case   :    List[str]			       =		os.stat(SCREAMING_SNAKE_CASE__       ).st_size
						else:
								snake_case   :    str			       =		0
				else:
						snake_case   :    List[Any]			       =		partial(tempfile.NamedTemporaryFile      ,    dir=SCREAMING_SNAKE_CASE__      ,    delete=SCREAMING_SNAKE_CASE__       )
						snake_case   :    Dict			       =		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'''      ,    SCREAMING_SNAKE_CASE__      ,    temp_file.name      ,    )

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

				os.replace(temp_file.name      ,    SCREAMING_SNAKE_CASE__       )

				snake_case   :    int			       =		{'''url''': url, '''etag''': etag}
				snake_case   :    List[Any]			       =		cache_path + '''.json'''
				with open(SCREAMING_SNAKE_CASE__      ,    '''w'''       ) as meta_file:
						json.dump(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )

		return cache_path




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__=None       )   ->				str:


		'''simple docstring'''


		snake_case   :    Any			       =		url.encode('''utf-8'''       )
		snake_case   :    Union[str, Any]			       =		shaaaa(SCREAMING_SNAKE_CASE__       )
		snake_case   :    List[str]			       =		url_hash.hexdigest()

		if etag:
				snake_case   :    Dict			       =		etag.encode('''utf-8'''       )
				snake_case   :    List[Any]			       =		shaaaa(SCREAMING_SNAKE_CASE__       )
				filename += "." + etag_hash.hexdigest()

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

		return filename




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__=None      ,    SCREAMING_SNAKE_CASE__=False      ,    SCREAMING_SNAKE_CASE__=None      ,    SCREAMING_SNAKE_CASE__=False      ,    SCREAMING_SNAKE_CASE__=None      ,    SCREAMING_SNAKE_CASE__=False      ,    SCREAMING_SNAKE_CASE__=False      ,    SCREAMING_SNAKE_CASE__=False      ,    )   ->				Union[str, Any]:


		'''simple docstring'''


		if cache_dir is None:
				snake_case   :    Optional[int]			       =		TRANSFORMERS_CACHE
		if isinstance(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       ):
				snake_case   :    str			       =		str(SCREAMING_SNAKE_CASE__       )
		if isinstance(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       ):
				snake_case   :    str			       =		str(SCREAMING_SNAKE_CASE__       )

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

		if extract_compressed_file:
				if not is_zipfile(SCREAMING_SNAKE_CASE__       ) and not tarfile.is_tarfile(SCREAMING_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/"
				snake_case       ,snake_case   :    Union[str, Any]			       =		os.path.split(SCREAMING_SNAKE_CASE__       )
				snake_case   :    Tuple			       =		output_file.replace('''.'''      ,    '''-'''       ) + '''-extracted'''
				snake_case   :    Tuple			       =		os.path.join(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )

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

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

				return output_path_extracted

		return output_path




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__=","       )   ->				Optional[Any]:


		'''simple docstring'''


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




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				Optional[Any]:


		'''simple docstring'''


		snake_case   :    Tuple			       =		requests.get(SCREAMING_SNAKE_CASE__       )
		snake_case   :    Optional[int]			       =		np.array(Image.open(BytesIO(response.content       )       )       )
		return img




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				int:


		'''simple docstring'''


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




def 					_UpperCamelCase		(    )   ->				str:


		'''simple docstring'''


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




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__="RGB"       )   ->				List[Any]:


		'''simple docstring'''


		assert isinstance(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )
		if os.path.isfile(SCREAMING_SNAKE_CASE__       ):
				snake_case   :    List[Any]			       =		cva.imread(SCREAMING_SNAKE_CASE__       )
		else:
				snake_case   :    Tuple			       =		get_image_from_url(SCREAMING_SNAKE_CASE__       )
				assert img is not None, F'could not connect to: {im}'
		snake_case   :    int			       =		cva.cvtColor(SCREAMING_SNAKE_CASE__      ,    cva.COLOR_BGR2RGB       )
		if input_format == "RGB":
				snake_case   :    Optional[Any]			       =		img[:, :, ::-1]
		return img




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__=1       )   ->				Optional[Any]:


		'''simple docstring'''


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




import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
lowerCamelCase_  :       int					      =     logging.get_logger(__name__)

lowerCamelCase_  :       Optional[int]					      =     {
    """post_extract_proj""": """feature_projection.projection""",
    """models.0.layer_norm""": """feature_projection.layer_norm""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
lowerCamelCase_  :       Any					      =     [
    """lm_head""",
]







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		List[Any]:
				for attribute in key.split("""."""  ):
								UpperCamelCase_:							List[str]		=  getattr(lowerCamelCase   ,			lowerCamelCase  )

				if weight_type is not None:
								UpperCamelCase_:							Tuple		=  getattr(lowerCamelCase   ,			lowerCamelCase  ).shape
				else:
								UpperCamelCase_:							List[Any]		=  hf_pointer.shape

				if hf_shape != value.shape:
								raise ValueError(
								    F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
								    F''' {value.shape} for {full_name}'''  )

				if weight_type == "weight":
								UpperCamelCase_:							str		=  value
				elif weight_type == "weight_g":
								UpperCamelCase_:							List[str]		=  value
				elif weight_type == "weight_v":
								UpperCamelCase_:							Tuple		=  value
				elif weight_type == "bias":
								UpperCamelCase_:							Any		=  value
				else:
								UpperCamelCase_:							Dict		=  value

				logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		Union[str, Any]:
				UpperCamelCase_:							Optional[int]		=  []
				UpperCamelCase_:							Tuple		=  fairseq_model.state_dict()

				if not is_headless:
								UpperCamelCase_:							Dict		=  hf_model.dataavec_audio.feature_extractor
								UpperCamelCase_:							Tuple		=  hf_model.dataavec_audio.encoder.pos_conv_embed

				else:
								UpperCamelCase_:							Tuple		=  hf_model.feature_extractor
								UpperCamelCase_:							int		=  hf_model.encoder.pos_conv_embed

				for name, value in fairseq_dict.items():
								UpperCamelCase_:							Union[str, Any]		=  False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			)
												UpperCamelCase_:							Optional[Any]		=  True
								elif "pos_conv" in name:
												load_pos_conv_layer(
												    lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			)
												UpperCamelCase_:							Optional[Any]		=  True
								else:
												for key, mapped_key in MAPPING.items():
																if not is_headless:
																				UpperCamelCase_:							List[str]		=  """data2vec_audio.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																if key in name or key.split("""w2v_model."""  )[-1] == name.split("""."""  )[0]:
																				UpperCamelCase_:							str		=  True
																				if "*" in mapped_key:
																								UpperCamelCase_:							Any		=  name.split(lowerCamelCase  )[0].split("""."""  )[-2]
																								UpperCamelCase_:							int		=  mapped_key.replace("""*"""   ,			lowerCamelCase  )
																				if "weight_g" in name:
																								UpperCamelCase_:							Tuple		=  """weight_g"""
																				elif "weight_v" in name:
																								UpperCamelCase_:							List[Any]		=  """weight_v"""
																				elif "bias" in name:
																								UpperCamelCase_:							Union[str, Any]		=  """bias"""
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								UpperCamelCase_:							str		=  """weight"""
																				else:
																								UpperCamelCase_:							Tuple		=  None
																				set_recursively(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )
																continue
								if not is_used:
												unused_weights.append(lowerCamelCase  )

				logger.warning(F'''Unused weights: {unused_weights}'''  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase  )     ->		Dict:
				UpperCamelCase_:							Union[str, Any]		=  path.split("""."""  )
				return reduce(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		str:
				UpperCamelCase_:							Union[str, Any]		=  access_by_string(lowerCamelCase   ,			lowerCamelCase  )
				UpperCamelCase_:							str		=  hf_weight.data

				if fsq_value.shape != hf_value.shape:
								raise ValueError(F'''{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'''  )
				UpperCamelCase_:							Optional[int]		=  fsq_value
				logger.info(F'''{full_name} was correctly initialized from {hf_weight_path}.'''  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		Optional[int]:
				UpperCamelCase_:							str		=  full_name.split("""conv_layers."""  )[-1]
				UpperCamelCase_:							Union[str, Any]		=  name.split("""."""  )
				UpperCamelCase_:							Union[str, Any]		=  int(items[0]  )
				UpperCamelCase_:							Union[str, Any]		=  int(items[1]  )

				UpperCamelCase_:							Tuple		=  name.split("""."""  )[-1]
				if type_id == 0:
								UpperCamelCase_:							List[Any]		=  """conv"""
				elif type_id == 2:
								UpperCamelCase_:							Tuple		=  """layer_norm"""
				else:
								unused_weights.append(lowerCamelCase  )
								return

				set_weights(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			F'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		Optional[Any]:
				UpperCamelCase_:							Optional[int]		=  full_name.split("""pos_conv."""  )[-1]
				UpperCamelCase_:							Any		=  name.split("""."""  )
				UpperCamelCase_:							List[str]		=  int(items[0]  )
				UpperCamelCase_:							str		=  int(items[1]  )

				UpperCamelCase_:							List[str]		=  name.split("""."""  )[-1]
				if type_id != 0:
								unused_weights.append(lowerCamelCase  )
								return
				else:
								UpperCamelCase_:							Tuple		=  """conv"""

				set_weights(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			F'''layers.{layer_id}.{layer_type}.{weight_type}'''  )







@torch.no_grad()
def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase=None   ,			lowerCamelCase=None   ,			lowerCamelCase=True  )     ->		Dict:
				if config_path is not None:
								UpperCamelCase_:							str		=  DataaVecAudioConfig.from_pretrained(lowerCamelCase  )
				else:
								UpperCamelCase_:							Tuple		=  DataaVecAudioConfig()

				if not is_finetuned:
								# Modify final_proj layer name
								UpperCamelCase_:							Union[str, Any]		=  DataaVecAudioModel(lowerCamelCase  )
								UpperCamelCase_:							Optional[int]		=  os.path.dirname(lowerCamelCase  )

								UpperCamelCase_:							Optional[Any]		=  torch.load(lowerCamelCase  )
								UpperCamelCase_:							List[str]		=  state_dict["""model"""].pop("""final_proj.0.weight"""  )
								UpperCamelCase_:							int		=  state_dict["""model"""].pop("""final_proj.0.bias"""  )
								UpperCamelCase_:							Optional[int]		=  os.path.join(lowerCamelCase   ,			"""converted.pt"""  )
								torch.save(lowerCamelCase   ,			lowerCamelCase  )
				else:
								UpperCamelCase_:							int		=  DataaVecAudioForCTC(lowerCamelCase  )
								UpperCamelCase_:							Optional[int]		=  checkpoint_path

				def load_dataavec(lowerCamelCase  ):
								UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_:							Optional[int]		=  fairseq.checkpoint_utils.load_model_ensemble_and_task([path]  )
								return model[0].eval()

				UpperCamelCase_:							List[Any]		=  load_dataavec(lowerCamelCase  )

				recursively_load_weights(lowerCamelCase   ,			lowerCamelCase   ,			not is_finetuned  )

				UpperCamelCase_:							Tuple		=  WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""  )

				UpperCamelCase_:							Dict		=  load_dataset("""patrickvonplaten/librispeech_asr_dummy"""   ,			"""clean"""   ,			split="""validation"""  )
				UpperCamelCase_:							Optional[Any]		=  [x["""array"""] for x in ds[:4]["""audio"""]]

				UpperCamelCase_:							List[Any]		=  processor(lowerCamelCase   ,			return_tensors="""pt"""   ,			padding=lowerCamelCase  )

				UpperCamelCase_:							str		=  inputs.input_values
				UpperCamelCase_:							Dict		=  inputs.attention_mask
				#    input_values = inputs.input_values[:, :-1]
				#    attention_mask = inputs.attention_mask[:, :-1]

				hf_wavavec.eval()
				model.eval()
				if is_finetuned:
								UpperCamelCase_:							Optional[Any]		=  model(source=lowerCamelCase   ,			padding_mask=(1 - attention_mask)   ,			mask=lowerCamelCase   ,			features_only=lowerCamelCase  )[
								    """encoder_out"""
								].transpose(0   ,			1  )
								UpperCamelCase_:							Tuple		=  hf_wavavec(lowerCamelCase   ,			attention_mask=lowerCamelCase  )["""logits"""]

								UpperCamelCase_:							Optional[Any]		=  torch.argmax(lowerCamelCase   ,			dim=-1  )
								UpperCamelCase_:							Any		=  processor.batch_decode(lowerCamelCase  )

								print(F'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}'''  )
				else:
								UpperCamelCase_:							Optional[Any]		=  model(source=lowerCamelCase   ,			padding_mask=(1 - attention_mask)   ,			mask=lowerCamelCase   ,			features_only=lowerCamelCase  )[
								    """layer_results"""
								][-1][0].transpose(0   ,			1  )
								UpperCamelCase_:							List[str]		=  hf_wavavec(lowerCamelCase   ,			attention_mask=lowerCamelCase  )["""last_hidden_state"""]

				print(our_output.shape   ,			their_output.shape  )
				UpperCamelCase_:							Optional[Any]		=  torch.max(torch.abs(our_output - their_output  )  ).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
				UpperCamelCase_:							List[str]		=  torch.allclose(lowerCamelCase   ,			lowerCamelCase   ,			atol=1E-3  )
				print("""Do both models output the same tensors?"""   ,			"""🔥""" if success else """💩"""  )
				if not success:
								raise Exception("""Something went wRoNg"""  )

				hf_wavavec.save_pretrained(lowerCamelCase  )

				if is_finetuned:
								processor.save_pretrained(lowerCamelCase  )
				else:
								processor.feature_extractor.save_pretrained(lowerCamelCase  )


if __name__ == "__main__":
							lowerCamelCase_  :       str					      =     argparse.ArgumentParser()
							parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
							parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							parser.add_argument(
							    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
							)
							lowerCamelCase_  :       Dict					      =     parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)






