
'''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    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE=OBJECTS				,   _SCREAMING_SNAKE_CASE=ATTRIBUTES ):
							_snake_case  =    []
							with open(_SCREAMING_SNAKE_CASE ) as f:
														for object in f.readlines():
																					vg_classes.append(object.split(""",""" )[0].lower().strip() )

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


class      _lowerCAmelCase       :







							'''simple docstring'''

							lowerCAmelCase_   		=		{}

							def __init__(self    ,    UpperCAmelCase    ,    UpperCAmelCase = "root"    ,    UpperCAmelCase=0    )	->  Union[str, 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(UpperCAmelCase    )
																					_snake_case  =    copy.deepcopy(UpperCAmelCase    )
																					if isinstance(UpperCAmelCase    ,    UpperCAmelCase    ):
																												_snake_case  =    Config(UpperCAmelCase    ,    name=UpperCAmelCase    ,    level=level + 1    )
																					_snake_case  =    v
																					setattr(self    ,    UpperCAmelCase    ,    UpperCAmelCase    )

														_snake_case  =    d

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

							def __setattr__(self    ,    UpperCAmelCase    ,    UpperCAmelCase    )	->  int:
														_snake_case  =    val
														_snake_case  =    val
														_snake_case  =    key.split("""."""    )
														_snake_case  =    len(UpperCAmelCase    ) - 1
														_snake_case  =    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  =    val
																												else:
																																			_snake_case  =    pointer[l]

							def 			lowercase				(self    )	->  int:
														return self._pointer

							def 			lowercase				(self    ,    UpperCAmelCase    ,    UpperCAmelCase    )	->  str:

														with open(f"""{file_name}"""    ,    """w"""    ) as stream:
																					dump(UpperCAmelCase    ,    UpperCAmelCase    )

							def 			lowercase				(self    ,    UpperCAmelCase    ,    UpperCAmelCase    )	->  List[str]:

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

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

														with open(UpperCAmelCase    ) as stream:
																					_snake_case  =    load(UpperCAmelCase    ,    Loader=UpperCAmelCase    )
														return data

							def __str__(self    )	->  Union[str, Any]:
														_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(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  =    level
														return r[:-1]

							@classmethod
							def 			lowercase				(cls    ,    UpperCAmelCase    ,    **UpperCAmelCase    )	->  Any:
														_snake_case,				_snake_case  =    cls.get_config_dict(UpperCAmelCase    ,    **UpperCAmelCase    )
														return cls(UpperCAmelCase    )





							@classmethod
							def 			lowercase				(cls    ,    UpperCAmelCase    ,    **UpperCAmelCase    )	->  Dict:
														_snake_case  =    kwargs.pop("""cache_dir"""    ,    UpperCAmelCase    )
														_snake_case  =    kwargs.pop("""force_download"""    ,    UpperCAmelCase    )
														_snake_case  =    kwargs.pop("""resume_download"""    ,    UpperCAmelCase    )
														_snake_case  =    kwargs.pop("""proxies"""    ,    UpperCAmelCase    )
														_snake_case  =    kwargs.pop("""local_files_only"""    ,    UpperCAmelCase    )

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

														try:
																					# Load from URL or cache if already cached
																					_snake_case  =    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  =    Config.load_yaml(UpperCAmelCase    )

														except EnvironmentError:
																					_snake_case  =    """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    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE ):
							_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(_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    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE ):
							_snake_case  =    urlparse(_SCREAMING_SNAKE_CASE )
							return parsed.scheme in ("http", "https")






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE=True ):
							_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    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE=None				,   _SCREAMING_SNAKE_CASE=0				,   _SCREAMING_SNAKE_CASE=None				,   ):
							_snake_case  =    """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  =    {"""user-agent""": ua}
							if resume_size > 0:
														_snake_case  =    """bytes=%d-""" % (resume_size,)
							_snake_case  =    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  =    response.headers.get("""Content-Length""" )
							_snake_case  =    resume_size + int(_SCREAMING_SNAKE_CASE ) if content_length is not None else None
							_snake_case  =    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    __SCREAMING_SNAKE_CASE (    _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				,   ):






							if cache_dir is None:
														_snake_case  =    TRANSFORMERS_CACHE
							if isinstance(_SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE ):
														_snake_case  =    str(_SCREAMING_SNAKE_CASE )

							os.makedirs(_SCREAMING_SNAKE_CASE				,   exist_ok=_SCREAMING_SNAKE_CASE )

							_snake_case  =    None
							if not local_files_only:
														try:
																					_snake_case  =    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  =    response.headers.get("""ETag""" )
														except (EnvironmentError, requests.exceptions.Timeout):
																					# etag is already None
																					pass

							_snake_case  =    url_to_filename(_SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE )

							# get cache path to put the file
							_snake_case  =    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  =    [
																					    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  =    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  =    cache_path + """.incomplete"""

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

																					_snake_case  =    _resumable_file_manager
																					if os.path.exists(_SCREAMING_SNAKE_CASE ):
																												_snake_case  =    os.stat(_SCREAMING_SNAKE_CASE ).st_size
																					else:
																												_snake_case  =    0
														else:
																					_snake_case  =    partial(tempfile.NamedTemporaryFile				,   dir=_SCREAMING_SNAKE_CASE				,   delete=_SCREAMING_SNAKE_CASE )
																					_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"""				,   _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  =    {"""url""": url, """etag""": etag}
														_snake_case  =    cache_path + """.json"""
														with open(_SCREAMING_SNAKE_CASE				,   """w""" ) as meta_file:
																					json.dump(_SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE )

							return cache_path






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE=None ):
							_snake_case  =    url.encode("""utf-8""" )
							_snake_case  =    shaaaa(_SCREAMING_SNAKE_CASE )
							_snake_case  =    url_hash.hexdigest()

							if etag:
														_snake_case  =    etag.encode("""utf-8""" )
														_snake_case  =    shaaaa(_SCREAMING_SNAKE_CASE )
														filename += "." + etag_hash.hexdigest()

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

							return filename






def    __SCREAMING_SNAKE_CASE (    _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				,   ):






							if cache_dir is None:
														_snake_case  =    TRANSFORMERS_CACHE
							if isinstance(_SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE ):
														_snake_case  =    str(_SCREAMING_SNAKE_CASE )
							if isinstance(_SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE ):
														_snake_case  =    str(_SCREAMING_SNAKE_CASE )

							if is_remote_url(_SCREAMING_SNAKE_CASE ):
														# URL, so get it from the cache (downloading if necessary)
														_snake_case  =    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  =    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  =    os.path.split(_SCREAMING_SNAKE_CASE )
														_snake_case  =    output_file.replace("""."""				,   """-""" ) + """-extracted"""
														_snake_case  =    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  =    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  =    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    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE="," ):
							assert isinstance(_SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE )
							if os.path.isfile(_SCREAMING_SNAKE_CASE ):
														with open(_SCREAMING_SNAKE_CASE ) as f:
																					_snake_case  =    eval(f.read() )
							else:
														_snake_case  =    requests.get(_SCREAMING_SNAKE_CASE )
														try:
																					_snake_case  =    requests.json()
														except Exception:
																					_snake_case  =    req.content.decode()
																					assert data is not None, "could not connect"
																					try:
																												_snake_case  =    eval(_SCREAMING_SNAKE_CASE )
																					except Exception:
																												_snake_case  =    data.split("""\n""" )
														req.close()
							return data






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE ):
							_snake_case  =    requests.get(_SCREAMING_SNAKE_CASE )
							_snake_case  =    np.array(Image.open(BytesIO(response.content ) ) )
							return img






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE ):
							_snake_case  =    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  =    pkl.load(_SCREAMING_SNAKE_CASE )
							_snake_case  =    weights.pop("""model""" )
							_snake_case  =    {}
							for k, v in model.items():
														_snake_case  =    torch.from_numpy(_SCREAMING_SNAKE_CASE )
														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    __SCREAMING_SNAKE_CASE (    ):
							print(f"""{os.path.abspath(os.path.join(_SCREAMING_SNAKE_CASE				,   os.pardir ) )}/demo.ipynb""" )






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE="RGB" ):
							assert isinstance(_SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE )
							if os.path.isfile(_SCREAMING_SNAKE_CASE ):
														_snake_case  =    cva.imread(_SCREAMING_SNAKE_CASE )
							else:
														_snake_case  =    get_image_from_url(_SCREAMING_SNAKE_CASE )
														assert img is not None, f"""could not connect to: {im}"""
							_snake_case  =    cva.cvtColor(_SCREAMING_SNAKE_CASE				,   cva.COLOR_BGR2RGB )
							if input_format == "RGB":
														_snake_case  =    img[:, :, ::-1]
							return img






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE=1 ):
							return (images[i : i + batch] for i in range(0				,   len(_SCREAMING_SNAKE_CASE )				,   _SCREAMING_SNAKE_CASE ))

import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

lowerCamelCase__						=				logging.getLogger()


def 				__A()		->					str:

		"""simple docstring"""






		_UpperCamelCase         =						argparse.ArgumentParser()
		parser.add_argument("""-f"""			)
		_UpperCamelCase         =						parser.parse_args()
		return args.f


def 				__A(lowerCAmelCase			)		->					Any:

		"""simple docstring"""






		_UpperCamelCase         =						{}
		_UpperCamelCase         =						os.path.join(lowerCAmelCase    ,	"""all_results.json"""			)
		if os.path.exists(lowerCAmelCase			):
				with open(lowerCAmelCase    ,	"""r"""			) as f:
						_UpperCamelCase         =						json.load(lowerCAmelCase			)
		else:
				raise ValueError(F'can\'t find {path}'			)
		return results


def 				__A()		->					Tuple:

		"""simple docstring"""






		_UpperCamelCase         =						torch.cuda.is_available() and torch_device == """cuda"""
		return is_using_cuda and is_apex_available()


lowerCamelCase__						=				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 							lowerCAmelCase__   ( __lowercase						):

						@classmethod
						def 			A_					(      cls					)       ->   Tuple:


								'''simple docstring'''

								_UpperCamelCase         =						tempfile.mkdtemp()
								_UpperCamelCase         =						os.path.join(cls.tmpdir							,				"""default_config.yml"""					)
								write_basic_config(save_location=cls.configPath					)
								_UpperCamelCase         =						["""accelerate""", """launch""", """--config_file""", cls.configPath]

						@classmethod
						def 			A_					(      cls					)       ->   str:


								'''simple docstring'''

								shutil.rmtree(cls.tmpdir					)

						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								if is_cuda_and_apex_available():
										testargs.append("""--fp16"""					)

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_accuracy"""]							,				0.75					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""glue_no_trainer"""					)					)					)

						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   List[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								if torch.cuda.device_count() > 1:
										# Skipping because there are not enough batches to train the model + would need a drop_last to work.
										return

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertLess(result["""perplexity"""]							,				1_00					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""clm_no_trainer"""					)					)					)

						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Tuple:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertLess(result["""perplexity"""]							,				42					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""mlm_no_trainer"""					)					)					)

						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Dict:


								'''simple docstring'''

								_UpperCamelCase         =						7 if get_gpu_count() > 1 else 2

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_accuracy"""]							,				0.75					)
								self.assertLess(result["""train_loss"""]							,				0.5					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""ner_no_trainer"""					)					)					)

						@unittest.skip(reason="""Fix me @muellerzr"""					)
						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Dict:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
								self.assertGreaterEqual(result["""eval_f1"""]							,				28					)
								self.assertGreaterEqual(result["""eval_exact"""]							,				28					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""qa_no_trainer"""					)					)					)

						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Dict:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_accuracy"""]							,				0.8					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""swag_no_trainer"""					)					)					)

						@slow
						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_rouge1"""]							,				10					)
								self.assertGreaterEqual(result["""eval_rouge2"""]							,				2					)
								self.assertGreaterEqual(result["""eval_rougeL"""]							,				7					)
								self.assertGreaterEqual(result["""eval_rougeLsum"""]							,				7					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""summarization_no_trainer"""					)					)					)

						@slow
						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_bleu"""]							,				30					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""translation_no_trainer"""					)					)					)

						@slow
						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						logging.StreamHandler(sys.stdout					)
								logger.addHandler(a					)

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_overall_accuracy"""]							,				0.10					)







						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   List[str]:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        '.split()

								if is_cuda_and_apex_available():
										testargs.append("""--fp16"""					)

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								# The base model scores a 25%
								self.assertGreaterEqual(result["""eval_accuracy"""]							,				0.6					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""step_1"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""image_classification_no_trainer"""					)					)					)



