





"""simple docstring"""




import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
						import torch

						UpperCamelCase_           =True
except ImportError:
						UpperCamelCase_           =False


try:
						from torch.hub import _get_torch_home

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

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

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





def        a_				(							_lowercase=OBJECTS       ,						_lowercase=ATTRIBUTES   ):
						_UpperCamelCase			:					Optional[int]     =    []
						with open(_lowercase   ) as f:
												for object in f.readlines():
																		vg_classes.append(object.split(''','''   )[0].lower().strip()   )

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





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




class  _a						:
				UpperCamelCase								=    {}






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




										'''simple docstring'''



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

										_UpperCamelCase			:					Optional[Any]     =    d






				def __repr__( self		:    List[str]							)	->      List[Any]:




										'''simple docstring'''



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






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




										'''simple docstring'''



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






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




										'''simple docstring'''



										return self._pointer






				def 				snake_case	( self		:    int, lowerCAmelCase__		:    List[str], lowerCAmelCase__		:    Union[str, Any]							)	->      Tuple:




										'''simple docstring'''



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






				def 				snake_case	( self		:    Tuple, lowerCAmelCase__		:    Optional[Any], lowerCAmelCase__		:    Tuple							)	->      Optional[Any]:




										'''simple docstring'''



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






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    List[Any]							)	->      List[str]:




										'''simple docstring'''



										with open(lowerCAmelCase__							) as stream:
																_UpperCamelCase			:					List[str]     =    load(lowerCAmelCase__, Loader=lowerCAmelCase__							)
										return data






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




										'''simple docstring'''



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






				@classmethod
				def 				snake_case	( cls		:    Optional[int], lowerCAmelCase__		:    str, **lowerCAmelCase__		:    Tuple							)	->      List[str]:




										'''simple docstring'''



										_UpperCamelCase   ,							_UpperCamelCase			:					int     =    cls.get_config_dict(lowerCAmelCase__, **lowerCAmelCase__							)
										return cls(lowerCAmelCase__							)






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




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    kwargs.pop('''cache_dir''', lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[int]     =    kwargs.pop('''force_download''', lowerCAmelCase__							)
										_UpperCamelCase			:					List[str]     =    kwargs.pop('''resume_download''', lowerCAmelCase__							)
										_UpperCamelCase			:					str     =    kwargs.pop('''proxies''', lowerCAmelCase__							)
										_UpperCamelCase			:					Any     =    kwargs.pop('''local_files_only''', lowerCAmelCase__							)

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

										try:
																# Load from URL or cache if already cached
																_UpperCamelCase			:					Union[str, Any]     =    cached_path(
																    lowerCAmelCase__, cache_dir=lowerCAmelCase__, force_download=lowerCAmelCase__, proxies=lowerCAmelCase__, resume_download=lowerCAmelCase__, local_files_only=lowerCAmelCase__, )
																# Load config dict
																if resolved_config_file is None:
																						raise EnvironmentError

																_UpperCamelCase			:					int     =    Config.load_yaml(lowerCAmelCase__							)

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

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

										return Config.load_yaml(lowerCAmelCase__							), kwargs





def        a_				(							_lowercase   ):
						_UpperCamelCase			:					List[Any]     =    torch.load('''dump.pt'''       ,						map_location=in_tensor.device   )
						_UpperCamelCase			:					Tuple     =    in_tensor.numpy()
						_UpperCamelCase			:					str     =    out_tensor.numpy()[0]
						print(na.shape       ,						na[0, 0, :5]   )
						print(na.shape       ,						na[0, 0, :5]   )
						assert np.allclose(_lowercase       ,						_lowercase       ,						rtol=0.01       ,						atol=0.1   ), (
						    F"""{sum([1 for x in np.isclose(_lowercase       ,						_lowercase       ,						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        a_				(							_lowercase   ):
						_UpperCamelCase			:					Dict     =    urlparse(_lowercase   )
						return parsed.scheme in ("http", "https")





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase=True   ):
						_UpperCamelCase			:					List[str]     =    CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
						_UpperCamelCase			:					Optional[int]     =    '''/''' not in model_id
						if legacy_format:
												return F"""{endpoint}/{model_id}-{filename}"""
						else:
												return F"""{endpoint}/{model_id}/{filename}"""





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





def        a_				(							_lowercase       ,						_lowercase=None       ,						_lowercase=False       ,						_lowercase=None       ,						_lowercase=10       ,						_lowercase=False       ,						_lowercase=None       ,						_lowercase=False       ,						):
						if cache_dir is None:
												_UpperCamelCase			:					Any     =    TRANSFORMERS_CACHE
						if isinstance(_lowercase       ,						_lowercase   ):
												_UpperCamelCase			:					int     =    str(_lowercase   )

						os.makedirs(_lowercase       ,						exist_ok=_lowercase   )

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

						_UpperCamelCase			:					Optional[Any]     =    url_to_filename(_lowercase       ,						_lowercase   )

						# get cache path to put the file
						_UpperCamelCase			:					Any     =    os.path.join(_lowercase       ,						_lowercase   )

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

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

												if resume_download:
																		_UpperCamelCase			:					Any     =    cache_path + '''.incomplete'''

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

																		_UpperCamelCase			:					List[str]     =    _resumable_file_manager
																		if os.path.exists(_lowercase   ):
																								_UpperCamelCase			:					str     =    os.stat(_lowercase   ).st_size
																		else:
																								_UpperCamelCase			:					Tuple     =    0
												else:
																		_UpperCamelCase			:					Tuple     =    partial(tempfile.NamedTemporaryFile       ,						dir=_lowercase       ,						delete=_lowercase   )
																		_UpperCamelCase			:					Optional[int]     =    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'''       ,						_lowercase       ,						temp_file.name       ,						)

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

												os.replace(temp_file.name       ,						_lowercase   )

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

						return cache_path





def        a_				(							_lowercase       ,						_lowercase=None   ):
						_UpperCamelCase			:					str     =    url.encode('''utf-8'''   )
						_UpperCamelCase			:					Any     =    shaaaa(_lowercase   )
						_UpperCamelCase			:					Union[str, Any]     =    url_hash.hexdigest()

						if etag:
												_UpperCamelCase			:					List[Any]     =    etag.encode('''utf-8'''   )
												_UpperCamelCase			:					Union[str, Any]     =    shaaaa(_lowercase   )
												filename += "." + etag_hash.hexdigest()

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

						return filename





def        a_				(							_lowercase       ,						_lowercase=None       ,						_lowercase=False       ,						_lowercase=None       ,						_lowercase=False       ,						_lowercase=None       ,						_lowercase=False       ,						_lowercase=False       ,						_lowercase=False       ,						):
						if cache_dir is None:
												_UpperCamelCase			:					Union[str, Any]     =    TRANSFORMERS_CACHE
						if isinstance(_lowercase       ,						_lowercase   ):
												_UpperCamelCase			:					Tuple     =    str(_lowercase   )
						if isinstance(_lowercase       ,						_lowercase   ):
												_UpperCamelCase			:					str     =    str(_lowercase   )

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

						if extract_compressed_file:
												if not is_zipfile(_lowercase   ) and not tarfile.is_tarfile(_lowercase   ):
																		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			:					str     =    os.path.split(_lowercase   )
												_UpperCamelCase			:					str     =    output_file.replace('''.'''       ,						'''-'''   ) + '''-extracted'''
												_UpperCamelCase			:					Tuple     =    os.path.join(_lowercase       ,						_lowercase   )

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

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

												return output_path_extracted

						return output_path





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





def        a_				(							_lowercase   ):
						_UpperCamelCase			:					Union[str, Any]     =    requests.get(_lowercase   )
						_UpperCamelCase			:					List[Any]     =    np.array(Image.open(BytesIO(response.content   )   )   )
						return img





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





def        a_				(							):
						print(F"""{os.path.abspath(os.path.join(_lowercase       ,						os.pardir   )   )}/demo.ipynb"""   )





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






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










'''simple docstring'''



import argparse
import logging
import os
import sys
from collections import namedtuple

import torch
from modeling_bertabs import BertAbs, build_predictor
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm

from transformers import BertTokenizer

from .utils_summarization import (
    CNNDMDataset,
    build_mask,
    compute_token_type_ids,
    encode_for_summarization,
    truncate_or_pad,
)


_SCREAMING_SNAKE_CASE    =			logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


_SCREAMING_SNAKE_CASE    =			namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])




def       _lowerCAmelCase  ( lowerCamelCase_	:     int ):
			__lowercase   =							BertTokenizer.from_pretrained('''bert-base-uncased'''						,				do_lower_case=lowerCamelCase_ )
			__lowercase   =							BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization''' )
			model.to(args.device )
			model.eval()

			__lowercase   =							{
			    '''BOS''': tokenizer.vocab['''[unused0]'''],
			    '''EOS''': tokenizer.vocab['''[unused1]'''],
			    '''PAD''': tokenizer.vocab['''[PAD]'''],
			}

			if args.compute_rouge:
						__lowercase   =							[]
						__lowercase   =							[]

						import nltk
						import rouge

						nltk.download('''punkt''' )
						__lowercase   =							rouge.Rouge(
						    metrics=['''rouge-n''', '''rouge-l''']						,				max_n=2						,				limit_length=lowerCamelCase_						,				length_limit=args.beam_size						,				length_limit_type='''words'''						,				apply_avg=lowerCamelCase_						,				apply_best=lowerCamelCase_						,				alpha=0.5						,				weight_factor=1.2						,				stemming=lowerCamelCase_						,				)

			# these (unused) arguments are defined to keep the compatibility
			# with the legacy code and will be deleted in a next iteration.
			__lowercase   =							''''''
			__lowercase   =							''''''

			__lowercase   =							build_data_iterator(lowerCamelCase_						,				lowerCamelCase_ )
			__lowercase   =							build_predictor(lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_ )

			logger.info('''***** Running evaluation *****''' )
			logger.info('''  Number examples = %d'''						,				len(data_iterator.dataset ) )
			logger.info('''  Batch size = %d'''						,				args.batch_size )
			logger.info('''''' )
			logger.info('''***** Beam Search parameters *****''' )
			logger.info('''  Beam size = %d'''						,				args.beam_size )
			logger.info('''  Minimum length = %d'''						,				args.min_length )
			logger.info('''  Maximum length = %d'''						,				args.max_length )
			logger.info('''  Alpha (length penalty) = %.2f'''						,				args.alpha )
			logger.info('''  Trigrams %s be blocked'''						,				('''will''' if args.block_trigram else '''will NOT''') )

			for batch in tqdm(lowerCamelCase_ ):
						__lowercase   =							predictor.translate_batch(lowerCamelCase_ )
						__lowercase   =							predictor.from_batch(lowerCamelCase_ )
						__lowercase   =							[format_summary(lowerCamelCase_ ) for t in translations]
						save_summaries(lowerCamelCase_						,				args.summaries_output_dir						,				batch.document_names )

						if args.compute_rouge:
									reference_summaries += batch.tgt_str
									generated_summaries += summaries

			if args.compute_rouge:
						__lowercase   =							rouge_evaluator.get_scores(lowerCamelCase_						,				lowerCamelCase_ )
						__lowercase   =							format_rouge_scores(lowerCamelCase_ )
						save_rouge_scores(lowerCamelCase_ )
						print(lowerCamelCase_ )




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[int]						,				lowerCamelCase_	:     str						,				lowerCamelCase_	:     Dict ):
			for summary, document_name in zip(lowerCamelCase_						,				lowerCamelCase_ ):
						# Prepare the summary file's name
						if "." in document_name:
									__lowercase   =							'''.'''.join(document_name.split('''.''' )[:-1] )
									__lowercase   =							document_name.split('''.''' )[-1]
									__lowercase   =							bare_document_name + '''_summary.''' + extension
						else:
									__lowercase   =							document_name + '''_summary'''

						__lowercase   =							os.path.join(lowerCamelCase_						,				lowerCamelCase_ )
						with open(lowerCamelCase_						,				'''w''' ) as output:
									output.write(lowerCamelCase_ )




def       _lowerCAmelCase  ( lowerCamelCase_	:     List[Any] ):
			__lowercase  ,						__lowercase  ,						__lowercase   =							translation
			__lowercase   =							(
			    raw_summary.replace('''[unused0]'''						,				'''''' )
			    .replace('''[unused3]'''						,				'''''' )
			    .replace('''[PAD]'''						,				'''''' )
			    .replace('''[unused1]'''						,				'''''' )
			    .replace(r''' +'''						,				''' ''' )
			    .replace(''' [unused2] '''						,				'''. ''' )
			    .replace('''[unused2]'''						,				'''''' )
			    .strip()
			)

			return summary




def       _lowerCAmelCase  ( lowerCamelCase_	:     int ):
			return """\n
****** ROUGE SCORES ******

** ROUGE 1
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE 2
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE L
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}""".format(
			    scores['''rouge-1''']['''f''']						,				scores['''rouge-1''']['''p''']						,				scores['''rouge-1''']['''r''']						,				scores['''rouge-2''']['''f''']						,				scores['''rouge-2''']['''p''']						,				scores['''rouge-2''']['''r''']						,				scores['''rouge-l''']['''f''']						,				scores['''rouge-l''']['''p''']						,				scores['''rouge-l''']['''r''']						,				)




def       _lowerCAmelCase  ( lowerCamelCase_	:     Dict ):
			with open('''rouge_scores.txt'''						,				'''w''' ) as output:
						output.write(lowerCamelCase_ )




def       _lowerCAmelCase  ( lowerCamelCase_	:     List[Any]						,				lowerCamelCase_	:     List[Any] ):
			__lowercase   =							load_and_cache_examples(lowerCamelCase_						,				lowerCamelCase_ )
			__lowercase   =							SequentialSampler(lowerCamelCase_ )

			def collate_fn(lowerCamelCase_	:     List[str] ):
						return collate(lowerCamelCase_						,				lowerCamelCase_						,				block_size=5_1_2						,				device=args.device )

			__lowercase   =							DataLoader(
			    lowerCamelCase_						,				sampler=lowerCamelCase_						,				batch_size=args.batch_size						,				collate_fn=lowerCamelCase_						,				)

			return iterator




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[int]						,				lowerCamelCase_	:     List[Any] ):
			__lowercase   =							CNNDMDataset(args.documents_dir )
			return dataset




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[int]						,				lowerCamelCase_	:     List[Any]						,				lowerCamelCase_	:     Any						,				lowerCamelCase_	:     Optional[Any] ):
			__lowercase   =							[x for x in data if not len(x[1] ) == 0]  # remove empty_files
			__lowercase   =							[name for name, _, _ in data]
			__lowercase   =							[''' '''.join(lowerCamelCase_ ) for _, _, summary_list in data]

			__lowercase   =							[encode_for_summarization(lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_ ) for _, story, summary in data]
			__lowercase   =							torch.tensor(
			    [truncate_or_pad(lowerCamelCase_						,				lowerCamelCase_						,				tokenizer.pad_token_id ) for story, _ in encoded_text] )
			__lowercase   =							compute_token_type_ids(lowerCamelCase_						,				tokenizer.cls_token_id )
			__lowercase   =							build_mask(lowerCamelCase_						,				tokenizer.pad_token_id )

			__lowercase   =							Batch(
			    document_names=lowerCamelCase_						,				batch_size=len(lowerCamelCase_ )						,				src=encoded_stories.to(lowerCamelCase_ )						,				segs=encoder_token_type_ids.to(lowerCamelCase_ )						,				mask_src=encoder_mask.to(lowerCamelCase_ )						,				tgt_str=lowerCamelCase_						,				)

			return batch




def       _lowerCAmelCase  ( lowerCamelCase_	:     List[Any]						,				lowerCamelCase_	:     Union[str, Any] ):
			__lowercase   =							summary_tokens.to('''cpu''' ).numpy()
			__lowercase   =							tokenizer.decode(lowerCamelCase_ )
			__lowercase   =							summary.split('''.''' )
			__lowercase   =							[s + '''.''' for s in sentences]
			return sentences




def       _lowerCAmelCase  ( ):
			__lowercase   =							argparse.ArgumentParser()
			parser.add_argument(
			    '''--documents_dir'''						,				default=lowerCamelCase_						,				type=lowerCamelCase_						,				required=lowerCamelCase_						,				help='''The folder where the documents to summarize are located.'''						,				)
			parser.add_argument(
			    '''--summaries_output_dir'''						,				default=lowerCamelCase_						,				type=lowerCamelCase_						,				required=lowerCamelCase_						,				help='''The folder in wich the summaries should be written. Defaults to the folder where the documents are'''						,				)
			parser.add_argument(
			    '''--compute_rouge'''						,				default=lowerCamelCase_						,				type=lowerCamelCase_						,				required=lowerCamelCase_						,				help='''Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.'''						,				)
			# EVALUATION options
			parser.add_argument(
			    '''--no_cuda'''						,				default=lowerCamelCase_						,				type=lowerCamelCase_						,				help='''Whether to force the execution on CPU.'''						,				)
			parser.add_argument(
			    '''--batch_size'''						,				default=4						,				type=lowerCamelCase_						,				help='''Batch size per GPU/CPU for training.'''						,				)
			# BEAM SEARCH arguments
			parser.add_argument(
			    '''--min_length'''						,				default=5_0						,				type=lowerCamelCase_						,				help='''Minimum number of tokens for the summaries.'''						,				)
			parser.add_argument(
			    '''--max_length'''						,				default=2_0_0						,				type=lowerCamelCase_						,				help='''Maixmum number of tokens for the summaries.'''						,				)
			parser.add_argument(
			    '''--beam_size'''						,				default=5						,				type=lowerCamelCase_						,				help='''The number of beams to start with for each example.'''						,				)
			parser.add_argument(
			    '''--alpha'''						,				default=0.95						,				type=lowerCamelCase_						,				help='''The value of alpha for the length penalty in the beam search.'''						,				)
			parser.add_argument(
			    '''--block_trigram'''						,				default=lowerCamelCase_						,				type=lowerCamelCase_						,				help='''Whether to block the existence of repeating trigrams in the text generated by beam search.'''						,				)
			__lowercase   =							parser.parse_args()

			# Select device (distibuted not available)
			__lowercase   =							torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''' )

			# Check the existence of directories
			if not args.summaries_output_dir:
						__lowercase   =							args.documents_dir

			if not documents_dir_is_valid(args.documents_dir ):
						raise FileNotFoundError(
						    '''We could not find the directory you specified for the documents to summarize, or it was empty. Please'''
						    ''' specify a valid path.''' )
			os.makedirs(args.summaries_output_dir						,				exist_ok=lowerCamelCase_ )

			evaluate(lowerCamelCase_ )




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[Any] ):
			if not os.path.exists(lowerCamelCase_ ):
						return False

			__lowercase   =							os.listdir(lowerCamelCase_ )
			if len(lowerCamelCase_ ) == 0:
						return False

			return True


if __name__ == "__main__":
	main()





