
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

					A  :			Optional[Any]     				=				True
except ImportError:
					A  :			List[Any]     				=				False


try:
					from torch.hub import _get_torch_home

					A  :			List[str]     				=				_get_torch_home()
except ImportError:
					A  :			Optional[int]     				=				os.path.expanduser(
					    os.getenv('TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch'))
					)

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

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


def 					__lowerCAmelCase    (    a__=OBJECTS		,					a__=ATTRIBUTES						) ->						int:
				__a    							=					[]
				with open(a__						) as f:
								for object in f.readlines():
												vg_classes.append(object.split(''','''						)[0].lower().strip()						)

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


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







class     __A:
				snake_case_		     =					{}




				def __init__(  self	,  _snake_case	,  _snake_case = "root"	,  _snake_case=0       )					->   Dict:


								'''simple docstring'''




								__a    							=					name
								__a    							=					level
								__a    							=					{}
								for k, v in dictionary.items():
												if v is None:
																raise ValueError()
												__a    							=					copy.deepcopy(_snake_case       )
												__a    							=					copy.deepcopy(_snake_case       )
												if isinstance(_snake_case	,  _snake_case       ):
																__a    							=					Config(_snake_case	,  name=_snake_case	,  level=level + 1       )
												__a    							=					v
												setattr(self	,  _snake_case	,  _snake_case       )

								__a    							=					d




				def __repr__(  self       )					->   int:


								'''simple docstring'''




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




				def __setattr__(  self	,  _snake_case	,  _snake_case       )					->   Optional[int]:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[int]:


								'''simple docstring'''




								return self._pointer




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case       )					->   Dict:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case       )					->   Tuple:


								'''simple docstring'''




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




				@staticmethod
				def        SCREAMING_SNAKE_CASE_	(  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




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




				def __str__(  self       )					->   str:


								'''simple docstring'''




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




				@classmethod
				def        SCREAMING_SNAKE_CASE_	(  cls	,  _snake_case	,  **_snake_case       )					->   int:


								'''simple docstring'''




								__a							,  __a    							=					cls.get_config_dict(_snake_case	,  **_snake_case       )
								return cls(_snake_case       )




				@classmethod
				def        SCREAMING_SNAKE_CASE_	(  cls	,  _snake_case	,  **_snake_case       )					->   Tuple:


								'''simple docstring'''




								__a    							=					kwargs.pop('''cache_dir'''	,  _snake_case       )
								__a    							=					kwargs.pop('''force_download'''	,  _snake_case       )
								__a    							=					kwargs.pop('''resume_download'''	,  _snake_case       )
								__a    							=					kwargs.pop('''proxies'''	,  _snake_case       )
								__a    							=					kwargs.pop('''local_files_only'''	,  _snake_case       )

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

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

												__a    							=					Config.load_yaml(_snake_case       )

								except EnvironmentError:
												__a    							=					'''Can\'t load config for'''
												raise EnvironmentError(_snake_case       )

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

								return Config.load_yaml(_snake_case       ), kwargs



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

				# Hugging face functions below


def 					__lowerCAmelCase    (    a__						) ->						Any:
				__a    							=					urlparse(a__						)
				return parsed.scheme in ("http", "https")


def 					__lowerCAmelCase    (    a__		,					a__		,					a__=True						) ->						str:
				__a    							=					CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
				__a    							=					'''/''' not in model_id
				if legacy_format:
								return F"""{endpoint}/{model_id}-{filename}"""
				else:
								return F"""{endpoint}/{model_id}/{filename}"""


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


def 					__lowerCAmelCase    (    a__		,					a__=None		,					a__=False		,					a__=None		,					a__=10		,					a__=False		,					a__=None		,					a__=False		,					) ->						List[Any]:
				if cache_dir is None:
								__a    							=					TRANSFORMERS_CACHE
				if isinstance(a__		,					a__						):
								__a    							=					str(a__						)

				os.makedirs(a__		,					exist_ok=a__						)

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

				__a    							=					url_to_filename(a__		,					a__						)

				# get cache path to put the file
				__a    							=					os.path.join(a__		,					a__						)

				# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
				# try to get the last downloaded one
				if etag is None:
								if os.path.exists(a__						):
												return cache_path
								else:
												__a    							=					[
												    file
												    for file in fnmatch.filter(os.listdir(a__						)		,					filename + '''.*'''						)
												    if not file.endswith('''.json'''						) and not file.endswith('''.lock'''						)
												]
												if len(a__						) > 0:
																return os.path.join(a__		,					matching_files[-1]						)
												else:
																# If files cannot be found and local_files_only=True,
																# the models might've been found if local_files_only=False
																# Notify the user about that
																if local_files_only:
																				raise ValueError(
																				    '''Cannot find the requested files in the cached path and outgoing traffic has been'''
																				    ''' disabled. To enable model look-ups and downloads online, set \'local_files_only\''''
																				    ''' to False.'''						)
																return None

    # From now on, etag is not None.
				if os.path.exists(a__						) and not force_download:
								return cache_path

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

								if resume_download:
												__a    							=					cache_path + '''.incomplete'''

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

												__a    							=					_resumable_file_manager
												if os.path.exists(a__						):
																__a    							=					os.stat(a__						).st_size
												else:
																__a    							=					0
								else:
												__a    							=					partial(tempfile.NamedTemporaryFile		,					dir=a__		,					delete=a__						)
												__a    							=					0

								# Download to temporary file, then copy to cache dir once finished.
								# Otherwise you get corrupt cache entries if the download gets interrupted.
								with temp_file_manager() as temp_file:
												print(
												    '''%s not found in cache or force_download set to True, downloading to %s'''		,					a__		,					temp_file.name		,					)

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

								os.replace(temp_file.name		,					a__						)

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

				return cache_path


def 					__lowerCAmelCase    (    a__		,					a__=None						) ->						Optional[int]:
				__a    							=					url.encode('''utf-8'''						)
				__a    							=					shaaaa(a__						)
				__a    							=					url_hash.hexdigest()

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

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

				return filename


def 					__lowerCAmelCase    (    a__		,					a__=None		,					a__=False		,					a__=None		,					a__=False		,					a__=None		,					a__=False		,					a__=False		,					a__=False		,					) ->						Optional[Any]:
				if cache_dir is None:
								__a    							=					TRANSFORMERS_CACHE
				if isinstance(a__		,					a__						):
								__a    							=					str(a__						)
				if isinstance(a__		,					a__						):
								__a    							=					str(a__						)

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

				if extract_compressed_file:
								if not is_zipfile(a__						) and not tarfile.is_tarfile(a__						):
												return output_path

								# Path where we extract compressed archives
								# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
								__a							,  __a    							=					os.path.split(a__						)
								__a    							=					output_file.replace('''.'''		,					'''-'''						) + '''-extracted'''
								__a    							=					os.path.join(a__		,					a__						)

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

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

								return output_path_extracted

				return output_path


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


def 					__lowerCAmelCase    (    a__						) ->						str:
				__a    							=					requests.get(a__						)
				__a    							=					np.array(Image.open(BytesIO(response.content						)						)						)
				return img


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


def 					__lowerCAmelCase    (    ) ->						Optional[int]:
				print(F"""{os.path.abspath(os.path.join(a__		,					os.pardir						)						)}/demo.ipynb"""						)


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






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

import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


A  :			Optional[int]     				=				2 << 2_0  # 2mb
A  :			Optional[int]     				=				get_logger(__name__)







class     __A(				a    ):




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


								'''simple docstring'''




								__a    							=					self._options.get_all_options().get('''runner'''       )
								return runner in [None, "DirectRunner", "PortableRunner"]



def 					__lowerCAmelCase    (    a__		,					a__		,					a__=False						) ->						Union[str, Any]:
				__a    							=					FileSystems
				if fs.exists(a__						):
								if force_upload:
												logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""						)
								else:
												logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""						)
												return
				with fs.create(a__						) as remote_file:
								with open(a__		,					'''rb'''						) as local_file:
												__a    							=					local_file.read(a__						)
												while chunk:
																remote_file.write(a__						)
																__a    							=					local_file.read(a__						)






def 					__lowerCAmelCase    (    a__		,					a__		,					a__=False						) ->						Dict:
				__a    							=					FileSystems
				if os.path.exists(a__						):
								if force_download:
												logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""						)
								else:
												logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""						)
												return
				with fs.open(a__						) as remote_file:
								with open(a__		,					'''wb'''						) as local_file:
												__a    							=					remote_file.read(a__						)
												while chunk:
																local_file.write(a__						)
																__a    							=					remote_file.read(a__						)