




'''simple docstring'''




import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
a_      :		Dict							      =       logging.getLogger(__name__)





def      a_							(      __snake_case							:			str   )							->						Union[str, Any]:



       """simple docstring"""
       lowerCamelCase_							=git.Repo(search_parent_directories=__snake_case   )
       lowerCamelCase_							={
           '''repo_id''': str(__snake_case   ),
           '''repo_sha''': str(repo.head.object.hexsha   ),
           '''repo_branch''': str(repo.active_branch   ),
       }

       with open(os.path.join(__snake_case   ,       '''git_log.json'''   )   ,       '''w'''   ) as f:
              json.dump(__snake_case   ,       __snake_case   ,       indent=4   )





def      a_							(      __snake_case							:			Union[str, Any]   )							->						List[Any]:



       """simple docstring"""
       if params.n_gpu <= 0:
              lowerCamelCase_							=0
              lowerCamelCase_							=-1
              lowerCamelCase_							=True
              lowerCamelCase_							=False
              return

       assert torch.cuda.is_available()

       logger.info('''Initializing GPUs'''   )
       if params.n_gpu > 1:
              assert params.local_rank != -1

              lowerCamelCase_							=int(os.environ['''WORLD_SIZE''']   )
              lowerCamelCase_							=int(os.environ['''N_GPU_NODE''']   )
              lowerCamelCase_							=int(os.environ['''RANK''']   )

              # number of nodes / node ID
              lowerCamelCase_							=params.world_size // params.n_gpu_per_node
              lowerCamelCase_							=params.global_rank // params.n_gpu_per_node
              lowerCamelCase_							=True

              assert params.n_nodes == int(os.environ['''N_NODES''']   )
              assert params.node_id == int(os.environ['''NODE_RANK''']   )

       # local job (single GPU)
       else:
              assert params.local_rank == -1

              lowerCamelCase_							=1
              lowerCamelCase_							=0
              lowerCamelCase_							=0
              lowerCamelCase_							=0
              lowerCamelCase_							=1
              lowerCamelCase_							=1
              lowerCamelCase_							=False

       # sanity checks
       assert params.n_nodes >= 1
       assert 0 <= params.node_id < params.n_nodes
       assert 0 <= params.local_rank <= params.global_rank < params.world_size
       assert params.world_size == params.n_nodes * params.n_gpu_per_node

       # define whether this is the master process / if we are in multi-node distributed mode
       lowerCamelCase_							=params.node_id == 0 and params.local_rank == 0
       lowerCamelCase_							=params.n_nodes > 1

       # summary
       lowerCamelCase_							=F'''--- Global rank: {params.global_rank} - '''
       logger.info(PREFIX + '''Number of nodes: %i''' % params.n_nodes   )
       logger.info(PREFIX + '''Node ID        : %i''' % params.node_id   )
       logger.info(PREFIX + '''Local rank     : %i''' % params.local_rank   )
       logger.info(PREFIX + '''World size     : %i''' % params.world_size   )
       logger.info(PREFIX + '''GPUs per node  : %i''' % params.n_gpu_per_node   )
       logger.info(PREFIX + '''Master         : %s''' % str(params.is_master   )   )
       logger.info(PREFIX + '''Multi-node     : %s''' % str(params.multi_node   )   )
       logger.info(PREFIX + '''Multi-GPU      : %s''' % str(params.multi_gpu   )   )
       logger.info(PREFIX + '''Hostname       : %s''' % socket.gethostname()   )

       # set GPU device
       torch.cuda.set_device(params.local_rank   )

       # initialize multi-GPU
       if params.multi_gpu:
              logger.info('''Initializing PyTorch distributed'''   )
              torch.distributed.init_process_group(
                  init_method='''env://'''   ,       backend='''nccl'''   ,       )





def      a_							(      __snake_case							:			Optional[int]   )							->						Tuple:



       """simple docstring"""
       np.random.seed(args.seed   )
       torch.manual_seed(args.seed   )
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed   )






# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				get_logger(__name__)





class 							UpperCAmelCase      :




	'''simple docstring'''





	snake_case_					    =     "dummy_data"
	snake_case_					    =     "datasets"
	snake_case_					    =     False






	def __init__(       self					: Optional[int]							,A					: str							,A					: str							,A					: Union[Version, str]							,A					: Optional[str] = None							,A					: bool = False							,A					: bool = True							,A					: Optional[List[Callable]] = None							,):
					__A   			= 0
					__A   			= dataset_name
					__A   			= cache_dir
					__A   			= use_local_dummy_data
					__A   			= config
					# download_callbacks take a single url as input
					__A   			= download_callbacks or []
					# if False, it doesn't load existing files and it returns the paths of the dummy files relative
					# to the dummy_data zip file root
					__A   			= load_existing_dummy_data

					# TODO(PVP, QL) might need to make this more general
					__A   			= str(A )
					# to be downloaded
					__A   			= None
					__A   			= None






	@property
	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					if self._dummy_file is None:
									__A   			= self.download_dummy_data()
					return self._dummy_file






	@property
	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					if self.config is not None:
									# structure is dummy / config_name / version_name
									return os.path.join("dummy"							,self.config.name							,self.version_name )
					# structure is dummy / version_name
					return os.path.join("dummy"							,self.version_name )






	@property
	def 				UpperCamelCase_  (       self					: List[Any] ):
					return os.path.join(self.dummy_data_folder							,"dummy_data.zip" )






	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= (
					    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
					)

					__A   			= cached_path(
					    A							,cache_dir=self.cache_dir							,extract_compressed_file=A							,force_extract=A )

					return os.path.join(A							,self.dummy_file_name )






	@property
	def 				UpperCamelCase_  (       self					: str ):
					return os.path.join(self.datasets_scripts_dir							,self.dataset_name							,self.dummy_zip_file )






	@property
	def 				UpperCamelCase_  (       self					: Any ):
					if self._bucket_url is None:
									__A   			= hf_github_url(self.dataset_name							,self.dummy_zip_file.replace(os.sep							,"/" ) )
					return self._bucket_url






	@property
	def 				UpperCamelCase_  (       self					: Tuple ):
					# return full path if its a dir
					if os.path.isdir(self.dummy_file ):
									return self.dummy_file
					# else cut off path to file -> example `xsum`.
					return "/".join(self.dummy_file.replace(os.sep							,"/" ).split("/" )[:-1] )






	def 				UpperCamelCase_  (       self					: List[str]							,A					: List[Any]							,*A					: Dict ):
					if self.load_existing_dummy_data:
									# dummy data is downloaded and tested
									__A   			= self.dummy_file
					else:
									# dummy data cannot be downloaded and only the path to dummy file is returned
									__A   			= self.dummy_file_name

					# special case when data_url is a dict
					if isinstance(A							,A ):
									return self.create_dummy_data_dict(A							,A )
					elif isinstance(A							,(list, tuple) ):
									return self.create_dummy_data_list(A							,A )
					else:
									return self.create_dummy_data_single(A							,A )






	def 				UpperCamelCase_  (       self					: str							,A					: List[Any]							,*A					: List[Any] ):
					return self.download_and_extract(A )






	def 				UpperCamelCase_  (       self					: List[str]							,A					: List[str]							,A					: Tuple ):
					return self.download_and_extract(A )






	def 				UpperCamelCase_  (       self					: Any							,A					: Any							,*A					: Optional[Any]							,**A					: List[str] ):
					return path






	def 				UpperCamelCase_  (       self					: str ):
					return {}






	def 				UpperCamelCase_  (       self					: int							,A					: int							,A					: Tuple ):
					__A   			= {}
					for key, single_urls in data_url.items():
									for download_callback in self.download_callbacks:
													if isinstance(A							,A ):
																	for single_url in single_urls:
																					download_callback(A )
													else:
																	__A   			= single_urls
																	download_callback(A )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
									if isinstance(A							,A ):
													__A   			= [os.path.join(A							,urllib.parse.quote_plus(Path(A ).name ) ) for x in single_urls]
									else:
													__A   			= single_urls
													__A   			= os.path.join(A							,urllib.parse.quote_plus(Path(A ).name ) )
									__A   			= value

					# make sure that values are unique
					if all(isinstance(A							,A ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
					    dummy_data_dict.values() ):
									# append key to value to make its name unique
									__A   			= {key: value + key for key, value in dummy_data_dict.items()}

					return dummy_data_dict






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: str							,A					: str ):
					__A   			= []
					# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
					__A   			= all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}"							,A ) ) for url in data_url )
					__A   			= all(
					    url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed" ) for url in data_url )
					if data_url and (is_tf_records or is_pubmed_records):
									__A   			= [data_url[0]] * len(A )
					for single_url in data_url:
									for download_callback in self.download_callbacks:
													download_callback(A )
									# we force the name of each key to be the last file / folder name of the url path
									# if the url has arguments, we need to encode them with urllib.parse.quote_plus
									__A   			= os.path.join(A							,urllib.parse.quote_plus(single_url.split("/" )[-1] ) )
									dummy_data_list.append(A )
					return dummy_data_list






	def 				UpperCamelCase_  (       self					: str							,A					: List[Any]							,A					: Optional[Any] ):
					for download_callback in self.download_callbacks:
									download_callback(A )
					# we force the name of each key to be the last file / folder name of the url path
					# if the url has arguments, we need to encode them with urllib.parse.quote_plus
					__A   			= os.path.join(A							,urllib.parse.quote_plus(data_url.split("/" )[-1] ) )
					if os.path.exists(A ) or not self.load_existing_dummy_data:
									return value
					else:
									# Backward compatibility, maybe deprecate at one point.
									# For many datasets with single url calls to dl_manager.download_and_extract,
									# the dummy_data.zip file is actually the zipped downloaded file
									# while now we expected the dummy_data.zip file to be a directory containing
									# the downloaded file.
									return path_to_dummy_data






	def 				UpperCamelCase_  (       self					: int ):
					pass






	def 				UpperCamelCase_  (       self					: Dict ):
					pass






	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: List[Any] ):
					def _iter_archive_members(A					: Optional[Any] ):
									# this preserves the order of the members inside the ZIP archive
									__A   			= Path(self.dummy_file ).parent
									__A   			= path.relative_to(A )
									with ZipFile(self.local_path_to_dummy_data ) as zip_file:
													__A   			= zip_file.namelist()
									for member in members:
													if member.startswith(relative_path.as_posix() ):
																	yield dummy_parent_path.joinpath(A )

					__A   			= Path(A )
					__A   			= _iter_archive_members(A ) if self.use_local_dummy_data else path.rglob("*" )
					for file_path in file_paths:
									if file_path.is_file() and not file_path.name.startswith((".", "__") ):
													yield file_path.relative_to(A ).as_posix(), file_path.open("rb" )







	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Any ):
					if not isinstance(A							,A ):
									__A   			= [paths]
					for path in paths:
									if os.path.isfile(A ):
													if os.path.basename(A ).startswith((".", "__") ):
																	return
													yield path
									else:
													for dirpath, dirnames, filenames in os.walk(A ):
																	if os.path.basename(A ).startswith((".", "__") ):
																					continue
																	dirnames.sort()
																	for filename in sorted(A ):
																					if filename.startswith((".", "__") ):
																									continue
																					yield os.path.join(A							,A )
