






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


UpperCamelCase			    =     logging.getLogger(__name__)



@dataclass
class 				__UpperCAmelCase		:
			__snake_case    :				str									=							field(
			    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}   )
			__snake_case    :				Optional[str]									=							field(
			    default=_UpperCAmelCase	,metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
			__snake_case    :				Optional[str]									=							field(
			    default=_UpperCAmelCase	,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}   )
			__snake_case    :				Optional[str]									=							field(
			    default=_UpperCAmelCase	,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}	,)
			__snake_case    :				bool									=							field(default=_UpperCAmelCase	,metadata={"help": "Whether tp freeze the encoder."}   )
			__snake_case    :				bool									=							field(default=_UpperCAmelCase	,metadata={"help": "Whether  to freeze the embeddings."}   )



@dataclass
class 				__UpperCAmelCase		:
			__snake_case    :				str									=							field(
			    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}   )
			__snake_case    :				Optional[str]									=							field(
			    default="summarization"	,metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}	,)
			__snake_case    :				Optional[int]									=							field(
			    default=1024	,metadata={
			        "help": (
			            "The maximum total input sequence length after tokenization. Sequences longer "
			            "than this will be truncated, sequences shorter will be padded."
			        )
			    }	,)
			__snake_case    :				Optional[int]									=							field(
			    default=128	,metadata={
			        "help": (
			            "The maximum total sequence length for target text after tokenization. Sequences longer "
			            "than this will be truncated, sequences shorter will be padded."
			        )
			    }	,)
			__snake_case    :				Optional[int]									=							field(
			    default=142	,metadata={
			        "help": (
			            "The maximum total sequence length for validation target text after tokenization. Sequences longer "
			            "than this will be truncated, sequences shorter will be padded. "
			            "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
			            "during ``evaluate`` and ``predict``."
			        )
			    }	,)
			__snake_case    :				Optional[int]									=							field(
			    default=142	,metadata={
			        "help": (
			            "The maximum total sequence length for test target text after tokenization. Sequences longer "
			            "than this will be truncated, sequences shorter will be padded."
			        )
			    }	,)
			__snake_case    :				Optional[int]									=							field(default=-1	,metadata={"help": "# training examples. -1 means use all."}   )
			__snake_case    :				Optional[int]									=							field(default=-1	,metadata={"help": "# validation examples. -1 means use all."}   )
			__snake_case    :				Optional[int]									=							field(default=-1	,metadata={"help": "# test examples. -1 means use all."}   )
			__snake_case    :				Optional[str]									=							field(default=_UpperCAmelCase	,metadata={"help": "Source language id for translation."}   )
			__snake_case    :				Optional[str]									=							field(default=_UpperCAmelCase	,metadata={"help": "Target language id for translation."}   )
			__snake_case    :				Optional[int]									=							field(default=_UpperCAmelCase	,metadata={"help": "# num_beams to use for evaluation."}   )
			__snake_case    :				bool									=							field(
			    default=_UpperCAmelCase	,metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}	,)

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  List[Any]:






						"""simple docstring"""





						logger.info(F'***** {split} metrics *****'		)
						for key in sorted(metrics.keys()		):
												logger.info(F'  {key} = {metrics[key]}'		)
						save_json(snake_case__	,os.path.join(snake_case__	,F'{split}_results.json'		)		)

def 	__lowerCamelCase      (		)							->  str:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)		)

						if len(sys.argv		) == 2 and sys.argv[1].endswith(""".json"""		):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
						else:
												_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  parser.parse_args_into_dataclasses()

						check_output_dir(snake_case__		)

						# Setup logging
						logging.basicConfig(
						    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""	,datefmt="""%m/%d/%Y %H:%M:%S"""	,level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN	,)
						logger.warning(
						    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""	,training_args.local_rank	,training_args.device	,training_args.n_gpu	,bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED		)	,training_args.fpaa	,)
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank		):
												transformers.utils.logging.set_verbosity_info()
						logger.info("""Training/evaluation parameters %s"""	,snake_case__		)

						# Set seed
						set_seed(training_args.seed		)

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						_SCREAMING_SNAKE_CASE     				=  AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,cache_dir=model_args.cache_dir	,)

						_SCREAMING_SNAKE_CASE     				=  ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
						for p in extra_model_params:
												if getattr(snake_case__	,snake_case__	,snake_case__		):
																		assert hasattr(snake_case__	,snake_case__		), F'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
																		setattr(snake_case__	,snake_case__	,getattr(snake_case__	,snake_case__		)		)

						_SCREAMING_SNAKE_CASE     				=  AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	,cache_dir=model_args.cache_dir	,)
						_SCREAMING_SNAKE_CASE     				=  AutoModelForSeqaSeqLM.from_pretrained(
						    model_args.model_name_or_path	,from_tf=""".ckpt""" in model_args.model_name_or_path	,config=snake_case__	,cache_dir=model_args.cache_dir	,)

						# use task specific params
						use_task_specific_params(snake_case__	,data_args.task		)

						# set num_beams for evaluation
						if data_args.eval_beams is None:
												_SCREAMING_SNAKE_CASE     				=  model.config.num_beams

						# set decoder_start_token_id for MBart
						if model.config.decoder_start_token_id is None and isinstance(snake_case__	,(MBartTokenizer, MBartTokenizerFast)		):
												assert (
												    data_args.tgt_lang is not None and data_args.src_lang is not None
												), "mBart requires --tgt_lang and --src_lang"
												if isinstance(snake_case__	,snake_case__		):
																		_SCREAMING_SNAKE_CASE     				=  tokenizer.lang_code_to_id[data_args.tgt_lang]
												else:
																		_SCREAMING_SNAKE_CASE     				=  tokenizer.convert_tokens_to_ids(data_args.tgt_lang		)

						if model_args.freeze_embeds:
												freeze_embeds(snake_case__		)
						if model_args.freeze_encoder:
												freeze_params(model.get_encoder()		)
												assert_all_frozen(model.get_encoder()		)

						_SCREAMING_SNAKE_CASE     				=  SeqaSeqDataset

						# Get datasets
						_SCREAMING_SNAKE_CASE     				=  (
						    dataset_class(
						        snake_case__	,type_path="""train"""	,data_dir=data_args.data_dir	,n_obs=data_args.n_train	,max_target_length=data_args.max_target_length	,max_source_length=data_args.max_source_length	,prefix=model.config.prefix or """"""	,)
						    if training_args.do_train
						    else None
						)
						_SCREAMING_SNAKE_CASE     				=  (
						    dataset_class(
						        snake_case__	,type_path="""val"""	,data_dir=data_args.data_dir	,n_obs=data_args.n_val	,max_target_length=data_args.val_max_target_length	,max_source_length=data_args.max_source_length	,prefix=model.config.prefix or """"""	,)
						    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
						    else None
						)
						_SCREAMING_SNAKE_CASE     				=  (
						    dataset_class(
						        snake_case__	,type_path="""test"""	,data_dir=data_args.data_dir	,n_obs=data_args.n_test	,max_target_length=data_args.test_max_target_length	,max_source_length=data_args.max_source_length	,prefix=model.config.prefix or """"""	,)
						    if training_args.do_predict
						    else None
						)

						# Initialize our Trainer
						_SCREAMING_SNAKE_CASE     				=  (
						    build_compute_metrics_fn(data_args.task	,snake_case__		) if training_args.predict_with_generate else None
						)
						_SCREAMING_SNAKE_CASE     				=  SeqaSeqTrainer(
						    model=snake_case__	,args=snake_case__	,data_args=snake_case__	,train_dataset=snake_case__	,eval_dataset=snake_case__	,data_collator=SeqaSeqDataCollator(
						        snake_case__	,snake_case__	,model.config.decoder_start_token_id	,training_args.tpu_num_cores		)	,compute_metrics=snake_case__	,tokenizer=snake_case__	,)

						_SCREAMING_SNAKE_CASE     				=  {}
						# Training
						if training_args.do_train:
												logger.info("""*** Train ***"""		)

												_SCREAMING_SNAKE_CASE     				=  trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path		) else None		)
												_SCREAMING_SNAKE_CASE     				=  train_result.metrics
												_SCREAMING_SNAKE_CASE     				=  data_args.n_train

												trainer.save_model()  # this also saves the tokenizer

												if trainer.is_world_process_zero():
																		handle_metrics("""train"""	,snake_case__	,training_args.output_dir		)
																		all_metrics.update(snake_case__		)

																		# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
																		trainer.state.save_to_json(os.path.join(training_args.output_dir	,"""trainer_state.json"""		)		)

																		# For convenience, we also re-save the tokenizer to the same directory,
																		# so that you can share your model easily on huggingface.co/models =)
																		tokenizer.save_pretrained(training_args.output_dir		)

    # Evaluation
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""		)

												_SCREAMING_SNAKE_CASE     				=  trainer.evaluate(metric_key_prefix="""val"""		)
												_SCREAMING_SNAKE_CASE     				=  data_args.n_val
												_SCREAMING_SNAKE_CASE     				=  round(metrics["""val_loss"""]	,4		)

												if trainer.is_world_process_zero():
																		handle_metrics("""val"""	,snake_case__	,training_args.output_dir		)
																		all_metrics.update(snake_case__		)

						if training_args.do_predict:
												logger.info("""*** Predict ***"""		)

												_SCREAMING_SNAKE_CASE     				=  trainer.predict(test_dataset=snake_case__	,metric_key_prefix="""test"""		)
												_SCREAMING_SNAKE_CASE     				=  test_output.metrics
												_SCREAMING_SNAKE_CASE     				=  data_args.n_test

												if trainer.is_world_process_zero():
																		_SCREAMING_SNAKE_CASE     				=  round(metrics["""test_loss"""]	,4		)
																		handle_metrics("""test"""	,snake_case__	,training_args.output_dir		)
																		all_metrics.update(snake_case__		)

																		if training_args.predict_with_generate:
																								_SCREAMING_SNAKE_CASE     				=  tokenizer.batch_decode(
																								    test_output.predictions	,skip_special_tokens=snake_case__	,clean_up_tokenization_spaces=snake_case__		)
																								_SCREAMING_SNAKE_CASE     				=  lmap(str.strip	,snake_case__		)
																								write_txt_file(snake_case__	,os.path.join(training_args.output_dir	,"""test_generations.txt"""		)		)

						if trainer.is_world_process_zero():
												save_json(snake_case__	,os.path.join(training_args.output_dir	,"""all_results.json"""		)		)

						return all_metrics

def 	__lowerCamelCase      (		snake_case__		)							->  int:






						"""simple docstring"""





						main()


if __name__ == "__main__":
		main()









# 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


UpperCamelCase			    =     get_logger(__name__)



class 				__UpperCAmelCase		:
			__snake_case    :				Tuple									=							"dummy_data"
			__snake_case    :				List[Any]									=							"datasets"
			__snake_case    :				List[Any]									=							False







			def __init__(     self:  Any				,     UpperCAmelCase_:  str				,     UpperCAmelCase_:  str				,     UpperCAmelCase_:  Union[Version, str]				,     UpperCAmelCase_:  Optional[str] = None				,     UpperCAmelCase_:  bool = False				,     UpperCAmelCase_:  bool = True				,     UpperCAmelCase_:  Optional[List[Callable]] = None				,     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  0
									_SCREAMING_SNAKE_CASE     				=  dataset_name
									_SCREAMING_SNAKE_CASE     				=  cache_dir
									_SCREAMING_SNAKE_CASE     				=  use_local_dummy_data
									_SCREAMING_SNAKE_CASE     				=  config
									# download_callbacks take a single url as input
									_SCREAMING_SNAKE_CASE     				=  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
									_SCREAMING_SNAKE_CASE     				=  load_existing_dummy_data

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







			@property
			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									if self._dummy_file is None:
															_SCREAMING_SNAKE_CASE     				=  self.download_dummy_data()
									return self._dummy_file







			@property
			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									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:  Optional[int]					):







									'''simple docstring'''




									return os.path.join(self.dummy_data_folder				,     """dummy_data.zip"""					)







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  (
									    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
									)

									_SCREAMING_SNAKE_CASE     				=  cached_path(
									    UpperCAmelCase_				,     cache_dir=self.cache_dir				,     extract_compressed_file=UpperCAmelCase_				,     force_extract=UpperCAmelCase_					)

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







			@property
			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									return os.path.join(self.datasets_scripts_dir				,     self.dataset_name				,     self.dummy_zip_file					)







			@property
			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									if self._bucket_url is None:
															_SCREAMING_SNAKE_CASE     				=  hf_github_url(self.dataset_name				,     self.dummy_zip_file.replace(os.sep				,     """/"""					)					)
									return self._bucket_url







			@property
			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									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:  str				,     UpperCAmelCase_:  str				,     *UpperCAmelCase_:  Dict					):







									'''simple docstring'''




									if self.load_existing_dummy_data:
															# dummy data is downloaded and tested
															_SCREAMING_SNAKE_CASE     				=  self.dummy_file
									else:
															# dummy data cannot be downloaded and only the path to dummy file is returned
															_SCREAMING_SNAKE_CASE     				=  self.dummy_file_name

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







			def 							UpperCamelCase				(     self:  int				,     UpperCAmelCase_:  Tuple				,     *UpperCAmelCase_:  Any					):







									'''simple docstring'''




									return self.download_and_extract(UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[Any]				,     UpperCAmelCase_:  int				,     UpperCAmelCase_:  Optional[Any]					):







									'''simple docstring'''




									return self.download_and_extract(UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Dict				,     UpperCAmelCase_:  Dict				,     *UpperCAmelCase_:  Tuple				,     **UpperCAmelCase_:  Union[str, Any]					):







									'''simple docstring'''




									return path







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									return {}







			def 							UpperCamelCase				(     self:  List[Any]				,     UpperCAmelCase_:  Dict				,     UpperCAmelCase_:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  {}
									for key, single_urls in data_url.items():
															for download_callback in self.download_callbacks:
																					if isinstance(UpperCAmelCase_				,     UpperCAmelCase_					):
																											for single_url in single_urls:
																																	download_callback(UpperCAmelCase_					)
																					else:
																											_SCREAMING_SNAKE_CASE     				=  single_urls
																											download_callback(UpperCAmelCase_					)
            # 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(UpperCAmelCase_				,     UpperCAmelCase_					):
																					_SCREAMING_SNAKE_CASE     				=  [os.path.join(UpperCAmelCase_				,     urllib.parse.quote_plus(Path(UpperCAmelCase_					).name					)					) for x in single_urls]
															else:
																					_SCREAMING_SNAKE_CASE     				=  single_urls
																					_SCREAMING_SNAKE_CASE     				=  os.path.join(UpperCAmelCase_				,     urllib.parse.quote_plus(Path(UpperCAmelCase_					).name					)					)
															_SCREAMING_SNAKE_CASE     				=  value

									# make sure that values are unique
									if all(isinstance(UpperCAmelCase_				,     UpperCAmelCase_					) 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
															_SCREAMING_SNAKE_CASE     				=  {key: value + key for key, value in dummy_data_dict.items()}

									return dummy_data_dict







			def 							UpperCamelCase				(     self:  int				,     UpperCAmelCase_:  Dict				,     UpperCAmelCase_:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  []
									# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
									_SCREAMING_SNAKE_CASE     				=  all(bool(re.findall("""[0-9]{3,}-of-[0-9]{3,}"""				,     UpperCAmelCase_					)					) for url in data_url					)
									_SCREAMING_SNAKE_CASE     				=  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):
															_SCREAMING_SNAKE_CASE     				=  [data_url[0]] * len(UpperCAmelCase_					)
									for single_url in data_url:
															for download_callback in self.download_callbacks:
																					download_callback(UpperCAmelCase_					)
															# 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
															_SCREAMING_SNAKE_CASE     				=  os.path.join(UpperCAmelCase_				,     urllib.parse.quote_plus(single_url.split("""/"""					)[-1]					)					)
															dummy_data_list.append(UpperCAmelCase_					)
									return dummy_data_list







			def 							UpperCamelCase				(     self:  Tuple				,     UpperCAmelCase_:  Dict				,     UpperCAmelCase_:  List[Any]					):







									'''simple docstring'''




									for download_callback in self.download_callbacks:
															download_callback(UpperCAmelCase_					)
									# 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
									_SCREAMING_SNAKE_CASE     				=  os.path.join(UpperCAmelCase_				,     urllib.parse.quote_plus(data_url.split("""/"""					)[-1]					)					)
									if os.path.exists(UpperCAmelCase_					) 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:  Optional[Any]					):







									'''simple docstring'''




									pass







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									pass







			def 							UpperCamelCase				(     self:  Tuple				,     UpperCAmelCase_:  Union[str, Any]					):







									'''simple docstring'''




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

									_SCREAMING_SNAKE_CASE     				=  Path(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  _iter_archive_members(UpperCAmelCase_					) 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(UpperCAmelCase_					).as_posix(), file_path.open("""rb"""					)







			def 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  List[Any]					):







									'''simple docstring'''




									if not isinstance(UpperCAmelCase_				,     UpperCAmelCase_					):
															_SCREAMING_SNAKE_CASE     				=  [paths]
									for path in paths:
															if os.path.isfile(UpperCAmelCase_					):
																					if os.path.basename(UpperCAmelCase_					).startswith((""".""", """__""")					):
																											return
																					yield path
															else:
																					for dirpath, dirnames, filenames in os.walk(UpperCAmelCase_					):
																											if os.path.basename(UpperCAmelCase_					).startswith((""".""", """__""")					):
																																	continue
																											dirnames.sort()
																											for filename in sorted(UpperCAmelCase_					):
																																	if filename.startswith((""".""", """__""")					):
																																							continue
																																	yield os.path.join(UpperCAmelCase_				,     UpperCAmelCase_					)

