





# 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


A_												=     get_logger(__name__)

class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					A__				 =    "dummy_data"
					A__				 =    "datasets"
					A__				 =    False





					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  _lowerCAmelCase = False	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  ):
										lowerCamelCase__       =       0
										lowerCamelCase__       =       dataset_name
										lowerCamelCase__       =       cache_dir
										lowerCamelCase__       =       use_local_dummy_data
										lowerCamelCase__       =       config
										# download_callbacks take a single url as input
										lowerCamelCase__       =       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
										lowerCamelCase__       =       load_existing_dummy_data

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





					@property
					def 					__magic_name__ ( self	):
										if self._dummy_file is None:
															lowerCamelCase__       =       self.download_dummy_data()
										return self._dummy_file





					@property
					def 					__magic_name__ ( self	):
										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 					__magic_name__ ( self	):
										return os.path.join(self.dummy_data_folder	,  "dummy_data.zip"	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       (
										    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
										)

										lowerCamelCase__       =       cached_path(
										    _lowerCAmelCase	,  cache_dir=self.cache_dir	,  extract_compressed_file=_lowerCAmelCase	,  force_extract=_lowerCAmelCase	)

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





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





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





					@property
					def 					__magic_name__ ( self	):
										# 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 					__magic_name__ ( self	,  _lowerCAmelCase	,  *_lowerCAmelCase	):
										if self.load_existing_dummy_data:
															# dummy data is downloaded and tested
															lowerCamelCase__       =       self.dummy_file
										else:
															# dummy data cannot be downloaded and only the path to dummy file is returned
															lowerCamelCase__       =       self.dummy_file_name

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





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  *_lowerCAmelCase	):
										return self.download_and_extract(_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										return self.download_and_extract(_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  *_lowerCAmelCase	,  **_lowerCAmelCase	):
										return path





					def 					__magic_name__ ( self	):
										return {}





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

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

										return dummy_data_dict





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





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										for download_callback in self.download_callbacks:
															download_callback(_lowerCAmelCase	)
										# 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
										lowerCamelCase__       =       os.path.join(_lowerCAmelCase	,  urllib.parse.quote_plus(data_url.split("/"	)[-1]	)	)
										if os.path.exists(_lowerCAmelCase	) 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 					__magic_name__ ( self	):
										pass





					def 					__magic_name__ ( self	):
										pass





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

										lowerCamelCase__       =       Path(_lowerCAmelCase	)
										lowerCamelCase__       =       _iter_archive_members(_lowerCAmelCase	) 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(_lowerCAmelCase	).as_posix(), file_path.open("rb"	)






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








import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


A_												=     logging.get_logger(__name__)

A_												=     list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
A_												=     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					A__				 =    field(
					    default=lowercase_							,       metadata={"help": "Model type selected in the list: " + ", ".join(lowercase_   )}   )
					A__				 =    field(
					    default=lowercase_							,       metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."}   )
					A__				 =    field(
					    default=1_2_8							,       metadata={
					        "help": (
					            "The maximum total input sequence length after tokenization. Sequences longer "
					            "than this will be truncated, sequences shorter will be padded."
					        )
					    }							,       )
					A__				 =    field(
					    default=1_2_8							,       metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."}							,       )
					A__				 =    field(
					    default=6_4							,       metadata={
					        "help": (
					            "The maximum number of tokens for the question. Questions longer than this will "
					            "be truncated to this length."
					        )
					    }							,       )
					A__				 =    field(
					    default=3_0							,       metadata={
					        "help": (
					            "The maximum length of an answer that can be generated. This is needed because the start "
					            "and end predictions are not conditioned on one another."
					        )
					    }							,       )
					A__				 =    field(
					    default=lowercase_							,       metadata={"help": "Overwrite the cached training and evaluation sets"}   )
					A__				 =    field(
					    default=lowercase_							,       metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."}   )
					A__				 =    field(
					    default=0.0							,       metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}   )
					A__				 =    field(
					    default=2_0							,       metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}   )
					A__				 =    field(
					    default=0							,       metadata={
					        "help": (
					            "language id of input for language-specific xlm models (see"
					            " tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
					        )
					    }							,       )
					A__				 =    field(default=1							,       metadata={"help": "multiple threads for converting example to features"}   )

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    "train"
					A__				 =    "dev"

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    42
					A__				 =    42
					A__				 =    42
					A__				 =    42





					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  _lowerCAmelCase = Split.train	,  _lowerCAmelCase = False	,  _lowerCAmelCase = None	,  _lowerCAmelCase = "pt"	,  ):
										lowerCamelCase__       =       args
										lowerCamelCase__       =       is_language_sensitive
										lowerCamelCase__       =       SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
										if isinstance(_lowerCAmelCase	,  _lowerCAmelCase	):
															try:
																				lowerCamelCase__       =       Split[mode]
															except KeyError:
																				raise KeyError("mode is not a valid split name"	)
										lowerCamelCase__       =       mode
										# Load data features from cache or dataset file
										lowerCamelCase__       =       "v2" if args.version_2_with_negative else "v1"
										lowerCamelCase__       =       os.path.join(
										    cache_dir if cache_dir is not None else args.data_dir	,  F"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}"	,  )

										# Make sure only the first process in distributed training processes the dataset,
										# and the others will use the cache.
										lowerCamelCase__       =       cached_features_file + ".lock"
										with FileLock(_lowerCAmelCase	):
															if os.path.exists(_lowerCAmelCase	) and not args.overwrite_cache:
																				lowerCamelCase__       =       time.time()
																				lowerCamelCase__       =       torch.load(_lowerCAmelCase	)

																				# Legacy cache files have only features, while new cache files
																				# will have dataset and examples also.
																				lowerCamelCase__       =       self.old_features["features"]
																				lowerCamelCase__       =       self.old_features.get("dataset"	,  _lowerCAmelCase	)
																				lowerCamelCase__       =       self.old_features.get("examples"	,  _lowerCAmelCase	)
																				logger.info(
																				    F"Loading features from cached file {cached_features_file} [took %.3f s]"	,  time.time() - start	)

																				if self.dataset is None or self.examples is None:
																									logger.warning(
																									    F"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"
																									    " future run"	)
															else:
																				if mode == Split.dev:
																									lowerCamelCase__       =       self.processor.get_dev_examples(args.data_dir	)
																				else:
																									lowerCamelCase__       =       self.processor.get_train_examples(args.data_dir	)

																				lowerCamelCase__    ,		lowerCamelCase__       =       squad_convert_examples_to_features(
																				    examples=self.examples	,  tokenizer=_lowerCAmelCase	,  max_seq_length=args.max_seq_length	,  doc_stride=args.doc_stride	,  max_query_length=args.max_query_length	,  is_training=mode == Split.train	,  threads=args.threads	,  return_dataset=_lowerCAmelCase	,  )

																				lowerCamelCase__       =       time.time()
																				torch.save(
																				    {"features": self.features, "dataset": self.dataset, "examples": self.examples}	,  _lowerCAmelCase	,  )
																				# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																				logger.info(
																				    F"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"	)





					def __len__( self	):
										return len(self.features	)






					def __getitem__( self	,  _lowerCAmelCase	):
										# Convert to Tensors and build dataset
										lowerCamelCase__       =       self.features[i]

										lowerCamelCase__       =       torch.tensor(feature.input_ids	,  dtype=torch.long	)
										lowerCamelCase__       =       torch.tensor(feature.attention_mask	,  dtype=torch.long	)
										lowerCamelCase__       =       torch.tensor(feature.token_type_ids	,  dtype=torch.long	)
										lowerCamelCase__       =       torch.tensor(feature.cls_index	,  dtype=torch.long	)
										lowerCamelCase__       =       torch.tensor(feature.p_mask	,  dtype=torch.float	)
										lowerCamelCase__       =       torch.tensor(feature.is_impossible	,  dtype=torch.float	)

										lowerCamelCase__       =       {
										    "input_ids": input_ids,
										    "attention_mask": attention_mask,
										    "token_type_ids": token_type_ids,
										}

										if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
															del inputs["token_type_ids"]

										if self.args.model_type in ["xlnet", "xlm"]:
															inputs.update({"cls_index": cls_index, "p_mask": p_mask}	)
															if self.args.version_2_with_negative:
																				inputs.update({"is_impossible": is_impossible}	)
															if self.is_language_sensitive:
																				inputs.update({"langs": (torch.ones(input_ids.shape	,  dtype=torch.intaa	) * self.args.lang_id)}	)

										if self.mode == Split.train:
															lowerCamelCase__       =       torch.tensor(feature.start_position	,  dtype=torch.long	)
															lowerCamelCase__       =       torch.tensor(feature.end_position	,  dtype=torch.long	)
															inputs.update({"start_positions": start_positions, "end_positions": end_positions}	)

										return inputs

