






# 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


__lowerCamelCase      :    int   		=   get_logger(__name__)



class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					a_			=	"dummy_data"
					a_			=	"datasets"
					a_			=	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							,	):
						snake_case__				:   Tuple     =						0
						snake_case__				:   Union[str, Any]     =						dataset_name
						snake_case__				:   List[str]     =						cache_dir
						snake_case__				:   str     =						use_local_dummy_data
						snake_case__				:   Any     =						config
						# download_callbacks take a single url as input
						snake_case__				:   List[Callable]     =						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
						snake_case__				:   Union[str, Any]     =						load_existing_dummy_data

						# TODO(PVP, QL) might need to make this more general
						snake_case__				:   Union[str, Any]     =						str(__A )
						# to be downloaded
						snake_case__				:   List[str]     =						None
						snake_case__				:   Union[str, Any]     =						None



					@property
					def  _lowercase  (      self  :						str ):
						if self._dummy_file is None:
							snake_case__				:   Dict     =						self.download_dummy_data()
						return self._dummy_file



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



					def  _lowercase  (      self  :						List[Any] ):
						snake_case__				:   List[Any]     =						(
						    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
						)

						snake_case__				:   Dict     =						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  _lowercase  (      self  :						str ):
						return os.path.join(self.datasets_scripts_dir							,	self.dataset_name							,	self.dummy_zip_file )



					@property
					def  _lowercase  (      self  :						List[str] ):
						if self._bucket_url is None:
							snake_case__				:   List[Any]     =						hf_github_url(self.dataset_name							,	self.dummy_zip_file.replace(os.sep							,	"/" ) )
						return self._bucket_url



					@property
					def  _lowercase  (      self  :						List[str] ):
						# 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  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Optional[Any]							,	*__A  :						Optional[int] ):
						if self.load_existing_dummy_data:
							# dummy data is downloaded and tested
							snake_case__				:   List[str]     =						self.dummy_file
						else:
							# dummy data cannot be downloaded and only the path to dummy file is returned
							snake_case__				:   Dict     =						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  _lowercase  (      self  :						Union[str, Any]							,	__A  :						List[str]							,	*__A  :						List[Any] ):
						return self.download_and_extract(__A )



					def  _lowercase  (      self  :						Any							,	__A  :						Union[str, Any]							,	__A  :						str ):
						return self.download_and_extract(__A )



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						int							,	*__A  :						List[str]							,	**__A  :						str ):
						return path



					def  _lowercase  (      self  :						Optional[Any] ):
						return {}



					def  _lowercase  (      self  :						Dict							,	__A  :						int							,	__A  :						Tuple ):
						snake_case__				:   str     =						{}
						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:
									snake_case__				:   Optional[Any]     =						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 ):
								snake_case__				:   List[str]     =						[os.path.join(__A							,	urllib.parse.quote_plus(Path(__A ).name ) ) for x in single_urls]
							else:
								snake_case__				:   List[str]     =						single_urls
								snake_case__				:   Dict     =						os.path.join(__A							,	urllib.parse.quote_plus(Path(__A ).name ) )
							snake_case__				:   List[str]     =						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
							snake_case__				:   str     =						{key: value + key for key, value in dummy_data_dict.items()}

						return dummy_data_dict



					def  _lowercase  (      self  :						List[Any]							,	__A  :						Optional[Any]							,	__A  :						Union[str, Any] ):
						snake_case__				:   Tuple     =						[]
						# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
						snake_case__				:   List[str]     =						all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}"							,	__A ) ) for url in data_url )
						snake_case__				:   Optional[int]     =						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):
							snake_case__				:   Optional[Any]     =						[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
							snake_case__				:   List[str]     =						os.path.join(__A							,	urllib.parse.quote_plus(single_url.split("/" )[-1] ) )
							dummy_data_list.append(__A )
						return dummy_data_list



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						List[Any]							,	__A  :						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
						snake_case__				:   int     =						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  _lowercase  (      self  :						List[str] ):
						pass



					def  _lowercase  (      self  :						Dict ):
						pass



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

						snake_case__				:   int     =						Path(__A )
						snake_case__				:   Any     =						_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  _lowercase  (      self  :						int							,	__A  :						Any ):
						if not isinstance(__A							,	__A ):
							snake_case__				:   Union[str, Any]     =						[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 )











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase      :    Tuple   		=   {
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Tuple   		=   [
	    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RobertaPreLayerNormForCausalLM""",
	    """RobertaPreLayerNormForMaskedLM""",
	    """RobertaPreLayerNormForMultipleChoice""",
	    """RobertaPreLayerNormForQuestionAnswering""",
	    """RobertaPreLayerNormForSequenceClassification""",
	    """RobertaPreLayerNormForTokenClassification""",
	    """RobertaPreLayerNormModel""",
	    """RobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Union[str, Any]   		=   [
	    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFRobertaPreLayerNormForCausalLM""",
	    """TFRobertaPreLayerNormForMaskedLM""",
	    """TFRobertaPreLayerNormForMultipleChoice""",
	    """TFRobertaPreLayerNormForQuestionAnswering""",
	    """TFRobertaPreLayerNormForSequenceClassification""",
	    """TFRobertaPreLayerNormForTokenClassification""",
	    """TFRobertaPreLayerNormMainLayer""",
	    """TFRobertaPreLayerNormModel""",
	    """TFRobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    List[Any]   		=   [
	    """FlaxRobertaPreLayerNormForCausalLM""",
	    """FlaxRobertaPreLayerNormForMaskedLM""",
	    """FlaxRobertaPreLayerNormForMultipleChoice""",
	    """FlaxRobertaPreLayerNormForQuestionAnswering""",
	    """FlaxRobertaPreLayerNormForSequenceClassification""",
	    """FlaxRobertaPreLayerNormForTokenClassification""",
	    """FlaxRobertaPreLayerNormModel""",
	    """FlaxRobertaPreLayerNormPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase      :    Dict   		=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



