






# 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 __lowerCamelCase :





	"""simple docstring"""






	UpperCamelCase__								=      "dummy_data"
	UpperCamelCase__								=      "datasets"
	UpperCamelCase__								=      False

	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase = None       ,    UpperCAmelCase = False       ,    UpperCAmelCase = True       ,    UpperCAmelCase = None       ,    ):
							"""simple docstring"""







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

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

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	@property
	def        UpperCamelCase    (							self    ):
							"""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    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

							_UpperCAmelCase									=			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    ):
							"""simple docstring"""







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

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	@property
	def        UpperCamelCase    (							self    ):
							"""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       ,    UpperCAmelCase       ,    *UpperCAmelCase    ):
							"""simple docstring"""







							if self.load_existing_dummy_data:
													# dummy data is downloaded and tested
													_UpperCAmelCase									=			self.dummy_file
							else:
													# dummy data cannot be downloaded and only the path to dummy file is returned
													_UpperCAmelCase									=			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       ,    UpperCAmelCase       ,    *UpperCAmelCase    ):
							"""simple docstring"""







							return self.download_and_extract(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return self.download_and_extract(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return path

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return {}

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			{}
							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:
																									_UpperCAmelCase									=			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    ):
																			_UpperCAmelCase									=			[os.path.join(UpperCAmelCase       ,    urllib.parse.quote_plus(Path(UpperCAmelCase    ).name    )    ) for x in single_urls]
													else:
																			_UpperCAmelCase									=			single_urls
																			_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    urllib.parse.quote_plus(Path(UpperCAmelCase    ).name    )    )
													_UpperCAmelCase									=			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
													_UpperCAmelCase									=			{key: value + key for key, value in dummy_data_dict.items()}

							return dummy_data_dict

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""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
							_UpperCAmelCase									=			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    ):
							"""simple docstring"""







							pass

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







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

							_UpperCAmelCase									=			Path(UpperCAmelCase    )
							_UpperCAmelCase									=			_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       ,    UpperCAmelCase    ):
							"""simple docstring"""







							if not isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
													_UpperCAmelCase									=			[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    )








from typing import TYPE_CHECKING

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


_a      	=  {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''OPTForCausalLM''',
		    '''OPTModel''',
		    '''OPTPreTrainedModel''',
		    '''OPTForSequenceClassification''',
		    '''OPTForQuestionAnswering''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''FlaxOPTForCausalLM''',
		    '''FlaxOPTModel''',
		    '''FlaxOPTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_opt import (
				    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OPTForCausalLM,
				    OPTForQuestionAnswering,
				    OPTForSequenceClassification,
				    OPTModel,
				    OPTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
		import sys

		_a      	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
