
'''simple docstring'''
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												=	get_logger(__name__)


class       _lowerCAmelCase   :







							'''simple docstring'''

							lowerCAmelCase_ 						=						"""dummy_data"""
							lowerCAmelCase_ 						=						"""datasets"""
							lowerCAmelCase_ 						=						False

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = None  ,  UpperCAmelCase = False  ,  UpperCAmelCase = True  ,  UpperCAmelCase = None  ,  )					->       List[Any]:
														_snake_case       =  0
														_snake_case       =  dataset_name
														_snake_case       =  cache_dir
														_snake_case       =  use_local_dummy_data
														_snake_case       =  config
														# download_callbacks take a single url as input
														_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
														_snake_case       =  load_existing_dummy_data

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

							@property
							def 	lowercase							(self  )					->       int:
														if self._dummy_file is None:
																					_snake_case       =  self.download_dummy_data()
														return self._dummy_file

							@property
							def 	lowercase							(self  )					->       Optional[int]:
														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  )					->       Dict:
														return os.path.join(self.dummy_data_folder  ,  """dummy_data.zip"""  )

							def 	lowercase							(self  )					->       str:
														_snake_case       =  (
														    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
														)

														_snake_case       =  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 	lowercase							(self  )					->       Union[str, Any]:
														return os.path.join(self.datasets_scripts_dir  ,  self.dataset_name  ,  self.dummy_zip_file  )

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

							@property
							def 	lowercase							(self  )					->       Any:
														# 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  ,  UpperCAmelCase  ,  *UpperCAmelCase  )					->       Optional[int]:
														if self.load_existing_dummy_data:
																					# dummy data is downloaded and tested
																					_snake_case       =  self.dummy_file
														else:
																					# dummy data cannot be downloaded and only the path to dummy file is returned
																					_snake_case       =  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 	lowercase							(self  ,  UpperCAmelCase  ,  *UpperCAmelCase  )					->       Tuple:
														return self.download_and_extract(lowerCAmelCase__  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       str:
														return self.download_and_extract(lowerCAmelCase__  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  *UpperCAmelCase  ,  **UpperCAmelCase  )					->       Optional[Any]:
														return path

							def 	lowercase							(self  )					->       List[str]:
														return {}

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Any:
														_snake_case       =  {}
														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:
																																			_snake_case       =  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__  ):
																												_snake_case       =  [os.path.join(lowerCAmelCase__  ,  urllib.parse.quote_plus(Path(lowerCAmelCase__  ).name  )  ) for x in single_urls]
																					else:
																												_snake_case       =  single_urls
																												_snake_case       =  os.path.join(lowerCAmelCase__  ,  urllib.parse.quote_plus(Path(lowerCAmelCase__  ).name  )  )
																					_snake_case       =  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
																					_snake_case       =  {key: value + key for key, value in dummy_data_dict.items()}

														return dummy_data_dict

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

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       List[Any]:
														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
														_snake_case       =  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 	lowercase							(self  )					->       Union[str, Any]:
														pass

							def 	lowercase							(self  )					->       Optional[Any]:
														pass

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       Union[str, Any]:
														def _iter_archive_members(UpperCAmelCase  ):
																					# this preserves the order of the members inside the ZIP archive
																					_snake_case       =  Path(self.dummy_file  ).parent
																					_snake_case       =  path.relative_to(lowerCAmelCase__  )
																					with ZipFile(self.local_path_to_dummy_data  ) as zip_file:
																												_snake_case       =  zip_file.namelist()
																					for member in members:
																												if member.startswith(relative_path.as_posix()  ):
																																			yield dummy_parent_path.joinpath(lowerCAmelCase__  )

														_snake_case       =  Path(lowerCAmelCase__  )
														_snake_case       =  _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 	lowercase							(self  ,  UpperCAmelCase  )					->       Tuple:
														if not isinstance(lowerCAmelCase__  ,  lowerCAmelCase__  ):
																					_snake_case       =  [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__  )

'''simple docstring'''
from typing import TYPE_CHECKING

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


__lowerCAmelCase												=	{
    'configuration_efficientformer': [
        'EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientFormerConfig',
    ]
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	['EfficientFormerImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'EfficientFormerForImageClassification',
				    'EfficientFormerForImageClassificationWithTeacher',
				    'EfficientFormerModel',
				    'EfficientFormerPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFEfficientFormerForImageClassification',
				    'TFEfficientFormerForImageClassificationWithTeacher',
				    'TFEfficientFormerModel',
				    'TFEfficientFormerPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_efficientformer import EfficientFormerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_efficientformer import (
								    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EfficientFormerForImageClassification,
								    EfficientFormerForImageClassificationWithTeacher,
								    EfficientFormerModel,
								    EfficientFormerPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_efficientformer import (
								    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFEfficientFormerForImageClassification,
								    TFEfficientFormerForImageClassificationWithTeacher,
								    TFEfficientFormerModel,
								    TFEfficientFormerPreTrainedModel,
								)

else:
				import sys

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