

'''simple docstring'''





import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu


class  UpperCAmelCase (							nn.Module    ):

		'''simple docstring'''






		def __init__(					self		)  ->     Optional[int]:






							'''simple docstring'''



							super().__init__()
							lowerCamelCase_  				=      nn.Linear(3   ,			4		)
							lowerCamelCase_  				=      nn.BatchNormad(4		)
							lowerCamelCase_  				=      nn.Linear(4   ,			5		)






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     int:






							'''simple docstring'''



							return self.lineara(self.batchnorm(self.lineara(SCREAMING_SNAKE_CASE_		)		)		)


class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			*SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)  ->     List[Any]:






							'''simple docstring'''



							return (args[0] + 1,) + args[1:], kwargs


class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     Optional[int]:






							'''simple docstring'''



							return output + 1


class  UpperCAmelCase (							unittest.TestCase    ):

		'''simple docstring'''






		def 			UpperCamelCase(					self		)  ->     List[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      ModelForTest()
							lowerCamelCase_  				=      ModelHook()

							add_hook_to_module(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
							self.assertEqual(test_model._hf_hook   ,			SCREAMING_SNAKE_CASE_		)
							self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,			'_old_forward'		)		)

							# Check adding the hook did not change the name or the signature
							self.assertEqual(test_model.forward.__name__   ,			'forward'		)
							self.assertListEqual(list(inspect.signature(test_model.forward		).parameters		)   ,			['x']		)

							remove_hook_from_module(SCREAMING_SNAKE_CASE_		)
							self.assertFalse(hasattr(SCREAMING_SNAKE_CASE_   ,			'_hf_hook'		)		)
							self.assertFalse(hasattr(SCREAMING_SNAKE_CASE_   ,			'_old_forward'		)		)
		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							lowerCamelCase_  				=      ModelForTest()
							lowerCamelCase_  				=      ModelHook()

							add_hook_to_module(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
							add_hook_to_module(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			append=SCREAMING_SNAKE_CASE_		)

							self.assertEqual(isinstance(test_model._hf_hook   ,			SCREAMING_SNAKE_CASE_		)   ,			SCREAMING_SNAKE_CASE_		)
							self.assertEqual(len(test_model._hf_hook.hooks		)   ,			2		)
							self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,			'_old_forward'		)		)

							# Check adding the hook did not change the name or the signature
							self.assertEqual(test_model.forward.__name__   ,			'forward'		)
							self.assertListEqual(list(inspect.signature(test_model.forward		).parameters		)   ,			['x']		)

							remove_hook_from_module(SCREAMING_SNAKE_CASE_		)
							self.assertFalse(hasattr(SCREAMING_SNAKE_CASE_   ,			'_hf_hook'		)		)
							self.assertFalse(hasattr(SCREAMING_SNAKE_CASE_   ,			'_old_forward'		)		)
		def 			UpperCamelCase(					self		)  ->     Dict:






							'''simple docstring'''



							lowerCamelCase_  				=      ModelForTest()
							lowerCamelCase_  				=      torch.randn(2   ,			3		)
							lowerCamelCase_  				=      test_model(x + 1		)
							lowerCamelCase_  				=      test_model(x + 2		)

							lowerCamelCase_  				=      PreForwardHook()
							add_hook_to_module(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      test_model(SCREAMING_SNAKE_CASE_		)
							self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			atol=1E-5		)		)

							# Attaching a hook to a model when it already has one replaces, does not chain
							lowerCamelCase_  				=      PreForwardHook()
							add_hook_to_module(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      test_model(SCREAMING_SNAKE_CASE_		)
							self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			atol=1E-5		)		)

							# You need to use the sequential hook to chain two or more hooks
							lowerCamelCase_  				=      SequentialHook(PreForwardHook()   ,			PreForwardHook()		)
							add_hook_to_module(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      test_model(SCREAMING_SNAKE_CASE_		)
							assert torch.allclose(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			atol=1E-5		)
		def 			UpperCamelCase(					self		)  ->     Optional[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      ModelForTest()
							lowerCamelCase_  				=      torch.randn(2   ,			3		)
							lowerCamelCase_  				=      test_model(SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      PostForwardHook()
							add_hook_to_module(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      test_model(SCREAMING_SNAKE_CASE_		)
							self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_   ,			output + 1   ,			atol=1E-5		)		)

							# Attaching a hook to a model when it already has one replaces, does not chain
							lowerCamelCase_  				=      PostForwardHook()
							add_hook_to_module(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      test_model(SCREAMING_SNAKE_CASE_		)
							self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_   ,			output + 1   ,			atol=1E-5		)		)

							# You need to use the sequential hook to chain two or more hooks
							lowerCamelCase_  				=      SequentialHook(PostForwardHook()   ,			PostForwardHook()		)
							add_hook_to_module(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      test_model(SCREAMING_SNAKE_CASE_		)
							assert torch.allclose(SCREAMING_SNAKE_CASE_   ,			output + 2   ,			atol=1E-5		)
		def 			UpperCamelCase(					self		)  ->     List[str]:






							'''simple docstring'''



							lowerCamelCase_  				=      ModelForTest()
							lowerCamelCase_  				=      torch.randn(2   ,			3		)
							lowerCamelCase_  				=      test_model(SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      PostForwardHook()
							add_hook_to_module(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      test_model(SCREAMING_SNAKE_CASE_		)
							self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_   ,			output + 1		)		)
							self.assertTrue(outputa.requires_grad		)

							lowerCamelCase_  				=      True
							lowerCamelCase_  				=      test_model(SCREAMING_SNAKE_CASE_		)
							self.assertFalse(outputa.requires_grad		)
		@require_multi_gpu
		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							lowerCamelCase_  				=      ModelForTest()
							# Everything is on CPU
							self.assertEqual(model.lineara.weight.device   ,			torch.device('cpu'		)		)
							self.assertEqual(model.batchnorm.weight.device   ,			torch.device('cpu'		)		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('cpu'		)		)

							# This will move each submodule on different devices
							add_hook_to_module(model.lineara   ,			AlignDevicesHook(execution_device=0		)		)
							add_hook_to_module(model.batchnorm   ,			AlignDevicesHook(execution_device=0		)		)
							add_hook_to_module(model.lineara   ,			AlignDevicesHook(execution_device=1		)		)

							self.assertEqual(model.lineara.weight.device   ,			torch.device(0		)		)
							self.assertEqual(model.batchnorm.weight.device   ,			torch.device(0		)		)
							self.assertEqual(model.batchnorm.running_mean.device   ,			torch.device(0		)		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device(1		)		)

							# We can still make a forward pass. The input does not need to be on any particular device
							lowerCamelCase_  				=      torch.randn(2   ,			3		)
							lowerCamelCase_  				=      model(SCREAMING_SNAKE_CASE_		)
							self.assertEqual(output.device   ,			torch.device(1		)		)

							# We can add a general hook to put back output on same device as input.
							add_hook_to_module(SCREAMING_SNAKE_CASE_   ,			AlignDevicesHook(io_same_device=SCREAMING_SNAKE_CASE_		)		)
							lowerCamelCase_  				=      torch.randn(2   ,			3		).to(0		)
							lowerCamelCase_  				=      model(SCREAMING_SNAKE_CASE_		)
							self.assertEqual(output.device   ,			torch.device(0		)		)
		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							lowerCamelCase_  				=      ModelForTest()

							# Everything is on CPU
							self.assertEqual(model.lineara.weight.device   ,			torch.device('cpu'		)		)
							self.assertEqual(model.batchnorm.weight.device   ,			torch.device('cpu'		)		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('cpu'		)		)

							# This will move each submodule on different devices
							lowerCamelCase_  				=      {'execution_device': 0 if torch.cuda.is_available() else 'cpu', 'offload': True}

							add_hook_to_module(model.lineara   ,			AlignDevicesHook(**SCREAMING_SNAKE_CASE_		)		)
							add_hook_to_module(model.batchnorm   ,			AlignDevicesHook(**SCREAMING_SNAKE_CASE_		)		)
							add_hook_to_module(model.lineara   ,			AlignDevicesHook(**SCREAMING_SNAKE_CASE_		)		)

							# Parameters have been offloaded, so on the meta device
							self.assertEqual(model.lineara.weight.device   ,			torch.device('meta'		)		)
							self.assertEqual(model.batchnorm.weight.device   ,			torch.device('meta'		)		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('meta'		)		)
							# Buffers are not included in the offload by default, so are on the execution device
							lowerCamelCase_  				=      torch.device(hook_kwargs['execution_device']		)
							self.assertEqual(model.batchnorm.running_mean.device   ,			SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      torch.randn(2   ,			3		)
							lowerCamelCase_  				=      model(SCREAMING_SNAKE_CASE_		)
							self.assertEqual(output.device   ,			SCREAMING_SNAKE_CASE_		)

							# Removing hooks loads back the weights in the model.
							remove_hook_from_module(model.lineara		)
							remove_hook_from_module(model.batchnorm		)
							remove_hook_from_module(model.lineara		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('cpu'		)		)
							self.assertEqual(model.batchnorm.weight.device   ,			torch.device('cpu'		)		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('cpu'		)		)

							# Now test with buffers included in the offload
							lowerCamelCase_  				=      {
							    'execution_device': 0 if torch.cuda.is_available() else 'cpu',
							    'offload': True,
							    'offload_buffers': True,
							}

							add_hook_to_module(model.lineara   ,			AlignDevicesHook(**SCREAMING_SNAKE_CASE_		)		)
							add_hook_to_module(model.batchnorm   ,			AlignDevicesHook(**SCREAMING_SNAKE_CASE_		)		)
							add_hook_to_module(model.lineara   ,			AlignDevicesHook(**SCREAMING_SNAKE_CASE_		)		)

							# Parameters have been offloaded, so on the meta device, buffers included
							self.assertEqual(model.lineara.weight.device   ,			torch.device('meta'		)		)
							self.assertEqual(model.batchnorm.weight.device   ,			torch.device('meta'		)		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('meta'		)		)
							self.assertEqual(model.batchnorm.running_mean.device   ,			torch.device('meta'		)		)

							lowerCamelCase_  				=      torch.randn(2   ,			3		)
							lowerCamelCase_  				=      model(SCREAMING_SNAKE_CASE_		)
							self.assertEqual(output.device   ,			SCREAMING_SNAKE_CASE_		)

							# Removing hooks loads back the weights in the model.
							remove_hook_from_module(model.lineara		)
							remove_hook_from_module(model.batchnorm		)
							remove_hook_from_module(model.lineara		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('cpu'		)		)
							self.assertEqual(model.batchnorm.weight.device   ,			torch.device('cpu'		)		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('cpu'		)		)
		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							lowerCamelCase_  				=      ModelForTest()

							# Everything is on CPU
							self.assertEqual(model.lineara.weight.device   ,			torch.device('cpu'		)		)
							self.assertEqual(model.batchnorm.weight.device   ,			torch.device('cpu'		)		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('cpu'		)		)

							# This will move each submodule on different devices
							lowerCamelCase_  				=      0 if torch.cuda.is_available() else 'cpu'
							attach_align_device_hook(SCREAMING_SNAKE_CASE_   ,			execution_device=SCREAMING_SNAKE_CASE_   ,			offload=SCREAMING_SNAKE_CASE_		)

							# Parameters have been offloaded, so on the meta device
							self.assertEqual(model.lineara.weight.device   ,			torch.device('meta'		)		)
							self.assertEqual(model.batchnorm.weight.device   ,			torch.device('meta'		)		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('meta'		)		)
							# Buffers are not included in the offload by default, so are on the execution device
							lowerCamelCase_  				=      torch.device(SCREAMING_SNAKE_CASE_		)
							self.assertEqual(model.batchnorm.running_mean.device   ,			SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      torch.randn(2   ,			3		)
							lowerCamelCase_  				=      model(SCREAMING_SNAKE_CASE_		)
							self.assertEqual(output.device   ,			SCREAMING_SNAKE_CASE_		)

							# Removing hooks loads back the weights in the model.
							remove_hook_from_submodules(SCREAMING_SNAKE_CASE_		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('cpu'		)		)
							self.assertEqual(model.batchnorm.weight.device   ,			torch.device('cpu'		)		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('cpu'		)		)

							# Now test with buffers included in the offload
							attach_align_device_hook(SCREAMING_SNAKE_CASE_   ,			execution_device=SCREAMING_SNAKE_CASE_   ,			offload=SCREAMING_SNAKE_CASE_   ,			offload_buffers=SCREAMING_SNAKE_CASE_		)

							# Parameters have been offloaded, so on the meta device, buffers included
							self.assertEqual(model.lineara.weight.device   ,			torch.device('meta'		)		)
							self.assertEqual(model.batchnorm.weight.device   ,			torch.device('meta'		)		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('meta'		)		)
							self.assertEqual(model.batchnorm.running_mean.device   ,			torch.device('meta'		)		)

							lowerCamelCase_  				=      torch.randn(2   ,			3		)
							lowerCamelCase_  				=      model(SCREAMING_SNAKE_CASE_		)
							self.assertEqual(output.device   ,			SCREAMING_SNAKE_CASE_		)

							# Removing hooks loads back the weights in the model.
							remove_hook_from_submodules(SCREAMING_SNAKE_CASE_		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('cpu'		)		)
							self.assertEqual(model.batchnorm.weight.device   ,			torch.device('cpu'		)		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('cpu'		)		)






		def 			UpperCamelCase(					self		)  ->     Dict:






							'''simple docstring'''



							lowerCamelCase_  				=      ModelForTest()

							# Everything is on CPU
							self.assertEqual(model.lineara.weight.device   ,			torch.device('cpu'		)		)
							self.assertEqual(model.batchnorm.weight.device   ,			torch.device('cpu'		)		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('cpu'		)		)

							# This will move each submodule on different devices
							lowerCamelCase_  				=      0 if torch.cuda.is_available() else 'cpu'
							attach_align_device_hook(
							    SCREAMING_SNAKE_CASE_   ,			execution_device=SCREAMING_SNAKE_CASE_   ,			offload=SCREAMING_SNAKE_CASE_   ,			weights_map=model.state_dict()		)

							# Parameters have been offloaded, so on the meta device
							self.assertEqual(model.lineara.weight.device   ,			torch.device('meta'		)		)
							self.assertEqual(model.batchnorm.weight.device   ,			torch.device('meta'		)		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('meta'		)		)
							# Buffers are not included in the offload by default, so are on the execution device
							lowerCamelCase_  				=      torch.device(SCREAMING_SNAKE_CASE_		)
							self.assertEqual(model.batchnorm.running_mean.device   ,			SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      torch.randn(2   ,			3		)
							lowerCamelCase_  				=      model(SCREAMING_SNAKE_CASE_		)
							self.assertEqual(output.device   ,			SCREAMING_SNAKE_CASE_		)

							# Removing hooks loads back the weights in the model.
							remove_hook_from_submodules(SCREAMING_SNAKE_CASE_		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('cpu'		)		)
							self.assertEqual(model.batchnorm.weight.device   ,			torch.device('cpu'		)		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('cpu'		)		)

							# Now test with buffers included in the offload
							attach_align_device_hook(
							    SCREAMING_SNAKE_CASE_   ,			execution_device=SCREAMING_SNAKE_CASE_   ,			offload=SCREAMING_SNAKE_CASE_   ,			weights_map=model.state_dict()   ,			offload_buffers=SCREAMING_SNAKE_CASE_   ,			)

							# Parameters have been offloaded, so on the meta device, buffers included
							self.assertEqual(model.lineara.weight.device   ,			torch.device('meta'		)		)
							self.assertEqual(model.batchnorm.weight.device   ,			torch.device('meta'		)		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('meta'		)		)
							self.assertEqual(model.batchnorm.running_mean.device   ,			torch.device('meta'		)		)

							lowerCamelCase_  				=      torch.randn(2   ,			3		)
							lowerCamelCase_  				=      model(SCREAMING_SNAKE_CASE_		)
							self.assertEqual(output.device   ,			SCREAMING_SNAKE_CASE_		)

							# Removing hooks loads back the weights in the model.
							remove_hook_from_submodules(SCREAMING_SNAKE_CASE_		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('cpu'		)		)
							self.assertEqual(model.batchnorm.weight.device   ,			torch.device('cpu'		)		)
							self.assertEqual(model.lineara.weight.device   ,			torch.device('cpu'		)		)





"""simple docstring"""




# 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     :			List[str]  						=			get_logger(__name__)

class 						a__		:
   __lowerCAmelCase				=							"""dummy_data"""
   __lowerCAmelCase				=							"""datasets"""
   __lowerCAmelCase				=							False
   def __init__(     self      ,       _a      ,       _a      ,       _a      ,       _a = None      ,       _a = False      ,       _a = True      ,       _a = None      ,       ):
          lowercase       :							int									=				0
          lowercase       :							Optional[Any]									=				dataset_name
          lowercase       :							List[str]									=				cache_dir
          lowercase       :							Union[str, Any]									=				use_local_dummy_data
          lowercase       :							str									=				config
          # download_callbacks take a single url as input
          lowercase       :							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
          lowercase       :							List[Any]									=				load_existing_dummy_data

          # TODO(PVP, QL) might need to make this more general
          lowercase       :							Tuple									=				str(_a							)
          # to be downloaded
          lowercase       :							Tuple									=				None
          lowercase       :							List[Any]									=				None
   @property
   def      __magic_name__     (     self							):
          if self._dummy_file is None:
                 lowercase       :							Optional[int]									=				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							):
          lowercase       :							Optional[Any]									=				(
              self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
          )

          lowercase       :							str									=				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      __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:
                 lowercase       :							Dict									=				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      ,       _a      ,       *_a							):
          if self.load_existing_dummy_data:
                 # dummy data is downloaded and tested
                 lowercase       :							Optional[int]									=				self.dummy_file
          else:
                 # dummy data cannot be downloaded and only the path to dummy file is returned
                 lowercase       :							Optional[Any]									=				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      __magic_name__     (     self      ,       _a      ,       *_a							):
          return self.download_and_extract(_a							)
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          return self.download_and_extract(_a							)
   def      __magic_name__     (     self      ,       _a      ,       *_a      ,       **_a							):
          return path
   def      __magic_name__     (     self							):
          return {}
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							List[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:
                               lowercase       :							Union[str, 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							):
                        lowercase       :							Any									=				[os.path.join(_a      ,       urllib.parse.quote_plus(Path(_a							).name							)							) for x in single_urls]
                 else:
                        lowercase       :							int									=				single_urls
                        lowercase       :							Tuple									=				os.path.join(_a      ,       urllib.parse.quote_plus(Path(_a							).name							)							)
                 lowercase       :							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
                 lowercase       :							str									=				{key: value + key for key, value in dummy_data_dict.items()}

          return dummy_data_dict
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							Union[str, Any]									=				[]
          # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
          lowercase       :							Any									=				all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}"      ,       _a							)							) for url in data_url							)
          lowercase       :							List[Any]									=				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):
                 lowercase       :							Tuple									=				[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
                 lowercase       :							Union[str, Any]									=				os.path.join(_a      ,       urllib.parse.quote_plus(single_url.split("/"							)[-1]							)							)
                 dummy_data_list.append(_a							)
          return dummy_data_list
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          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
          lowercase       :							Tuple									=				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      __magic_name__     (     self							):
          pass
   def      __magic_name__     (     self							):
          pass
   def      __magic_name__     (     self      ,       _a							):
          def _iter_archive_members(_a							):
                 # this preserves the order of the members inside the ZIP archive
                 lowercase       :							Optional[int]									=				Path(self.dummy_file							).parent
                 lowercase       :							List[str]									=				path.relative_to(_a							)
                 with ZipFile(self.local_path_to_dummy_data							) as zip_file:
                        lowercase       :							List[str]									=				zip_file.namelist()
                 for member in members:
                        if member.startswith(relative_path.as_posix()							):
                               yield dummy_parent_path.joinpath(_a							)

          lowercase       :							Union[str, Any]									=				Path(_a							)
          lowercase       :							List[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      __magic_name__     (     self      ,       _a							):
          if not isinstance(_a      ,       _a							):
                 lowercase       :							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							)


