

import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


A__							=				[nn.LayerNorm]

A__							=				logging.get_logger(__name__)

A__							=				version.parse(version.parse(torch.__version__).base_version)

A__							=				parsed_torch_version_base >= version.parse("2.0")
A__							=				parsed_torch_version_base >= version.parse("1.12")
A__							=				parsed_torch_version_base >= version.parse("1.11")
A__							=				parsed_torch_version_base < version.parse("1.11")




def        _lowercase  (					a_					:       int       ,a_					:       Union[str, Any]       ,a_					:       List[str]       ,a_					:       Any       ,self					:       Optional[int]					)					->						int:




					'''simple docstring'''







					from torch import _softmax_backward_data

					if is_torch_less_than_1_11:
										return _softmax_backward_data(a_       ,a_       ,parent.dim       ,self					)
					else:
										return _softmax_backward_data(a_       ,a_       ,parent.dim       ,self.dtype					)




def        _lowercase  (					a_					:       nn.Linear       ,a_					:       torch.LongTensor       ,a_					:       int = 0					)					->						nn.Linear:




					'''simple docstring'''







					__magic_name__				=     index.to(layer.weight.device					)
					__magic_name__				=     layer.weight.index_select(a_       ,a_					).clone().detach()
					if layer.bias is not None:
										if dim == 1:
															__magic_name__				=     layer.bias.clone().detach()
										else:
															__magic_name__				=     layer.bias[index].clone().detach()
					__magic_name__				=     list(layer.weight.size()					)
					__magic_name__				=     len(a_					)
					__magic_name__				=     nn.Linear(new_size[1]       ,new_size[0]       ,bias=layer.bias is not None					).to(layer.weight.device					)
					__magic_name__				=     False
					new_layer.weight.copy_(W.contiguous()					)
					__magic_name__				=     True
					if layer.bias is not None:
										__magic_name__				=     False
										new_layer.bias.copy_(b.contiguous()					)
										__magic_name__				=     True
					return new_layer

class 					__UpperCamelCase					(       nn.Module      ):







	def __init__( self:			Union[str, Any]	,			__UpperCamelCase:			Optional[Any]	,			__UpperCamelCase:			Any   ):

						'''simple docstring'''





						super().__init__()
						__magic_name__				=     nf
						__magic_name__				=     nn.Parameter(torch.empty(__UpperCamelCase	,			__UpperCamelCase   )   )
						__magic_name__				=     nn.Parameter(torch.zeros(__UpperCamelCase   )   )
						nn.init.normal_(self.weight	,			std=0.02   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			List[Any]   ):

						'''simple docstring'''





						__magic_name__				=     x.size()[:-1] + (self.nf,)
						__magic_name__				=     torch.addmm(self.bias	,			x.view(-1	,			x.size(-1   )   )	,			self.weight   )
						__magic_name__				=     x.view(__UpperCamelCase   )
						return x




def        _lowercase  (					a_					:       ConvaD       ,a_					:       torch.LongTensor       ,a_					:       int = 1					)					->						ConvaD:




					'''simple docstring'''







					__magic_name__				=     index.to(layer.weight.device					)
					__magic_name__				=     layer.weight.index_select(a_       ,a_					).clone().detach()
					if dim == 0:
										__magic_name__				=     layer.bias.clone().detach()
					else:
										__magic_name__				=     layer.bias[index].clone().detach()
					__magic_name__				=     list(layer.weight.size()					)
					__magic_name__				=     len(a_					)
					__magic_name__				=     ConvaD(new_size[1]       ,new_size[0]					).to(layer.weight.device					)
					__magic_name__				=     False
					new_layer.weight.copy_(W.contiguous()					)
					__magic_name__				=     True
					__magic_name__				=     False
					new_layer.bias.copy_(b.contiguous()					)
					__magic_name__				=     True
					return new_layer




def        _lowercase  (					a_					:       Union[nn.Linear, ConvaD]       ,a_					:       torch.LongTensor       ,a_					:       Optional[int] = None					)					->						Union[nn.Linear, ConvaD]:




					'''simple docstring'''







					if isinstance(a_       ,nn.Linear					):
										return prune_linear_layer(a_       ,a_       ,dim=0 if dim is None else dim					)
					elif isinstance(a_       ,a_					):
										return prune_convad_layer(a_       ,a_       ,dim=1 if dim is None else dim					)
					else:
										raise ValueError(F'Can\'t prune layer of class {layer.__class__}'					)




def        _lowercase  (					a_					:       Callable[..., torch.Tensor]       ,a_					:       int       ,a_					:       int       ,*a_					:       Union[str, Any]					)					->						torch.Tensor:




					'''simple docstring'''







					assert len(a_					) > 0, F'{input_tensors} has to be a tuple/list of tensors'

					# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
					__magic_name__				=     len(inspect.signature(a_					).parameters					)
					if num_args_in_forward_chunk_fn != len(a_					):
										raise ValueError(
										    F'forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(a_					)} input '
										    'tensors are given'					)

					if chunk_size > 0:
										__magic_name__				=     input_tensors[0].shape[chunk_dim]
										for input_tensor in input_tensors:
															if input_tensor.shape[chunk_dim] != tensor_shape:
																				raise ValueError(
																				    F'All input tenors have to be of the same shape: {tensor_shape}, '
																				    F'found shape {input_tensor.shape[chunk_dim]}'					)

										if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
															raise ValueError(
															    F'The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '
															    F'size {chunk_size}'					)

										__magic_name__				=     input_tensors[0].shape[chunk_dim] // chunk_size

										# chunk input tensor into tuples
										__magic_name__				=     tuple(input_tensor.chunk(a_       ,dim=a_					) for input_tensor in input_tensors					)
										# apply forward fn to every tuple
										__magic_name__				=     tuple(forward_fn(*a_					) for input_tensors_chunk in zip(*a_					)					)
										# concatenate output at same dimension
										return torch.cat(a_       ,dim=a_					)

					return forward_fn(*a_					)




def        _lowercase  (					a_					:       List[int]       ,a_					:       int       ,a_					:       int       ,a_					:       Set[int]					)					->						Tuple[Set[int], torch.LongTensor]:




					'''simple docstring'''







					__magic_name__				=     torch.ones(a_       ,a_					)
					__magic_name__				=     set(a_					) - already_pruned_heads  # Convert to set and remove already pruned heads
					for head in heads:
										# Compute how many pruned heads are before the head and move the index accordingly
										__magic_name__				=     head - sum(1 if h < head else 0 for h in already_pruned_heads					)
										__magic_name__				=     0
					__magic_name__				=     mask.view(-1					).contiguous().eq(1					)
					__magic_name__				=     torch.arange(len(a_					)					)[mask].long()
					return heads, index




def        _lowercase  (					*a_					:       Union[torch.Tensor, List[torch.Tensor]]       ,a_					:       Optional[str] = None					)					->						Tuple[torch.Tensor, ...]:




					'''simple docstring'''







					return torch.meshgrid(*a_       ,indexing=a_					)




def        _lowercase  (					a_					:       torch.Tensor					)					->						Tuple[torch.device, int, int]:




					'''simple docstring'''







					return tensor.device, storage_ptr(a_					), storage_size(a_					)







import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


A__							=				get_logger(__name__)




def        _lowercase  (					a_					:       int					)					->						Optional[int]:




					'''simple docstring'''







					return TestCommand(
					    args.dataset       ,args.name       ,args.cache_dir       ,args.data_dir       ,args.all_configs       ,args.save_info or args.save_infos       ,args.ignore_verifications       ,args.force_redownload       ,args.clear_cache       ,)


class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: List[Any]												=	False  # to tell pytest it's not a test class







	@staticmethod
	def 						_SCREAMING_SNAKE_CASE				( __UpperCamelCase:			ArgumentParser   ):

						'''simple docstring'''





						__magic_name__				=     parser.add_parser('test'	,			help='Test dataset implementation.'   )
						test_parser.add_argument('--name'	,			type=__UpperCamelCase	,			default=__UpperCamelCase	,			help='Dataset processing name'   )
						test_parser.add_argument(
						    '--cache_dir'	,			type=__UpperCamelCase	,			default=__UpperCamelCase	,			help='Cache directory where the datasets are stored.'	,			)
						test_parser.add_argument(
						    '--data_dir'	,			type=__UpperCamelCase	,			default=__UpperCamelCase	,			help='Can be used to specify a manual directory to get the files from.'	,			)
						test_parser.add_argument('--all_configs'	,			action='store_true'	,			help='Test all dataset configurations'   )
						test_parser.add_argument(
						    '--save_info'	,			action='store_true'	,			help='Save the dataset infos in the dataset card (README.md)'   )
						test_parser.add_argument(
						    '--ignore_verifications'	,			action='store_true'	,			help='Run the test without checksums and splits checks.'	,			)
						test_parser.add_argument('--force_redownload'	,			action='store_true'	,			help='Force dataset redownload'   )
						test_parser.add_argument(
						    '--clear_cache'	,			action='store_true'	,			help='Remove downloaded files and cached datasets after each config test'	,			)
						# aliases
						test_parser.add_argument('--save_infos'	,			action='store_true'	,			help='alias to save_info'   )
						test_parser.add_argument('dataset'	,			type=__UpperCamelCase	,			help='Name of the dataset to download'   )
						test_parser.set_defaults(func=_test_command_factory   )







	def __init__( self:			List[str]	,			__UpperCamelCase:			str	,			__UpperCamelCase:			str	,			__UpperCamelCase:			str	,			__UpperCamelCase:			str	,			__UpperCamelCase:			bool	,			__UpperCamelCase:			bool	,			__UpperCamelCase:			bool	,			__UpperCamelCase:			bool	,			__UpperCamelCase:			bool	,			):

						'''simple docstring'''





						__magic_name__				=     dataset
						__magic_name__				=     name
						__magic_name__				=     cache_dir
						__magic_name__				=     data_dir
						__magic_name__				=     all_configs
						__magic_name__				=     save_infos
						__magic_name__				=     ignore_verifications
						__magic_name__				=     force_redownload
						__magic_name__				=     clear_cache
						if clear_cache and not cache_dir:
											print(
											    'When --clear_cache is used, specifying a cache directory is mandatory.\n'
											    'The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'
											    'Please provide a --cache_dir that will be used to test the dataset script.'   )
											exit(1   )
						if save_infos:
											__magic_name__				=     True







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						fl_logger().setLevel(__UpperCamelCase   )
						if self._name is not None and self._all_configs:
											print('Both parameters `config` and `all_configs` can\'t be used at once.'   )
											exit(1   )
						__magic_name__,						__magic_name__				=     self._dataset, self._name
						__magic_name__				=     dataset_module_factory(__UpperCamelCase   )
						__magic_name__				=     import_main_class(module.module_path   )
						__magic_name__				=     len(builder_cls.BUILDER_CONFIGS   ) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

						def get_builders() -> Generator[DatasetBuilder, None, None]:
											if self._all_configs and builder_cls.BUILDER_CONFIGS:
																for i, config in enumerate(builder_cls.BUILDER_CONFIGS   ):
																					if "config_name" in module.builder_kwargs:
																										yield builder_cls(
																										    cache_dir=self._cache_dir	,			data_dir=self._data_dir	,			**module.builder_kwargs	,			)
																					else:
																										yield builder_cls(
																										    config_name=config.name	,			cache_dir=self._cache_dir	,			data_dir=self._data_dir	,			**module.builder_kwargs	,			)
											else:
																if "config_name" in module.builder_kwargs:
																					yield builder_cls(cache_dir=self._cache_dir	,			data_dir=self._data_dir	,			**module.builder_kwargs   )
																else:
																					yield builder_cls(
																					    config_name=__UpperCamelCase	,			cache_dir=self._cache_dir	,			data_dir=self._data_dir	,			**module.builder_kwargs	,			)

						for j, builder in enumerate(get_builders()   ):
											print(F'Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})'   )
											__magic_name__				=     os.path.exists(
											    os.path.join(builder.get_imported_module_dir()	,			datasets.config.DATASETDICT_INFOS_FILENAME   )   )  # record checksums only if we need to update a (deprecated) dataset_infos.json
											builder.download_and_prepare(
											    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
											    if not self._force_redownload
											    else DownloadMode.FORCE_REDOWNLOAD	,			verification_mode=VerificationMode.NO_CHECKS
											    if self._ignore_verifications
											    else VerificationMode.ALL_CHECKS	,			try_from_hf_gcs=__UpperCamelCase	,			)
											builder.as_dataset()
											if self._save_infos:
																builder._save_infos()

											# If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
											# The dataset_infos are saved in the YAML part of the README.md

											# Let's move it to the original directory of the dataset script, to allow the user to
											# upload them on S3 at the same time afterwards.
											if self._save_infos:
																__magic_name__				=     os.path.join(builder_cls.get_imported_module_dir()	,			'README.md'   )
																__magic_name__				=     Path(__UpperCamelCase   ).name + '.py'
																__magic_name__				=     os.path.join(__UpperCamelCase	,			__UpperCamelCase   )
																if os.path.isfile(__UpperCamelCase   ):
																					__magic_name__				=     os.path.dirname(__UpperCamelCase   )
																elif os.path.isfile(__UpperCamelCase   ):
																					__magic_name__				=     path
																elif os.path.isdir(__UpperCamelCase   ):  # for local directories containing only data files
																					__magic_name__				=     path
																else:  # in case of a remote dataset
																					__magic_name__				=     None
																					print(F'Dataset card saved at {dataset_readme_path}'   )

																# Move dataset_info back to the user
																if dataset_dir is not None:
																					__magic_name__				=     os.path.join(__UpperCamelCase	,			'README.md'   )
																					copyfile(__UpperCamelCase	,			__UpperCamelCase   )
																					print(F'Dataset card saved at {user_dataset_readme_path}'   )

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
											if self._clear_cache:
																if os.path.isdir(builder._cache_dir   ):
																					logger.warning(F'Clearing cache at {builder._cache_dir}'   )
																					rmtree(builder._cache_dir   )
																__magic_name__				=     os.path.join(self._cache_dir	,			datasets.config.DOWNLOADED_DATASETS_DIR   )
																if os.path.isdir(__UpperCamelCase   ):
																					logger.warning(F'Clearing cache at {download_dir}'   )
																					rmtree(__UpperCamelCase   )

						print('Test successful.'   )




