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


lowercase__      :    List[Any]          =     get_logger(__name__)

def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     Optional[Any]:
							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        lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	UpperCAmelCase_  : List[Any]       =    False  # to tell pytest it's not a test class


	@staticmethod
	def       SCREAMING_SNAKE_CASE_    (							__SCREAMING_SNAKE_CASE    )						->Dict:
								lowerCAmelCase     		=		parser.add_parser('''test'''		,      help='''Test dataset implementation.'''    )
								test_parser.add_argument('''--name'''		,      type=__SCREAMING_SNAKE_CASE		,      default=__SCREAMING_SNAKE_CASE		,      help='''Dataset processing name'''    )
								test_parser.add_argument(
								    '''--cache_dir'''		,      type=__SCREAMING_SNAKE_CASE		,      default=__SCREAMING_SNAKE_CASE		,      help='''Cache directory where the datasets are stored.'''		,      )
								test_parser.add_argument(
								    '''--data_dir'''		,      type=__SCREAMING_SNAKE_CASE		,      default=__SCREAMING_SNAKE_CASE		,      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=__SCREAMING_SNAKE_CASE		,      help='''Name of the dataset to download'''    )
								test_parser.set_defaults(func=_test_command_factory    )


	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      )						->Optional[int]:
								lowerCAmelCase     		=		dataset
								lowerCAmelCase     		=		name
								lowerCAmelCase     		=		cache_dir
								lowerCAmelCase     		=		data_dir
								lowerCAmelCase     		=		all_configs
								lowerCAmelCase     		=		save_infos
								lowerCAmelCase     		=		ignore_verifications
								lowerCAmelCase     		=		force_redownload
								lowerCAmelCase     		=		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:
															lowerCAmelCase     		=		True




	def       SCREAMING_SNAKE_CASE_    (							self    )						->str:
								fl_logger().setLevel(__SCREAMING_SNAKE_CASE    )
								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    )
								lowerCAmelCase		,				lowerCAmelCase     		=		self._dataset, self._name
								lowerCAmelCase     		=		dataset_module_factory(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		import_main_class(module.module_path    )
								lowerCAmelCase     		=		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=__SCREAMING_SNAKE_CASE		,      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})"    )
															lowerCAmelCase     		=		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=__SCREAMING_SNAKE_CASE		,      )
															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:
																						lowerCAmelCase     		=		os.path.join(builder_cls.get_imported_module_dir()		,      '''README.md'''    )
																						lowerCAmelCase     		=		Path(__SCREAMING_SNAKE_CASE    ).name + '''.py'''
																						lowerCAmelCase     		=		os.path.join(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )
																						if os.path.isfile(__SCREAMING_SNAKE_CASE    ):
																													lowerCAmelCase     		=		os.path.dirname(__SCREAMING_SNAKE_CASE    )
																						elif os.path.isfile(__SCREAMING_SNAKE_CASE    ):
																													lowerCAmelCase     		=		path
																						elif os.path.isdir(__SCREAMING_SNAKE_CASE    ):  # for local directories containing only data files
																													lowerCAmelCase     		=		path
																						else:  # in case of a remote dataset
																													lowerCAmelCase     		=		None
																													print(F"Dataset card saved at {dataset_readme_path}"    )

																						# Move dataset_info back to the user
																						if dataset_dir is not None:
																													lowerCAmelCase     		=		os.path.join(__SCREAMING_SNAKE_CASE		,      '''README.md'''    )
																													copyfile(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )
																													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    )
																						lowerCAmelCase     		=		os.path.join(self._cache_dir		,      datasets.config.DOWNLOADED_DATASETS_DIR    )
																						if os.path.isdir(__SCREAMING_SNAKE_CASE    ):
																													logger.warning(F"Clearing cache at {download_dir}"    )
																													rmtree(__SCREAMING_SNAKE_CASE    )

								print('''Test successful.'''    )




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


lowercase__      :    str          =     [nn.LayerNorm]

lowercase__      :    str          =     logging.get_logger(__name__)

lowercase__      :    List[Any]          =     version.parse(version.parse(torch.__version__).base_version)

lowercase__      :    Dict          =     parsed_torch_version_base >= version.parse('''2.0''')
lowercase__      :    int          =     parsed_torch_version_base >= version.parse('''1.12''')
lowercase__      :    Optional[int]          =     parsed_torch_version_base >= version.parse('''1.11''')
lowercase__      :    int          =     parsed_torch_version_base < version.parse('''1.11''')

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  self							)					->     Union[str, Any]:
							from torch import _softmax_backward_data

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

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




class        lowercase_  (  nn.Module						):



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Optional[Any]:
								super().__init__()
								lowerCAmelCase     		=		nf
								lowerCAmelCase     		=		nn.Parameter(torch.empty(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )    )
								lowerCAmelCase     		=		nn.Parameter(torch.zeros(__SCREAMING_SNAKE_CASE    )    )
								nn.init.normal_(self.weight		,      std=0.0_2    )




	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->List[str]:
								lowerCAmelCase     		=		x.size()[:-1] + (self.nf,)
								lowerCAmelCase     		=		torch.addmm(self.bias		,      x.view(-1		,      x.size(-1    )    )		,      self.weight    )
								lowerCAmelCase     		=		x.view(__SCREAMING_SNAKE_CASE    )
								return x

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

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__ = None							)					->     Union[nn.Linear, ConvaD]:
							if isinstance(snake_case__       ,  nn.Linear							):
														return prune_linear_layer(snake_case__       ,  snake_case__       ,  dim=0 if dim is None else dim							)
							elif isinstance(snake_case__       ,  snake_case__							):
														return prune_convad_layer(snake_case__       ,  snake_case__       ,  dim=1 if dim is None else dim							)
							else:
														raise ValueError(f"Can't prune layer of class {layer.__class__}"							)

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__       ,  *snake_case__							)					->     torch.Tensor:
							assert len(snake_case__							) > 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
							lowerCAmelCase     		=		len(inspect.signature(snake_case__							).parameters							)
							if num_args_in_forward_chunk_fn != len(snake_case__							):
														raise ValueError(
														    f"forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(snake_case__							)} input "
														    '''tensors are given'''							)

							if chunk_size > 0:
														lowerCAmelCase     		=		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}"							)

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

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

							return forward_fn(*snake_case__							)

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__							)					->     Tuple[Set[int], torch.LongTensor]:
							lowerCAmelCase     		=		torch.ones(snake_case__       ,  snake_case__							)
							lowerCAmelCase     		=		set(snake_case__							) - 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
														lowerCAmelCase     		=		head - sum(1 if h < head else 0 for h in already_pruned_heads							)
														lowerCAmelCase     		=		0
							lowerCAmelCase     		=		mask.view(-1							).contiguous().eq(1							)
							lowerCAmelCase     		=		torch.arange(len(snake_case__							)							)[mask].long()
							return heads, index

def        SCREAMING_SNAKE_CASE_					(						*snake_case__       ,  snake_case__ = None							)					->     Tuple[torch.Tensor, ...]:
							return torch.meshgrid(*snake_case__       ,  indexing=snake_case__							)






def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     Tuple[torch.device, int, int]:
							return tensor.device, storage_ptr(snake_case__							), storage_size(snake_case__							)



