


'''simple docstring'''


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


lowerCamelCase__     						=  [nn.LayerNorm]

lowerCamelCase__     						=  logging.get_logger(__name__)

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

lowerCamelCase__     						=  parsed_torch_version_base >= version.parse('2.0')
lowerCamelCase__     						=  parsed_torch_version_base >= version.parse('1.12')
lowerCamelCase__     						=  parsed_torch_version_base >= version.parse('1.11')
lowerCamelCase__     						=  parsed_torch_version_base < version.parse('1.11')






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			self ):
		from torch import _softmax_backward_data

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






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase = 0 ):
		_UpperCAmelCase		:							Union[str, Any]             =   index.to(layer.weight.device )
		_UpperCAmelCase		:							int             =   layer.weight.index_select(__lowerCAmelCase   ,			__lowerCAmelCase ).clone().detach()
		if layer.bias is not None:
				if dim == 1:
						_UpperCAmelCase		:							Union[str, Any]             =   layer.bias.clone().detach()
				else:
						_UpperCAmelCase		:							List[str]             =   layer.bias[index].clone().detach()
		_UpperCAmelCase		:							int             =   list(layer.weight.size() )
		_UpperCAmelCase		:							Dict             =   len(__lowerCAmelCase )
		_UpperCAmelCase		:							Tuple             =   nn.Linear(new_size[1]   ,			new_size[0]   ,			bias=layer.bias is not None ).to(layer.weight.device )
		_UpperCAmelCase		:							Optional[int]             =   False
		new_layer.weight.copy_(W.contiguous() )
		_UpperCAmelCase		:							int             =   True
		if layer.bias is not None:
				_UpperCAmelCase		:							Any             =   False
				new_layer.bias.copy_(b.contiguous() )
				_UpperCAmelCase		:							str             =   True
		return new_layer






class 						lowerCAmelCase__      (      nn.Module						):




			def __init__(		self	:  Union[str, Any]						,	lowerCamelCase__	:  Union[str, Any]						,	lowerCamelCase__	:  List[str]			) ->str:



					'''simple docstring'''







					super().__init__()
					_UpperCAmelCase		:							int             =   nf
					_UpperCAmelCase		:							Optional[int]             =   nn.Parameter(torch.empty(lowerCamelCase__						,	lowerCamelCase__			)			)
					_UpperCAmelCase		:							Optional[Any]             =   nn.Parameter(torch.zeros(lowerCamelCase__			)			)
					nn.init.normal_(self.weight						,	std=0.0_2			)






			def 				lowerCAmelCase__     (		self	:  Any						,	lowerCamelCase__	:  Tuple			) ->str:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   x.size()[:-1] + (self.nf,)
					_UpperCAmelCase		:							Optional[Any]             =   torch.addmm(self.bias						,	x.view(-1						,	x.size(-1			)			)						,	self.weight			)
					_UpperCAmelCase		:							Optional[int]             =   x.view(lowerCamelCase__			)
					return x






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase = 1 ):
		_UpperCAmelCase		:							Union[str, Any]             =   index.to(layer.weight.device )
		_UpperCAmelCase		:							Optional[Any]             =   layer.weight.index_select(__lowerCAmelCase   ,			__lowerCAmelCase ).clone().detach()
		if dim == 0:
				_UpperCAmelCase		:							Tuple             =   layer.bias.clone().detach()
		else:
				_UpperCAmelCase		:							List[Any]             =   layer.bias[index].clone().detach()
		_UpperCAmelCase		:							Union[str, Any]             =   list(layer.weight.size() )
		_UpperCAmelCase		:							Dict             =   len(__lowerCAmelCase )
		_UpperCAmelCase		:							int             =   ConvaD(new_size[1]   ,			new_size[0] ).to(layer.weight.device )
		_UpperCAmelCase		:							List[Any]             =   False
		new_layer.weight.copy_(W.contiguous() )
		_UpperCAmelCase		:							Union[str, Any]             =   True
		_UpperCAmelCase		:							Dict             =   False
		new_layer.bias.copy_(b.contiguous() )
		_UpperCAmelCase		:							List[str]             =   True
		return new_layer






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






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			*__lowerCAmelCase ):
		assert len(__lowerCAmelCase ) > 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
		_UpperCAmelCase		:							Any             =   len(inspect.signature(__lowerCAmelCase ).parameters )
		if num_args_in_forward_chunk_fn != len(__lowerCAmelCase ):
				raise ValueError(
				    F"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(__lowerCAmelCase )} input """
				    "tensors are given" )

		if chunk_size > 0:
				_UpperCAmelCase		:							List[Any]             =   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}""" )

				_UpperCAmelCase		:							int             =   input_tensors[0].shape[chunk_dim] // chunk_size

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

		return forward_fn(*__lowerCAmelCase )






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase ):
		_UpperCAmelCase		:							Any             =   torch.ones(__lowerCAmelCase   ,			__lowerCAmelCase )
		_UpperCAmelCase		:							Dict             =   set(__lowerCAmelCase ) - 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
				_UpperCAmelCase		:							str             =   head - sum(1 if h < head else 0 for h in already_pruned_heads )
				_UpperCAmelCase		:							Dict             =   0
		_UpperCAmelCase		:							List[Any]             =   mask.view(-1 ).contiguous().eq(1 )
		_UpperCAmelCase		:							torch.LongTensor             =   torch.arange(len(__lowerCAmelCase ) )[mask].long()
		return heads, index






def 			__lowerCAmelCase			(*__lowerCAmelCase   ,			__lowerCAmelCase = None ):
		return torch.meshgrid(*__lowerCAmelCase   ,			indexing=__lowerCAmelCase )






def 			__lowerCAmelCase			(__lowerCAmelCase ):
		return tensor.device, storage_ptr(__lowerCAmelCase ), storage_size(__lowerCAmelCase )







'''simple docstring'''


import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			**__lowerCAmelCase ):
		return RunBeamCommand(
		    args.dataset   ,			args.name   ,			args.cache_dir   ,			args.beam_pipeline_options   ,			args.data_dir   ,			args.all_configs   ,			args.save_info or args.save_infos   ,			args.ignore_verifications   ,			args.force_redownload   ,			**__lowerCAmelCase   ,			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  ArgumentParser			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Union[str, Any]             =   parser.add_parser("run_beam"						,	help="Run a Beam dataset processing pipeline"			)
					run_beam_parser.add_argument("dataset"						,	type=lowerCamelCase__						,	help="Name of the dataset to download"			)
					run_beam_parser.add_argument("--name"						,	type=lowerCamelCase__						,	default=lowerCamelCase__						,	help="Dataset config name"			)
					run_beam_parser.add_argument(
					    "--cache_dir"						,	type=lowerCamelCase__						,	default=lowerCamelCase__						,	help="Cache directory where the datasets are stored"						,	)
					run_beam_parser.add_argument(
					    "--beam_pipeline_options"						,	type=lowerCamelCase__						,	default=""						,	help="Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`"						,	)
					run_beam_parser.add_argument(
					    "--data_dir"						,	type=lowerCamelCase__						,	default=lowerCamelCase__						,	help="Can be used to specify a manual directory to get the files from"						,	)
					run_beam_parser.add_argument("--all_configs"						,	action="store_true"						,	help="Test all dataset configurations"			)
					run_beam_parser.add_argument("--save_info"						,	action="store_true"						,	help="Save the dataset infos file"			)
					run_beam_parser.add_argument(
					    "--ignore_verifications"						,	action="store_true"						,	help="Run the test without checksums and splits checks"			)
					run_beam_parser.add_argument("--force_redownload"						,	action="store_true"						,	help="Force dataset redownload"			)
					# aliases
					run_beam_parser.add_argument("--save_infos"						,	action="store_true"						,	help="alias for save_info"			)
					run_beam_parser.set_defaults(func=lowerCamelCase__			)




			def __init__(		self	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  bool						,	lowerCamelCase__	:  bool						,	lowerCamelCase__	:  bool						,	lowerCamelCase__	:  bool						,	**lowerCamelCase__	:  Optional[Any]						,	) ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:							Any             =   dataset
					_UpperCAmelCase		:							str             =   name
					_UpperCAmelCase		:							int             =   cache_dir
					_UpperCAmelCase		:							Optional[int]             =   beam_pipeline_options
					_UpperCAmelCase		:							int             =   data_dir
					_UpperCAmelCase		:							Optional[int]             =   all_configs
					_UpperCAmelCase		:							Any             =   save_infos
					_UpperCAmelCase		:							Optional[Any]             =   ignore_verifications
					_UpperCAmelCase		:							Any             =   force_redownload
					_UpperCAmelCase		:							Tuple             =   config_kwargs






			def 				lowerCAmelCase__     (		self	:  Dict			) ->Union[str, Any]:



					'''simple docstring'''







					import apache_beam as beam

					if self._name is not None and self._all_configs:
							print("Both parameters `name` and `all_configs` can't be used at once."			)
							exit(1			)
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Union[str, Any]             =   self._dataset, self._name
					_UpperCAmelCase		:							Union[str, Any]             =   dataset_module_factory(lowerCamelCase__			)
					_UpperCAmelCase		:							Any             =   import_main_class(dataset_module.module_path			)
					_UpperCAmelCase		:							List[DatasetBuilder]             =   []
					if self._beam_pipeline_options:
							_UpperCAmelCase		:							Optional[int]             =   beam.options.pipeline_options.PipelineOptions(
							    flags=[F"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(","			) if opt]			)
					else:
							_UpperCAmelCase		:							str             =   None
					if self._all_configs and len(builder_cls.BUILDER_CONFIGS			) > 0:
							for builder_config in builder_cls.BUILDER_CONFIGS:
									builders.append(
									    builder_cls(
									        config_name=builder_config.name						,	data_dir=self._data_dir						,	hash=dataset_module.hash						,	beam_options=lowerCamelCase__						,	cache_dir=self._cache_dir						,	base_path=dataset_module.builder_kwargs.get("base_path"			)						,	)			)
					else:
							builders.append(
							    builder_cls(
							        config_name=lowerCamelCase__						,	data_dir=self._data_dir						,	beam_options=lowerCamelCase__						,	cache_dir=self._cache_dir						,	base_path=dataset_module.builder_kwargs.get("base_path"			)						,	**self._config_kwargs						,	)			)

					for builder in builders:
							builder.download_and_prepare(
							    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
							    if not self._force_redownload
							    else DownloadMode.FORCE_REDOWNLOAD						,	download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH			)						,	verification_mode=VerificationMode.NO_CHECKS
							    if self._ignore_verifications
							    else VerificationMode.ALL_CHECKS						,	try_from_hf_gcs=lowerCamelCase__						,	)
							if self._save_infos:
									builder._save_infos()

					print("Apache beam run successful."			)

					# If save_infos=True, the dataset infos file is created next to the loaded module file.
					# 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:
							_UpperCAmelCase		:							Optional[Any]             =   os.path.join(builder_cls.get_imported_module_dir()						,	config.DATASETDICT_INFOS_FILENAME			)

							_UpperCAmelCase		:							List[str]             =   Path(lowerCamelCase__			).name + ".py"

							_UpperCAmelCase		:							int             =   os.path.join(lowerCamelCase__						,	lowerCamelCase__			)
							if os.path.isfile(lowerCamelCase__			):
									_UpperCAmelCase		:							Any             =   os.path.dirname(lowerCamelCase__			)
							elif os.path.isfile(lowerCamelCase__			):
									_UpperCAmelCase		:							Tuple             =   path
							else:  # in case of a remote dataset
									print(F"""Dataset Infos file saved at {dataset_infos_path}"""			)
									exit(1			)

							# Move datasetinfo back to the user
							_UpperCAmelCase		:							Union[str, Any]             =   os.path.join(lowerCamelCase__						,	config.DATASETDICT_INFOS_FILENAME			)
							copyfile(lowerCamelCase__						,	lowerCamelCase__			)
							print(F"""Dataset Infos file saved at {user_dataset_infos_path}"""			)



