






import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


__UpperCamelCase   :						int							=  logging.getLogger(__name__)
def        a_	( )     ->     Tuple:







				"""simple docstring"""
				snake_case__													=       argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'					)
				parser.add_argument(
				    '--dataset_name' ,				type=_A ,				default='wikitext' ,				help='Name of the training. Explore datasets at: hf.co/datasets.' ,				)
				parser.add_argument(
				    '--dataset_config' ,				type=_A ,				default='wikitext-103-raw-v1' ,				help='Configuration name of the dataset.'					)
				parser.add_argument(
				    '--batch_size' ,				type=_A ,				default=1000 ,				help='Batch size during training.' ,				)
				parser.add_argument(
				    '--vocab_size' ,				type=_A ,				default=10048 ,				help='Size of the desired vocabulary.' ,				)
				parser.add_argument(
				    '--limit' ,				default=_A ,				type=_A ,				help='Limit the number of shards (used for debugging).' ,				)
				parser.add_argument(
				    '--export_to_hub' ,				action='store_true' ,				)

				snake_case__													=       parser.parse_args()
				return args
def        a_	( _A					)     ->     Union[str, Any]:







				"""simple docstring"""
				snake_case__													=       datasets.load_dataset(args.dataset_name ,				args.dataset_config ,				split='train'					)

				if args.limit is not None:
								snake_case__													=       min(len(_A					) ,				args.limit					)
								snake_case__													=       dataset.select(range(_A					)					)
								logger.info(f'''Limiting the dataset to {args.limit} entries.'''					)

				def batch_iterator():
								for i in range(0 ,				len(_A					) ,				args.batch_size					):
												yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
				snake_case__													=       Tokenizer(Unigram()					)
				snake_case__													=       normalizers.Sequence([normalizers.Replace('``' ,				'"'					), normalizers.Replace('\'\'' ,				'"'					)]					)
				snake_case__													=       pre_tokenizers.Metaspace()

				# Prepare the trainer.
				snake_case__													=       UnigramTrainer(
				    unk_token='<unk>' ,				special_tokens=['[CLS]', '[SEP]', '<unk>', '<pad>', '[MASK]'] ,				vocab_size=args.vocab_size ,				)

				logger.info('Training the tokenizer.'					)
				tokenizer.train_from_iterator(batch_iterator() ,				trainer=_A					)
				logger.info('Tokenizer training complete!'					)

				snake_case__													=       tokenizer.token_to_id('[CLS]'					)
				snake_case__													=       tokenizer.token_to_id('[SEP]'					)
				snake_case__													=       processors.TemplateProcessing(
				    single='[CLS]:0 $A:0 [SEP]:0' ,				pair='[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1' ,				special_tokens=[
				        ('[CLS]', cls_token_id),
				        ('[SEP]', sep_token_id),
				    ] ,				)
				snake_case__													=       decoders.Metaspace()

				if args.export_to_hub:
								logger.info('Exporting the trained tokenzier to Hub.'					)
								snake_case__													=       AlbertTokenizerFast(tokenizer_object=_A					)
								new_tokenizer.push_to_hub('unigram-tokenizer-dataset'					)


if __name__ == "__main__":
							__UpperCamelCase   :						int							=  parse_args()
							main(args)












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


__UpperCamelCase   :						int							=  [nn.LayerNorm]

__UpperCamelCase   :						List[str]							=  logging.get_logger(__name__)

__UpperCamelCase   :						str							=  version.parse(version.parse(torch.__version__).base_version)

__UpperCamelCase   :						List[str]							=  parsed_torch_version_base >= version.parse("""2.0""")
__UpperCamelCase   :						Dict							=  parsed_torch_version_base >= version.parse("""1.12""")
__UpperCamelCase   :						Tuple							=  parsed_torch_version_base >= version.parse("""1.11""")
__UpperCamelCase   :						Union[str, Any]							=  parsed_torch_version_base < version.parse("""1.11""")
def        a_	( _A ,				_A ,				_A ,				_A ,				self					)     ->     Any:







				"""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        a_	( _A ,				_A ,				_A = 0					)     ->     nn.Linear:







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


class 					__SCREAMING_SNAKE_CASE(		nn.Module		):
							def __init__(       self: Union[str, Any]    ,		UpperCamelCase: Dict    ,		UpperCamelCase: List[Any]  )						->						Any:
											super().__init__()
											snake_case__													=       nf
											snake_case__													=       nn.Parameter(torch.empty(UpperCamelCase    ,		UpperCamelCase  )  )
											snake_case__													=       nn.Parameter(torch.zeros(UpperCamelCase  )  )
											nn.init.normal_(self.weight    ,		std=0.02  )


							def lowerCAmelCase_	(       self: Optional[Any]    ,		UpperCamelCase: int  )						->						List[Any]:
											snake_case__													=       x.size()[:-1] + (self.nf,)
											snake_case__													=       torch.addmm(self.bias    ,		x.view(-1    ,		x.size(-1  )  )    ,		self.weight  )
											snake_case__													=       x.view(UpperCamelCase  )
											return x


def        a_	( _A ,				_A ,				_A = 1					)     ->     ConvaD:







				"""simple docstring"""
				snake_case__													=       index.to(layer.weight.device					)
				snake_case__													=       layer.weight.index_select(_A ,				_A					).clone().detach()
				if dim == 0:
								snake_case__													=       layer.bias.clone().detach()
				else:
								snake_case__													=       layer.bias[index].clone().detach()
				snake_case__													=       list(layer.weight.size()					)
				snake_case__													=       len(_A					)
				snake_case__													=       ConvaD(new_size[1] ,				new_size[0]					).to(layer.weight.device					)
				snake_case__													=       False
				new_layer.weight.copy_(W.contiguous()					)
				snake_case__													=       True
				snake_case__													=       False
				new_layer.bias.copy_(b.contiguous()					)
				snake_case__													=       True
				return new_layer
def        a_	( _A ,				_A ,				_A = 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        a_	( _A ,				_A ,				_A ,				*_A					)     ->     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
				snake_case__													=       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:
								snake_case__													=       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}'''					)

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

								# chunk input tensor into tuples
								snake_case__													=       tuple(input_tensor.chunk(_A ,				dim=_A					) for input_tensor in input_tensors					)
								# apply forward fn to every tuple
								snake_case__													=       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        a_	( _A ,				_A ,				_A ,				_A					)     ->     Tuple[Set[int], torch.LongTensor]:







				"""simple docstring"""
				snake_case__													=       torch.ones(_A ,				_A					)
				snake_case__													=       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
								snake_case__													=       head - sum(1 if h < head else 0 for h in already_pruned_heads					)
								snake_case__													=       0
				snake_case__													=       mask.view(-1					).contiguous().eq(1					)
				snake_case__													=       torch.arange(len(_A					)					)[mask].long()
				return heads, index
def        a_	( *_A ,				_A = None					)     ->     Tuple[torch.Tensor, ...]:







				"""simple docstring"""
				return torch.meshgrid(*_A ,				indexing=_A					)


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







				"""simple docstring"""
				return tensor.device, storage_ptr(_A					), storage_size(_A					)




