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


lowerCAmelCase__	:  Dict						=  logging.getLogger(__name__)
def 					UpperCamelCase__		(							)  ->   str:
				snake_case__   :      int   				= 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=1_0048     ,					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__   :      Any   				= parser.parse_args()
				return args
def 					UpperCamelCase__		(							A__       )  ->   List[Any]:
				snake_case__   :      str   				= datasets.load_dataset(args.dataset_name     ,					args.dataset_config     ,					split='train'       )

				if args.limit is not None:
								snake_case__   :      List[str]   				= min(len(A__       )     ,					args.limit       )
								snake_case__   :      Dict   				= 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__   :      int   				= Tokenizer(Unigram()       )
				snake_case__   :      List[Any]   				= normalizers.Sequence([normalizers.Replace('``'     ,					'"'       ), normalizers.Replace('\'\''     ,					'"'       )]       )
				snake_case__   :      Union[str, Any]   				= pre_tokenizers.Metaspace()

				# Prepare the trainer.
				snake_case__   :      str   				= 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__   :      List[str]   				= tokenizer.token_to_id('[CLS]'       )
				snake_case__   :      List[Any]   				= tokenizer.token_to_id('[SEP]'       )
				snake_case__   :      Optional[int]   				= 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__   :      List[str]   				= decoders.Metaspace()

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


if __name__ == "__main__":
				lowerCAmelCase__	:  List[Any]						=  parse_args()
				main(args)



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowerCAmelCase__	:  List[Any]						=  logging.get_logger(__name__)

lowerCAmelCase__	:  str						=  {
    '''google/bigbird-pegasus-large-arxiv''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'''
    ),
    '''google/bigbird-pegasus-large-pubmed''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'''
    ),
    '''google/bigbird-pegasus-large-bigpatent''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'''
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}





class 					__snake_case    (     _lowerCamelCase      ):
					__lowerCamelCase			   =  """bigbird_pegasus"""
					__lowerCamelCase			   =  ["""past_key_values"""]
					__lowerCamelCase			   =  {
					    """num_attention_heads""": """encoder_attention_heads""",
					    """hidden_size""": """d_model""",
					    """attention_probs_dropout_prob""": """attention_dropout""",
					}




					def __init__(  self       ,			__UpperCamelCase=96103       ,			__UpperCamelCase=4096       ,			__UpperCamelCase=16       ,			__UpperCamelCase=4096       ,			__UpperCamelCase=16       ,			__UpperCamelCase=16       ,			__UpperCamelCase=4096       ,			__UpperCamelCase=16       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=True       ,			__UpperCamelCase=True       ,			__UpperCamelCase="gelu_new"       ,			__UpperCamelCase=1024       ,			__UpperCamelCase=0.1       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=0.0_2       ,			__UpperCamelCase=2       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=True       ,			__UpperCamelCase=0       ,			__UpperCamelCase=2       ,			__UpperCamelCase=1       ,			__UpperCamelCase="block_sparse"       ,			__UpperCamelCase=64       ,			__UpperCamelCase=3       ,			__UpperCamelCase=False       ,			**__UpperCamelCase       ,			)	->    Tuple:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= vocab_size
									snake_case__   :      Optional[Any]   				= max_position_embeddings
									snake_case__   :      Tuple   				= d_model
									snake_case__   :      str   				= encoder_ffn_dim
									snake_case__   :      List[str]   				= encoder_layers
									snake_case__   :      Any   				= encoder_attention_heads
									snake_case__   :      Any   				= decoder_ffn_dim
									snake_case__   :      List[Any]   				= decoder_layers
									snake_case__   :      Dict   				= decoder_attention_heads
									snake_case__   :      List[Any]   				= dropout
									snake_case__   :      List[Any]   				= attention_dropout
									snake_case__   :      List[Any]   				= activation_dropout
									snake_case__   :      str   				= activation_function
									snake_case__   :      Optional[Any]   				= init_std
									snake_case__   :      Optional[int]   				= encoder_layerdrop
									snake_case__   :      List[Any]   				= decoder_layerdrop
									snake_case__   :      Tuple   				= classifier_dropout
									snake_case__   :      Optional[Any]   				= use_cache
									snake_case__   :      List[Any]   				= encoder_layers
									snake_case__   :      Union[str, Any]   				= scale_embedding  # scale factor will be sqrt(d_model) if True

									# extra config
									snake_case__   :      Optional[Any]   				= attention_type
									snake_case__   :      Optional[int]   				= block_size
									snake_case__   :      Optional[Any]   				= num_random_blocks
									snake_case__   :      List[str]   				= use_bias

									super().__init__(
									    pad_token_id=__UpperCamelCase       ,			bos_token_id=__UpperCamelCase       ,			eos_token_id=__UpperCamelCase       ,			is_encoder_decoder=__UpperCamelCase       ,			decoder_start_token_id=__UpperCamelCase       ,			**__UpperCamelCase       ,			)





class 					__snake_case    (     _lowerCamelCase      ):




					@property
					def      __a     (  self						)	->    Mapping[str, Mapping[int, str]]:



									'''simple docstring'''







									if self.task in ["default", "seq2seq-lm"]:
													snake_case__   :      str   				= OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													    ]						)

													if self.use_past:
																	snake_case__   :      Optional[int]   				= {0: 'batch'}
																	snake_case__   :      str   				= {0: 'batch', 1: 'past_decoder_sequence + sequence'}
													else:
																	snake_case__   :      Any   				= {0: 'batch', 1: 'decoder_sequence'}
																	snake_case__   :      List[str]   				= {0: 'batch', 1: 'decoder_sequence'}

													if self.use_past:
																	self.fill_with_past_key_values_(__UpperCamelCase       ,			direction='inputs'						)
									elif self.task == "causal-lm":
													# TODO: figure this case out.
													snake_case__   :      Any   				= OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													    ]						)
													if self.use_past:
																	snake_case__				,    snake_case__   :      Optional[Any]   				= self.num_layers
																	for i in range(__UpperCamelCase						):
																					snake_case__   :      str   				= {0: 'batch', 2: 'past_sequence + sequence'}
																					snake_case__   :      Optional[int]   				= {0: 'batch', 2: 'past_sequence + sequence'}
									else:
													snake_case__   :      Union[str, Any]   				= OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
													        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
													    ]						)

									return common_inputs




					@property
					def      __a     (  self						)	->    Mapping[str, Mapping[int, str]]:



									'''simple docstring'''







									if self.task in ["default", "seq2seq-lm"]:
													snake_case__   :      int   				= super().outputs
									else:
													snake_case__   :      Dict   				= super(__UpperCamelCase       ,			self						).outputs
													if self.use_past:
																	snake_case__				,    snake_case__   :      List[Any]   				= self.num_layers
																	for i in range(__UpperCamelCase						):
																					snake_case__   :      List[Any]   				= {0: 'batch', 2: 'past_sequence + sequence'}
																					snake_case__   :      List[Any]   				= {0: 'batch', 2: 'past_sequence + sequence'}
									return common_outputs




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = False       ,			__UpperCamelCase = None       ,			)	->    Mapping[str, Any]:



									'''simple docstring'''







									snake_case__   :      List[str]   				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)

									# Generate decoder inputs
									snake_case__   :      Optional[Any]   				= seq_length if not self.use_past else 1
									snake_case__   :      int   				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)
									snake_case__   :      str   				= {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									snake_case__   :      Any   				= dict(**__UpperCamelCase       ,			**__UpperCamelCase						)

									if self.use_past:
													if not is_torch_available():
																	raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'						)
													else:
																	import torch
													snake_case__				,    snake_case__   :      int   				= common_inputs['input_ids'].shape
													snake_case__   :      int   				= common_inputs['decoder_input_ids'].shape[1]
													snake_case__				,    snake_case__   :      List[Any]   				= self.num_attention_heads
													snake_case__   :      Any   				= (
													    batch,
													    num_encoder_attention_heads,
													    encoder_seq_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													snake_case__   :      Optional[int]   				= decoder_seq_length + 3
													snake_case__   :      Union[str, Any]   				= (
													    batch,
													    num_decoder_attention_heads,
													    decoder_past_length,
													    self._config.hidden_size // num_decoder_attention_heads,
													)

													snake_case__   :      Union[str, Any]   				= torch.cat(
													    [common_inputs['decoder_attention_mask'], torch.ones(__UpperCamelCase       ,			__UpperCamelCase						)]       ,			dim=1						)

													snake_case__   :      Optional[Any]   				= []
													# If the number of encoder and decoder layers are present in the model configuration, both are considered
													snake_case__				,    snake_case__   :      str   				= self.num_layers
													snake_case__   :      str   				= min(__UpperCamelCase       ,			__UpperCamelCase						)
													snake_case__   :      Optional[int]   				= max(__UpperCamelCase       ,			__UpperCamelCase						) - min_num_layers
													snake_case__   :      int   				= 'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

													for _ in range(__UpperCamelCase						):
																	common_inputs["past_key_values"].append(
																	    (
																	        torch.zeros(__UpperCamelCase						),
																	        torch.zeros(__UpperCamelCase						),
																	        torch.zeros(__UpperCamelCase						),
																	        torch.zeros(__UpperCamelCase						),
																	    )						)
													# TODO: test this.
													snake_case__   :      Dict   				= encoder_shape if remaining_side_name == 'encoder' else decoder_shape
													for _ in range(__UpperCamelCase       ,			__UpperCamelCase						):
																	common_inputs["past_key_values"].append((torch.zeros(__UpperCamelCase						), torch.zeros(__UpperCamelCase						))						)
									return common_inputs




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = False       ,			__UpperCamelCase = None       ,			)	->    Mapping[str, Any]:



									'''simple docstring'''







									snake_case__   :      List[str]   				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)

									if self.use_past:
													if not is_torch_available():
																	raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'						)
													else:
																	import torch
													snake_case__				,    snake_case__   :      Optional[int]   				= common_inputs['input_ids'].shape
													# Not using the same length for past_key_values
													snake_case__   :      str   				= seqlen + 2
													snake_case__				,    snake_case__   :      List[str]   				= self.num_layers
													snake_case__				,    snake_case__   :      str   				= self.num_attention_heads
													snake_case__   :      Tuple   				= (
													    batch,
													    num_encoder_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)

													snake_case__   :      Dict   				= common_inputs['attention_mask'].dtype
													snake_case__   :      Optional[Any]   				= torch.cat(
													    [common_inputs['attention_mask'], torch.ones(__UpperCamelCase       ,			__UpperCamelCase       ,			dtype=__UpperCamelCase						)]       ,			dim=1						)
													snake_case__   :      int   				= [
													    (torch.zeros(__UpperCamelCase						), torch.zeros(__UpperCamelCase						)) for _ in range(__UpperCamelCase						)
													]
									return common_inputs




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = False       ,			__UpperCamelCase = None       ,			)	->    Mapping[str, Any]:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= compute_effective_axis_dimension(
									    __UpperCamelCase       ,			fixed_dimension=OnnxConfig.default_fixed_batch       ,			num_token_to_add=0						)

									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
									snake_case__   :      str   				= tokenizer.num_special_tokens_to_add(__UpperCamelCase						)
									snake_case__   :      List[str]   				= compute_effective_axis_dimension(
									    __UpperCamelCase       ,			fixed_dimension=OnnxConfig.default_fixed_sequence       ,			num_token_to_add=__UpperCamelCase						)

									# Generate dummy inputs according to compute batch and sequence
									snake_case__   :      Optional[Any]   				= [' '.join([tokenizer.unk_token]						) * seq_length] * batch_size
									snake_case__   :      Optional[Any]   				= dict(tokenizer(__UpperCamelCase       ,			return_tensors=__UpperCamelCase						)						)
									return common_inputs




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = False       ,			__UpperCamelCase = None       ,			)	->    Mapping[str, Any]:



									'''simple docstring'''







									if self.task in ["default", "seq2seq-lm"]:
													snake_case__   :      Dict   				= self._generate_dummy_inputs_for_default_and_seqaseq_lm(
													    __UpperCamelCase       ,			batch_size=__UpperCamelCase       ,			seq_length=__UpperCamelCase       ,			is_pair=__UpperCamelCase       ,			framework=__UpperCamelCase						)

									elif self.task == "causal-lm":
													snake_case__   :      Union[str, Any]   				= self._generate_dummy_inputs_for_causal_lm(
													    __UpperCamelCase       ,			batch_size=__UpperCamelCase       ,			seq_length=__UpperCamelCase       ,			is_pair=__UpperCamelCase       ,			framework=__UpperCamelCase						)
									else:
													snake_case__   :      Any   				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    __UpperCamelCase       ,			batch_size=__UpperCamelCase       ,			seq_length=__UpperCamelCase       ,			is_pair=__UpperCamelCase       ,			framework=__UpperCamelCase						)

									return common_inputs




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Dict:



									'''simple docstring'''







									if self.task in ["default", "seq2seq-lm"]:
													snake_case__   :      Optional[int]   				= super()._flatten_past_key_values_(__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)
									else:
													snake_case__   :      Optional[Any]   				= super(__UpperCamelCase       ,			self						)._flatten_past_key_values_(
													    __UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)


