



"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers.models.mpnet.modeling_tf_mpnet import (
		    TFMPNetForMaskedLM,
		    TFMPNetForMultipleChoice,
		    TFMPNetForQuestionAnswering,
		    TFMPNetForSequenceClassification,
		    TFMPNetForTokenClassification,
		    TFMPNetModel,
		)





class   UpperCamelCase	:




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_				,UpperCAmelCase_=13				,UpperCAmelCase_=7				,UpperCAmelCase_=True				,UpperCAmelCase_=True				,UpperCAmelCase_=False				,UpperCAmelCase_=True				,UpperCAmelCase_=99				,UpperCAmelCase_=64				,UpperCAmelCase_=2				,UpperCAmelCase_=4				,UpperCAmelCase_=64				,UpperCAmelCase_="gelu"				,UpperCAmelCase_=0.1				,UpperCAmelCase_=0.1				,UpperCAmelCase_=5_12				,UpperCAmelCase_=16				,UpperCAmelCase_=2				,UpperCAmelCase_=0.02				,UpperCAmelCase_=3				,UpperCAmelCase_=4				,UpperCAmelCase_=None				,):
			_lowercase				: str     =   parent
			_lowercase				: Any     =   batch_size
			_lowercase				: str     =   seq_length
			_lowercase				: List[str]     =   is_training
			_lowercase				: Tuple     =   use_input_mask
			_lowercase				: List[Any]     =   use_token_type_ids
			_lowercase				: List[Any]     =   use_labels
			_lowercase				: Optional[int]     =   vocab_size
			_lowercase				: Any     =   hidden_size
			_lowercase				: Optional[int]     =   num_hidden_layers
			_lowercase				: Optional[int]     =   num_attention_heads
			_lowercase				: Optional[Any]     =   intermediate_size
			_lowercase				: Any     =   hidden_act
			_lowercase				: Dict     =   hidden_dropout_prob
			_lowercase				: Union[str, Any]     =   attention_probs_dropout_prob
			_lowercase				: Union[str, Any]     =   max_position_embeddings
			_lowercase				: str     =   type_vocab_size
			_lowercase				: List[str]     =   type_sequence_label_size
			_lowercase				: List[str]     =   initializer_range
			_lowercase				: List[str]     =   num_labels
			_lowercase				: List[Any]     =   num_choices
			_lowercase				: List[Any]     =   scope






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[int]     =   ids_tensor([self.batch_size, self.seq_length]				,self.vocab_size   )

			_lowercase				: Optional[int]     =   None
			if self.use_input_mask:
					_lowercase				: Union[str, Any]     =   random_attention_mask([self.batch_size, self.seq_length]   )

			_lowercase				: Optional[Any]     =   None
			_lowercase				: int     =   None
			_lowercase				: List[str]     =   None
			if self.use_labels:
					_lowercase				: List[str]     =   ids_tensor([self.batch_size]				,self.type_sequence_label_size   )
					_lowercase				: Tuple     =   ids_tensor([self.batch_size, self.seq_length]				,self.num_labels   )
					_lowercase				: List[Any]     =   ids_tensor([self.batch_size]				,self.num_choices   )

			_lowercase				: Dict     =   MPNetConfig(
			    vocab_size=self.vocab_size				,hidden_size=self.hidden_size				,num_hidden_layers=self.num_hidden_layers				,num_attention_heads=self.num_attention_heads				,intermediate_size=self.intermediate_size				,hidden_act=self.hidden_act				,hidden_dropout_prob=self.hidden_dropout_prob				,attention_probs_dropout_prob=self.attention_probs_dropout_prob				,max_position_embeddings=self.max_position_embeddings				,initializer_range=self.initializer_range				,)
			return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
			_lowercase				: List[str]     =   TFMPNetModel(config=UpperCAmelCase_   )
			_lowercase				: int     =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
			_lowercase				: Any     =   model(UpperCAmelCase_   )
			_lowercase				: Optional[int]     =   [input_ids, input_mask]
			_lowercase				: Tuple     =   model(UpperCAmelCase_   )
			self.parent.assertEqual(result.last_hidden_state.shape				,(self.batch_size, self.seq_length, self.hidden_size)   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
			_lowercase				: Dict     =   TFMPNetForMaskedLM(config=UpperCAmelCase_   )
			_lowercase				: Union[str, Any]     =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
			_lowercase				: str     =   model(UpperCAmelCase_   )
			self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.seq_length, self.vocab_size)   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
			_lowercase				: Optional[Any]     =   TFMPNetForQuestionAnswering(config=UpperCAmelCase_   )
			_lowercase				: Dict     =   {
			    """input_ids""": input_ids,
			    """attention_mask""": input_mask,
			}
			_lowercase				: List[str]     =   model(UpperCAmelCase_   )
			self.parent.assertEqual(result.start_logits.shape				,(self.batch_size, self.seq_length)   )
			self.parent.assertEqual(result.end_logits.shape				,(self.batch_size, self.seq_length)   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
			_lowercase				: str     =   self.num_labels
			_lowercase				: int     =   TFMPNetForSequenceClassification(UpperCAmelCase_   )
			_lowercase				: Union[str, Any]     =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
			_lowercase				: Union[str, Any]     =   model(UpperCAmelCase_   )
			self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.num_labels)   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
			_lowercase				: str     =   self.num_choices
			_lowercase				: Optional[Any]     =   TFMPNetForMultipleChoice(UpperCAmelCase_   )
			_lowercase				: Tuple     =   tf.tile(tf.expand_dims(UpperCAmelCase_				,1   )				,(1, self.num_choices, 1)   )
			_lowercase				: List[str]     =   tf.tile(tf.expand_dims(UpperCAmelCase_				,1   )				,(1, self.num_choices, 1)   )
			_lowercase				: Any     =   {
			    """input_ids""": multiple_choice_inputs_ids,
			    """attention_mask""": multiple_choice_input_mask,
			}
			_lowercase				: Union[str, Any]     =   model(UpperCAmelCase_   )
			self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.num_choices)   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
			_lowercase				: Dict     =   self.num_labels
			_lowercase				: Tuple     =   TFMPNetForTokenClassification(UpperCAmelCase_   )
			_lowercase				: List[Any]     =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
			_lowercase				: List[str]     =   model(UpperCAmelCase_   )
			self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.seq_length, self.num_labels)   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: int     =   self.prepare_config_and_inputs()
			((_lowercase)     ,  (_lowercase)     ,  (_lowercase)     ,  (_lowercase)     ,  (_lowercase)     ,  (_lowercase))				: Optional[int]     =   config_and_inputs
			_lowercase				: Optional[int]     =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
			return config, inputs_dict







@require_tf
class   UpperCamelCase	(     snake_case					,   snake_case					,   unittest.TestCase       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       str						       =  (
	    (
	        TFMPNetForMaskedLM,
	        TFMPNetForMultipleChoice,
	        TFMPNetForQuestionAnswering,
	        TFMPNetForSequenceClassification,
	        TFMPNetForTokenClassification,
	        TFMPNetModel,
	    )
	    if is_tf_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE_							:       str						       =  (
	    {
	        "feature-extraction": TFMPNetModel,
	        "fill-mask": TFMPNetForMaskedLM,
	        "question-answering": TFMPNetForQuestionAnswering,
	        "text-classification": TFMPNetForSequenceClassification,
	        "token-classification": TFMPNetForTokenClassification,
	        "zero-shot": TFMPNetForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	SCREAMING_SNAKE_CASE_							:       List[str]						       =  False
	SCREAMING_SNAKE_CASE_							:       str						       =  False






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[Any]     =   TFMPNetModelTester(self   )
			_lowercase				: Tuple     =   ConfigTester(self				,config_class=UpperCAmelCase_				,hidden_size=37   )






	def    lowerCamelCase__					(     self   ):
			self.config_tester.run_common_tests()






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Dict     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_mpnet_model(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Dict     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_mpnet_for_masked_lm(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_mpnet_for_question_answering(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[Any]     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_mpnet_for_sequence_classification(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[str]     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_mpnet_for_multiple_choice(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Dict     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_mpnet_for_token_classification(*UpperCAmelCase_   )






	@slow
	def    lowerCamelCase__					(     self   ):
			for model_name in ["microsoft/mpnet-base"]:
					_lowercase				: Optional[Any]     =   TFMPNetModel.from_pretrained(UpperCAmelCase_   )
					self.assertIsNotNone(UpperCAmelCase_   )







@require_tf
class   UpperCamelCase	(     unittest.TestCase       ):




	"""simple docstring"""






	@slow
	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   TFMPNetModel.from_pretrained("""microsoft/mpnet-base"""   )
			_lowercase				: str     =   tf.constant([[0, 1, 2, 3, 4, 5]]   )
			_lowercase				: Tuple     =   model(UpperCAmelCase_   )[0]

			_lowercase				: Optional[int]     =   [1, 6, 7_68]
			self.assertEqual(output.shape				,UpperCAmelCase_   )

			_lowercase				: Dict     =   tf.constant(
			    [
			        [
			            [-0.1067172, 0.08216473, 0.0024543],
			            [-0.03465879, 0.8354118, -0.03252288],
			            [-0.06569476, -0.12424111, -0.0494436],
			        ]
			    ]   )
			tf.debugging.assert_near(output[:, :3, :3]				,UpperCAmelCase_				,atol=1E-4   )








"""simple docstring"""







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:  List[str]			     =				logging.getLogger(__name__)


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

		_lowercase				: Any     =   parser.parse_args()
		return args


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		_lowercase				: List[Any]     =   datasets.load_dataset(args.dataset_name   ,				args.dataset_config   ,				split="""train"""      )

		if args.limit is not None:
				_lowercase				: Tuple     =   min(len(__UpperCAmelCase      )   ,				args.limit      )
				_lowercase				: Optional[Any]     =   dataset.select(range(__UpperCAmelCase      )      )
				logger.info(F"""Limiting the dataset to {args.limit} entries."""      )

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

    # Prepare the tokenizer.
		_lowercase				: Optional[int]     =   Tokenizer(Unigram()      )
		_lowercase				: List[str]     =   normalizers.Sequence([normalizers.Replace("""``"""   ,				"""\""""      ), normalizers.Replace("""''"""   ,				"""\""""      )]      )
		_lowercase				: str     =   pre_tokenizers.Metaspace()

		# Prepare the trainer.
		_lowercase				: int     =   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=__UpperCAmelCase      )
		logger.info("""Tokenizer training complete!"""      )

		_lowercase				: int     =   tokenizer.token_to_id("""[CLS]"""      )
		_lowercase				: Optional[Any]     =   tokenizer.token_to_id("""[SEP]"""      )
		_lowercase				: 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),
		    ]   ,				)
		_lowercase				: Union[str, Any]     =   decoders.Metaspace()

		if args.export_to_hub:
				logger.info("""Exporting the trained tokenzier to Hub."""      )
				_lowercase				: List[Any]     =   AlbertTokenizerFast(tokenizer_object=__UpperCAmelCase      )
				new_tokenizer.push_to_hub("""unigram-tokenizer-dataset"""      )


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



