
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
							import datasets

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			@slow
			@require_torch
			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							Optional[int]		=  EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""  ,		"""prajjwal1/bert-tiny""" )
							UpperCamelCase_:							Union[str, Any]		=  BertTokenizer.from_pretrained("""bert-base-uncased""" )

							UpperCamelCase_:							Optional[int]		=  bertabert.config.encoder.vocab_size
							UpperCamelCase_:							Union[str, Any]		=  tokenizer.sep_token_id
							UpperCamelCase_:							Dict		=  tokenizer.cls_token_id
							UpperCamelCase_:							Tuple		=  128

							UpperCamelCase_:							Union[str, Any]		=  datasets.load_dataset("""cnn_dailymail"""  ,		"""3.0.0"""  ,		split="""train[:1%]""" )
							UpperCamelCase_:							Optional[int]		=  datasets.load_dataset("""cnn_dailymail"""  ,		"""3.0.0"""  ,		split="""validation[:1%]""" )

							UpperCamelCase_:							List[str]		=  train_dataset.select(range(32 ) )
							UpperCamelCase_:							List[str]		=  val_dataset.select(range(16 ) )

							UpperCamelCase_:							Dict		=  4

							def _map_to_encoder_decoder_inputs(snake_case_	:    List[str] ):
											# Tokenizer will automatically set [BOS] <text> [EOS]
											UpperCamelCase_:							Optional[int]		=  tokenizer(batch["""article"""]  ,		padding="""max_length"""  ,		truncation=snake_case_  ,		max_length=512 )
											UpperCamelCase_:							Dict		=  tokenizer(batch["""highlights"""]  ,		padding="""max_length"""  ,		truncation=snake_case_  ,		max_length=128 )
											UpperCamelCase_:							Dict		=  inputs.input_ids
											UpperCamelCase_:							Union[str, Any]		=  inputs.attention_mask

											UpperCamelCase_:							Tuple		=  outputs.input_ids
											UpperCamelCase_:							Optional[int]		=  outputs.input_ids.copy()
											UpperCamelCase_:							Tuple		=  [
											    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
											]
											UpperCamelCase_:							str		=  outputs.attention_mask

											assert all(len(snake_case_ ) == 512 for x in inputs.input_ids )
											assert all(len(snake_case_ ) == 128 for x in outputs.input_ids )

											return batch

							def _compute_metrics(snake_case_	:    List[Any] ):
											UpperCamelCase_:							Tuple		=  pred.label_ids
											UpperCamelCase_:							Optional[Any]		=  pred.predictions

											# all unnecessary tokens are removed
											UpperCamelCase_:							List[Any]		=  tokenizer.batch_decode(snake_case_  ,		skip_special_tokens=snake_case_ )
											UpperCamelCase_:							Any		=  tokenizer.batch_decode(snake_case_  ,		skip_special_tokens=snake_case_ )

											UpperCamelCase_:							int		=  sum([int(pred_str[i] == label_str[i] ) for i in range(len(snake_case_ ) )] ) / len(snake_case_ )

											return {"accuracy": accuracy}

							# map train dataset
							UpperCamelCase_:							List[str]		=  train_dataset.map(
							    _map_to_encoder_decoder_inputs  ,		batched=snake_case_  ,		batch_size=snake_case_  ,		remove_columns=["""article""", """highlights"""]  ,		)
							train_dataset.set_format(
							    type="""torch"""  ,		columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]  ,		)

							# same for validation dataset
							UpperCamelCase_:							Optional[int]		=  val_dataset.map(
							    _map_to_encoder_decoder_inputs  ,		batched=snake_case_  ,		batch_size=snake_case_  ,		remove_columns=["""article""", """highlights"""]  ,		)
							val_dataset.set_format(
							    type="""torch"""  ,		columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]  ,		)

							UpperCamelCase_:							Optional[Any]		=  self.get_auto_remove_tmp_dir()

							UpperCamelCase_:							List[Any]		=  SeqaSeqTrainingArguments(
							    output_dir=snake_case_  ,		per_device_train_batch_size=snake_case_  ,		per_device_eval_batch_size=snake_case_  ,		predict_with_generate=snake_case_  ,		evaluation_strategy="""steps"""  ,		do_train=snake_case_  ,		do_eval=snake_case_  ,		warmup_steps=0  ,		eval_steps=2  ,		logging_steps=2  ,		)

							# instantiate trainer
							UpperCamelCase_:							Union[str, Any]		=  SeqaSeqTrainer(
							    model=snake_case_  ,		args=snake_case_  ,		compute_metrics=_compute_metrics  ,		train_dataset=snake_case_  ,		eval_dataset=snake_case_  ,		tokenizer=snake_case_  ,		)

							# start training
							trainer.train()








import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_rjieba
@require_tokenizers
class     _UpperCamelCase			(   _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  Union[str, Any]    =	RoFormerTokenizer
			__UpperCamelCase :  Union[str, Any]    =	RoFormerTokenizerFast
			__UpperCamelCase :  Optional[Any]    =	True
			__UpperCamelCase :  Tuple    =	True

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							super().setUp()

			def  lowerCAmelCase__		(    self	:    List[str]  ,		**snake_case_	:    Union[str, Any] ):
							return self.tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base"""  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict  ,		**snake_case_	:    Union[str, Any] ):
							return self.rust_tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base"""  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Any ):
							UpperCamelCase_:							int		=  """永和服装饰品有限公司,今天天气非常好"""
							UpperCamelCase_:							int		=  """永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"""
							return input_text, output_text

			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							Dict		=  self.get_tokenizer()
							UpperCamelCase_,				UpperCamelCase_:							Optional[int]		=  self.get_chinese_input_output_texts()
							UpperCamelCase_:							Optional[int]		=  tokenizer.tokenize(snake_case_ )

							self.assertListEqual(snake_case_  ,		output_text.split() )

							UpperCamelCase_:							Dict		=  tokens + [tokenizer.unk_token]
							UpperCamelCase_:							int		=  [2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ )  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							Optional[int]		=  self.get_rust_tokenizer()
							UpperCamelCase_,				UpperCamelCase_:							Any		=  self.get_chinese_input_output_texts()
							UpperCamelCase_:							int		=  tokenizer.tokenize(snake_case_ )
							self.assertListEqual(snake_case_  ,		output_text.split() )
							UpperCamelCase_:							Union[str, Any]		=  tokens + [tokenizer.unk_token]
							UpperCamelCase_:							Optional[Any]		=  [2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ )  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict ):
							pass

			def  lowerCAmelCase__		(    self	:    Tuple ):
							pass




			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							pass






