



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				(  UpperCamelCase__  ):




	'''simple docstring'''





	@slow
	@require_torch
	def     UpperCamelCase_  (	self :			int			):
					__A             =			EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"							,"prajjwal1/bert-tiny"			)
					__A             =			BertTokenizer.from_pretrained("bert-base-uncased"			)

					__A             =			bertabert.config.encoder.vocab_size
					__A             =			tokenizer.sep_token_id
					__A             =			tokenizer.cls_token_id
					__A             =			1_28

					__A             =			datasets.load_dataset("cnn_dailymail"							,"3.0.0"							,split="train[:1%]"			)
					__A             =			datasets.load_dataset("cnn_dailymail"							,"3.0.0"							,split="validation[:1%]"			)

					__A             =			train_dataset.select(range(32			)			)
					__A             =			val_dataset.select(range(16			)			)

					__A             =			4

					def _map_to_encoder_decoder_inputs(A :			int			):
									# Tokenizer will automatically set [BOS] <text> [EOS]
									__A             =			tokenizer(batch["article"]							,padding="max_length"							,truncation=__lowerCamelCase							,max_length=5_12			)
									__A             =			tokenizer(batch["highlights"]							,padding="max_length"							,truncation=__lowerCamelCase							,max_length=1_28			)
									__A             =			inputs.input_ids
									__A             =			inputs.attention_mask

									__A             =			outputs.input_ids
									__A             =			outputs.input_ids.copy()
									__A             =			[
									    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
									]
									__A             =			outputs.attention_mask

									assert all(len(__lowerCamelCase			) == 5_12 for x in inputs.input_ids			)
									assert all(len(__lowerCamelCase			) == 1_28 for x in outputs.input_ids			)

									return batch

					def _compute_metrics(A :			Optional[Any]			):
									__A             =			pred.label_ids
									__A             =			pred.predictions

									# all unnecessary tokens are removed
									__A             =			tokenizer.batch_decode(__lowerCamelCase							,skip_special_tokens=__lowerCamelCase			)
									__A             =			tokenizer.batch_decode(__lowerCamelCase							,skip_special_tokens=__lowerCamelCase			)

									__A             =			sum([int(pred_str[i] == label_str[i]			) for i in range(len(__lowerCamelCase			)			)]			) / len(__lowerCamelCase			)

									return {"accuracy": accuracy}

					# map train dataset
					__A             =			train_dataset.map(
					    _map_to_encoder_decoder_inputs							,batched=__lowerCamelCase							,batch_size=__lowerCamelCase							,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
					__A             =			val_dataset.map(
					    _map_to_encoder_decoder_inputs							,batched=__lowerCamelCase							,batch_size=__lowerCamelCase							,remove_columns=["article", "highlights"]							,)
					val_dataset.set_format(
					    type="torch"							,columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							,)

					__A             =			self.get_auto_remove_tmp_dir()

					__A             =			SeqaSeqTrainingArguments(
					    output_dir=__lowerCamelCase							,per_device_train_batch_size=__lowerCamelCase							,per_device_eval_batch_size=__lowerCamelCase							,predict_with_generate=__lowerCamelCase							,evaluation_strategy="steps"							,do_train=__lowerCamelCase							,do_eval=__lowerCamelCase							,warmup_steps=0							,eval_steps=2							,logging_steps=2							,)

					# instantiate trainer
					__A             =			SeqaSeqTrainer(
					    model=__lowerCamelCase							,args=__lowerCamelCase							,compute_metrics=_compute_metrics							,train_dataset=__lowerCamelCase							,eval_dataset=__lowerCamelCase							,tokenizer=__lowerCamelCase							,)

					# start training
					trainer.train()


import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					CTRLTokenizer
	snake_case_    =					False
	snake_case_    =					False






	def     UpperCamelCase_  (	self :			Dict			):
					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					__A             =			["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
					__A             =			dict(zip(A							,range(len(A			)			)			)			)
					__A             =			["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
					__A             =			{"unk_token": "<unk>"}

					__A             =			os.path.join(self.tmpdirname							,VOCAB_FILES_NAMES["vocab_file"]			)
					__A             =			os.path.join(self.tmpdirname							,VOCAB_FILES_NAMES["merges_file"]			)
					with open(self.vocab_file							,"w"							,encoding="utf-8"			) as fp:
									fp.write(json.dumps(A			) + "\n"			)
					with open(self.merges_file							,"w"							,encoding="utf-8"			) as fp:
									fp.write("\n".join(A			)			)






	def     UpperCamelCase_  (	self :			List[str]							,**A :			List[str]			):
					kwargs.update(self.special_tokens_map			)
					return CTRLTokenizer.from_pretrained(self.tmpdirname							,**A			)






	def     UpperCamelCase_  (	self :			Optional[int]							,A :			Tuple			):
					__A             =			"adapt react readapt apt"
					__A             =			"adapt react readapt apt"
					return input_text, output_text







	def     UpperCamelCase_  (	self :			Any			):
					__A             =			CTRLTokenizer(self.vocab_file							,self.merges_file							,**self.special_tokens_map			)
					__A             =			"adapt react readapt apt"
					__A             =			"adapt re@@ a@@ c@@ t re@@ adapt apt".split()
					__A             =			tokenizer.tokenize(A			)
					self.assertListEqual(A							,A			)

					__A             =			tokens + [tokenizer.unk_token]

					__A             =			[0, 1, 2, 4, 5, 1, 0, 3, 6]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)							,A			)
