
from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
				from fairscale.optim import OSS


__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    """constant""": get_constant_schedule,
    """constant_w_warmup""": get_constant_schedule_with_warmup,
}




class 						lowercase__  (						_UpperCAmelCase  ):
	def __init__(			self							:   str       ,   UpperCAmelCase_							:   int=None       ,   UpperCAmelCase_							:   List[str]=None       ,   *UpperCAmelCase_							:   Union[str, Any]       ,   **UpperCAmelCase_							:   Optional[Any]							):
						super().__init__(*UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if config is None:
											assert isinstance(self.model       ,   UpperCAmelCase_							), (
											    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
											    F' {self.model.__class__}'
											)
											SCREAMING_SNAKE_CASE__													= self.model.config
						else:
											SCREAMING_SNAKE_CASE__													= config

						SCREAMING_SNAKE_CASE__													= data_args
						SCREAMING_SNAKE_CASE__													= self.config.tgt_vocab_size if isinstance(self.config       ,   UpperCAmelCase_							) else self.config.vocab_size

						if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
											assert self.config.pad_token_id is not None, (
											    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
											    " calculation or doing label smoothing."
											)

						if self.config.pad_token_id is None and self.config.eos_token_id is not None:
											logger.warning(
											    F'The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'
											    ' padding..'							)

						if self.args.label_smoothing == 0:
											SCREAMING_SNAKE_CASE__													= torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id							)
						else:
											# dynamically import label_smoothed_nll_loss
											from utils import label_smoothed_nll_loss

											SCREAMING_SNAKE_CASE__													= label_smoothed_nll_loss
	def      A_				(			self							:   Tuple       ,   UpperCAmelCase_							:   int							):
						if self.optimizer is None:
											SCREAMING_SNAKE_CASE__													= ['bias', 'LayerNorm.weight']
											SCREAMING_SNAKE_CASE__													= [
											    {
											        'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay							)],
											        'weight_decay': self.args.weight_decay,
											    },
											    {
											        'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay							)],
											        'weight_decay': 0.0,
											    },
											]
											SCREAMING_SNAKE_CASE__													= Adafactor if self.args.adafactor else AdamW
											if self.args.adafactor:
																SCREAMING_SNAKE_CASE__													= Adafactor
																SCREAMING_SNAKE_CASE__													= {'scale_parameter': False, 'relative_step': False}
											else:
																SCREAMING_SNAKE_CASE__													= AdamW
																SCREAMING_SNAKE_CASE__													= {
																    'betas': (self.args.adam_betaa, self.args.adam_betaa),
																    'eps': self.args.adam_epsilon,
																}
											SCREAMING_SNAKE_CASE__													= self.args.learning_rate
											if self.sharded_ddp:
																SCREAMING_SNAKE_CASE__													= OSS(
																    params=UpperCAmelCase_       ,   optim=UpperCAmelCase_       ,   **UpperCAmelCase_       ,   )
											else:
																SCREAMING_SNAKE_CASE__													= optimizer_cls(UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if self.lr_scheduler is None:
											SCREAMING_SNAKE_CASE__													= self._get_lr_scheduler(UpperCAmelCase_							)
						else:  # ignoring --lr_scheduler
											logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'							)
	def      A_				(			self							:   str       ,   UpperCAmelCase_							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= arg_to_scheduler[self.args.lr_scheduler]
						if self.args.lr_scheduler == "constant":
											SCREAMING_SNAKE_CASE__													= schedule_func(self.optimizer							)
						elif self.args.lr_scheduler == "constant_w_warmup":
											SCREAMING_SNAKE_CASE__													= schedule_func(self.optimizer       ,   num_warmup_steps=self.args.warmup_steps							)
						else:
											SCREAMING_SNAKE_CASE__													= schedule_func(
											    self.optimizer       ,   num_warmup_steps=self.args.warmup_steps       ,   num_training_steps=UpperCAmelCase_							)
						return scheduler
	def      A_				(			self							:   List[str]							):
						if isinstance(self.train_dataset       ,   torch.utils.data.IterableDataset							):
											return None
						elif is_torch_tpu_available():
											return get_tpu_sampler(self.train_dataset							)
						else:
											if self.args.sortish_sampler:
																self.train_dataset.make_sortish_sampler(
																    self.args.per_device_train_batch_size       ,   distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)       ,   )

											return (
											    RandomSampler(self.train_dataset							)
											    if self.args.local_rank == -1
											    else DistributedSampler(self.train_dataset							)
											)
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   Optional[Any]       ,   UpperCAmelCase_							:   Dict							):
						if self.args.label_smoothing == 0:
											if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
																# force training to ignore pad token
																SCREAMING_SNAKE_CASE__													= model(**UpperCAmelCase_       ,   use_cache=UpperCAmelCase_							)[0]
																SCREAMING_SNAKE_CASE__													= self.loss_fn(logits.view(-1       ,   logits.shape[-1]							)       ,   labels.view(-1							)							)
											else:
																# compute usual loss via models
																SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= model(**UpperCAmelCase_       ,   labels=UpperCAmelCase_       ,   use_cache=UpperCAmelCase_							)[:2]
						else:
											# compute label smoothed loss
											SCREAMING_SNAKE_CASE__													= model(**UpperCAmelCase_       ,   use_cache=UpperCAmelCase_							)[0]
											SCREAMING_SNAKE_CASE__													= torch.nn.functional.log_softmax(UpperCAmelCase_       ,   dim=-1							)
											SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self.loss_fn(UpperCAmelCase_       ,   UpperCAmelCase_       ,   self.args.label_smoothing       ,   ignore_index=self.config.pad_token_id							)
						return loss, logits
	def      A_				(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= inputs.pop('labels'							)
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self._compute_loss(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)
						return loss
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   nn.Module       ,   UpperCAmelCase_							:   Dict[str, Union[torch.Tensor, Any]]       ,   UpperCAmelCase_							:   bool       ,   UpperCAmelCase_							:   Optional[List[str]] = None       ,   ):
						SCREAMING_SNAKE_CASE__													= self._prepare_inputs(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= {
						    'max_length': self.data_args.val_max_target_length
						    if self.data_args is not None
						    else self.config.max_length,
						    'num_beams': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
						}

						if self.args.predict_with_generate and not self.args.prediction_loss_only:
											SCREAMING_SNAKE_CASE__													= self.model.generate(
											    inputs['input_ids']       ,   attention_mask=inputs['attention_mask']       ,   **UpperCAmelCase_       ,   )
											# in case the batch is shorter than max length, the output should be padded
											if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																SCREAMING_SNAKE_CASE__													= self._pad_tensors_to_max_len(UpperCAmelCase_       ,   gen_kwargs['max_length']							)

						SCREAMING_SNAKE_CASE__													= inputs.pop('labels'							)
						with torch.no_grad():
											# compute loss on predict data
											SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self._compute_loss(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= loss.mean().detach()
						if self.args.prediction_loss_only:
											return (loss, None, None)

						SCREAMING_SNAKE_CASE__													= generated_tokens if self.args.predict_with_generate else logits

						if labels.shape[-1] < gen_kwargs["max_length"]:
											SCREAMING_SNAKE_CASE__													= self._pad_tensors_to_max_len(UpperCAmelCase_       ,   gen_kwargs['max_length']							)

						return (loss, logits, labels)





	def      A_				(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   List[str]							):
						# If PAD token is not defined at least EOS token has to be defined
						SCREAMING_SNAKE_CASE__													= self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

						if pad_token_id is None:
											raise ValueError(
											    'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
											    F' padded to `max_length`={max_length}'							)

						SCREAMING_SNAKE_CASE__													= pad_token_id * torch.ones(
						    (tensor.shape[0], max_length)       ,   dtype=tensor.dtype       ,   device=tensor.device							)
						SCREAMING_SNAKE_CASE__													= tensor
						return padded_tensor







import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Any				       =GPTSanJapaneseTokenizer
	A__    :   str				       =False
	A__    :   int				       ={"""do_clean_text""": False, """add_prefix_space""": False}
	def      A_				(			self							:   Any							):
						super().setUp()

						# fmt: off
						SCREAMING_SNAKE_CASE__													= ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
						# fmt: on
						SCREAMING_SNAKE_CASE__													= {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
						SCREAMING_SNAKE_CASE__													= {'unk_token': '<unk>'}

						SCREAMING_SNAKE_CASE__													= os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES['vocab_file']							)
						SCREAMING_SNAKE_CASE__													= os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES['emoji_file']							)
						with open(self.vocab_file       ,   'w'       ,   encoding='utf-8'							) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]							)							)
						with open(self.emoji_file       ,   'w'							) as emoji_writer:
											emoji_writer.write(json.dumps(UpperCAmelCase_							)							)
	def      A_				(			self							:   str       ,   **UpperCAmelCase_							:   Optional[Any]							):
						kwargs.update(self.special_tokens_map							)
						return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname       ,   **UpperCAmelCase_							)
	def      A_				(			self							:   int       ,   UpperCAmelCase_							:   List[str]							):
						SCREAMING_SNAKE_CASE__													= 'こんにちは、世界。 \nこんばんは、㔺界。😀'
						SCREAMING_SNAKE_CASE__													= 'こんにちは、世界。 \nこんばんは、世界。😀'
						return input_text, output_text
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   str							):
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self.get_input_output_texts(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= tokenizer.encode(UpperCAmelCase_       ,   add_special_tokens=UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= tokenizer.decode(UpperCAmelCase_       ,   clean_up_tokenization_spaces=UpperCAmelCase_							)
						return text, ids
	def      A_				(			self							:   str							):
						pass  # TODO add if relevant
	def      A_				(			self							:   Tuple							):
						pass  # TODO add if relevant
	def      A_				(			self							:   int							):
						pass  # TODO add if relevant
	def      A_				(			self							:   Optional[int]							):
						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()

						# Testing tokenization
						SCREAMING_SNAKE_CASE__													= 'こんにちは、世界。　こんばんは、㔺界。'
						SCREAMING_SNAKE_CASE__													= ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
						SCREAMING_SNAKE_CASE__													= tokenizer.tokenize(UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)

						# Testing conversion to ids without special tokens
						SCREAMING_SNAKE_CASE__													= [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
						SCREAMING_SNAKE_CASE__													= tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)

						# Testing conversion to ids with special tokens
						SCREAMING_SNAKE_CASE__													= tokens + [tokenizer.unk_token]
						SCREAMING_SNAKE_CASE__													= [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
						SCREAMING_SNAKE_CASE__													= tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()

						# Testing tokenization
						SCREAMING_SNAKE_CASE__													= 'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
						SCREAMING_SNAKE_CASE__													= 'こんにちは、、、、世界。こんばんは、、、、世界。'
						SCREAMING_SNAKE_CASE__													= tokenizer.encode(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= tokenizer.decode(UpperCAmelCase_							)
						self.assertEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
	@slow
	def      A_				(			self							:   str							):
						SCREAMING_SNAKE_CASE__													= self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'							)

						# Testing tokenization
						SCREAMING_SNAKE_CASE__													= 'こんにちは、世界。'
						SCREAMING_SNAKE_CASE__													= 'こんばんは、㔺界。😀'
						SCREAMING_SNAKE_CASE__													= 'こんにちは、世界。こんばんは、世界。😀'
						SCREAMING_SNAKE_CASE__													= tokenizer.encode(prefix_text + input_text							)
						SCREAMING_SNAKE_CASE__													= tokenizer.encode(''       ,   prefix_text=prefix_text + input_text							)
						SCREAMING_SNAKE_CASE__													= tokenizer.encode(UpperCAmelCase_       ,   prefix_text=UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= tokenizer.decode(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= tokenizer.decode(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= tokenizer.decode(UpperCAmelCase_							)
						self.assertEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
						self.assertEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
						self.assertEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
	@slow
	def      A_				(			self							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'							)

						# Testing tokenization
						SCREAMING_SNAKE_CASE__													= 'こんにちは、世界。'
						SCREAMING_SNAKE_CASE__													= 'こんばんは、㔺界。😀'

						SCREAMING_SNAKE_CASE__													= len(tokenizer.encode(UpperCAmelCase_							)							) - 2
						SCREAMING_SNAKE_CASE__													= len(tokenizer.encode(UpperCAmelCase_							)							) - 2

						SCREAMING_SNAKE_CASE__													= [1] + [0] * (len_prefix + len_text + 1)
						SCREAMING_SNAKE_CASE__													= [1] * (len_prefix + len_text + 1) + [0]
						SCREAMING_SNAKE_CASE__													= [1] + [1] * (len_prefix) + [0] * (len_text + 1)

						SCREAMING_SNAKE_CASE__													= tokenizer(prefix_text + input_text							).token_type_ids
						SCREAMING_SNAKE_CASE__													= tokenizer(''       ,   prefix_text=prefix_text + input_text							).token_type_ids
						SCREAMING_SNAKE_CASE__													= tokenizer(UpperCAmelCase_       ,   prefix_text=UpperCAmelCase_							).token_type_ids
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
	@slow
	def      A_				(			self							:   Tuple							):
						SCREAMING_SNAKE_CASE__													= self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'							)

						SCREAMING_SNAKE_CASE__													= tokenizer.encode('あンいワ'							)
						SCREAMING_SNAKE_CASE__													= tokenizer.encode(''       ,   prefix_text='あンいワ'							)
						SCREAMING_SNAKE_CASE__													= tokenizer.encode('いワ'       ,   prefix_text='あン'							)

						self.assertEqual(tokenizer.decode(UpperCAmelCase_							)       ,   tokenizer.decode(UpperCAmelCase_							)							)
						self.assertEqual(tokenizer.decode(UpperCAmelCase_							)       ,   tokenizer.decode(UpperCAmelCase_							)							)
						self.assertNotEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
						self.assertNotEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
						self.assertEqual(x_token_a[1]       ,   x_token_a[-1]							)  # SEG token
						self.assertEqual(x_token_a[1]       ,   x_token_a[3]							)  # SEG token
	@slow
	def      A_				(			self							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'							)

						SCREAMING_SNAKE_CASE__													= [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
						SCREAMING_SNAKE_CASE__													= tokenizer(UpperCAmelCase_       ,   padding=UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= tokenizer.batch_encode_plus(UpperCAmelCase_       ,   padding=UpperCAmelCase_							)

						# fmt: off
						SCREAMING_SNAKE_CASE__													= [[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]]
						SCREAMING_SNAKE_CASE__													= [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
						SCREAMING_SNAKE_CASE__													= [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
						# fmt: on
						self.assertListEqual(x_token.input_ids       ,   UpperCAmelCase_							)
						self.assertListEqual(x_token.token_type_ids       ,   UpperCAmelCase_							)
						self.assertListEqual(x_token.attention_mask       ,   UpperCAmelCase_							)
						self.assertListEqual(x_token_a.input_ids       ,   UpperCAmelCase_							)
						self.assertListEqual(x_token_a.token_type_ids       ,   UpperCAmelCase_							)
						self.assertListEqual(x_token_a.attention_mask       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Tuple							):
						# Intentionally convert some words to accommodate character fluctuations unique to Japanese
						pass





	def      A_				(			self							:   List[str]							):
						# tokenizer has no padding token
						pass





