


import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_SCREAMING_SNAKE_CASE      :			Optional[int]      				=   16
_SCREAMING_SNAKE_CASE      :			int      				=   32
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_ = 16							,UpperCamelCase_ = "bert-base-cased"    ):



					"""simple docstring"""






					snake_case					=    AutoTokenizer.from_pretrained(UpperCamelCase_    )
					snake_case					=    load_dataset('''glue'''							,'''mrpc'''    )

					def tokenize_function(UpperCamelCase_    ):
										# max_length=None => use the model max length (it's actually the default)
										snake_case					=    tokenizer(examples['''sentence1''']							,examples['''sentence2''']							,truncation=UpperCamelCase_							,max_length=UpperCamelCase_    )
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					snake_case					=    datasets.map(
					    UpperCamelCase_							,batched=UpperCamelCase_							,remove_columns=['''idx''', '''sentence1''', '''sentence2''']							,load_from_cache_file=UpperCamelCase_    )

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					snake_case					=    tokenized_datasets.rename_column('''label'''							,'''labels'''    )

					def collate_fn(UpperCamelCase_    ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										if accelerator.distributed_type == DistributedType.TPU:
															return tokenizer.pad(UpperCamelCase_							,padding='''max_length'''							,max_length=1_28							,return_tensors='''pt'''    )
										return tokenizer.pad(UpperCamelCase_							,padding='''longest'''							,return_tensors='''pt'''    )

					# Instantiate dataloaders.
					snake_case					=    DataLoader(
					    tokenized_datasets['''train''']							,shuffle=UpperCamelCase_							,collate_fn=UpperCamelCase_							,batch_size=UpperCamelCase_    )
					snake_case					=    DataLoader(
					    tokenized_datasets['''validation''']							,shuffle=UpperCamelCase_							,collate_fn=UpperCamelCase_							,batch_size=UpperCamelCase_    )

					return train_dataloader, eval_dataloader
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



					"""simple docstring"""






					model.eval()
					snake_case					=    0
					for step, batch in enumerate(UpperCamelCase_    ):
										# We could avoid this line since we set the accelerator with `device_placement=True`.
										batch.to(accelerator.device    )
										with torch.no_grad():
															snake_case					=    model(**UpperCamelCase_    )
										snake_case					=    outputs.logits.argmax(dim=-1    )
										# It is slightly faster to call this once, than multiple times
										snake_case			,  snake_case					=    accelerator.gather(
										    (predictions, batch['''labels'''])    )  # If we are in a multiprocess environment, the last batch has duplicates
										if accelerator.use_distributed:
															if step == len(UpperCamelCase_    ) - 1:
																				snake_case					=    predictions[: len(eval_dataloader.dataset    ) - samples_seen]
																				snake_case					=    references[: len(eval_dataloader.dataset    ) - samples_seen]
															else:
																				samples_seen += references.shape[0]
										metric.add_batch(
										    predictions=UpperCamelCase_							,references=UpperCamelCase_							,)

					snake_case					=    metric.compute()
					return eval_metric["accuracy"]
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



					"""simple docstring"""






					snake_case					=    Accelerator()

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					snake_case					=    config['''lr''']
					snake_case					=    int(config['''num_epochs''']    )
					snake_case					=    int(config['''seed''']    )
					snake_case					=    int(config['''batch_size''']    )
					snake_case					=    args.model_name_or_path

					set_seed(UpperCamelCase_    )
					snake_case			,  snake_case					=    get_dataloaders(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					snake_case					=    AutoModelForSequenceClassification.from_pretrained(UpperCamelCase_							,return_dict=UpperCamelCase_    )

					# Instantiate optimizer
					snake_case					=    (
					    AdamW
					    if accelerator.state.deepspeed_plugin is None
					    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
					    else DummyOptim
					)
					snake_case					=    optimizer_cls(params=model.parameters()							,lr=UpperCamelCase_    )

					if accelerator.state.deepspeed_plugin is not None:
										snake_case					=    accelerator.state.deepspeed_plugin.deepspeed_config[
										    '''gradient_accumulation_steps'''
										]
					else:
										snake_case					=    1
					snake_case					=    (len(UpperCamelCase_    ) * num_epochs) // gradient_accumulation_steps

					# Instantiate scheduler
					if (
					    accelerator.state.deepspeed_plugin is None
					    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
					):
										snake_case					=    get_linear_schedule_with_warmup(
										    optimizer=UpperCamelCase_							,num_warmup_steps=0							,num_training_steps=UpperCamelCase_							,)
					else:
										snake_case					=    DummyScheduler(UpperCamelCase_							,total_num_steps=UpperCamelCase_							,warmup_num_steps=0    )

					# Prepare everything
					# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
					# prepare method.
					snake_case			,  snake_case			,  snake_case			,  snake_case			,  snake_case					=    accelerator.prepare(
					    UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )

					# We need to keep track of how many total steps we have iterated over
					snake_case					=    0
					# We also need to keep track of the stating epoch so files are named properly
					snake_case					=    0
					snake_case					=    evaluate.load('''glue'''							,'''mrpc'''    )
					snake_case					=    num_epochs

					if args.partial_train_epoch is not None:
										snake_case					=    args.partial_train_epoch

					if args.resume_from_checkpoint:
										accelerator.load_state(args.resume_from_checkpoint    )
										snake_case					=    args.resume_from_checkpoint.split('''epoch_'''    )[1]
										snake_case					=    ''''''
										for char in epoch_string:
															if char.isdigit():
																				state_epoch_num += char
															else:
																				break
										snake_case					=    int(UpperCamelCase_    ) + 1
										snake_case					=    evaluation_loop(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )
										accelerator.print('''resumed checkpoint performance:'''							,UpperCamelCase_    )
										accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''							,lr_scheduler.get_lr()[0]    )
										accelerator.print('''resumed optimizers\'s lr:'''							,optimizer.param_groups[0]['''lr''']    )
										with open(os.path.join(args.output_dir							,F'''state_{starting_epoch-1}.json'''    )							,'''r'''    ) as f:
															snake_case					=    json.load(UpperCamelCase_    )
															assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
															assert (
															    resumed_state["lr"] == lr_scheduler.get_lr()[0]
															), "Scheduler learning rate mismatch, loading from checkpoint failed"
															assert (
															    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
															), "Optimizer learning rate mismatch, loading from checkpoint failed"
															assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
															return

    # Now we train the model
					snake_case					=    {}
					for epoch in range(UpperCamelCase_							,UpperCamelCase_    ):
										model.train()
										for step, batch in enumerate(UpperCamelCase_    ):
															snake_case					=    model(**UpperCamelCase_    )
															snake_case					=    outputs.loss
															snake_case					=    loss / gradient_accumulation_steps
															accelerator.backward(UpperCamelCase_    )
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1
										snake_case					=    F'''epoch_{epoch}'''
										snake_case					=    os.path.join(args.output_dir							,UpperCamelCase_    )
										accelerator.save_state(UpperCamelCase_    )
										snake_case					=    evaluation_loop(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )
										snake_case					=    accuracy
										snake_case					=    lr_scheduler.get_lr()[0]
										snake_case					=    optimizer.param_groups[0]['''lr''']
										snake_case					=    epoch
										snake_case					=    overall_step
										accelerator.print(F'''epoch {epoch}:'''							,UpperCamelCase_    )

										accelerator.wait_for_everyone()
										if accelerator.is_main_process:
															with open(os.path.join(args.output_dir							,F'''state_{epoch}.json'''    )							,'''w'''    ) as f:
																				json.dump(UpperCamelCase_							,UpperCamelCase_    )
def       UpperCAmelCase__						():



					"""simple docstring"""






					snake_case					=    argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''    )
					parser.add_argument(
					    '''--model_name_or_path'''							,type=UpperCamelCase_							,default='''bert-base-cased'''							,help='''Path to pretrained model or model identifier from huggingface.co/models.'''							,required=UpperCamelCase_							,)
					parser.add_argument(
					    '''--output_dir'''							,type=UpperCamelCase_							,default='''.'''							,help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''							,)
					parser.add_argument(
					    '''--resume_from_checkpoint'''							,type=UpperCamelCase_							,default=UpperCamelCase_							,help='''If the training should continue from a checkpoint folder.'''							,)
					parser.add_argument(
					    '''--partial_train_epoch'''							,type=UpperCamelCase_							,default=UpperCamelCase_							,help='''If passed, the training will stop after this number of epochs.'''							,)
					parser.add_argument(
					    '''--num_epochs'''							,type=UpperCamelCase_							,default=2							,help='''Number of train epochs.'''							,)
					snake_case					=    parser.parse_args()
					snake_case					=    {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

					training_function(UpperCamelCase_							,UpperCamelCase_    )


if __name__ == "__main__":
	main()








import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case=1_3      , __snake_case=7      , __snake_case=True      , __snake_case=True      , __snake_case=True      , __snake_case=True      , __snake_case=9_9      , __snake_case=3_2      , __snake_case=5      , __snake_case=4      , __snake_case=4      , __snake_case="gelu"      , __snake_case=0.0      , __snake_case=0.1      , __snake_case=True      , __snake_case=5_1_2      , __snake_case=1_6      , __snake_case=2      , __snake_case=0.02      , __snake_case=3      , __snake_case=4      , __snake_case=None      , ):
         snake_case					=    parent
         snake_case					=    batch_size
         snake_case					=    seq_length
         snake_case					=    is_training
         snake_case					=    use_input_mask
         snake_case					=    use_token_type_ids
         snake_case					=    use_labels
         snake_case					=    vocab_size
         snake_case					=    hidden_size
         snake_case					=    num_hidden_layers
         snake_case					=    num_attention_heads
         snake_case					=    intermediate_multiple_size
         snake_case					=    hidden_act
         snake_case					=    hidden_dropout
         snake_case					=    attention_dropout
         snake_case					=    weight_tying
         snake_case					=    max_position_embeddings
         snake_case					=    type_vocab_size
         snake_case					=    type_sequence_label_size
         snake_case					=    initializer_range
         snake_case					=    num_labels
         snake_case					=    num_choices
         snake_case					=    scope
    def 		a_      (	self   ):
         snake_case					=    ids_tensor([self.batch_size, self.seq_length]      , self.vocab_size   )

         snake_case					=    None
         if self.use_input_mask:
              snake_case					=    random_attention_mask([self.batch_size, self.seq_length]   )

         snake_case					=    None
         if self.use_labels:
              snake_case					=    ids_tensor([self.batch_size, self.seq_length]      , self.num_labels   )

         snake_case					=    self.get_config()

         return config, input_ids, input_mask, token_labels
    def 		a_      (	self   ):
         return GPTNeoXJapaneseConfig(
             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_multiple_size=self.intermediate_multiple_size      , hidden_act=self.hidden_act      , hidden_dropout=self.hidden_dropout      , attention_dropout=self.attention_dropout      , weight_tying=self.weight_tying      , max_position_embeddings=self.max_position_embeddings      , type_vocab_size=self.type_vocab_size      , is_decoder=__snake_case      , initializer_range=self.initializer_range      , )
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.prepare_config_and_inputs()

         snake_case					=    True

         return config, input_ids, input_mask, token_labels
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    GPTNeoXJapaneseModel(config=__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case      , attention_mask=__snake_case   )
         snake_case					=    model(__snake_case   )
         self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    True
         snake_case					=    GPTNeoXJapaneseModel(__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case      , attention_mask=__snake_case   )
         self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    GPTNeoXJapaneseForCausalLM(config=__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case      , attention_mask=__snake_case      , labels=__snake_case   )
         self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.seq_length, self.vocab_size)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    True
         snake_case					=    GPTNeoXJapaneseForCausalLM(config=__snake_case   )
         model.to(__snake_case   )
         model.eval()

         # first forward pass
         snake_case					=    model(__snake_case      , attention_mask=__snake_case      , use_cache=__snake_case   )
         snake_case					=    outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         snake_case					=    ids_tensor((self.batch_size, 3)      , config.vocab_size   )
         snake_case					=    ids_tensor((self.batch_size, 3)      , vocab_size=2   )

         # append to next input_ids and
         snake_case					=    torch.cat([input_ids, next_tokens]      , dim=-1   )
         snake_case					=    torch.cat([input_mask, next_mask]      , dim=-1   )

         snake_case					=    model(__snake_case      , attention_mask=__snake_case      , output_hidden_states=__snake_case   )
         snake_case					=    output_from_no_past['''hidden_states'''][0]
         snake_case					=    model(
             __snake_case      , attention_mask=__snake_case      , past_key_values=__snake_case      , output_hidden_states=__snake_case      , )['''hidden_states'''][0]

         # select random slice
         snake_case					=    ids_tensor((1,)      , output_from_past.shape[-1]   ).item()
         snake_case					=    output_from_no_past[:, -3:, random_slice_idx].detach()
         snake_case					=    output_from_past[:, :, random_slice_idx].detach()

         self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]   )

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(__snake_case      , __snake_case      , atol=1E-3   )   )




    def 		a_      (	self   ):
         snake_case					=    self.prepare_config_and_inputs()
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    config_and_inputs
         snake_case					=    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
         return config, inputs_dict






@require_torch
class   A__ (   snake_case__	, snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
    __magic_name__        =		(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
    __magic_name__        =		(
        {'feature-extraction': GPTNeoXJapaneseModel, 'text-generation': GPTNeoXJapaneseForCausalLM}
        if is_torch_available()
        else {}
    )
    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		False
    def 		a_      (	self   ):
         snake_case					=    GPTNeoXJapaneseModelTester(self   )
         snake_case					=    ConfigTester(self      , config_class=__snake_case      , hidden_size=3_7   )
    def 		a_      (	self   ):
         self.config_tester.run_common_tests()
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         # This regression test was failing with PyTorch < 1.3
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_decoder()

         snake_case					=    None

         self.model_tester.create_and_check_model_as_decoder(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_causal_lm(*__snake_case   )




    @slow
    def 		a_      (	self   ):
         snake_case					=    '''abeja/gpt-neox-japanese-2.7b'''

         snake_case					=    ['''データサイエンティストとは、''', '''100年後に必要とされる会社は、''', '''フルリモートの環境で働くために必要なことは、''', '''国境の長いトンネルを抜けると''', '''美味しい日本食といえば、''']

         snake_case					=    [
             '''データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。''',
             '''100年後に必要とされる会社は、「人」が中心の会社です。''',
             '''フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。''',
             '''国境の長いトンネルを抜けると、そこは雪国だった。''',
             '''美味しい日本食といえば、やっぱりお寿司ですよね。''',
         ]

         snake_case					=    GPTNeoXJapaneseTokenizer.from_pretrained(__snake_case   )
         snake_case					=    GPTNeoXJapaneseForCausalLM.from_pretrained(__snake_case   )

         snake_case					=    []
         for prompt in prompts:
              snake_case					=    tokenizer(__snake_case      , return_tensors='''pt'''   ).input_ids
              snake_case					=    model.generate(__snake_case      , max_length=5_0   )
              snake_case					=    tokenizer.batch_decode(__snake_case      , skip_special_tokens=__snake_case   )
              predicted_outputs += generated_string
         self.assertListEqual(__snake_case      , __snake_case   )




