
'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_							: Optional[Any] =							logging.get_logger(__name__)

UpperCAmelCase_							: List[str] =							{
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}

class 			UpperCAmelCase__    (							A			):
    lowerCAmelCase_					     =		'gpt_neox'



    def __init__(						self   :							Optional[Any],__A   :							Any=5_0_4_3_2,__A   :							Optional[Any]=6_1_4_4,__A   :							Optional[int]=4_4,__A   :							int=6_4,__A   :							List[str]=2_4_5_7_6,__A   :							Union[str, Any]="gelu",__A   :							Optional[int]=0.25,__A   :							List[str]=1_0_0_0_0,__A   :							Optional[Any]=0.0,__A   :							Optional[Any]=0.0,__A   :							Tuple=0.1,__A   :							List[Any]=2_0_4_8,__A   :							int=0.02,__A   :							Tuple=1e-5,__A   :							Dict=True,__A   :							int=0,__A   :							Optional[int]=2,__A   :							int=False,__A   :							List[Any]=True,__A   :							List[Any]=None,**__A   :							Dict,):
          super().__init__(bos_token_id=__A,eos_token_id=__A,**__A  )
          _lowerCamelCase					: int		    = vocab_size
          _lowerCamelCase					: Dict		    = max_position_embeddings
          _lowerCamelCase					: List[Any]		    = hidden_size
          _lowerCamelCase					: Optional[int]		    = num_hidden_layers
          _lowerCamelCase					: str		    = num_attention_heads
          _lowerCamelCase					: Optional[Any]		    = intermediate_size
          _lowerCamelCase					: Optional[int]		    = hidden_act
          _lowerCamelCase					: List[str]		    = rotary_pct
          _lowerCamelCase					: Tuple		    = rotary_emb_base
          _lowerCamelCase					: Optional[int]		    = attention_dropout
          _lowerCamelCase					: List[Any]		    = hidden_dropout
          _lowerCamelCase					: str		    = classifier_dropout
          _lowerCamelCase					: List[str]		    = initializer_range
          _lowerCamelCase					: Any		    = layer_norm_eps
          _lowerCamelCase					: Dict		    = use_cache
          _lowerCamelCase					: Dict		    = tie_word_embeddings
          _lowerCamelCase					: List[str]		    = use_parallel_residual
          _lowerCamelCase					: Tuple		    = rope_scaling
          self._rope_scaling_validation()

          if self.hidden_size % self.num_attention_heads != 0:
                raise ValueError(
                    "The hidden size is not divisble by the number of attention heads! Make sure to update them!"  )



    def 	lowerCamelCase_	(						self   :							Optional[Any]  ):
          if self.rope_scaling is None:
                return

          if not isinstance(self.rope_scaling,__A  ) or len(self.rope_scaling  ) != 2:
                raise ValueError(
                    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
                    f'got {self.rope_scaling}'  )
          _lowerCamelCase					: Union[str, Any]		    = self.rope_scaling.get("type",__A  )
          _lowerCamelCase					: Tuple		    = self.rope_scaling.get("factor",__A  )
          if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
                raise ValueError(
                    f'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'  )
          if rope_scaling_factor is None or not isinstance(__A,__A  ) or rope_scaling_factor <= 1.0:
                raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'  )







import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


SCREAMING_SNAKE_CASE__   :      List[Any]				=		os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"{bindir}/../../examples/pytorch/translation"):
					from run_translation import main  # noqa


set_seed(42)
SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		"""sshleifer/student_marian_en_ro_6_1"""
SCREAMING_SNAKE_CASE__   :      List[Any]				=		"""sshleifer/tiny-mbart"""
@require_torch
class     lowerCamelCase_					(   lowerCamelCase			):







				def       A						(    self							,       __lowerCAmelCase=False							,       __lowerCAmelCase=None							,       __lowerCAmelCase=True							,       __lowerCAmelCase=True							,       __lowerCAmelCase=True							,       __lowerCAmelCase=True							,       ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.run_trainer(
								    eval_steps=1							,       max_len=1_2							,       model_name=__lowerCAmelCase							,       num_train_epochs=1							,       distributed=__lowerCAmelCase							,       extra_args_str=__lowerCAmelCase							,       predict_with_generate=__lowerCAmelCase							,       do_train=__lowerCAmelCase							,       do_eval=__lowerCAmelCase							,       do_predict=__lowerCAmelCase							,       )
								__magic_name__		:Any   =   TrainerState.load_from_json(os.path.join(__lowerCAmelCase							,       '''trainer_state.json'''  )  ).log_history

								if not do_eval:
												return

								__magic_name__		:Any   =   [log for log in logs if '''eval_loss''' in log.keys()]

								__magic_name__		:str   =   eval_metrics[0]
								if predict_with_generate:
												assert "eval_bleu" in first_step_stats

												__magic_name__		:Tuple   =   eval_metrics[-1]
												assert isinstance(last_step_stats['''eval_bleu''']							,       __lowerCAmelCase  )
												assert not math.isnan(float(last_step_stats['''eval_loss''']  )  ), "eval_loss must not be `nan`"







				@require_torch_non_multi_gpu
				def       A						(    self  ):
								"""simple docstring"""
								self.run_seqaseq_quick()







				@require_torch_multi_gpu
				def       A						(    self  ):
								"""simple docstring"""
								self.run_seqaseq_quick(distributed=__lowerCAmelCase  )







				@require_torch_multi_gpu
				def       A						(    self  ):
								"""simple docstring"""
								self.run_seqaseq_quick(distributed=__lowerCAmelCase  )







				@unittest.skip('''Requires an update of the env running those tests'''  )
				@require_torch_multi_gpu
				@require_fairscale
				def       A						(    self  ):
								"""simple docstring"""
								self.run_seqaseq_quick(distributed=__lowerCAmelCase							,       extra_args_str='''--sharded_ddp simple'''  )







				@unittest.skip('''Requires an update of the env running those tests'''  )
				@require_torch_multi_gpu
				@require_fairscale
				def       A						(    self  ):
								"""simple docstring"""
								self.run_seqaseq_quick(distributed=__lowerCAmelCase							,       extra_args_str='''--sharded_ddp simple --fp16'''  )







				@unittest.skip('''Requires an update of the env running those tests'''  )
				@require_torch_multi_gpu
				@require_fairscale
				def       A						(    self  ):
								"""simple docstring"""
								self.run_seqaseq_quick(distributed=__lowerCAmelCase							,       extra_args_str='''--sharded_ddp zero_dp_2'''							,       predict_with_generate=__lowerCAmelCase  )







				@unittest.skip('''Requires an update of the env running those tests'''  )
				@require_torch_multi_gpu
				@require_fairscale
				def       A						(    self  ):
								"""simple docstring"""
								self.run_seqaseq_quick(
								    distributed=__lowerCAmelCase							,       extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''							,       predict_with_generate=__lowerCAmelCase  )







				@require_apex
				@require_torch_gpu
				def       A						(    self  ):
								"""simple docstring"""
								# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
								# program and it breaks other tests that run from the same pytest worker, therefore until this is
								# sorted out it must be run only in an external program, that is distributed=True in this
								# test and only under one or more gpus - if we want cpu will need to make a special test
								#
								# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
								# 2nd main() call it botches the future eval.
								#
								self.run_seqaseq_quick(distributed=__lowerCAmelCase							,       extra_args_str='''--fp16 --fp16_backend=apex'''  )
								# test 2nd time - was getting eval_loss': nan'
								# to reproduce the problem set distributed=False
								self.run_seqaseq_quick(distributed=__lowerCAmelCase							,       extra_args_str='''--fp16 --fp16_backend=apex'''  )







				@parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']  )
				@require_torch_multi_gpu
				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
								__magic_name__		:Any   =   {
								    # test with the default log_level - should be info and thus log info once
								    '''base''': {'''extra_args_str''': '''''', '''n_matches''': 1},
								    # test with low log_level and log_level_replica - should be noisy on all processes
								    # now the info string should appear twice on 2 processes
								    '''low''': {'''extra_args_str''': '''--log_level debug --log_level_replica debug''', '''n_matches''': 2},
								    # test with high log_level and low log_level_replica
								    # now the info string should appear once only on the replica
								    '''high''': {'''extra_args_str''': '''--log_level error --log_level_replica debug''', '''n_matches''': 1},
								    # test with high log_level and log_level_replica - should be quiet on all processes
								    '''mixed''': {'''extra_args_str''': '''--log_level error --log_level_replica error''', '''n_matches''': 0},
								}

								__magic_name__		:Optional[Any]   =   experiments[experiment_id]
								__magic_name__		:List[Any]   =   {'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
								__magic_name__		:Optional[int]   =   '''Running training'''
								with CaptureStderr() as cl:
												self.run_seqaseq_quick(**__lowerCAmelCase							,       extra_args_str=data['''extra_args_str''']  )
								__magic_name__		:int   =   len(re.findall(__lowerCAmelCase							,       cl.err  )  )
								self.assertEqual(__lowerCAmelCase							,       data['''n_matches''']  )







				@slow
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   self.run_trainer(
								    eval_steps=2							,       max_len=1_2_8							,       model_name=__lowerCAmelCase							,       learning_rate=3E-4							,       num_train_epochs=1_0							,       distributed=__lowerCAmelCase							,       )

								# Check metrics
								__magic_name__		:Optional[int]   =   TrainerState.load_from_json(os.path.join(__lowerCAmelCase							,       '''trainer_state.json'''  )  ).log_history
								__magic_name__		:List[str]   =   [log for log in logs if '''eval_loss''' in log.keys()]
								__magic_name__		:Any   =   eval_metrics[0]
								__magic_name__		:int   =   eval_metrics[-1]

								assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
								assert isinstance(last_step_stats['''eval_bleu''']							,       __lowerCAmelCase  )

								# test if do_predict saves generations and metrics
								__magic_name__		:List[Any]   =   os.listdir(__lowerCAmelCase  )
								__magic_name__		:List[str]   =   {os.path.basename(__lowerCAmelCase  ) for p in contents}
								assert "generated_predictions.txt" in contents
								assert "predict_results.json" in contents







				@slow
				@require_bitsandbytes
				def       A						(    self  ):
								"""simple docstring"""
								from transformers.training_args import OptimizerNames

								def train_and_return_metrics(__lowerCAmelCase  ) -> Tuple[int, float]:
												__magic_name__		:str   =   '''--skip_memory_metrics 0'''

												__magic_name__		:Dict   =   self.run_trainer(
												    max_len=1_2_8							,       model_name=__lowerCAmelCase							,       learning_rate=3E-4							,       num_train_epochs=1							,       optim=__lowerCAmelCase							,       distributed=__lowerCAmelCase							,       extra_args_str=__lowerCAmelCase							,       do_eval=__lowerCAmelCase							,       do_predict=__lowerCAmelCase							,       n_gpus_to_use=1							,       )

												# Check metrics
												__magic_name__		:Optional[Any]   =   TrainerState.load_from_json(Path(__lowerCAmelCase							,       '''trainer_state.json'''  )  ).log_history
												__magic_name__		:int   =   int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**2_0  )
												__magic_name__		:Optional[Any]   =   int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**2_0  )

												__magic_name__		:Any   =   logs[0]['''train_loss''']
												return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

								__magic_name__  ,					__magic_name__  ,					__magic_name__		:int   =   train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value  )
								__magic_name__  ,					__magic_name__  ,					__magic_name__		:Tuple   =   train_and_return_metrics(OptimizerNames.ADAMW_BNB.value  )

								__magic_name__		:Tuple   =   gpu_alloc_mem_orig - gpu_alloc_mem_bnb

								__magic_name__		:Tuple   =   gpu_peak_mem_orig + gpu_alloc_mem_orig
								__magic_name__		:List[Any]   =   gpu_peak_mem_bnb + gpu_alloc_mem_bnb
								__magic_name__		:Optional[int]   =   gpu_total_mem_orig - gpu_total_mem_bnb

								# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
								# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
								# in 2 bytes and the diff in optim memory usage is derived as so:
								#
								# - normal 25*8=~200MB (8 bytes per param)
								# - bnb    25*2= ~50MB (2 bytes per param)
								#
								# Thus we should expect ~150MB total memory saved.
								#
								# Peak memory should be the same - the total should be different by about that same margin
								#
								# After leaving a small margin to accommodate for differences between gpus let's check
								# that we have at least 120MB in savings
								__magic_name__		:Optional[Any]   =   1_2_0

								# uncomment the following if this test starts failing - requires py38 for a new print feature
								# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
								# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
								# print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
								# print(f"{gpu_alloc_mem_diff=}MB")
								# print(f"{gpu_peak_mem_diff=}MB")
								# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
								# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

								self.assertGreater(
								    __lowerCAmelCase							,       __lowerCAmelCase							,       '''should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'''
								    F''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
								    F''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''							,       )

								self.assertGreater(
								    __lowerCAmelCase							,       __lowerCAmelCase							,       '''should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'''
								    F''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
								    F''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''							,       )

								self.assertEqual(
								    __lowerCAmelCase							,       __lowerCAmelCase							,       F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase = 3E-3							,       __lowerCAmelCase = "adafactor"							,       __lowerCAmelCase = False							,       __lowerCAmelCase = None							,       __lowerCAmelCase = 0							,       __lowerCAmelCase = True							,       __lowerCAmelCase = True							,       __lowerCAmelCase = True							,       __lowerCAmelCase = True							,       __lowerCAmelCase = None							,       ):
								"""simple docstring"""
								__magic_name__		:int   =   self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
								__magic_name__		:Dict   =   self.get_auto_remove_tmp_dir()
								__magic_name__		:Tuple   =   F'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(__lowerCAmelCase  )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(__lowerCAmelCase  )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

								__magic_name__		:str   =   F'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(__lowerCAmelCase  )}
        '''.split()

								__magic_name__		:Dict   =   '''
            --do_predict
        '''.split()

								__magic_name__		:Optional[int]   =   []
								if do_train:
												args += args_train

								if do_eval:
												args += args_eval

								if do_predict:
												args += args_predict

								if predict_with_generate:
												args += "--predict_with_generate".split()

								if do_train:
												if optim == "adafactor":
																args += "--adafactor".split()
												else:
																args += F'''--optim {optim}'''.split()

								if extra_args_str is not None:
												args += extra_args_str.split()

								if distributed:
												if n_gpus_to_use is None:
																__magic_name__		:List[Any]   =   get_gpu_count()
												__magic_name__		:Tuple   =   get_torch_dist_unique_port()
												__magic_name__		:Union[str, Any]   =   F'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
												__magic_name__		:Any   =   [sys.executable] + distributed_args + args
												# keep for quick debug
												# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
												execute_subprocess_async(__lowerCAmelCase							,       env=self.get_env()  )
								else:
												__magic_name__		:List[Any]   =   ['''run_translation.py'''] + args
												with patch.object(__lowerCAmelCase							,       '''argv'''							,       __lowerCAmelCase  ):
																main()

								return output_dir






