


'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case							   =  logging.get_logger(__name__)

__snake_case							   =  {
    '''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      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'gpt_neox'




		def __init__(	self					,     UpperCamelCase_=50432					,     UpperCamelCase_=6144					,     UpperCamelCase_=44					,     UpperCamelCase_=64					,     UpperCamelCase_=24576					,     UpperCamelCase_="gelu"					,     UpperCamelCase_=0.25					,     UpperCamelCase_=10000					,     UpperCamelCase_=0.0					,     UpperCamelCase_=0.0					,     UpperCamelCase_=0.1					,     UpperCamelCase_=2048					,     UpperCamelCase_=0.02					,     UpperCamelCase_=1e-5					,     UpperCamelCase_=True					,     UpperCamelCase_=0					,     UpperCamelCase_=2					,     UpperCamelCase_=False					,     UpperCamelCase_=True					,     UpperCamelCase_=None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							super().__init__(bos_token_id=UpperCamelCase_					,     eos_token_id=UpperCamelCase_					,     **UpperCamelCase_							)
							UpperCamelCase__     :Tuple							      =				vocab_size
							UpperCamelCase__     :Tuple							      =				max_position_embeddings
							UpperCamelCase__     :Any							      =				hidden_size
							UpperCamelCase__     :Any							      =				num_hidden_layers
							UpperCamelCase__     :Optional[Any]							      =				num_attention_heads
							UpperCamelCase__     :Union[str, Any]							      =				intermediate_size
							UpperCamelCase__     :Union[str, Any]							      =				hidden_act
							UpperCamelCase__     :Any							      =				rotary_pct
							UpperCamelCase__     :Optional[Any]							      =				rotary_emb_base
							UpperCamelCase__     :Dict							      =				attention_dropout
							UpperCamelCase__     :Optional[int]							      =				hidden_dropout
							UpperCamelCase__     :List[Any]							      =				classifier_dropout
							UpperCamelCase__     :List[str]							      =				initializer_range
							UpperCamelCase__     :Union[str, Any]							      =				layer_norm_eps
							UpperCamelCase__     :Optional[int]							      =				use_cache
							UpperCamelCase__     :str							      =				tie_word_embeddings
							UpperCamelCase__     :List[str]							      =				use_parallel_residual
							UpperCamelCase__     :Any							      =				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							):

							'''simple docstring'''


							if self.rope_scaling is None:
												return

							if not isinstance(self.rope_scaling					,     UpperCamelCase_							) 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}'''							)
							UpperCamelCase__     :List[str]							      =				self.rope_scaling.get('''type'''					,     UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				self.rope_scaling.get('''factor'''					,     UpperCamelCase_							)
							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(UpperCamelCase_					,     UpperCamelCase_							) or rope_scaling_factor <= 1.0:
												raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''							)



'''simple docstring'''
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

__snake_case							   =  logging.getLogger()


def     a   (    __a     )						->			Dict:



					'''simple docstring'''






					UpperCamelCase__     :str							      =				{}
					UpperCamelCase__     :Dict							      =				os.path.join(__a				,      '''all_results.json'''     )
					if os.path.exists(__a     ):
										with open(__a				,      '''r'''     ) as f:
															UpperCamelCase__     :int							      =				json.load(__a     )
					else:
										raise ValueError(f'''can\'t find {path}'''     )
					return results


__snake_case							   =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@require_torch_tpu
class      lowercase			(							A__      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							import xla_spawn

							UpperCamelCase__     :Any							      =				self.get_auto_remove_tmp_dir()
							UpperCamelCase__     :List[str]							      =				F'''
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --debug tpu_metrics_debug
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

							with patch.object(UpperCamelCase_					,     '''argv'''					,     UpperCamelCase_							):
												UpperCamelCase__     :List[Any]							      =				time()
												xla_spawn.main()
												UpperCamelCase__     :List[Any]							      =				time()

												UpperCamelCase__     :Tuple							      =				get_results(UpperCamelCase_							)
												self.assertGreaterEqual(result['''eval_accuracy''']					,     0.75							)

												# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
												self.assertLess(end - start					,     500							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							import xla_spawn

							UpperCamelCase__     :Tuple							      =				'''
            ./tests/test_trainer_tpu.py
            --num_cores=8
            ./tests/test_trainer_tpu.py
            '''.split()
							with patch.object(UpperCamelCase_					,     '''argv'''					,     UpperCamelCase_							):
												xla_spawn.main()