


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


__snake_case							   =  logging.get_logger(__name__)

__snake_case							   =  {
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'xglm'
		_a									=       ['past_key_values']

		_a									=       {
		    'num_attention_heads': 'attention_heads',
		    'hidden_size': 'd_model',
		    'num_hidden_layers': 'num_layers',
		}




		def __init__(	self					,     UpperCamelCase_=256008					,     UpperCamelCase_=2048					,     UpperCamelCase_=1024					,     UpperCamelCase_=4096					,     UpperCamelCase_=24					,     UpperCamelCase_=16					,     UpperCamelCase_="gelu"					,     UpperCamelCase_=0.1					,     UpperCamelCase_=0.1					,     UpperCamelCase_=0.0					,     UpperCamelCase_=0.0					,     UpperCamelCase_=0.02					,     UpperCamelCase_=True					,     UpperCamelCase_=True					,     UpperCamelCase_=2					,     UpperCamelCase_=1					,     UpperCamelCase_=0					,     UpperCamelCase_=2					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				vocab_size
							UpperCamelCase__     :List[str]							      =				max_position_embeddings
							UpperCamelCase__     :Tuple							      =				d_model
							UpperCamelCase__     :Any							      =				ffn_dim
							UpperCamelCase__     :List[str]							      =				num_layers
							UpperCamelCase__     :List[Any]							      =				attention_heads
							UpperCamelCase__     :Any							      =				activation_function
							UpperCamelCase__     :Any							      =				dropout
							UpperCamelCase__     :Any							      =				attention_dropout
							UpperCamelCase__     :Dict							      =				activation_dropout
							UpperCamelCase__     :Any							      =				layerdrop
							UpperCamelCase__     :Tuple							      =				init_std
							UpperCamelCase__     :Tuple							      =				scale_embedding  # scale factor will be sqrt(d_model) if True
							UpperCamelCase__     :Any							      =				use_cache

							super().__init__(
							    pad_token_id=UpperCamelCase_					,     bos_token_id=UpperCamelCase_					,     eos_token_id=UpperCamelCase_					,     decoder_start_token_id=UpperCamelCase_					,     **UpperCamelCase_					,     )



'''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()