


'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__snake_case							   =  logging.get_logger(__name__)

__snake_case							   =  {
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}
class      lowercase			(							a__			,	a__      ):

		"""simple docstring"""

		_a									=       'bit'
		_a									=       ['preactivation', 'bottleneck']
		_a									=       ['SAME', 'VALID']




		def __init__(	self					,     UpperCamelCase_=3					,     UpperCamelCase_=64					,     UpperCamelCase_=[256, 512, 1024, 2048]					,     UpperCamelCase_=[3, 4, 6, 3]					,     UpperCamelCase_="preactivation"					,     UpperCamelCase_="relu"					,     UpperCamelCase_=None					,     UpperCamelCase_=32					,     UpperCamelCase_=0.0					,     UpperCamelCase_=False					,     UpperCamelCase_=32					,     UpperCamelCase_=1					,     UpperCamelCase_=None					,     UpperCamelCase_=None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							super().__init__(**_A							)
							if layer_type not in self.layer_types:
												raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types							)}'''							)
							if global_padding is not None:
												if global_padding.upper() in self.supported_padding:
																	UpperCamelCase__     :Dict							      =				global_padding.upper()
												else:
																	raise ValueError(F'''Padding strategy {global_padding} not supported'''							)
							UpperCamelCase__     :Optional[int]							      =				num_channels
							UpperCamelCase__     :str							      =				embedding_size
							UpperCamelCase__     :Optional[int]							      =				hidden_sizes
							UpperCamelCase__     :Optional[int]							      =				depths
							UpperCamelCase__     :Dict							      =				layer_type
							UpperCamelCase__     :str							      =				hidden_act
							UpperCamelCase__     :Tuple							      =				global_padding
							UpperCamelCase__     :Dict							      =				num_groups
							UpperCamelCase__     :Optional[Any]							      =				drop_path_rate
							UpperCamelCase__     :Tuple							      =				embedding_dynamic_padding
							UpperCamelCase__     :Union[str, Any]							      =				output_stride
							UpperCamelCase__     :Dict							      =				width_factor

							UpperCamelCase__     :List[Any]							      =				['stem'] + [F'''stage{idx}''' for idx in range(1					,     len(_A							) + 1							)]
							UpperCamelCase__     :Dict							      =				get_aligned_output_features_output_indices(
							    out_features=_A					,     out_indices=_A					,     stage_names=self.stage_names							)



'''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()