


'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__A	=logging.get_logger(__name__)

__A	={
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class 		_snake_case					(     a__       ,   a__	):
					lowerCAmelCase      :Dict   =					'''nat'''

					lowerCAmelCase      :Tuple   =					{
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}




					def __init__(      self							,							_lowerCamelCase=4							,							_lowerCamelCase=3							,							_lowerCamelCase=64							,							_lowerCamelCase=[3, 4, 6, 5]							,							_lowerCamelCase=[2, 4, 8, 16]							,							_lowerCamelCase=7							,							_lowerCamelCase=3.0							,							_lowerCamelCase=True							,							_lowerCamelCase=0.0							,							_lowerCamelCase=0.0							,							_lowerCamelCase=0.1							,							_lowerCamelCase="gelu"							,							_lowerCamelCase=0.02							,							_lowerCamelCase=1e-5							,							_lowerCamelCase=0.0							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							**_lowerCamelCase							,							):
								super().__init__(**_lowerCamelCase)

								UpperCAmelCase__  :					List[str]        =     patch_size
								UpperCAmelCase__  :					Any        =     num_channels
								UpperCAmelCase__  :					str        =     embed_dim
								UpperCAmelCase__  :					Tuple        =     depths
								UpperCAmelCase__  :					Union[str, Any]        =     len(_lowerCamelCase)
								UpperCAmelCase__  :					Optional[int]        =     num_heads
								UpperCAmelCase__  :					Tuple        =     kernel_size
								UpperCAmelCase__  :					Any        =     mlp_ratio
								UpperCAmelCase__  :					Optional[int]        =     qkv_bias
								UpperCAmelCase__  :					Optional[Any]        =     hidden_dropout_prob
								UpperCAmelCase__  :					Optional[Any]        =     attention_probs_dropout_prob
								UpperCAmelCase__  :					List[str]        =     drop_path_rate
								UpperCAmelCase__  :					int        =     hidden_act
								UpperCAmelCase__  :					Optional[Any]        =     layer_norm_eps
								UpperCAmelCase__  :					List[str]        =     initializer_range
								# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								UpperCAmelCase__  :					int        =     int(embed_dim * 2 ** (len(_lowerCamelCase) - 1))
								UpperCAmelCase__  :					Optional[int]        =     layer_scale_init_value
								UpperCAmelCase__  :					Tuple        =     ["""stem"""] + [f'''stage{idx}''' for idx in range(1							,							len(_lowerCamelCase) + 1)]
								UpperCAmelCase__  :					Optional[Any]        =     get_aligned_output_features_output_indices(
								    out_features=_lowerCamelCase							,							out_indices=_lowerCamelCase							,							stage_names=self.stage_names)



'''simple docstring'''



import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

__A	=logging.getLogger()







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					List[Any]        =     argparse.ArgumentParser()
			parser.add_argument("""-f"""					)
			UpperCAmelCase__  :					Optional[Any]        =     parser.parse_args()
			return args.f


class 		_snake_case					(     a__	):




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Tuple        =     logging.StreamHandler(sys.stdout)
								logger.addHandler(_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Any        =     get_gpu_count()

								if n_gpu > 1:
											pass
											# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
											# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
											# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
											# cmd = [sys.executable] + distributed_args + args
											# execute_subprocess_async(cmd, env=self.get_env())
											# XXX: test the results - need to save them first into .json file
								else:
											args.insert(0							,							"""run_glue_deebert.py""")
											with patch.object(_lowerCamelCase							,							"""argv"""							,							_lowerCamelCase):
														UpperCAmelCase__  :					Union[str, Any]        =     run_glue_deebert.main()
														for value in result.values():
																	self.assertGreaterEqual(_lowerCamelCase							,							0.666)




					@slow
					@require_torch_non_multi_gpu
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[str]        =     """
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            """.split()
								self.run_and_check(_lowerCamelCase)

								UpperCAmelCase__  :					Dict        =     """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
								self.run_and_check(_lowerCamelCase)

								UpperCAmelCase__  :					Any        =     """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
								self.run_and_check(_lowerCamelCase)