



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


__snake_case     :Tuple			       =logging.get_logger(__name__)

__snake_case     :int			       ={
    'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     int								=     'canine'


				def __init__(							self      :  Optional[int]						, __UpperCamelCase      :  Dict=768						, __UpperCamelCase      :  Tuple=12						, __UpperCamelCase      :  Union[str, Any]=12						, __UpperCamelCase      :  Optional[Any]=3_072						, __UpperCamelCase      :  int="gelu"						, __UpperCamelCase      :  int=0.1						, __UpperCamelCase      :  Optional[Any]=0.1						, __UpperCamelCase      :  Optional[int]=16_384						, __UpperCamelCase      :  Union[str, Any]=16						, __UpperCamelCase      :  Tuple=0.0_2						, __UpperCamelCase      :  Dict=1e-12						, __UpperCamelCase      :  int=0						, __UpperCamelCase      :  Optional[int]=0XE000						, __UpperCamelCase      :  List[Any]=0XE001						, __UpperCamelCase      :  Union[str, Any]=4						, __UpperCamelCase      :  Dict=4						, __UpperCamelCase      :  Tuple=8						, __UpperCamelCase      :  Any=16_384						, __UpperCamelCase      :  int=128						, **__UpperCamelCase      :  Tuple						, )		->     List[Any]:
											super().__init__(pad_token_id=__UpperCamelCase						, bos_token_id=__UpperCamelCase						, eos_token_id=__UpperCamelCase						, **__UpperCamelCase       )

											A      						=    max_position_embeddings
											A      						=    hidden_size
											A      						=    num_hidden_layers
											A      						=    num_attention_heads
											A      						=    intermediate_size
											A      						=    hidden_act
											A      						=    hidden_dropout_prob
											A      						=    attention_probs_dropout_prob
											A      						=    initializer_range
											A      						=    type_vocab_size
											A      						=    layer_norm_eps

											# Character config:
											A      						=    downsampling_rate
											A      						=    upsampling_kernel_size
											A      						=    num_hash_functions
											A      						=    num_hash_buckets
											A      						=    local_transformer_stride



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)

SCREAMING_SNAKE_CASE							    =							logging.getLogger()




def  a   ():
   __a					=  argparse.ArgumentParser()
   parser.add_argument("""-f"""   )
   __a					=  parser.parse_args()
   return args.f


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def 	snake_case_  (	self    ):
        __a					=  logging.StreamHandler(sys.stdout    )
        logger.addHandler(__A    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  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(__A  ,       """argv"""  ,       __A    ):
              __a					=  run_glue_deebert.main()
              for value in result.values():
                 self.assertGreaterEqual(__A  ,       0.666    )


     @slow
     @require_torch_non_multi_gpu
     def 	snake_case_  (	self    ):
        __a					=  """
            --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(__A    )

        __a					=  """
            --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(__A    )

        __a					=  """
            --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(__A    )



