


'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a   :   Any												=   logging.get_logger(__name__)

_a   :   Optional[Any]												=   {
    "xlm-mlm-en-2048": "https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json",
    "xlm-mlm-ende-1024": "https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json",
    "xlm-mlm-enfr-1024": "https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json",
    "xlm-mlm-enro-1024": "https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json",
    "xlm-mlm-tlm-xnli15-1024": "https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json",
    "xlm-mlm-xnli15-1024": "https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json",
    "xlm-clm-enfr-1024": "https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json",
    "xlm-clm-ende-1024": "https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json",
    "xlm-mlm-17-1280": "https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json",
    "xlm-mlm-100-1280": "https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json",
}




class 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  int          =	"xlm"
   _SCREAMING_SNAKE_CASE  :  Dict          =	{
       "hidden_size": "emb_dim",
       "num_attention_heads": "n_heads",
       "num_hidden_layers": "n_layers",
       "n_words": "vocab_size",  # For backward compatibility
   }
   def __init__(						self       :      int       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=3_0145       ,   SCREAMING_SNAKE_CASE_       :      Dict=2048       ,   SCREAMING_SNAKE_CASE_       :      Dict=12       ,   SCREAMING_SNAKE_CASE_       :      List[str]=16       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=0.1       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=0.1       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=True       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=False       ,   SCREAMING_SNAKE_CASE_       :      List[str]=False       ,   SCREAMING_SNAKE_CASE_       :      List[str]=False       ,   SCREAMING_SNAKE_CASE_       :      int=1       ,   SCREAMING_SNAKE_CASE_       :      Dict=True       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=512       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=2048**-0.5       ,   SCREAMING_SNAKE_CASE_       :      Tuple=1e-12       ,   SCREAMING_SNAKE_CASE_       :      Dict=0.0_2       ,   SCREAMING_SNAKE_CASE_       :      Any=0       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=1       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=2       ,   SCREAMING_SNAKE_CASE_       :      Dict=3       ,   SCREAMING_SNAKE_CASE_       :      int=5       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]=True       ,   SCREAMING_SNAKE_CASE_       :      int="first"       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=True       ,   SCREAMING_SNAKE_CASE_       :      int=None       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=True       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=0.1       ,   SCREAMING_SNAKE_CASE_       :      Any=5       ,   SCREAMING_SNAKE_CASE_       :      int=5       ,   SCREAMING_SNAKE_CASE_       :      Dict=0       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=0       ,   SCREAMING_SNAKE_CASE_       :      str=2       ,   SCREAMING_SNAKE_CASE_       :      List[str]=0       ,   **SCREAMING_SNAKE_CASE_       :      Optional[int]       ,   ) ->							Optional[int]:
        __snake_case           =						vocab_size
        __snake_case           =						emb_dim
        __snake_case           =						n_layers
        __snake_case           =						n_heads
        __snake_case           =						dropout
        __snake_case           =						attention_dropout
        __snake_case           =						gelu_activation
        __snake_case           =						sinusoidal_embeddings
        __snake_case           =						causal
        __snake_case           =						asm
        __snake_case           =						n_langs
        __snake_case           =						use_lang_emb
        __snake_case           =						layer_norm_eps
        __snake_case           =						bos_index
        __snake_case           =						eos_index
        __snake_case           =						pad_index
        __snake_case           =						unk_index
        __snake_case           =						mask_index
        __snake_case           =						is_encoder
        __snake_case           =						max_position_embeddings
        __snake_case           =						embed_init_std
        __snake_case           =						init_std
        __snake_case           =						summary_type
        __snake_case           =						summary_use_proj
        __snake_case           =						summary_activation
        __snake_case           =						summary_proj_to_labels
        __snake_case           =						summary_first_dropout
        __snake_case           =						start_n_top
        __snake_case           =						end_n_top
        __snake_case           =						mask_token_id
        __snake_case           =						lang_id

        if "n_words" in kwargs:
             __snake_case           =						kwargs['n_words']

        super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_       ,   bos_token_id=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )




class 			_lowercase (	__lowercase							):
   @property
   def a  (						self       :      Union[str, Any]      ) ->							Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
             __snake_case           =						{0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
             __snake_case           =						{0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
                ('token_type_ids', dynamic_axis),
            ]      )









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__   :					Optional[Any] 						=      logging.getLogger()

def 	_lowerCAmelCase (    ):
      lowercase__  =	argparse.ArgumentParser()
      parser.add_argument('-f'						)
      lowercase__  =	parser.parse_args()
      return args.f



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   def       UpperCAmelCase		(    self	:      List[str])						->					None:




         """simple docstring"""

         lowercase__  =	logging.StreamHandler(sys.stdout)
         logger.addHandler(lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      Union[str, Any])						->					List[Any]:




         """simple docstring"""

         lowercase__  =	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):
                     lowercase__  =	run_glue_deebert.main()
                     for value in result.values():
                           self.assertGreaterEqual(lowerCAmelCase      ,					0.6_66)



   @slow
   @require_torch_non_multi_gpu
   def       UpperCAmelCase		(    self	:      Optional[Any])						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	'\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            '.split()
         self.run_and_check(lowerCAmelCase)

         lowercase__  =	'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
         self.run_and_check(lowerCAmelCase)

         lowercase__  =	'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
         self.run_and_check(lowerCAmelCase)
