



import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__						):
      if isinstance(A__    ,	A__						):
            lowercase__  =	np.full((len(A__						), sequence_length, 2)    ,	A__						)
      else:
            lowercase__  =	np.full((len(A__						), sequence_length)    ,	A__						)

      for i, tensor in enumerate(A__						):
            if padding_side == "right":
                  if isinstance(A__    ,	A__						):
                        lowercase__  =	tensor[:sequence_length]
                  else:
                        lowercase__  =	tensor[:sequence_length]
            else:
                  if isinstance(A__    ,	A__						):
                        lowercase__  =	tensor[:sequence_length]
                  else:
                        lowercase__  =	tensor[:sequence_length]

      return out_tensor.tolist()

def 	_lowerCAmelCase (    A__						):
      lowercase__  =	ord(A__						)
      if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
            return True
      lowercase__  =	unicodedata.category(A__						)
      if cat.startswith('P'						):
            return True
      return False



@dataclass
class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   A     :	PreTrainedTokenizerBase
   A     :	Union[bool, str, PaddingStrategy]												=     True
   A     :	Optional[int]												=     None
   A     :	Optional[int]												=     None
   A     :	int												=     -100
   A     :	str												=     "pt"


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      int)						->					Tuple:




         """simple docstring"""

         import torch

         lowercase__  =	'label' if 'label' in features[0].keys() else 'labels'
         lowercase__  =	[feature[label_name] for feature in features] if label_name in features[0].keys() else None
         lowercase__  =	self.tokenizer.pad(
             lowerCAmelCase      ,					padding=self.padding      ,					max_length=self.max_length      ,					pad_to_multiple_of=self.pad_to_multiple_of      ,					return_tensors='pt' if labels is None else None      ,					)

         if labels is None:
               return batch

         lowercase__  =	torch.tensor(batch['entity_ids']).shape[1]
         lowercase__  =	self.tokenizer.padding_side
         if padding_side == "right":
               lowercase__  =	[
                   list(lowerCAmelCase) + [self.label_pad_token_id] * (sequence_length - len(lowerCAmelCase)) for label in labels
               ]
         else:
               lowercase__  =	[
                   [self.label_pad_token_id] * (sequence_length - len(lowerCAmelCase)) + list(lowerCAmelCase) for label in labels
               ]

         lowercase__  =	[feature['ner_tags'] for feature in features]
         lowercase__  =	padding_tensor(lowerCAmelCase      ,					-1      ,					lowerCAmelCase      ,					lowerCAmelCase)
         lowercase__  =	[feature['original_entity_spans'] for feature in features]
         lowercase__  =	padding_tensor(lowerCAmelCase      ,					(-1, -1)      ,					lowerCAmelCase      ,					lowerCAmelCase)
         lowercase__  =	{k: torch.tensor(lowerCAmelCase      ,					dtype=torch.intaa) for k, v in batch.items()}

         return batch





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)
