
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)

lowercase__          =logging.getLogger()




def        __UpperCamelCase					(    ):
 __a			:  List[Any]		    =  argparse.ArgumentParser()
 parser.add_argument('''-f'''  )
 __a			:  Any		    =  parser.parse_args()
 return args.f



class 		UpperCamelCase__  (	__lowercase   ):
   def 				lowerCAmelCase      (self			: Tuple       ):
    __a			:  Dict		    =  logging.StreamHandler(sys.stdout       )
    logger.addHandler(snake_case_       )
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: Any       ):
    __a			:  Optional[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(snake_case_ ,    '''argv''' ,    snake_case_       ):
      __a			:  Optional[int]		    =  run_glue_deebert.main()
      for value in result.values():
       self.assertGreaterEqual(snake_case_ ,    0.666       )







   @slow
   @require_torch_non_multi_gpu
   def 				lowerCAmelCase      (self			: List[Any]       ):
    __a			:  List[Any]		    =  '''
            --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(snake_case_       )

    __a			:  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
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
    self.run_and_check(snake_case_       )

    __a			:  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(snake_case_       )


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
      from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__  (	unittest.TestCase   ):
   @slow
   def 				lowerCAmelCase      (self			: Tuple       ):
    __a			:  List[str]		    =  AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' ,    return_dict=snake_case_       ).to(snake_case_       )
    __a			:  List[Any]		    =  AutoTokenizer.from_pretrained('''google/mt5-small'''       )

    __a			:  Optional[int]		    =  tokenizer('''Hello there''' ,    return_tensors='''pt'''       ).input_ids
    __a			:  Dict		    =  tokenizer('''Hi I am''' ,    return_tensors='''pt'''       ).input_ids

    __a			:  Optional[Any]		    =  model(input_ids.to(snake_case_       ) ,    labels=labels.to(snake_case_       )       ).loss
    __a			:  Tuple		    =  -(labels.shape[-1] * loss.item())

    __a			:  Dict		    =  -84.9127
    self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1E-4       )
