


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


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							{
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      """pegasus"""
     _lowerCamelCase   =      ["""past_key_values"""]
     _lowerCamelCase   =      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


     def __init__(	self  ,       __A=50265  ,       __A=1024  ,       __A=12  ,       __A=4096  ,       __A=16  ,       __A=12  ,       __A=4096  ,       __A=16  ,       __A=0.0  ,       __A=0.0  ,       __A=True  ,       __A=True  ,       __A="gelu"  ,       __A=1024  ,       __A=0.1  ,       __A=0.0  ,       __A=0.0  ,       __A=0.02  ,       __A=0  ,       __A=False  ,       __A=0  ,       __A=1  ,       __A=1  ,       **__A  ,       ):
        __a					=  vocab_size
        __a					=  max_position_embeddings
        __a					=  d_model
        __a					=  encoder_ffn_dim
        __a					=  encoder_layers
        __a					=  encoder_attention_heads
        __a					=  decoder_ffn_dim
        __a					=  decoder_layers
        __a					=  decoder_attention_heads
        __a					=  dropout
        __a					=  attention_dropout
        __a					=  activation_dropout
        __a					=  activation_function
        __a					=  init_std
        __a					=  encoder_layerdrop
        __a					=  decoder_layerdrop
        __a					=  use_cache
        __a					=  encoder_layers
        __a					=  scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=__A  ,       eos_token_id=__A  ,       is_encoder_decoder=__A  ,       decoder_start_token_id=__A  ,       forced_eos_token_id=__A  ,       **__A  ,       )


     @property
     def 	snake_case_  (	self    ):
        return self.encoder_attention_heads


     @property
     def 	snake_case_  (	self    ):
        return self.d_model







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    )



