




from typing import Mapping

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


__SCREAMING_SNAKE_CASE    :   Optional[Any]       =logging.get_logger(__name__)






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Any     ="mt5"
     _snake_case:	List[str]     =["past_key_values"]




     def __init__(    self  :   List[str]	,  A__  :   Tuple=2_5_0_1_1_2	,  A__  :   Union[str, Any]=5_1_2	,  A__  :   Any=6_4	,  A__  :   Any=1_0_2_4	,  A__  :   Any=8	,  A__  :   Union[str, Any]=None	,  A__  :   Union[str, Any]=6	,  A__  :   str=3_2	,  A__  :   Optional[int]=1_2_8	,  A__  :   int=0.1	,  A__  :   Dict=1E-6	,  A__  :   Optional[int]=1.0	,  A__  :   Union[str, Any]="gated-gelu"	,  A__  :   Dict=True	,  A__  :   Any=True	,  A__  :   int="T5Tokenizer"	,  A__  :   str=False	,  A__  :   Union[str, Any]=0	,  A__  :   List[Any]=1	,  A__  :   Tuple=0	,  **A__  :   int	,  )				->       Tuple:
           super().__init__(
               is_encoder_decoder=A__	,  tokenizer_class=A__	,  tie_word_embeddings=A__	,  pad_token_id=A__	,  eos_token_id=A__	,  decoder_start_token_id=A__	,  **A__	,  )
           lowerCamelCase__        =				vocab_size
           lowerCamelCase__        =				d_model
           lowerCamelCase__        =				d_kv
           lowerCamelCase__        =				d_ff
           lowerCamelCase__        =				num_layers
           lowerCamelCase__        =				(
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           lowerCamelCase__        =				num_heads
           lowerCamelCase__        =				relative_attention_num_buckets
           lowerCamelCase__        =				relative_attention_max_distance
           lowerCamelCase__        =				dropout_rate
           lowerCamelCase__        =				layer_norm_epsilon
           lowerCamelCase__        =				initializer_factor
           lowerCamelCase__        =				feed_forward_proj
           lowerCamelCase__        =				use_cache

           lowerCamelCase__        =				self.feed_forward_proj.split('-'			)
           lowerCamelCase__        =				act_info[-1]
           lowerCamelCase__        =				act_info[0] == 'gated'

           if len(A__			) > 1 and act_info[0] != "gated" or len(A__			) > 2:
                 raise ValueError(
                     F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                     'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                     '\'gated-gelu\' or \'relu\''			)

           # for backwards compatibility
           if feed_forward_proj == "gated-gelu":
                 lowerCamelCase__        =				'gelu_new'




     @property
     def  __snake_case   (    self  :   Dict			)				->       int:
           return self.d_model




     @property
     def  __snake_case   (    self  :   Tuple			)				->       str:
           return self.num_heads




     @property
     def  __snake_case   (    self  :   Tuple			)				->       Tuple:
           return self.num_layers






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def  __snake_case   (    self  :   str			)				->       Mapping[str, Mapping[int, str]]:
           lowerCamelCase__        =				{
               'input_ids': {0: 'batch', 1: 'encoder_sequence'},
               'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
           }
           if self.use_past:
                 lowerCamelCase__        =				'past_encoder_sequence + sequence'
                 lowerCamelCase__        =				{0: 'batch'}
                 lowerCamelCase__        =				{0: 'batch', 1: 'past_decoder_sequence + sequence'}
           else:
                 lowerCamelCase__        =				{0: 'batch', 1: 'decoder_sequence'}
                 lowerCamelCase__        =				{0: 'batch', 1: 'decoder_sequence'}

           if self.use_past:
                 self.fill_with_past_key_values_(A__	,  direction='inputs'			)

           return common_inputs




     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def  __snake_case   (    self  :   Optional[Any]			)				->       int:
           return 1_3




     @property
     def  __snake_case   (    self  :   Optional[Any]			)				->       float:
           return 5E-4





import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__SCREAMING_SNAKE_CASE    :   List[str]       =logging.getLogger()

def 			lowerCAmelCase_			(  )					->			Any:


      '''simple docstring'''




      lowerCamelCase__        =				argparse.ArgumentParser()
      parser.add_argument('-f'						)
      lowerCamelCase__        =				parser.parse_args()
      return args.f






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     def  __snake_case   (    self  :   Optional[Any]			)				->       Optional[Any]:
           lowerCamelCase__        =				logging.StreamHandler(sys.stdout			)
           logger.addHandler(A__			)

           lowerCamelCase__        =				self.get_auto_remove_tmp_dir()
           lowerCamelCase__        =				F"""
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

           with patch.object(A__	,  'argv'	,  A__			):
                 lowerCamelCase__        =				run_glue_with_pabee.main()
                 for value in result.values():
                       self.assertGreaterEqual(A__	,  0.75			)