'''simple docstring'''




from typing import Mapping

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


__UpperCAmelCase     							=logging.get_logger(__name__)

__UpperCAmelCase     							={
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}




class     a__      (   UpperCAmelCase__ ):
     lowerCamelCase  :     Any							="umt5"
     lowerCamelCase  :     Dict							=["past_key_values"]






     def __init__( self		:		Optional[Any]  ,				a		:		Optional[Any]=25_01_12  ,				a		:		str=5_12  ,				a		:		Union[str, Any]=64  ,				a		:		Union[str, Any]=10_24  ,				a		:		Dict=8  ,				a		:		Any=None  ,				a		:		str=6  ,				a		:		Optional[int]=32  ,				a		:		List[str]=1_28  ,				a		:		Optional[int]=0.1  ,				a		:		Any=1e-6  ,				a		:		List[Any]=1.0  ,				a		:		int="gated-gelu"  ,				a		:		str=True  ,				a		:		Dict=True  ,				a		:		Optional[int]="T5Tokenizer"  ,				a		:		List[Any]=True  ,				a		:		Optional[Any]=0  ,				a		:		Any=1  ,				a		:		Union[str, Any]=0  ,				**a		:		str  ,				):





         """simple docstring"""



         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\''''    )

         if feed_forward_proj == "gated-gelu":
             __lowerCamelCase		=  '''gelu_new'''






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		List[str]    ):





         """simple docstring"""



         return self.d_model






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		Union[str, Any]    ):





         """simple docstring"""



         return self.num_heads






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]    ):





         """simple docstring"""



         return self.num_layers




class     a__      (   UpperCAmelCase__ ):






     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def    SCREAMING_SNAKE_CASE__   ( self		:		int    ):





         """simple docstring"""



         __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    SCREAMING_SNAKE_CASE__   ( self		:		Dict    ):





         """simple docstring"""



         return 13






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple    ):





         """simple docstring"""



         return 5e-4







'''simple docstring'''
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
    'split_dict'				,  [
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'				,  num_bytes=1337				,  num_examples=42				,  dataset_name='my_dataset'     )}     ),
        SplitDict({'train': SplitInfo(name='train'				,  num_bytes=1337				,  num_examples=42     )}     ),
        SplitDict({'train': SplitInfo()}     ),
    ]				,  )
def 				lowercase__   (   __lowercase						:  SplitDict     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  split_dict._to_yaml_list()
       assert len(__lowercase     ) == len(__lowercase     )
       __UpperCamelCase       =  SplitDict._from_yaml_list(__lowercase     )
       for split_name, split_info in split_dict.items():
              # dataset_name field is deprecated, and is therefore not part of the YAML dump
              __UpperCamelCase       =  None
              # the split name of split_dict takes over the name of the split info object
              __UpperCamelCase       =  split_name
       assert split_dict == reloaded


@pytest.mark.parametrize(
    'split_info'				,  [SplitInfo(), SplitInfo(dataset_name=__lowercase     ), SplitInfo(dataset_name='my_dataset'     )]     )
def 				lowercase__   (   __lowercase						:  Dict     ) ->      Any:



       """simple docstring"""






       __UpperCamelCase       =  asdict(SplitDict({'train': split_info}     )     )
       assert "dataset_name" in split_dict_asdict["train"]
       assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
