
'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


__snake_case:  List[Any]								=			logging.get_logger(__name__)

__snake_case:  Union[str, Any]								=			{
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					"xlm-roberta-xl"


     def __init__(      self							,		lowerCAmelCase_=25_08_80							,		lowerCAmelCase_=25_60							,		lowerCAmelCase_=36							,		lowerCAmelCase_=32							,		lowerCAmelCase_=1_02_40							,		lowerCAmelCase_="gelu"							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=5_14							,		lowerCAmelCase_=1							,		lowerCAmelCase_=0.02							,		lowerCAmelCase_=1E-0_5							,		lowerCAmelCase_=1							,		lowerCAmelCase_=0							,		lowerCAmelCase_=2							,		lowerCAmelCase_="absolute"							,		lowerCAmelCase_=True							,		lowerCAmelCase_=None							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       super().__init__(pad_token_id=lowerCAmelCase_							,		bos_token_id=lowerCAmelCase_							,		eos_token_id=lowerCAmelCase_							,		**lowerCAmelCase_ )
       a_      :	Tuple      =							vocab_size
       a_      :	str      =							hidden_size
       a_      :	Optional[Any]      =							num_hidden_layers
       a_      :	List[str]      =							num_attention_heads
       a_      :	str      =							hidden_act
       a_      :	Tuple      =							intermediate_size
       a_      :	List[Any]      =							hidden_dropout_prob
       a_      :	List[str]      =							attention_probs_dropout_prob
       a_      :	Optional[Any]      =							max_position_embeddings
       a_      :	List[str]      =							type_vocab_size
       a_      :	int      =							initializer_range
       a_      :	Optional[Any]      =							layer_norm_eps
       a_      :	Union[str, Any]      =							position_embedding_type
       a_      :	List[Any]      =							use_cache
       a_      :	int      =							classifier_dropout







class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       if self.task == "multiple-choice":
         a_      :	Any      =							{0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
         a_      :	Tuple      =							{0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ] )


'''simple docstring'''

from typing import Mapping

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


__snake_case:  Tuple								=			logging.get_logger(__name__)

__snake_case:  Tuple								=			{
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					"t5"
     a_    	=					["past_key_values"]
     a_    	=					{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}


     def __init__(      self							,		lowerCAmelCase_=3_21_28							,		lowerCAmelCase_=5_12							,		lowerCAmelCase_=64							,		lowerCAmelCase_=20_48							,		lowerCAmelCase_=6							,		lowerCAmelCase_=None							,		lowerCAmelCase_=8							,		lowerCAmelCase_=32							,		lowerCAmelCase_=1_28							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=1E-6							,		lowerCAmelCase_=1.0							,		lowerCAmelCase_="relu"							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=0							,		lowerCAmelCase_=1							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	List[Any]      =							vocab_size
       a_      :	List[str]      =							d_model
       a_      :	Union[str, Any]      =							d_kv
       a_      :	List[Any]      =							d_ff
       a_      :	int      =							num_layers
       a_      :	Any      =							(
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       a_      :	Union[str, Any]      =							num_heads
       a_      :	int      =							relative_attention_num_buckets
       a_      :	Dict      =							relative_attention_max_distance
       a_      :	Union[str, Any]      =							dropout_rate
       a_      :	Union[str, Any]      =							layer_norm_epsilon
       a_      :	List[Any]      =							initializer_factor
       a_      :	Any      =							feed_forward_proj
       a_      :	List[Any]      =							use_cache

       a_      :	Dict      =							self.feed_forward_proj.split("""-""" )
       a_      :	Union[str, Any]      =							act_info[-1]
       a_      :	str      =							act_info[0] == """gated"""

       if len(lowerCAmelCase_ ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_ ) > 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":
         a_      :	List[str]      =							"""gelu_new"""

       super().__init__(
           pad_token_id=lowerCAmelCase_							,		eos_token_id=lowerCAmelCase_							,		is_encoder_decoder=lowerCAmelCase_							,		**lowerCAmelCase_							,		)







class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							{
           """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
           """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
       }
       if self.use_past:
         a_      :	Tuple      =							"""past_encoder_sequence + sequence"""
         a_      :	str      =							{0: """batch"""}
         a_      :	Any      =							{0: """batch""", 1: """past_decoder_sequence + sequence"""}
       else:
         a_      :	List[Any]      =							{0: """batch""", 1: """decoder_sequence"""}
         a_      :	Optional[int]      =							{0: """batch""", 1: """decoder_sequence"""}

       if self.use_past:
         self.fill_with_past_key_values_(lowerCAmelCase_							,		direction="""inputs""" )

       return common_inputs






     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return 13


