
'''simple docstring'''

from typing import Mapping

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


__snake_case:  str								=			logging.get_logger(__name__)

__snake_case:  str								=			{
    "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  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					"umt5"
     a_    	=					["past_key_values"]


     def __init__(      self							,		lowerCAmelCase_=25_01_12							,		lowerCAmelCase_=5_12							,		lowerCAmelCase_=64							,		lowerCAmelCase_=10_24							,		lowerCAmelCase_=8							,		lowerCAmelCase_=None							,		lowerCAmelCase_=6							,		lowerCAmelCase_=32							,		lowerCAmelCase_=1_28							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=1E-6							,		lowerCAmelCase_=1.0							,		lowerCAmelCase_="gated-gelu"							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_="T5Tokenizer"							,		lowerCAmelCase_=True							,		lowerCAmelCase_=0							,		lowerCAmelCase_=1							,		lowerCAmelCase_=0							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       super().__init__(
           is_encoder_decoder=lowerCAmelCase_							,		tokenizer_class=lowerCAmelCase_							,		tie_word_embeddings=lowerCAmelCase_							,		pad_token_id=lowerCAmelCase_							,		eos_token_id=lowerCAmelCase_							,		decoder_start_token_id=lowerCAmelCase_							,		**lowerCAmelCase_							,		)
       a_      :	List[Any]      =							vocab_size
       a_      :	List[str]      =							d_model
       a_      :	Optional[int]      =							d_kv
       a_      :	Optional[Any]      =							d_ff
       a_      :	Union[str, Any]      =							num_layers
       a_      :	Optional[Any]      =							(
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       a_      :	int      =							num_heads
       a_      :	Any      =							relative_attention_num_buckets
       a_      :	List[Any]      =							relative_attention_max_distance
       a_      :	int      =							dropout_rate
       a_      :	List[Any]      =							layer_norm_epsilon
       a_      :	int      =							initializer_factor
       a_      :	Tuple      =							feed_forward_proj
       a_      :	List[Any]      =							use_cache

       a_      :	List[str]      =							self.feed_forward_proj.split("""-""" )
       a_      :	List[str]      =							act_info[-1]
       a_      :	Any      =							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'""" )

       if feed_forward_proj == "gated-gelu":
         a_      :	Optional[Any]      =							"""gelu_new"""


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return self.d_model


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return self.num_heads






     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return self.num_layers







class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

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

       '''simple docstring'''

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

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

       return common_inputs


     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return 13






     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return 5E-4




'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case:  Dict								=			{"configuration_encoder_decoder": ["EncoderDecoderConfig"]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  List[str]								=			["EncoderDecoderModel"]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  Any								=			["TFEncoderDecoderModel"]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  List[str]								=			["FlaxEncoderDecoderModel"]

if TYPE_CHECKING:
    from .configuration_encoder_decoder import EncoderDecoderConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_encoder_decoder import EncoderDecoderModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
    import sys

    __snake_case:  Tuple								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


