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 			__magic_name__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     Dict	      = '''umt5'''
   lowerCAmelCase   :     List[Any]	      = ['''past_key_values''']



   def __init__(						self       :	Any		,	_lowercase       :	str=250_112		,	_lowercase       :	str=512		,	_lowercase       :	str=64		,	_lowercase       :	Tuple=1_024		,	_lowercase       :	str=8		,	_lowercase       :	List[Any]=None		,	_lowercase       :	List[str]=6		,	_lowercase       :	int=32		,	_lowercase       :	Union[str, Any]=128		,	_lowercase       :	Union[str, Any]=0.1		,	_lowercase       :	Any=1E-6		,	_lowercase       :	List[Any]=1.0		,	_lowercase       :	Dict="gated-gelu"		,	_lowercase       :	Dict=True		,	_lowercase       :	str=True		,	_lowercase       :	Any="T5Tokenizer"		,	_lowercase       :	Tuple=True		,	_lowercase       :	Any=0		,	_lowercase       :	Optional[Any]=1		,	_lowercase       :	Dict=0		,	**_lowercase       :	Optional[Any]		,	):



    """simple docstring"""



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

    _UpperCamelCase:  List[str]    						=			self.feed_forward_proj.split('''-'''				)
    _UpperCamelCase:  List[str]    						=			act_info[-1]
    _UpperCamelCase:  str    						=			act_info[0] == '''gated'''

    if len(_lowercase				) > 1 and act_info[0] != "gated" or len(_lowercase				) > 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":
     _UpperCamelCase:  Any    						=			'''gelu_new'''



   @property
   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    return self.d_model



   @property
   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    return self.num_heads



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



    """simple docstring"""



    return self.num_layers




class 			__magic_name__	(		__a     ):





   """simple docstring"""




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



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			{
        '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
        '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
    }
    if self.use_past:
     _UpperCamelCase:  Optional[Any]    						=			'''past_encoder_sequence + sequence'''
     _UpperCamelCase:  Dict    						=			{0: '''batch'''}
     _UpperCamelCase:  Any    						=			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
    else:
     _UpperCamelCase:  Dict    						=			{0: '''batch''', 1: '''decoder_sequence'''}
     _UpperCamelCase:  str    						=			{0: '''batch''', 1: '''decoder_sequence'''}

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

    return common_inputs



   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    return 13



   @property
   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    return 5E-4
from typing import TYPE_CHECKING

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


UpperCAmelCase_           =				{'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				['''EncoderDecoderModel''']

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				['''TFEncoderDecoderModel''']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				['''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

  UpperCAmelCase_           =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)