


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase__    :List[str]       =				{
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :Dict       =				[
		    '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BioGptForCausalLM''',
		    '''BioGptForTokenClassification''',
		    '''BioGptForSequenceClassification''',
		    '''BioGptModel''',
		    '''BioGptPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
		from .tokenization_biogpt import BioGptTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_biogpt import (
				    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BioGptForCausalLM,
				    BioGptForSequenceClassification,
				    BioGptForTokenClassification,
				    BioGptModel,
				    BioGptPreTrainedModel,
				)


else:
		import sys

		lowerCAmelCase__    :Optional[Any]       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




"""simple docstring"""



from typing import Mapping

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


__snake_case       =       logging.get_logger(__name__)

__snake_case       =       {
    """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 _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			Dict				    =    '''umt5'''
   __UpperCAmelCase					:			str				    =    ['''past_key_values''']







   def __init__(      self		,      UpperCamelCase__=25_0112		,      UpperCamelCase__=512		,      UpperCamelCase__=64		,      UpperCamelCase__=1024		,      UpperCamelCase__=8		,      UpperCamelCase__=None		,      UpperCamelCase__=6		,      UpperCamelCase__=32		,      UpperCamelCase__=128		,      UpperCamelCase__=0.1		,      UpperCamelCase__=1e-6		,      UpperCamelCase__=1.0		,      UpperCamelCase__="gated-gelu"		,      UpperCamelCase__=True		,      UpperCamelCase__=True		,      UpperCamelCase__="T5Tokenizer"		,      UpperCamelCase__=True		,      UpperCamelCase__=0		,      UpperCamelCase__=1		,      UpperCamelCase__=0		,      **UpperCamelCase__		,      )      ->						Dict:

       '''simple docstring'''




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

       snake_case       : Any    =       self.feed_forward_proj.split("-"		)
       snake_case       : Tuple    =       act_info[-1]
       snake_case       : Optional[int]    =       act_info[0] == "gated"

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







   @property
   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       return self.d_model







   @property
   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       return self.num_heads







   @property
   def 					lowerCamelCase						(      self		)      ->						Union[str, Any]:

       '''simple docstring'''




       return self.num_layers







class _lowerCAmelCase							( snake_case_      ):







   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
   def 					lowerCamelCase						(      self		)      ->						Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




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

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

       return common_inputs







   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       return 13







   @property
   def 					lowerCamelCase						(      self		)      ->						float:

       '''simple docstring'''




       return 5e-4





