from typing import Mapping

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


__lowerCamelCase			: Union[str, Any]							   =		logging.get_logger(__name__)

__lowerCamelCase			: Optional[Any]							   =		{
    '''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 __snake_case				(						UpperCAmelCase__		):
   lowerCAmelCase_														=							"""umt5"""
   lowerCAmelCase_														=							["""past_key_values"""]




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



     """simple docstring"""






     super().__init__(
         is_encoder_decoder=SCREAMING_SNAKE_CASE__       ,	tokenizer_class=SCREAMING_SNAKE_CASE__       ,	tie_word_embeddings=SCREAMING_SNAKE_CASE__       ,	pad_token_id=SCREAMING_SNAKE_CASE__       ,	eos_token_id=SCREAMING_SNAKE_CASE__       ,	decoder_start_token_id=SCREAMING_SNAKE_CASE__       ,	**SCREAMING_SNAKE_CASE__       ,	)
     SCREAMING_SNAKE_CASE__										=      vocab_size
     SCREAMING_SNAKE_CASE__										=      d_model
     SCREAMING_SNAKE_CASE__										=      d_kv
     SCREAMING_SNAKE_CASE__										=      d_ff
     SCREAMING_SNAKE_CASE__										=      num_layers
     SCREAMING_SNAKE_CASE__										=      (
         num_decoder_layers if num_decoder_layers is not None else self.num_layers
     )  # default = symmetry
     SCREAMING_SNAKE_CASE__										=      num_heads
     SCREAMING_SNAKE_CASE__										=      relative_attention_num_buckets
     SCREAMING_SNAKE_CASE__										=      relative_attention_max_distance
     SCREAMING_SNAKE_CASE__										=      dropout_rate
     SCREAMING_SNAKE_CASE__										=      layer_norm_epsilon
     SCREAMING_SNAKE_CASE__										=      initializer_factor
     SCREAMING_SNAKE_CASE__										=      feed_forward_proj
     SCREAMING_SNAKE_CASE__										=      use_cache

     SCREAMING_SNAKE_CASE__										=      self.feed_forward_proj.split("""-"""      )
     SCREAMING_SNAKE_CASE__										=      act_info[-1]
     SCREAMING_SNAKE_CASE__										=      act_info[0] == """gated"""

     if len(SCREAMING_SNAKE_CASE__      ) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE__      ) > 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":
       SCREAMING_SNAKE_CASE__										=      """gelu_new"""




   @property
   def 	__a						(    self			:				str      ):



     """simple docstring"""






     return self.d_model




   @property
   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     return self.num_heads




   @property
   def 	__a						(    self			:				str      ):



     """simple docstring"""






     return self.num_layers







class __snake_case				(						UpperCAmelCase__		):




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      {
         """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
         """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
     }
     if self.use_past:
       SCREAMING_SNAKE_CASE__										=      """past_encoder_sequence + sequence"""
       SCREAMING_SNAKE_CASE__										=      {0: """batch"""}
       SCREAMING_SNAKE_CASE__										=      {0: """batch""", 1: """past_decoder_sequence + sequence"""}
     else:
       SCREAMING_SNAKE_CASE__										=      {0: """batch""", 1: """decoder_sequence"""}
       SCREAMING_SNAKE_CASE__										=      {0: """batch""", 1: """decoder_sequence"""}

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

     return common_inputs




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



     """simple docstring"""






     return 13




   @property
   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     return 5E-4

'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A			:      int					      =				logging.get_logger(__name__)

_A			:      Any					      =				{'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Any      	=					"""ctrl"""
 _SCREAMING_SNAKE_CASE     :       Optional[Any]      	=					["""past_key_values"""]
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					{
     """max_position_embeddings""": """n_positions""",
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }
 def __init__(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=24_65_34      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=2_56      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=12_80      ,       SCREAMING_SNAKE_CASE__   :	str=81_92      ,       SCREAMING_SNAKE_CASE__   :	List[str]=48      ,       SCREAMING_SNAKE_CASE__   :	Tuple=16      ,       SCREAMING_SNAKE_CASE__   :	str=0.1      ,       SCREAMING_SNAKE_CASE__   :	Dict=0.1      ,       SCREAMING_SNAKE_CASE__   :	Dict=1e-6      ,       SCREAMING_SNAKE_CASE__   :	Any=0.0_2      ,       SCREAMING_SNAKE_CASE__   :	Tuple=True      ,       **SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       )  ->    Tuple:
  __lowerCAmelCase											=						vocab_size
  __lowerCAmelCase											=						n_positions
  __lowerCAmelCase											=						n_embd
  __lowerCAmelCase											=						n_layer
  __lowerCAmelCase											=						n_head
  __lowerCAmelCase											=						dff
  __lowerCAmelCase											=						resid_pdrop
  __lowerCAmelCase											=						embd_pdrop
  __lowerCAmelCase											=						layer_norm_epsilon
  __lowerCAmelCase											=						initializer_range

  __lowerCAmelCase											=						use_cache

  super().__init__(**SCREAMING_SNAKE_CASE__							)






