


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


lowerCAmelCase__       :	Optional[int]     				=logging.get_logger(__name__)

lowerCAmelCase__       :	List[str]     				={
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							_UpperCAmelCase     =		"""qdqbert"""






							def __init__(	self       ,		lowerCAmelCase__=3_0_5_2_2       ,		lowerCAmelCase__=7_6_8       ,		lowerCAmelCase__=1_2       ,		lowerCAmelCase__=1_2       ,		lowerCAmelCase__=3_0_7_2       ,		lowerCAmelCase__="gelu"       ,		lowerCAmelCase__=0.1       ,		lowerCAmelCase__=0.1       ,		lowerCAmelCase__=5_1_2       ,		lowerCAmelCase__=2       ,		lowerCAmelCase__=0.02       ,		lowerCAmelCase__=1E-12       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=1       ,		lowerCAmelCase__=0       ,		lowerCAmelCase__=2       ,		**lowerCAmelCase__       ,		):





										"""simple docstring"""




										super().__init__(pad_token_id=lowerCAmelCase__       ,		bos_token_id=lowerCAmelCase__       ,		eos_token_id=lowerCAmelCase__       ,		**lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Dict       						= vocab_size
										SCREAMING_SNAKE_CASE_						:			int       						= max_position_embeddings
										SCREAMING_SNAKE_CASE_						:			int       						= hidden_size
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= num_hidden_layers
										SCREAMING_SNAKE_CASE_						:			Dict       						= num_attention_heads
										SCREAMING_SNAKE_CASE_						:			int       						= intermediate_size
										SCREAMING_SNAKE_CASE_						:			Any       						= hidden_act
										SCREAMING_SNAKE_CASE_						:			Dict       						= hidden_dropout_prob
										SCREAMING_SNAKE_CASE_						:			List[str]       						= attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_						:			Tuple       						= initializer_range
										SCREAMING_SNAKE_CASE_						:			int       						= type_vocab_size
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= layer_norm_eps
										SCREAMING_SNAKE_CASE_						:			int       						= use_cache







'''simple docstring'''



from typing import Mapping

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


UpperCamelCase__					:						Any											=     logging.get_logger(__name__)

class       _a (_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     'mt5'
       SCREAMING_SNAKE_CASE					=     ['past_key_values']






       def __init__(							self				,							A__=25_01_12				,							A__=5_12				,							A__=64				,							A__=10_24				,							A__=8				,							A__=None				,							A__=6				,							A__=32				,							A__=1_28				,							A__=0.1				,							A__=1E-6				,							A__=1.0				,							A__="gated-gelu"				,							A__=True				,							A__=True				,							A__="T5Tokenizer"				,							A__=False				,							A__=0				,							A__=1				,							A__=0				,							**A__				,							)			->	Union[str, Any]:
          super().__init__(
              is_encoder_decoder=A__				,							tokenizer_class=A__				,							tie_word_embeddings=A__				,							pad_token_id=A__				,							eos_token_id=A__				,							decoder_start_token_id=A__				,							**A__				,							)
          _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(A__					) > 1 and act_info[0] != "gated" or len(A__					) > 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":
             _SCREAMING_SNAKE_CASE          = """gelu_new"""






       @property
       def 				UpperCamelCase (							self					)			->	int:
          return self.d_model






       @property
       def 				UpperCamelCase (							self					)			->	Optional[Any]:
          return self.num_heads






       @property
       def 				UpperCamelCase (							self					)			->	Dict:
          return self.num_layers






class       _a (_lowerCamelCase):





       """simple docstring"""




       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
       def 				UpperCamelCase (							self					)			->	Mapping[str, Mapping[int, str]]:
          _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_(A__				,							direction="""inputs"""					)

          return common_inputs






       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
       def 				UpperCamelCase (							self					)			->	int:
          return 13






       @property
       def 				UpperCamelCase (							self					)			->	float:
          return 5E-4






