




'''simple docstring'''





from typing import Mapping

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


UpperCamelCase_		:     Tuple          =		logging.get_logger(__name__)

UpperCamelCase_		:     Tuple          =		{
    '''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   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       int       							=		"""umt5"""
     SCREAMING_SNAKE_CASE_				:       Optional[Any]       							=		["""past_key_values"""]






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=250_112   ,_SCREAMING_SNAKE_CASE=512   ,_SCREAMING_SNAKE_CASE=64   ,_SCREAMING_SNAKE_CASE=1_024   ,_SCREAMING_SNAKE_CASE=8   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=6   ,_SCREAMING_SNAKE_CASE=32   ,_SCREAMING_SNAKE_CASE=128   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=1e-6   ,_SCREAMING_SNAKE_CASE=1.0   ,_SCREAMING_SNAKE_CASE="gated-gelu"   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE="T5Tokenizer"   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=0   ,_SCREAMING_SNAKE_CASE=1   ,_SCREAMING_SNAKE_CASE=0   ,**_SCREAMING_SNAKE_CASE   ,)				->     Dict:
          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   ,)
          _snake_case				= vocab_size
          _snake_case				= d_model
          _snake_case				= d_kv
          _snake_case				= d_ff
          _snake_case				= num_layers
          _snake_case				= (
              num_decoder_layers if num_decoder_layers is not None else self.num_layers
          )  # default = symmetry
          _snake_case				= num_heads
          _snake_case				= relative_attention_num_buckets
          _snake_case				= relative_attention_max_distance
          _snake_case				= dropout_rate
          _snake_case				= layer_norm_epsilon
          _snake_case				= initializer_factor
          _snake_case				= feed_forward_proj
          _snake_case				= use_cache

          _snake_case				= self.feed_forward_proj.split("-"       )
          _snake_case				= act_info[-1]
          _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":
               _snake_case				= "gelu_new"






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






     @property
     def 							_lowercase      (					self       )				->     List[str]:
          return self.num_heads






     @property
     def 							_lowercase      (					self       )				->     Optional[Any]:
          return self.num_layers







class   _a							(		__lowerCAmelCase				):






     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def 							_lowercase      (					self       )				->     Mapping[str, Mapping[int, str]]:
          _snake_case				= {
              "input_ids": {0: "batch", 1: "encoder_sequence"},
              "attention_mask": {0: "batch", 1: "encoder_sequence"},
          }
          if self.use_past:
               _snake_case				= "past_encoder_sequence + sequence"
               _snake_case				= {0: "batch"}
               _snake_case				= {0: "batch", 1: "past_decoder_sequence + sequence"}
          else:
               _snake_case				= {0: "batch", 1: "decoder_sequence"}
               _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 							_lowercase      (					self       )				->     int:
          return 13






     @property
     def 							_lowercase      (					self       )				->     float:
          return 5e-4








'''simple docstring'''





from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version





def       __a      (  _UpperCamelCase:       str   ,		_UpperCamelCase:       str   ,		_UpperCamelCase:       Optional[str] = None       )		->					str:

     """simple docstring"""




     if version.parse(hfh.__version__       ).release < version.parse("0.11.0"       ).release:
          # old versions of hfh don't url-encode the file path
          _snake_case				= quote(_UpperCamelCase       )
     return hfh.hf_hub_url(_UpperCamelCase   ,		_UpperCamelCase   ,		repo_type="dataset"   ,		revision=_UpperCamelCase       )


