





from typing import Mapping

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


_lowerCamelCase 					=      logging.get_logger(__name__)

_lowerCamelCase 					=      {
    '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 	UpperCamelCase_							(  __lowerCamelCase     ):
							lowerCamelCase_							     =       'umt5'
							lowerCamelCase_							     =       ['past_key_values']


							def __init__(				self     :int				,					__A     :Any=25_0112				,					__A     :Union[str, Any]=512				,					__A     :Tuple=64				,					__A     :Tuple=1024				,					__A     :Optional[int]=8				,					__A     :Dict=None				,					__A     :Any=6				,					__A     :List[Any]=32				,					__A     :Tuple=128				,					__A     :List[str]=0.1				,					__A     :List[str]=1E-6				,					__A     :Dict=1.0				,					__A     :List[Any]="gated-gelu"				,					__A     :int=True				,					__A     :List[Any]=True				,					__A     :Any="T5Tokenizer"				,					__A     :List[str]=True				,					__A     :Any=0				,					__A     :str=1				,					__A     :Optional[Any]=0				,					**__A     :str				,					) -> str:



									"""simple docstring"""



									super().__init__(
									    is_encoder_decoder=lowerCamelCase__				,					tokenizer_class=lowerCamelCase__				,					tie_word_embeddings=lowerCamelCase__				,					pad_token_id=lowerCamelCase__				,					eos_token_id=lowerCamelCase__				,					decoder_start_token_id=lowerCamelCase__				,					**lowerCamelCase__				,					)
									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(lowerCamelCase__		) > 1 and act_info[0] != "gated" or len(lowerCamelCase__		) > 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   _snake_case	(				self     :List[Any]		) -> Any:



									"""simple docstring"""



									return self.d_model


							@property
							def   _snake_case	(				self     :List[str]		) -> Optional[int]:



									"""simple docstring"""



									return self.num_heads


							@property
							def   _snake_case	(				self     :int		) -> List[Any]:



									"""simple docstring"""



									return self.num_layers




class 	UpperCamelCase_							(  __lowerCamelCase     ):


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



									"""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_(lowerCamelCase__				,					direction="""inputs"""		)

									return common_inputs


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



									"""simple docstring"""



									return 13


							@property
							def   _snake_case	(				self     :List[str]		) -> float:



									"""simple docstring"""



									return 5E-4


from __future__ import annotations







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None   ):
      '''simple docstring'''







      if start is None:
            A_							       =		0

      if end is None:
            A_							       =		len(SCREAMING_SNAKE_CASE   ) - 1

      if start >= end:
            return

      A_							       =		(start + end) // 2

      slowsort(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
      slowsort(SCREAMING_SNAKE_CASE		,				mid + 1		,				SCREAMING_SNAKE_CASE   )

      if sequence[end] < sequence[mid]:
            A_       ,A_							       =		sequence[mid], sequence[end]

      slowsort(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				end - 1   )


if __name__ == "__main__":
      from doctest import testmod

      testmod()





