



'''simple docstring'''





from typing import Mapping

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


_UpperCamelCase											=		logging.get_logger(__name__)

_UpperCamelCase											=		{
    '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_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   ="""umt5"""
  a_				   =["""past_key_values"""]




  def __init__(					self :      int     ,      _a :      List[Any]=25_0112     ,      _a :      Optional[Any]=512     ,      _a :      Optional[Any]=64     ,      _a :      List[str]=1024     ,      _a :      Union[str, Any]=8     ,      _a :      Optional[int]=None     ,      _a :      Any=6     ,      _a :      Any=32     ,      _a :      Union[str, Any]=128     ,      _a :      int=0.1     ,      _a :      Optional[int]=1e-6     ,      _a :      List[Any]=1.0     ,      _a :      Any="gated-gelu"     ,      _a :      Optional[Any]=True     ,      _a :      str=True     ,      _a :      Optional[Any]="T5Tokenizer"     ,      _a :      List[Any]=True     ,      _a :      Optional[Any]=0     ,      _a :      List[Any]=1     ,      _a :      List[Any]=0     ,      **_a :      Dict     ,      )   ->   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     ,      )
    __lowerCamelCase			:			Tuple       			=					vocab_size
    __lowerCamelCase			:			Tuple       			=					d_model
    __lowerCamelCase			:			List[Any]       			=					d_kv
    __lowerCamelCase			:			Tuple       			=					d_ff
    __lowerCamelCase			:			Tuple       			=					num_layers
    __lowerCamelCase			:			Optional[int]       			=					(
        num_decoder_layers if num_decoder_layers is not None else self.num_layers
    )  # default = symmetry
    __lowerCamelCase			:			Optional[Any]       			=					num_heads
    __lowerCamelCase			:			Union[str, Any]       			=					relative_attention_num_buckets
    __lowerCamelCase			:			Dict       			=					relative_attention_max_distance
    __lowerCamelCase			:			Tuple       			=					dropout_rate
    __lowerCamelCase			:			Dict       			=					layer_norm_epsilon
    __lowerCamelCase			:			int       			=					initializer_factor
    __lowerCamelCase			:			Optional[Any]       			=					feed_forward_proj
    __lowerCamelCase			:			Any       			=					use_cache

    __lowerCamelCase			:			Optional[Any]       			=					self.feed_forward_proj.split('-'  )
    __lowerCamelCase			:			Optional[int]       			=					act_info[-1]
    __lowerCamelCase			:			Optional[Any]       			=					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\''  )

    if feed_forward_proj == "gated-gelu":
      __lowerCamelCase			:			str       			=					'gelu_new'




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




  @property
  def 		_lowercase	(					self :      Any  )   ->   Any:
    return self.num_heads




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






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




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




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











'''simple docstring'''





import functools

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> int:

  # Validation
  if not isinstance(_lowerCAmelCase			,_lowerCAmelCase			) or not all(isinstance(_lowerCAmelCase			,_lowerCAmelCase			) for day in days			):
    raise ValueError('The parameter days should be a list of integers'			)

  if len(_lowerCAmelCase			) != 3 or not all(isinstance(_lowerCAmelCase			,_lowerCAmelCase			) for cost in costs			):
    raise ValueError('The parameter costs should be a list of three integers'			)

  if len(_lowerCAmelCase			) == 0:
    return 0

  if min(_lowerCAmelCase			) <= 0:
    raise ValueError('All days elements should be greater than 0'			)

  if max(_lowerCAmelCase			) >= 366:
    raise ValueError('All days elements should be less than 366'			)

  __lowerCamelCase			:			int       			=					set(_lowerCAmelCase			)

  @functools.cache
  def dynamic_programming(_lowerCAmelCase			) -> int:
    if index > 365:
      return 0

    if index not in days_set:
      return dynamic_programming(index + 1			)

    return min(
        costs[0] + dynamic_programming(index + 1			)			,costs[1] + dynamic_programming(index + 7			)			,costs[2] + dynamic_programming(index + 30			)			,)

  return dynamic_programming(1			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()






