




'''simple docstring'''





from __future__ import annotations


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   if not list_of_ints:
      return []

   if min(snake_case ) < 0:
      raise ValueError('''All numbers must be positive''' )

   SCREAMING_SNAKE_CASE__      :					int							    =			max(len(bin(snake_case )[2:] ) for x in list_of_ints )
   return _msd_radix_sort(snake_case						,  snake_case )


def 					UpperCamelCase						(	snake_case						,  snake_case ):




   '''simple docstring'''

   if bit_position == 0 or len(snake_case ) in [0, 1]:
      return list_of_ints

   SCREAMING_SNAKE_CASE__      :					int							    =			[]
   SCREAMING_SNAKE_CASE__      :					Dict							    =			[]
   # Split numbers based on bit at bit_position from the right
   for number in list_of_ints:
      if (number >> (bit_position - 1)) & 1:
         # number has a one at bit bit_position
         ones.append(snake_case )
      else:
         # number has a zero at bit bit_position
         zeros.append(snake_case )

    # recursively split both lists further
   SCREAMING_SNAKE_CASE__      :					str							    =			_msd_radix_sort(snake_case						,  bit_position - 1 )
   SCREAMING_SNAKE_CASE__      :					str							    =			_msd_radix_sort(snake_case						,  bit_position - 1 )

   # recombine lists
   SCREAMING_SNAKE_CASE__      :					Tuple							    =			zeros
   res.extend(snake_case )

   return res


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			len(snake_case )
   if not list_of_ints or length == 1:
      return

   if min(snake_case ) < 0:
      raise ValueError('''All numbers must be positive''' )

   SCREAMING_SNAKE_CASE__      :					int							    =			max(len(bin(snake_case )[2:] ) for x in list_of_ints )
   _msd_radix_sort_inplace(snake_case						,  snake_case						,  0						,  snake_case )


def 					UpperCamelCase						(	snake_case						,  snake_case						,  snake_case						,  snake_case ):




   '''simple docstring'''

   if bit_position == 0 or end_index - begin_index <= 1:
      return

   bit_position -= 1

   SCREAMING_SNAKE_CASE__      :					Dict							    =			begin_index
   SCREAMING_SNAKE_CASE__      :					Dict							    =			end_index - 1
   while i <= j:
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			False
      if not (list_of_ints[i] >> bit_position) & 1:
         # found zero at the beginning
         i += 1
         SCREAMING_SNAKE_CASE__      :					int							    =			True
      if (list_of_ints[j] >> bit_position) & 1:
         # found one at the end
         j -= 1
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			True

      if changed:
         continue

      SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			list_of_ints[j], list_of_ints[i]
      j -= 1
      if j != i:
         i += 1

   _msd_radix_sort_inplace(snake_case						,  snake_case						,  snake_case						,  snake_case )
   _msd_radix_sort_inplace(snake_case						,  snake_case						,  snake_case						,  snake_case )


if __name__ == "__main__":
       import doctest

       doctest.testmod()







'''simple docstring'''





from typing import Mapping

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


lowercase__								=						logging.get_logger(__name__)






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	'''mt5'''
      __lowercase					=	['''past_key_values''']






      def __init__(self			,	a__=2_5_0_1_1_2			,	a__=5_1_2			,	a__=6_4			,	a__=1_0_2_4			,	a__=8			,	a__=None			,	a__=6			,	a__=3_2			,	a__=1_2_8			,	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__			,	)							-> List[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__      :					Optional[int]							    =			vocab_size
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			d_model
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			d_kv
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			d_ff
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			num_layers
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			(
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         SCREAMING_SNAKE_CASE__      :					Any							    =			num_heads
         SCREAMING_SNAKE_CASE__      :					Any							    =			relative_attention_num_buckets
         SCREAMING_SNAKE_CASE__      :					Any							    =			relative_attention_max_distance
         SCREAMING_SNAKE_CASE__      :					Any							    =			dropout_rate
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			layer_norm_epsilon
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			initializer_factor
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			feed_forward_proj
         SCREAMING_SNAKE_CASE__      :					Any							    =			use_cache

         SCREAMING_SNAKE_CASE__      :					int							    =			self.feed_forward_proj.split('''-'''       )
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			act_info[-1]
         SCREAMING_SNAKE_CASE__      :					Union[str, 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\''''       )

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
            SCREAMING_SNAKE_CASE__      :					int							    =			'''gelu_new'''






      @property
      def  A__(self       )							-> Dict:
         return self.d_model






      @property
      def  A__(self       )							-> Tuple:
         return self.num_heads






      @property
      def  A__(self       )							-> List[str]:
         return self.num_layers






class     lowercase  ( UpperCAmelCase_):






      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
      def  A__(self       )							-> Mapping[str, Mapping[int, str]]:
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			{
             '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
             '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
         }
         if self.use_past:
            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			'''past_encoder_sequence + sequence'''
            SCREAMING_SNAKE_CASE__      :					str							    =			{0: '''batch'''}
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         else:
            SCREAMING_SNAKE_CASE__      :					int							    =			{0: '''batch''', 1: '''decoder_sequence'''}
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			{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  A__(self       )							-> int:
         return 1_3






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

