





'''simple docstring'''




from typing import Mapping

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


lowercase__       : Union[str, Any]         =       logging.get_logger(__name__)

lowercase__       : List[str]         =       {
    '''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 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			'''umt5'''
     lowerCAmelCase						     =			['''past_key_values''']


     def __init__(		self		,      _UpperCAmelCase=25_0112		,      _UpperCAmelCase=512		,      _UpperCAmelCase=64		,      _UpperCAmelCase=1024		,      _UpperCAmelCase=8		,      _UpperCAmelCase=None		,      _UpperCAmelCase=6		,      _UpperCAmelCase=32		,      _UpperCAmelCase=128		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=1e-6		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase="gated-gelu"		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase="T5Tokenizer"		,      _UpperCAmelCase=True		,      _UpperCAmelCase=0		,      _UpperCAmelCase=1		,      _UpperCAmelCase=0		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          super().__init__(
              is_encoder_decoder=_UpperCAmelCase		,      tokenizer_class=_UpperCAmelCase		,      tie_word_embeddings=_UpperCAmelCase		,      pad_token_id=_UpperCAmelCase		,      eos_token_id=_UpperCAmelCase		,      decoder_start_token_id=_UpperCAmelCase		,      **_UpperCAmelCase		,      )
          __A		:					List[str]						=			vocab_size
          __A		:					List[Any]						=			d_model
          __A		:					Union[str, Any]						=			d_kv
          __A		:					Dict						=			d_ff
          __A		:					Tuple						=			num_layers
          __A		:					Any						=			(
              num_decoder_layers if num_decoder_layers is not None else self.num_layers
          )  # default = symmetry
          __A		:					List[Any]						=			num_heads
          __A		:					Any						=			relative_attention_num_buckets
          __A		:					Tuple						=			relative_attention_max_distance
          __A		:					Optional[int]						=			dropout_rate
          __A		:					List[Any]						=			layer_norm_epsilon
          __A		:					Optional[int]						=			initializer_factor
          __A		:					str						=			feed_forward_proj
          __A		:					int						=			use_cache

          __A		:					str						=			self.feed_forward_proj.split('-')
          __A		:					Dict						=			act_info[-1]
          __A		:					str						=			act_info[0] == 'gated'

          if len(_UpperCAmelCase) > 1 and act_info[0] != "gated" or len(_UpperCAmelCase) > 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":
               __A		:					Tuple						=			'gelu_new'


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return self.d_model


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return self.num_heads


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return self.num_layers




class 					SCREAMING_SNAKE_CASE       (a__				):


     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			{
              'input_ids': {0: 'batch', 1: 'encoder_sequence'},
              'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
          }
          if self.use_past:
               __A		:					int						=			'past_encoder_sequence + sequence'
               __A		:					List[str]						=			{0: 'batch'}
               __A		:					Dict						=			{0: 'batch', 1: 'past_decoder_sequence + sequence'}
          else:
               __A		:					List[Any]						=			{0: 'batch', 1: 'decoder_sequence'}
               __A		:					Optional[Any]						=			{0: 'batch', 1: 'decoder_sequence'}

          if self.use_past:
               self.fill_with_past_key_values_(_UpperCAmelCase		,      direction='inputs')

          return common_inputs


     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return 13


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return 5e-4






'''simple docstring'''




import mpmath  # for roots of unity
import numpy as np


class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None):

          '''simple docstring'''






          __A		:					List[Any]						=			list(poly_a or [0])[:]
          __A		:					Optional[int]						=			list(poly_b or [0])[:]

          # Remove leading zero coefficients
          while self.polyA[-1] == 0:
               self.polyA.pop()
          __A		:					Union[str, Any]						=			len(self.polyA)

          while self.polyB[-1] == 0:
               self.polyB.pop()
          __A		:					Optional[int]						=			len(self.polyB)

          # Add 0 to make lengths equal a power of 2
          __A		:					Optional[Any]						=			int(
              2 ** np.ceil(np.loga(len(self.polyA) + len(self.polyB) - 1)))

          while len(self.polyA) < self.c_max_length:
               self.polyA.append(0)
          while len(self.polyB) < self.c_max_length:
               self.polyB.append(0)
          # A complex root used for the fourier transform
          __A		:					str						=			complex(mpmath.root(x=1		,      n=self.c_max_length		,      k=1))

          # The product
          __A		:					Tuple						=			self.__multiply()


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Optional[int]						=			[[x] for x in self.polyA] if which == 'A' else [[x] for x in self.polyB]
          # Corner case
          if len(_UpperCAmelCase) <= 1:
               return dft[0]
          #
          __A		:					Dict						=			self.c_max_length // 2
          while next_ncol > 0:
               __A		:					Optional[Any]						=			[[] for i in range(_UpperCAmelCase)]
               __A		:					Tuple						=			self.root**next_ncol

               # First half of next step
               __A		:					Optional[Any]						=			1
               for j in range(self.c_max_length // (next_ncol * 2)):
                    for i in range(_UpperCAmelCase):
                         new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j])
                    current_root *= root
               # Second half of next step
               __A		:					List[str]						=			1
               for j in range(self.c_max_length // (next_ncol * 2)):
                    for i in range(_UpperCAmelCase):
                         new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j])
                    current_root *= root
               # Update
               __A		:					Optional[int]						=			new_dft
               __A		:					Tuple						=			next_ncol // 2
          return dft[0]


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					int						=			self.__dft('A')
          __A		:					Optional[Any]						=			self.__dft('B')
          __A		:					str						=			[[dft_a[i] * dft_b[i] for i in range(self.c_max_length)]]
          del dft_a
          del dft_b

          # Corner Case
          if len(inverce_c[0]) <= 1:
               return inverce_c[0]
          # Inverse DFT
          __A		:					Dict						=			2
          while next_ncol <= self.c_max_length:
               __A		:					Optional[int]						=			[[] for i in range(_UpperCAmelCase)]
               __A		:					Any						=			self.root ** (next_ncol // 2)
               __A		:					Tuple						=			1
               # First half of next step
               for j in range(self.c_max_length // next_ncol):
                    for i in range(next_ncol // 2):
                         # Even positions
                         new_inverse_c[i].append(
                             (
                                 inverce_c[i][j]
                                 + inverce_c[i][j + self.c_max_length // next_ncol]
                             )
                             / 2)
                         # Odd positions
                         new_inverse_c[i + next_ncol // 2].append(
                             (
                                 inverce_c[i][j]
                                 - inverce_c[i][j + self.c_max_length // next_ncol]
                             )
                             / (2 * current_root))
                    current_root *= root
               # Update
               __A		:					int						=			new_inverse_c
               next_ncol *= 2
          # Unpack
          __A		:					Optional[int]						=			[round(x[0].real		,      8) + round(x[0].imag		,      8) * 1j for x in inverce_c]

          # Remove leading 0's
          while inverce_c[-1] == 0:
               inverce_c.pop()
          return inverce_c


     def __str__(		self):

          '''simple docstring'''






          __A		:					int						=			'A = ' + ' + '.join(
              F'{coef}*x^{i}' for coef, i in enumerate(self.polyA[: self.len_A]))
          __A		:					Optional[Any]						=			'B = ' + ' + '.join(
              F'{coef}*x^{i}' for coef, i in enumerate(self.polyB[: self.len_B]))
          __A		:					str						=			'A*B = ' + ' + '.join(
              F'{coef}*x^{i}' for coef, i in enumerate(self.product))

          return F'{a}\n{b}\n{c}'


# Unit tests
if __name__ == "__main__":
  import doctest

  doctest.testmod()