


from typing import Mapping

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


UpperCAmelCase__     			=							logging.get_logger(__name__)







class    lowerCamelCase__     ( lowerCAmelCase):
     SCREAMING_SNAKE_CASE__       =    '''mt5'''
     SCREAMING_SNAKE_CASE__       =    ['''past_key_values''']





     def __init__(self	,  UpperCAmelCase=2_5_0_1_1_2	,  UpperCAmelCase=5_1_2	,  UpperCAmelCase=6_4	,  UpperCAmelCase=1_0_2_4	,  UpperCAmelCase=8	,  UpperCAmelCase=None	,  UpperCAmelCase=6	,  UpperCAmelCase=3_2	,  UpperCAmelCase=1_2_8	,  UpperCAmelCase=0.1	,  UpperCAmelCase=1e-6	,  UpperCAmelCase=1.0	,  UpperCAmelCase="gated-gelu"	,  UpperCAmelCase=True	,  UpperCAmelCase=True	,  UpperCAmelCase="T5Tokenizer"	,  UpperCAmelCase=False	,  UpperCAmelCase=0	,  UpperCAmelCase=1	,  UpperCAmelCase=0	,  **UpperCAmelCase	,  )  ->     List[str]:
      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	,  )
      _lowercase							   =vocab_size
      _lowercase							   =d_model
      _lowercase							   =d_kv
      _lowercase							   =d_ff
      _lowercase							   =num_layers
      _lowercase							   =(
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      _lowercase							   =num_heads
      _lowercase							   =relative_attention_num_buckets
      _lowercase							   =relative_attention_max_distance
      _lowercase							   =dropout_rate
      _lowercase							   =layer_norm_epsilon
      _lowercase							   =initializer_factor
      _lowercase							   =feed_forward_proj
      _lowercase							   =use_cache

      _lowercase							   =self.feed_forward_proj.split('''-''' )
      _lowercase							   =act_info[-1]
      _lowercase							   =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\'''' )

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





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





     @property
     def   __A      (self )  ->     List[Any]:
      return self.num_heads





     @property
     def   __A      (self )  ->     int:
      return self.num_layers







class    lowerCamelCase__     ( lowerCAmelCase):





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





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





import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
  import tensorflow as tf

  from transformers.activations_tf import get_tf_activation







@require_tf
class    lowerCamelCase__     ( unittest.TestCase):





     def   __A      (self )  ->     Optional[Any]:
      _lowercase							   =tf.constant([-1_0_0, -1.0, -0.1, 0, 0.1, 1.0, 100.0] )
      _lowercase							   =get_tf_activation('''gelu''' )
      _lowercase							   =get_tf_activation('''gelu_10''' )

      _lowercase							   =gelu(UpperCAmelCase )
      _lowercase							   =geluaa(UpperCAmelCase )

      _lowercase							   =tf.where(y_gelu_aa < 10.0	,  1.0	,  0.0 )

      self.assertEqual(tf.math.reduce_max(UpperCAmelCase ).numpy().item()	,  10.0 )
      self.assertTrue(np.allclose(y_gelu * clipped_mask	,  y_gelu_aa * clipped_mask ) )





     def   __A      (self )  ->     Optional[Any]:
      get_tf_activation('''gelu''' )
      get_tf_activation('''gelu_10''' )
      get_tf_activation('''gelu_fast''' )
      get_tf_activation('''gelu_new''' )
      get_tf_activation('''glu''' )
      get_tf_activation('''mish''' )
      get_tf_activation('''quick_gelu''' )
      get_tf_activation('''relu''' )
      get_tf_activation('''sigmoid''' )
      get_tf_activation('''silu''' )
      get_tf_activation('''swish''' )
      get_tf_activation('''tanh''' )
      with self.assertRaises(UpperCAmelCase ):
       get_tf_activation('''bogus''' )
      with self.assertRaises(UpperCAmelCase ):
       get_tf_activation(UpperCAmelCase )

