


from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
      from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import MobileViTImageProcessor


class     lowerCamelCase_   ( _lowerCAmelCase):




 def 						a					( self       ):
   __UpperCAmelCase  :     List[str]		=    self.config_class(**self.inputs_dict       )
   self.parent.assertTrue(hasattr(_UpperCAmelCase     ,     'hidden_sizes'       )       )
   self.parent.assertTrue(hasattr(_UpperCAmelCase     ,     'neck_hidden_sizes'       )       )
   self.parent.assertTrue(hasattr(_UpperCAmelCase     ,     'num_attention_heads'       )       )



class     lowerCamelCase_   :




 def __init__( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase=13     ,     _UpperCAmelCase=32     ,     _UpperCAmelCase=2     ,     _UpperCAmelCase=3     ,     _UpperCAmelCase=640     ,     _UpperCAmelCase=4     ,     _UpperCAmelCase="silu"     ,     _UpperCAmelCase=3     ,     _UpperCAmelCase=32     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=0.0_2     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=10     ,     _UpperCAmelCase=None     ,     ):
   __UpperCAmelCase  :     Any		=    parent
   __UpperCAmelCase  :     List[str]		=    batch_size
   __UpperCAmelCase  :     Tuple		=    image_size
   __UpperCAmelCase  :     Dict		=    patch_size
   __UpperCAmelCase  :     Any		=    num_channels
   __UpperCAmelCase  :     Tuple		=    last_hidden_size
   __UpperCAmelCase  :     Optional[int]		=    num_attention_heads
   __UpperCAmelCase  :     Optional[Any]		=    hidden_act
   __UpperCAmelCase  :     List[str]		=    conv_kernel_size
   __UpperCAmelCase  :     Optional[int]		=    output_stride
   __UpperCAmelCase  :     str		=    hidden_dropout_prob
   __UpperCAmelCase  :     int		=    attention_probs_dropout_prob
   __UpperCAmelCase  :     List[Any]		=    classifier_dropout_prob
   __UpperCAmelCase  :     Dict		=    use_labels
   __UpperCAmelCase  :     Optional[int]		=    is_training
   __UpperCAmelCase  :     int		=    num_labels
   __UpperCAmelCase  :     Any		=    initializer_range
   __UpperCAmelCase  :     List[str]		=    scope




 def 						a					( self       ):
   __UpperCAmelCase  :     Tuple		=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

   __UpperCAmelCase  :     Optional[Any]		=    None
   __UpperCAmelCase  :     Optional[Any]		=    None
   if self.use_labels:
     __UpperCAmelCase  :     Tuple		=    ids_tensor([self.batch_size]     ,     self.num_labels       )
     __UpperCAmelCase  :     str		=    ids_tensor([self.batch_size, self.image_size, self.image_size]     ,     self.num_labels       )

   __UpperCAmelCase  :     int		=    self.get_config()

   return config, pixel_values, labels, pixel_labels




 def 						a					( self       ):
   return MobileViTConfig(
       image_size=self.image_size     ,     patch_size=self.patch_size     ,     num_channels=self.num_channels     ,     num_attention_heads=self.num_attention_heads     ,     hidden_act=self.hidden_act     ,     conv_kernel_size=self.conv_kernel_size     ,     output_stride=self.output_stride     ,     hidden_dropout_prob=self.hidden_dropout_prob     ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,     classifier_dropout_prob=self.classifier_dropout_prob     ,     initializer_range=self.initializer_range     ,     )




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     str		=    TFMobileViTModel(config=_UpperCAmelCase       )
   __UpperCAmelCase  :     List[Any]		=    model(_UpperCAmelCase     ,     training=_UpperCAmelCase       )
   __UpperCAmelCase  :     Dict		=    self.image_size // self.output_stride
   self.parent.assertEqual(
       result.last_hidden_state.shape     ,     (self.batch_size, self.last_hidden_size, expected_height, expected_width)       )




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Tuple		=    self.num_labels
   __UpperCAmelCase  :     Tuple		=    TFMobileViTForImageClassification(_UpperCAmelCase       )
   __UpperCAmelCase  :     Optional[Any]		=    model(_UpperCAmelCase     ,     labels=_UpperCAmelCase     ,     training=_UpperCAmelCase       )
   self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.num_labels)       )




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Tuple		=    self.num_labels
   __UpperCAmelCase  :     str		=    TFMobileViTForSemanticSegmentation(_UpperCAmelCase       )
   __UpperCAmelCase  :     int		=    self.image_size // self.output_stride

   __UpperCAmelCase  :     Optional[Any]		=    model(_UpperCAmelCase     ,     training=_UpperCAmelCase       )
   self.parent.assertEqual(
       result.logits.shape     ,     (self.batch_size, self.num_labels, expected_height, expected_width)       )

   __UpperCAmelCase  :     Tuple		=    model(_UpperCAmelCase     ,     labels=_UpperCAmelCase     ,     training=_UpperCAmelCase       )
   self.parent.assertEqual(
       result.logits.shape     ,     (self.batch_size, self.num_labels, expected_height, expected_width)       )




 def 						a					( self       ):
   __UpperCAmelCase  :     int		=    self.prepare_config_and_inputs()
   __UpperCAmelCase  :     int		=    config_and_inputs
   __UpperCAmelCase  :     List[Any]		=    {'pixel_values': pixel_values}
   return config, inputs_dict



@require_tf
class     lowerCamelCase_   ( _lowerCAmelCase  ,							_lowerCAmelCase  ,							unittest.TestCase):
 _lowerCAmelCase       		=  (
     (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
     if is_tf_available()
     else ()
 )
 _lowerCAmelCase       		=  (
     {'''feature-extraction''': TFMobileViTModel, '''image-classification''': TFMobileViTForImageClassification}
     if is_tf_available()
     else {}
 )

 _lowerCAmelCase       		=  False
 _lowerCAmelCase       		=  False
 _lowerCAmelCase       		=  False
 _lowerCAmelCase       		=  False
 _lowerCAmelCase       		=  False




 def 						a					( self       ):
   __UpperCAmelCase  :     str		=    TFMobileViTModelTester(self       )
   __UpperCAmelCase  :     str		=    TFMobileViTConfigTester(self     ,     config_class=_UpperCAmelCase     ,     has_text_modality=_UpperCAmelCase       )




 def 						a					( self       ):
   self.config_tester.run_common_tests()




 @unittest.skip(reason='MobileViT does not use inputs_embeds'       )
 def 						a					( self       ):
   pass




 @unittest.skip(reason='MobileViT does not support input and output embeddings'       )
 def 						a					( self       ):
   pass




 @unittest.skip(reason='MobileViT does not output attentions'       )
 def 						a					( self       ):
   pass




 def 						a					( self       ):
   __UpperCAmelCase  :     Tuple		=    self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     __UpperCAmelCase  :     List[str]		=    model_class(_UpperCAmelCase       )
     __UpperCAmelCase  :     str		=    inspect.signature(model.call       )
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     __UpperCAmelCase  :     Union[str, Any]		=    [*signature.parameters.keys()]

     __UpperCAmelCase  :     Optional[int]		=    ['pixel_values']
     self.assertListEqual(arg_names[:1]     ,     _UpperCAmelCase       )




 def 						a					( self       ):
   __UpperCAmelCase  :     Tuple		=    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*_UpperCAmelCase       )




 def 						a					( self       ):
   def check_hidden_states_output(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
     __UpperCAmelCase  :     List[str]		=    model_class(_UpperCAmelCase       )

     __UpperCAmelCase  :     str		=    model(**self._prepare_for_class(_UpperCAmelCase     ,     _UpperCAmelCase       )       )

     __UpperCAmelCase  :     List[Any]		=    outputs.hidden_states

     __UpperCAmelCase  :     int		=    5
     self.assertEqual(len(_UpperCAmelCase       )     ,     _UpperCAmelCase       )

     # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
     # with the width and height being successively divided by 2.
     __UpperCAmelCase  :     Any		=    2
     for i in range(len(_UpperCAmelCase       )       ):
       self.assertListEqual(
           list(hidden_states[i].shape[-2:]       )     ,     [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]     ,     )
       divisor *= 2

     self.assertEqual(self.model_tester.output_stride     ,     divisor // 2       )

   __UpperCAmelCase  :     str		=    self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     __UpperCAmelCase  :     Dict		=    True
     check_hidden_states_output(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       )

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     __UpperCAmelCase  :     int		=    True

     check_hidden_states_output(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       )




 def 						a					( self       ):
   __UpperCAmelCase  :     List[Any]		=    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase       )




 def 						a					( self       ):
   __UpperCAmelCase  :     List[Any]		=    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase       )




 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices('GPU'       )       ) == 0     ,     reason='TF does not support backprop for grouped convolutions on CPU.'     ,     )
 def 						a					( self       ):
   super().test_dataset_conversion()




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase=2E-1     ,     _UpperCAmelCase=2E-1       ):
   self.assertTrue(np.allclose(_UpperCAmelCase     ,     _UpperCAmelCase     ,     atol=_UpperCAmelCase     ,     rtol=_UpperCAmelCase       )       )




 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices('GPU'       )       ) == 0     ,     reason='TF does not support backprop for grouped convolutions on CPU.'     ,     )
 @slow
 def 						a					( self       ):
   __UpperCAmelCase  :     Union[str, Any]		=    self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     # Since `TFMobileViTModel` cannot operate with the default `fit()` method.
     if model_class.__name__ != "TFMobileViTModel":
       __UpperCAmelCase  :     List[str]		=    model_class(_UpperCAmelCase       )
       if getattr(_UpperCAmelCase     ,     'hf_compute_loss'     ,     _UpperCAmelCase       ):
         super().test_keras_fit()




 def 						a					( self       ):
   __UpperCAmelCase  :     Any		=    self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes:
     # set an ignore index to correctly test the masked loss used in
     # `TFMobileViTForSemanticSegmentation`.
     if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
       __UpperCAmelCase  :     str		=    5

     __UpperCAmelCase  :     int		=    model_class(_UpperCAmelCase       )
     if getattr(_UpperCAmelCase     ,     'hf_compute_loss'     ,     _UpperCAmelCase       ):
       # The number of elements in the loss should be the same as the number of elements in the label
       __UpperCAmelCase  :     Tuple		=    self._prepare_for_class(inputs_dict.copy()     ,     _UpperCAmelCase     ,     return_labels=_UpperCAmelCase       )
       __UpperCAmelCase  :     Optional[int]		=    prepared_for_class[
           sorted(prepared_for_class.keys() - inputs_dict.keys()     ,     reverse=_UpperCAmelCase       )[0]
       ]
       __UpperCAmelCase  :     List[Any]		=    added_label.shape.as_list()[:1]

       # Test that model correctly compute the loss with kwargs
       __UpperCAmelCase  :     int		=    self._prepare_for_class(inputs_dict.copy()     ,     _UpperCAmelCase     ,     return_labels=_UpperCAmelCase       )
       __UpperCAmelCase  :     List[Any]		=    {'input_ids', 'pixel_values', 'input_features'}
       __UpperCAmelCase  :     int		=    possible_input_names.intersection(set(_UpperCAmelCase       )       ).pop()
       __UpperCAmelCase  :     Union[str, Any]		=    prepared_for_class.pop(_UpperCAmelCase       )

       __UpperCAmelCase  :     Union[str, Any]		=    model(_UpperCAmelCase     ,     **_UpperCAmelCase       )[0]
       self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )

       # Test that model correctly compute the loss when we mask some positions
       __UpperCAmelCase  :     Dict		=    self._prepare_for_class(inputs_dict.copy()     ,     _UpperCAmelCase     ,     return_labels=_UpperCAmelCase       )
       __UpperCAmelCase  :     Tuple		=    {'input_ids', 'pixel_values', 'input_features'}
       __UpperCAmelCase  :     Tuple		=    possible_input_names.intersection(set(_UpperCAmelCase       )       ).pop()
       __UpperCAmelCase  :     Union[str, Any]		=    prepared_for_class.pop(_UpperCAmelCase       )
       if "labels" in prepared_for_class:
         __UpperCAmelCase  :     str		=    prepared_for_class['labels'].numpy()
         if len(labels.shape       ) > 1 and labels.shape[1] != 1:
           # labels[0] = -100
           __UpperCAmelCase  :     List[Any]		=    tf.convert_to_tensor(_UpperCAmelCase       )
           __UpperCAmelCase  :     int		=    model(_UpperCAmelCase     ,     **_UpperCAmelCase       )[0]
           self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )
           self.assertTrue(not np.any(np.isnan(loss.numpy()       )       )       )

                # Test that model correctly compute the loss with a dict
       __UpperCAmelCase  :     Any		=    self._prepare_for_class(inputs_dict.copy()     ,     _UpperCAmelCase     ,     return_labels=_UpperCAmelCase       )
       __UpperCAmelCase  :     Tuple		=    model(_UpperCAmelCase       )[0]
       self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )

       # Test that model correctly compute the loss with a tuple
       __UpperCAmelCase  :     Tuple		=    self._prepare_for_class(inputs_dict.copy()     ,     _UpperCAmelCase     ,     return_labels=_UpperCAmelCase       )

       # Get keys that were added with the _prepare_for_class function
       __UpperCAmelCase  :     List[str]		=    prepared_for_class.keys() - inputs_dict.keys()
       __UpperCAmelCase  :     List[Any]		=    inspect.signature(model.call       ).parameters
       __UpperCAmelCase  :     Union[str, Any]		=    list(signature.keys()       )

       # Create a dictionary holding the location of the tensors in the tuple
       __UpperCAmelCase  :     List[str]		=    {0: input_name}
       for label_key in label_keys:
         __UpperCAmelCase  :     str		=    signature_names.index(_UpperCAmelCase       )
         __UpperCAmelCase  :     Tuple		=    label_key
       __UpperCAmelCase  :     Optional[Any]		=    sorted(tuple_index_mapping.items()       )
       # Initialize a list with their default values, update the values and convert to a tuple
       __UpperCAmelCase  :     Optional[Any]		=    []

       for name in signature_names:
         if name != "kwargs":
           list_input.append(signature[name].default       )

       for index, value in sorted_tuple_index_mapping:
         __UpperCAmelCase  :     Dict		=    prepared_for_class[value]

       __UpperCAmelCase  :     str		=    tuple(_UpperCAmelCase       )

       # Send to model
       __UpperCAmelCase  :     Tuple		=    model(tuple_input[:-1]       )[0]

       self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )




 @slow
 def 						a					( self       ):
   for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     __UpperCAmelCase  :     Any		=    TFMobileViTModel.from_pretrained(_UpperCAmelCase       )
     self.assertIsNotNone(_UpperCAmelCase       )





def    _snake_case		()       ->		List[str]:
  __UpperCAmelCase  :     Optional[Any]		=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
  return image







@require_tf
class     lowerCamelCase_   ( unittest.TestCase):




 @slow
 def 						a					( self       ):
   __UpperCAmelCase  :     Optional[Any]		=    TFMobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'       )

   __UpperCAmelCase  :     Optional[Any]		=    MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small'       )
   __UpperCAmelCase  :     Dict		=    prepare_img()
   __UpperCAmelCase  :     int		=    image_processor(images=_UpperCAmelCase     ,     return_tensors='tf'       )

   # forward pass
   __UpperCAmelCase  :     List[Any]		=    model(**_UpperCAmelCase     ,     training=_UpperCAmelCase       )

   # verify the logits
   __UpperCAmelCase  :     Optional[int]		=    tf.TensorShape((1, 1_000)       )
   self.assertEqual(outputs.logits.shape     ,     _UpperCAmelCase       )

   __UpperCAmelCase  :     List[str]		=    tf.constant([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]       )

   tf.debugging.assert_near(outputs.logits[0, :3]     ,     _UpperCAmelCase     ,     atol=1E-4     ,     rtol=1E-04       )




 @slow
 def 						a					( self       ):
   # `from_pt` will be removed
   __UpperCAmelCase  :     int		=    TFMobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'       )

   __UpperCAmelCase  :     List[Any]		=    MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'       )

   __UpperCAmelCase  :     List[str]		=    prepare_img()
   __UpperCAmelCase  :     Optional[Any]		=    image_processor(images=_UpperCAmelCase     ,     return_tensors='tf'       )

   # forward pass
   __UpperCAmelCase  :     List[str]		=    model(inputs.pixel_values     ,     training=_UpperCAmelCase       )
   __UpperCAmelCase  :     int		=    outputs.logits

   # verify the logits
   __UpperCAmelCase  :     int		=    tf.TensorShape((1, 21, 32, 32)       )
   self.assertEqual(logits.shape     ,     _UpperCAmelCase       )

   __UpperCAmelCase  :     Optional[Any]		=    tf.constant(
       [
           [[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
           [[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9_8_6_8, -9.7_1_3_2], [-11.0_405, -11.0_221, -10.7_318]],
           [[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
       ]       )

   tf.debugging.assert_near(logits[0, :3, :3, :3]     ,     _UpperCAmelCase     ,     rtol=1E-4     ,     atol=1E-4       )








from typing import Mapping

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


_SCREAMING_SNAKE_CASE          = logging.get_logger(__name__)


class     lowerCamelCase_   ( _lowerCAmelCase):
 _lowerCAmelCase       		=  '''mt5'''
 _lowerCAmelCase       		=  ['''past_key_values''']




 def __init__( self     ,     _UpperCAmelCase=250_112     ,     _UpperCAmelCase=512     ,     _UpperCAmelCase=64     ,     _UpperCAmelCase=1_024     ,     _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=False     ,     _UpperCAmelCase=0     ,     _UpperCAmelCase=1     ,     _UpperCAmelCase=0     ,     **_UpperCAmelCase     ,     ):
   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     ,     )
   __UpperCAmelCase  :     Dict		=    vocab_size
   __UpperCAmelCase  :     List[str]		=    d_model
   __UpperCAmelCase  :     str		=    d_kv
   __UpperCAmelCase  :     List[str]		=    d_ff
   __UpperCAmelCase  :     int		=    num_layers
   __UpperCAmelCase  :     List[Any]		=    (
       num_decoder_layers if num_decoder_layers is not None else self.num_layers
   )  # default = symmetry
   __UpperCAmelCase  :     List[Any]		=    num_heads
   __UpperCAmelCase  :     Tuple		=    relative_attention_num_buckets
   __UpperCAmelCase  :     Optional[int]		=    relative_attention_max_distance
   __UpperCAmelCase  :     Dict		=    dropout_rate
   __UpperCAmelCase  :     Union[str, Any]		=    layer_norm_epsilon
   __UpperCAmelCase  :     Tuple		=    initializer_factor
   __UpperCAmelCase  :     Union[str, Any]		=    feed_forward_proj
   __UpperCAmelCase  :     Optional[Any]		=    use_cache

   __UpperCAmelCase  :     Any		=    self.feed_forward_proj.split('-'       )
   __UpperCAmelCase  :     Dict		=    act_info[-1]
   __UpperCAmelCase  :     Any		=    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":
     __UpperCAmelCase  :     List[str]		=    'gelu_new'




 @property
 def 						a					( self       ):
   return self.d_model




 @property
 def 						a					( self       ):
   return self.num_heads




 @property
 def 						a					( self       ):
   return self.num_layers



class     lowerCamelCase_   ( _lowerCAmelCase):




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




 @property
 def 						a					( self       ):
   return 5E-4





