





"""simple docstring"""




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     __A     =    "platform"

     import jax
     import jax.numpy as jnp

     from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
         FlaxBlenderbotSmallForConditionalGeneration,
         FlaxBlenderbotSmallModel,
         shift_tokens_right,
     )





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a=None     ,   __a=None     ,   __a=None     ,   __a=None     ,   __a=None     ,   __a=None     ,   )					->		Union[str, Any]:







  """simple docstring"""





  if attention_mask is None:
    lowerCamelCase__:				Optional[Any]        =np.where(input_ids != config.pad_token_id     ,   1     ,   0		)
  if decoder_attention_mask is None:
    lowerCamelCase__:				List[Any]        =np.where(decoder_input_ids != config.pad_token_id     ,   1     ,   0		)
  if head_mask is None:
    lowerCamelCase__:				Optional[int]        =np.ones((config.encoder_layers, config.encoder_attention_heads)		)
  if decoder_head_mask is None:
    lowerCamelCase__:				Dict        =np.ones((config.decoder_layers, config.decoder_attention_heads)		)
  if cross_attn_head_mask is None:
    lowerCamelCase__:				int        =np.ones((config.decoder_layers, config.decoder_attention_heads)		)
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": attention_mask,
  }



class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self    :       int	,			UpperCAmelCase_    :       Tuple	,			UpperCAmelCase_    :       Optional[Any]=13	,			UpperCAmelCase_    :       Optional[Any]=7	,			UpperCAmelCase_    :       Optional[int]=True	,			UpperCAmelCase_    :       Tuple=False	,			UpperCAmelCase_    :       List[str]=99	,			UpperCAmelCase_    :       Dict=16	,			UpperCAmelCase_    :       Any=2	,			UpperCAmelCase_    :       Union[str, Any]=4	,			UpperCAmelCase_    :       List[Any]=4	,			UpperCAmelCase_    :       str="gelu"	,			UpperCAmelCase_    :       Optional[Any]=0.1	,			UpperCAmelCase_    :       Union[str, Any]=0.1	,			UpperCAmelCase_    :       Optional[Any]=32	,			UpperCAmelCase_    :       Dict=2	,			UpperCAmelCase_    :       List[str]=1	,			UpperCAmelCase_    :       Any=0	,			UpperCAmelCase_    :       Union[str, Any]=0.02	,			)	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				int        =parent
   lowerCamelCase__:				Union[str, Any]        =batch_size
   lowerCamelCase__:				Union[str, Any]        =seq_length
   lowerCamelCase__:				Union[str, Any]        =is_training
   lowerCamelCase__:				int        =use_labels
   lowerCamelCase__:				int        =vocab_size
   lowerCamelCase__:				Dict        =hidden_size
   lowerCamelCase__:				int        =num_hidden_layers
   lowerCamelCase__:				Union[str, Any]        =num_attention_heads
   lowerCamelCase__:				str        =intermediate_size
   lowerCamelCase__:				Tuple        =hidden_act
   lowerCamelCase__:				Any        =hidden_dropout_prob
   lowerCamelCase__:				Optional[int]        =attention_probs_dropout_prob
   lowerCamelCase__:				Optional[Any]        =max_position_embeddings
   lowerCamelCase__:				Optional[Any]        =eos_token_id
   lowerCamelCase__:				List[str]        =pad_token_id
   lowerCamelCase__:				List[str]        =bos_token_id
   lowerCamelCase__:				List[Any]        =initializer_range

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:				str        =np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	,			self.vocab_size)	,			3	,			self.vocab_size)
   lowerCamelCase__:				int        =np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)	,			dtype=np.intaa))	,			-1)

   lowerCamelCase__:				int        =shift_tokens_right(_UpperCAmelCase	,			1	,			2)

   lowerCamelCase__:				List[Any]        =BlenderbotSmallConfig(
       vocab_size=self.vocab_size	,			d_model=self.hidden_size	,			encoder_layers=self.num_hidden_layers	,			decoder_layers=self.num_hidden_layers	,			encoder_attention_heads=self.num_attention_heads	,			decoder_attention_heads=self.num_attention_heads	,			encoder_ffn_dim=self.intermediate_size	,			decoder_ffn_dim=self.intermediate_size	,			dropout=self.hidden_dropout_prob	,			attention_dropout=self.attention_probs_dropout_prob	,			max_position_embeddings=self.max_position_embeddings	,			eos_token_id=self.eos_token_id	,			bos_token_id=self.bos_token_id	,			pad_token_id=self.pad_token_id	,			initializer_range=self.initializer_range	,			use_cache=_UpperCAmelCase	,			)
   lowerCamelCase__:				int        =prepare_blenderbot_inputs_dict(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase)
   return config, inputs_dict

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->int:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Dict        =self.prepare_config_and_inputs()
   return config, inputs_dict

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			UpperCAmelCase_    :       Dict	,			UpperCAmelCase_    :       Union[str, Any]	,			UpperCAmelCase_    :       List[Any])	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				Any        =20
   lowerCamelCase__:				List[str]        =model_class_name(_UpperCAmelCase)

   lowerCamelCase__:				int        =model.encode(inputs_dict["input_ids"])

   lowerCamelCase__  , lowerCamelCase__:				Optional[int]        =(
       inputs_dict["decoder_input_ids"],
       inputs_dict["decoder_attention_mask"],
   )

   lowerCamelCase__:				Optional[Any]        =model.init_cache(decoder_input_ids.shape[0]	,			_UpperCAmelCase	,			_UpperCAmelCase)
   lowerCamelCase__:				List[str]        =jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,			dtype="i4")

   lowerCamelCase__:				Any        =jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]	,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,			)
   lowerCamelCase__:				List[str]        =model.decode(
       decoder_input_ids[:, :-1]	,			_UpperCAmelCase	,			decoder_attention_mask=_UpperCAmelCase	,			past_key_values=_UpperCAmelCase	,			decoder_position_ids=_UpperCAmelCase	,			)

   lowerCamelCase__:				Any        =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,			dtype="i4")
   lowerCamelCase__:				Any        =model.decode(
       decoder_input_ids[:, -1:]	,			_UpperCAmelCase	,			decoder_attention_mask=_UpperCAmelCase	,			past_key_values=outputs_cache.past_key_values	,			decoder_position_ids=_UpperCAmelCase	,			)

   lowerCamelCase__:				int        =model.decode(_UpperCAmelCase	,			_UpperCAmelCase)

   lowerCamelCase__:				Dict        =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
   self.parent.assertTrue(diff < 1E-3	,			msg=F"""Max diff is {diff}""")







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       str)	->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:				str        =20
   lowerCamelCase__:				Tuple        =model_class_name(_UpperCAmelCase)

   lowerCamelCase__:				Optional[Any]        =model.encode(inputs_dict["input_ids"])

   lowerCamelCase__  , lowerCamelCase__:				List[Any]        =(
       inputs_dict["decoder_input_ids"],
       inputs_dict["decoder_attention_mask"],
   )

   lowerCamelCase__:				Dict        =jnp.concatenate(
       [
           decoder_attention_mask,
           jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
       ]	,			axis=-1	,			)

   lowerCamelCase__:				Union[str, Any]        =model.init_cache(decoder_input_ids.shape[0]	,			_UpperCAmelCase	,			_UpperCAmelCase)
   lowerCamelCase__:				List[Any]        =jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]	,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,			)

   lowerCamelCase__:				Union[str, Any]        =model.decode(
       decoder_input_ids[:, :-1]	,			_UpperCAmelCase	,			decoder_attention_mask=_UpperCAmelCase	,			past_key_values=_UpperCAmelCase	,			decoder_position_ids=_UpperCAmelCase	,			)
   lowerCamelCase__:				int        =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,			dtype="i4")
   lowerCamelCase__:				str        =model.decode(
       decoder_input_ids[:, -1:]	,			_UpperCAmelCase	,			past_key_values=outputs_cache.past_key_values	,			decoder_attention_mask=_UpperCAmelCase	,			decoder_position_ids=_UpperCAmelCase	,			)

   lowerCamelCase__:				List[Any]        =model.decode(_UpperCAmelCase	,			_UpperCAmelCase	,			decoder_attention_mask=_UpperCAmelCase)

   lowerCamelCase__:				Dict        =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
   self.parent.assertTrue(diff < 1E-3	,			msg=F"""Max diff is {diff}""")



@require_flax
class        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''
 lowercase_		=      99

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Any        =np.array(
       [
           [71, 82, 18, 33, 46, 91, 2],
           [68, 34, 26, 58, 30, 82, 2],
           [5, 97, 17, 39, 94, 40, 2],
           [76, 83, 94, 25, 70, 78, 2],
           [87, 59, 41, 35, 48, 66, 2],
           [55, 13, 16, 58, 5, 2, 1],  # note padding
           [64, 27, 31, 51, 12, 75, 2],
           [52, 64, 86, 17, 83, 39, 2],
           [48, 61, 9, 24, 71, 82, 2],
           [26, 1, 60, 48, 22, 13, 2],
           [21, 5, 62, 28, 14, 76, 2],
           [45, 98, 37, 86, 59, 48, 2],
           [70, 70, 50, 9, 28, 0, 2],
       ]	,			dtype=np.intaa	,			)

   lowerCamelCase__:				int        =input_ids.shape[0]
   lowerCamelCase__:				Any        =BlenderbotSmallConfig(
       vocab_size=self.vocab_size	,			d_model=24	,			encoder_layers=2	,			decoder_layers=2	,			encoder_attention_heads=2	,			decoder_attention_heads=2	,			encoder_ffn_dim=32	,			decoder_ffn_dim=32	,			max_position_embeddings=48	,			eos_token_id=2	,			pad_token_id=1	,			bos_token_id=0	,			)
   return config, input_ids, batch_size

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Tuple        =self._get_config_and_data()
   lowerCamelCase__:				Dict        =FlaxBlenderbotSmallForConditionalGeneration(_UpperCAmelCase)
   lowerCamelCase__:				Tuple        =lm_model(input_ids=_UpperCAmelCase)
   lowerCamelCase__:				Tuple        =(batch_size, input_ids.shape[1], config.vocab_size)
   self.assertEqual(outputs["logits"].shape	,			_UpperCAmelCase)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =BlenderbotSmallConfig(
       vocab_size=self.vocab_size	,			d_model=14	,			encoder_layers=2	,			decoder_layers=2	,			encoder_attention_heads=2	,			decoder_attention_heads=2	,			encoder_ffn_dim=8	,			decoder_ffn_dim=8	,			max_position_embeddings=48	,			)
   lowerCamelCase__:				List[Any]        =FlaxBlenderbotSmallForConditionalGeneration(_UpperCAmelCase)
   lowerCamelCase__:				List[Any]        =np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]	,			dtype=np.intaa)
   lowerCamelCase__:				List[str]        =np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]	,			dtype=np.intaa)
   lowerCamelCase__:				Any        =lm_model(input_ids=_UpperCAmelCase	,			decoder_input_ids=_UpperCAmelCase)
   lowerCamelCase__:				Union[str, Any]        =(*summary.shape, config.vocab_size)
   self.assertEqual(outputs["logits"].shape	,			_UpperCAmelCase)







 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]	,			dtype=np.intaa)
   lowerCamelCase__:				Union[str, Any]        =shift_tokens_right(_UpperCAmelCase	,			1	,			2)
   lowerCamelCase__:				Dict        =np.equal(_UpperCAmelCase	,			1).astype(np.floataa).sum()
   lowerCamelCase__:				Tuple        =np.equal(_UpperCAmelCase	,			1).astype(np.floataa).sum()
   self.assertEqual(shifted.shape	,			input_ids.shape)
   self.assertEqual(_UpperCAmelCase	,			n_pad_before - 1)
   self.assertTrue(np.equal(shifted[:, 0]	,			2).all())



@require_flax
class        _SCREAMING_SNAKE_CASE						(     lowerCamelCase_   ,							unittest.TestCase   ,							lowerCamelCase_   ):







 '''simple docstring'''
 lowercase_		=      True
 lowercase_		=      (
     (
         FlaxBlenderbotSmallModel,
         FlaxBlenderbotSmallForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 lowercase_		=      (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->int:



   '''simple docstring'''



   lowerCamelCase__:				str        =FlaxBlenderbotSmallModelTester(self)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Optional[int]        =self.model_tester.prepare_config_and_inputs()
   for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				str        =self.model_tester.prepare_config_and_inputs()
   for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward_with_attn_mask(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->str:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Any        =self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__):
       lowerCamelCase__:				List[str]        =self._prepare_for_class(_UpperCAmelCase	,			_UpperCAmelCase)
       lowerCamelCase__:				Optional[int]        =model_class(_UpperCAmelCase)

       @jax.jit
       def encode_jitted(UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       Any=None	,			**UpperCAmelCase_    :       Tuple):
         return model.encode(input_ids=_UpperCAmelCase	,			attention_mask=_UpperCAmelCase)

       with self.subTest("JIT Enabled"):
         lowerCamelCase__:				Tuple        =encode_jitted(**_UpperCAmelCase).to_tuple()

       with self.subTest("JIT Disabled"):
         with jax.disable_jit():
           lowerCamelCase__:				Tuple        =encode_jitted(**_UpperCAmelCase).to_tuple()

       self.assertEqual(len(_UpperCAmelCase)	,			len(_UpperCAmelCase))
       for jitted_output, output in zip(_UpperCAmelCase	,			_UpperCAmelCase):
         self.assertEqual(jitted_output.shape	,			output.shape)

 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Tuple        =self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__):
       lowerCamelCase__:				List[str]        =model_class(_UpperCAmelCase)
       lowerCamelCase__:				Union[str, Any]        =model.encode(inputs_dict["input_ids"]	,			inputs_dict["attention_mask"])

       lowerCamelCase__:				Dict        ={
           "decoder_input_ids": inputs_dict["decoder_input_ids"],
           "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
           "encoder_outputs": encoder_outputs,
       }

       @jax.jit
       def decode_jitted(UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       str):
         return model.decode(
             decoder_input_ids=_UpperCAmelCase	,			decoder_attention_mask=_UpperCAmelCase	,			encoder_outputs=_UpperCAmelCase	,			)

       with self.subTest("JIT Enabled"):
         lowerCamelCase__:				str        =decode_jitted(**_UpperCAmelCase).to_tuple()

       with self.subTest("JIT Disabled"):
         with jax.disable_jit():
           lowerCamelCase__:				Optional[Any]        =decode_jitted(**_UpperCAmelCase).to_tuple()

       self.assertEqual(len(_UpperCAmelCase)	,			len(_UpperCAmelCase))
       for jitted_output, output in zip(_UpperCAmelCase	,			_UpperCAmelCase):
         self.assertEqual(jitted_output.shape	,			output.shape)







 @slow
 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->List[Any]:



   '''simple docstring'''



   for model_class_name in self.all_model_classes:
     lowerCamelCase__:				Union[str, Any]        =model_class_name.from_pretrained("facebook/blenderbot_small-90M")
     # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
     lowerCamelCase__:				Optional[int]        =np.ones((1, 1)) * model.config.eos_token_id
     lowerCamelCase__:				Any        =model(_UpperCAmelCase)
     self.assertIsNotNone(_UpperCAmelCase)








from collections import OrderedDict
from typing import Mapping

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


__A     =    logging.get_logger(__name__)

__A     =    {
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      "distilbert"
 lowercase_		=      {
     "hidden_size": "dim",
     "num_attention_heads": "n_heads",
     "num_hidden_layers": "n_layers",
 }

 def __init__(self    :       Any	,			UpperCAmelCase_    :       str=30_522	,			UpperCAmelCase_    :       Union[str, Any]=512	,			UpperCAmelCase_    :       int=False	,			UpperCAmelCase_    :       Optional[Any]=6	,			UpperCAmelCase_    :       Optional[Any]=12	,			UpperCAmelCase_    :       Any=768	,			UpperCAmelCase_    :       List[Any]=4 * 768	,			UpperCAmelCase_    :       Tuple=0.1	,			UpperCAmelCase_    :       List[Any]=0.1	,			UpperCAmelCase_    :       Any="gelu"	,			UpperCAmelCase_    :       int=0.02	,			UpperCAmelCase_    :       Optional[Any]=0.1	,			UpperCAmelCase_    :       Optional[int]=0.2	,			UpperCAmelCase_    :       int=0	,			**UpperCAmelCase_    :       List[Any]	,			)	->Any:



   '''simple docstring'''



   lowerCamelCase__:				int        =vocab_size
   lowerCamelCase__:				Any        =max_position_embeddings
   lowerCamelCase__:				Optional[int]        =sinusoidal_pos_embds
   lowerCamelCase__:				str        =n_layers
   lowerCamelCase__:				str        =n_heads
   lowerCamelCase__:				str        =dim
   lowerCamelCase__:				Optional[Any]        =hidden_dim
   lowerCamelCase__:				Dict        =dropout
   lowerCamelCase__:				Optional[Any]        =attention_dropout
   lowerCamelCase__:				int        =activation
   lowerCamelCase__:				Dict        =initializer_range
   lowerCamelCase__:				Optional[Any]        =qa_dropout
   lowerCamelCase__:				int        =seq_classif_dropout
   super().__init__(**UpperCAmelCase_	,			pad_token_id=UpperCAmelCase_)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Mapping[str, Mapping[int, str]]:



   '''simple docstring'''



   if self.task == "multiple-choice":
     lowerCamelCase__:				Dict        ={0: "batch", 1: "choice", 2: "sequence"}
   else:
     lowerCamelCase__:				Optional[int]        ={0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
       ])

