

'''simple docstring'''



from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFTransfoXLForSequenceClassification,
      TFTransfoXLLMHeadModel,
      TFTransfoXLModel,
  )







class 		lowerCAmelCase__  :



 """simple docstring"""



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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = parent
      __SCREAMING_SNAKE_CASE      = 13
      __SCREAMING_SNAKE_CASE      = 7
      __SCREAMING_SNAKE_CASE      = 30
      __SCREAMING_SNAKE_CASE      = self.seq_length + self.mem_len
      __SCREAMING_SNAKE_CASE      = 15
      __SCREAMING_SNAKE_CASE      = True
      __SCREAMING_SNAKE_CASE      = True
      __SCREAMING_SNAKE_CASE      = 99
      __SCREAMING_SNAKE_CASE      = [10, 50, 80]
      __SCREAMING_SNAKE_CASE      = 32
      __SCREAMING_SNAKE_CASE      = 32
      __SCREAMING_SNAKE_CASE      = 4
      __SCREAMING_SNAKE_CASE      = 8
      __SCREAMING_SNAKE_CASE      = 128
      __SCREAMING_SNAKE_CASE      = 2
      __SCREAMING_SNAKE_CASE      = 2
      __SCREAMING_SNAKE_CASE      = None
      __SCREAMING_SNAKE_CASE      = 1
      __SCREAMING_SNAKE_CASE      = 0
      __SCREAMING_SNAKE_CASE      = 3
      __SCREAMING_SNAKE_CASE      = self.vocab_size - 1
      __SCREAMING_SNAKE_CASE      = 0.01
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size     )
      __SCREAMING_SNAKE_CASE      = ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size     )

      __SCREAMING_SNAKE_CASE      = None
      if self.use_labels:
           __SCREAMING_SNAKE_CASE      = ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size     )

      __SCREAMING_SNAKE_CASE      = TransfoXLConfig(
          vocab_size=self.vocab_size   ,				mem_len=self.mem_len   ,				clamp_len=self.clamp_len   ,				cutoffs=self.cutoffs   ,				d_model=self.hidden_size   ,				d_embed=self.d_embed   ,				n_head=self.num_attention_heads   ,				d_head=self.d_head   ,				d_inner=self.d_inner   ,				div_val=self.div_val   ,				n_layer=self.num_hidden_layers   ,				eos_token_id=self.eos_token_id   ,				pad_token_id=self.vocab_size - 1   ,				init_range=self.init_range   ,				num_labels=self.num_labels   ,				)

      return (config, input_ids_a, input_ids_a, lm_labels)
 def  UpperCAmelCase__						(						self   :			int     )    ->					Optional[Any]:


      """simple docstring"""




      random.seed(self.seed     )
      tf.random.set_seed(self.seed     )
 def  UpperCAmelCase__						(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			Any     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = TFTransfoXLModel(__UpperCAmelCase     )

      __SCREAMING_SNAKE_CASE      = model(__UpperCAmelCase     ).to_tuple()

      __SCREAMING_SNAKE_CASE      = {'input_ids': input_ids_a, 'mems': mems_a}

      __SCREAMING_SNAKE_CASE      = model(__UpperCAmelCase     ).to_tuple()

      self.parent.assertEqual(hidden_states_a.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)     )
      self.parent.assertEqual(hidden_states_a.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)     )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]   ,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,				)
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]   ,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,				)
 def  UpperCAmelCase__						(						self   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			Dict     )    ->					Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = TFTransfoXLLMHeadModel(__UpperCAmelCase     )

      __SCREAMING_SNAKE_CASE      = model(__UpperCAmelCase     ).to_tuple()

      __SCREAMING_SNAKE_CASE      = {'input_ids': input_ids_a, 'labels': lm_labels}
      __SCREAMING_SNAKE_CASE      = model(__UpperCAmelCase     ).to_tuple()

      __SCREAMING_SNAKE_CASE      = model([input_ids_a, mems_a]     ).to_tuple()

      __SCREAMING_SNAKE_CASE      = {'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}

      __SCREAMING_SNAKE_CASE      = model(__UpperCAmelCase     ).to_tuple()

      self.parent.assertEqual(lm_logits_a.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)     )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]   ,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,				)

      self.parent.assertEqual(lm_logits_a.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)     )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]   ,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,				)
 def  UpperCAmelCase__						(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			Any   ,				__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			Any     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = TFTransfoXLForSequenceClassification(__UpperCAmelCase     )
      __SCREAMING_SNAKE_CASE      = model(__UpperCAmelCase     )
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)     )


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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.prepare_config_and_inputs()
      (__SCREAMING_SNAKE_CASE)      = config_and_inputs
      __SCREAMING_SNAKE_CASE      = {'input_ids': input_ids_a}
      return config, inputs_dict







@require_tf
class 		lowerCAmelCase__  ( a					,   a					,   unittest.TestCase   ):



 """simple docstring"""



 lowerCAmelCase__       = (
     (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
 )
 lowerCAmelCase__       = () if is_tf_available() else ()
 lowerCAmelCase__       = (
     {
         """feature-extraction""": TFTransfoXLModel,
         """text-classification""": TFTransfoXLForSequenceClassification,
         """text-generation""": TFTransfoXLLMHeadModel,
         """zero-shot""": TFTransfoXLForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
 lowerCAmelCase__       = False
 lowerCAmelCase__       = False
 lowerCAmelCase__       = False
 lowerCAmelCase__       = False
 def  UpperCAmelCase__						(						self   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any     )    ->					Optional[Any]:


      """simple docstring"""




      if pipeline_test_casse_name == "TextGenerationPipelineTests":
           # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
           # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
           # tokenizer.
           return True

      return False
 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = TFTransfoXLModelTester(self     )
      __SCREAMING_SNAKE_CASE      = ConfigTester(self   ,				config_class=__UpperCAmelCase   ,				d_embed=37     )
 def  UpperCAmelCase__						(						self   :			str     )    ->					Dict:


      """simple docstring"""




      self.config_tester.run_common_tests()
 def  UpperCAmelCase__						(						self   :			str     )    ->					str:


      """simple docstring"""




      self.model_tester.set_seed()
      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_model(*__UpperCAmelCase     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Optional[int]:


      """simple docstring"""




      self.model_tester.set_seed()
      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_lm_head(*__UpperCAmelCase     )
 def  UpperCAmelCase__						(						self   :			int     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__UpperCAmelCase     )
 def  UpperCAmelCase__						(						self   :			int     )    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE      = [TFTransfoXLForSequenceClassification]

      for model_class in self.all_model_classes:
           __SCREAMING_SNAKE_CASE      = model_class(__UpperCAmelCase     )
           assert isinstance(model.get_input_embeddings()   ,				tf.keras.layers.Layer     )
           if model_class in list_other_models_with_output_ebd:
                __SCREAMING_SNAKE_CASE      = model.get_output_embeddings()
                assert isinstance(__UpperCAmelCase   ,				tf.keras.layers.Layer     )
                __SCREAMING_SNAKE_CASE      = model.get_bias()
                assert name is None
           else:
                __SCREAMING_SNAKE_CASE      = model.get_output_embeddings()
                assert x is None
                __SCREAMING_SNAKE_CASE      = model.get_bias()
                assert name is None
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					str:


      """simple docstring"""




      pass
 @slow
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					Union[str, Any]:


      """simple docstring"""




      for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __SCREAMING_SNAKE_CASE      = TFTransfoXLModel.from_pretrained(__UpperCAmelCase     )
           self.assertIsNotNone(__UpperCAmelCase     )


 @unittest.skip(reason="""This model doesn\'t play well with fit() due to not returning a single loss."""     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					Optional[Any]:


      """simple docstring"""




      pass







@require_tf
class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 @unittest.skip("""Skip test until #12651 is resolved."""     )
 @slow
 def  UpperCAmelCase__						(						self   :			List[str]     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""     )
      # fmt: off
      __SCREAMING_SNAKE_CASE      = tf.convert_to_tensor([[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0]]   ,				dtype=tf.intaa     )  # noqa: E231
      # fmt: on
      #  In 1991 , the remains of Russian Tsar Nicholas II and his family
      #  ( except for Alexei and Maria ) are discovered .
      #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
      #  remainder of the story . 1883 Western Siberia ,
      #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
      #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
      #  father initially slaps him for making such an accusation , Rasputin watches as the
      #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
      #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
      #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

      # fmt: off
      __SCREAMING_SNAKE_CASE      = [33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0,33,1,1_857,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,28,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,0]  # noqa: E231
      # fmt: on
      #  In 1991, the remains of Russian Tsar Nicholas II and his family (
      #  except for Alexei and Maria ) are discovered. The voice of young son,
      #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
      #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
      #  and a group of men to perform magic. Rasputin has a vision and
      #  denounces one of the men as a horse thief. Although his father initially
      #  slaps him for making such an accusation, Rasputin watches as the man
      #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
      #  of the Virgin Mary, prompting him to become a priest.
      #  Rasputin quickly becomes famous, with people, even a bishop, begging for
      #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
      # Nicholas II and his family were discovered. The voice of <unk> young son,
      # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

      __SCREAMING_SNAKE_CASE      = model.generate(__UpperCAmelCase   ,				max_length=200   ,				do_sample=__UpperCAmelCase     )
      self.assertListEqual(output_ids[0].numpy().tolist()   ,				__UpperCAmelCase     )













"""simple docstring"""





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
  import tensorflow as tf

if is_keras_nlp_available():
  from transformers.models.gpta import TFGPTaTokenizer


__A											=						["""gpt2"""]
__A											=						"""gpt2"""

if is_tf_available():






  class    _lowerCAmelCase  (	tf.Module			):
         """simple docstring"""




         def __init__(       self       ,					__UpperCAmelCase ):



           '''simple docstring'''

           super().__init__()
           lowerCAmelCase__     :List[str]											=				tokenizer
           lowerCAmelCase__     :Optional[int]											=				AutoConfig.from_pretrained(__UpperCAmelCase )
           lowerCAmelCase__     :Optional[Any]											=				TFGPTaLMHeadModel.from_config(__UpperCAmelCase )






         @tf.function(input_signature=(tf.TensorSpec((None,)       ,					tf.string       ,					name='text' ),) )
         def 	snake_case		(       self       ,					__UpperCAmelCase ):



           '''simple docstring'''

           lowerCAmelCase__     :str											=				self.tokenizer(__UpperCAmelCase )
           lowerCAmelCase__     :int											=				tokenized['input_ids'].to_tensor()

           lowerCAmelCase__     :Optional[Any]											=				tf.cast(input_ids_dense > 0       ,					tf.intaa )
           # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

           lowerCAmelCase__     :int											=				self.model(input_ids=__UpperCAmelCase       ,					attention_mask=__UpperCAmelCase )['logits']

           return outputs






@require_tf
@require_keras_nlp
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         super().setUp()

         lowerCAmelCase__     :List[str]											=				[GPTaTokenizer.from_pretrained(__UpperCAmelCase ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
         lowerCAmelCase__     :Union[str, Any]											=				[TFGPTaTokenizer.from_pretrained(__UpperCAmelCase ) for checkpoint in TOKENIZER_CHECKPOINTS]
         assert len(self.tokenizers ) == len(self.tf_tokenizers )

         lowerCAmelCase__     :int											=				[
             'This is a straightforward English test sentence.',
             'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
             'Now we\'re going to add some Chinese: 一 二 三 一二三',
             'And some much more rare Chinese: 齉 堃 齉堃',
             'Je vais aussi écrire en français pour tester les accents',
             'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
         ]
         lowerCAmelCase__     :str											=				list(zip(self.test_sentences       ,					self.test_sentences[::-1] ) )






       def 	snake_case		(       self ):



         '''simple docstring'''

         for tokenizer, tf_tokenizer in zip(self.tokenizers       ,					self.tf_tokenizers ):
           for test_inputs in self.test_sentences:
             lowerCAmelCase__     :int											=				tokenizer([test_inputs]       ,					return_tensors='tf' )
             lowerCAmelCase__     :Optional[int]											=				tf_tokenizer([test_inputs] )

             for key in python_outputs.keys():
               # convert them to numpy to avoid messing with ragged tensors
               lowerCAmelCase__     :Optional[int]											=				python_outputs[key].numpy()
               lowerCAmelCase__     :List[str]											=				tf_outputs[key].numpy()

               self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape ) )
               self.assertTrue(tf.reduce_all(tf.cast(__UpperCAmelCase       ,					tf.intaa ) == tf_outputs_values ) )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for tf_tokenizer in self.tf_tokenizers:
           lowerCAmelCase__     :Optional[int]											=				tf.function(__UpperCAmelCase )
           for test_inputs in self.test_sentences:
             lowerCAmelCase__     :Any											=				tf.constant(__UpperCAmelCase )
             lowerCAmelCase__     :int											=				compiled_tokenizer(__UpperCAmelCase )
             lowerCAmelCase__     :Any											=				tf_tokenizer(__UpperCAmelCase )

             for key in eager_outputs.keys():
               self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for tf_tokenizer in self.tf_tokenizers:
           lowerCAmelCase__     :List[str]											=				ModelToSave(tokenizer=__UpperCAmelCase )
           lowerCAmelCase__     :Union[str, Any]											=				tf.convert_to_tensor([self.test_sentences[0]] )
           lowerCAmelCase__     :List[str]											=				model.serving(__UpperCAmelCase )  # Build model with some sample inputs
           with TemporaryDirectory() as tempdir:
             lowerCAmelCase__     :Union[str, Any]											=				Path(__UpperCAmelCase ) / 'saved.model'
             tf.saved_model.save(__UpperCAmelCase       ,					__UpperCAmelCase       ,					signatures={'serving_default': model.serving} )
             lowerCAmelCase__     :str											=				tf.saved_model.load(__UpperCAmelCase )
           lowerCAmelCase__     :Dict											=				loaded_model.signatures['serving_default'](__UpperCAmelCase )['output_0']
           # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
           self.assertTrue(tf.reduce_all(out == loaded_output ) )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for tf_tokenizer in self.tf_tokenizers:
           lowerCAmelCase__     :List[str]											=				tf.convert_to_tensor([self.test_sentences[0]] )
           lowerCAmelCase__     :List[str]											=				tf_tokenizer(__UpperCAmelCase )  # Build model with some sample inputs

           lowerCAmelCase__     :Union[str, Any]											=				tf_tokenizer.get_config()
           lowerCAmelCase__     :Tuple											=				TFGPTaTokenizer.from_config(__UpperCAmelCase )
           lowerCAmelCase__     :Tuple											=				model_from_config(__UpperCAmelCase )

           for key in from_config_output.keys():
             self.assertTrue(tf.reduce_all(from_config_output[key] == out[key] ) )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for tf_tokenizer in self.tf_tokenizers:
           # for the test to run
           lowerCAmelCase__     :int											=				1_2_3_1_2_3

           for max_length in [3, 5, 1_0_2_4]:
             lowerCAmelCase__     :Tuple											=				tf.convert_to_tensor([self.test_sentences[0]] )
             lowerCAmelCase__     :List[str]											=				tf_tokenizer(__UpperCAmelCase       ,					max_length=__UpperCAmelCase )

             lowerCAmelCase__     :Union[str, Any]											=				out['input_ids'].numpy().shape[1]

             assert out_length == max_length
