


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 							a						:




							"""simple docstring"""


							def __init__(				self			:    Any       ,      lowerCamelCase			:    Optional[int]       ,      )      ->       Tuple:
														__snake_case						:     Union[str, Any]									=  parent
														__snake_case						:     Dict									=  13
														__snake_case						:     Tuple									=  7
														__snake_case						:     Dict									=  30
														__snake_case						:     Any									=  self.seq_length + self.mem_len
														__snake_case						:     int									=  15
														__snake_case						:     Union[str, Any]									=  True
														__snake_case						:     List[Any]									=  True
														__snake_case						:     List[Any]									=  99
														__snake_case						:     Optional[int]									=  [10, 50, 80]
														__snake_case						:     List[str]									=  32
														__snake_case						:     List[Any]									=  32
														__snake_case						:     int									=  4
														__snake_case						:     Optional[Any]									=  8
														__snake_case						:     Union[str, Any]									=  128
														__snake_case						:     Optional[Any]									=  2
														__snake_case						:     Any									=  2
														__snake_case						:     str									=  None
														__snake_case						:     Optional[int]									=  1
														__snake_case						:     str									=  0
														__snake_case						:     Union[str, Any]									=  3
														__snake_case						:     Tuple									=  self.vocab_size - 1
														__snake_case						:     List[str]									=  0.01

							def        __snake_case   (				self			:    Optional[int]							)      ->       Optional[int]:
														__snake_case						:     Tuple									=  ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size							)
														__snake_case						:     Optional[int]									=  ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size							)

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

														__snake_case						:     Dict									=  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        __snake_case   (				self			:    Any							)      ->       Union[str, Any]:
														random.seed(self.seed							)
														tf.random.set_seed(self.seed							)

							def        __snake_case   (				self			:    str       ,      lowerCamelCase			:    Tuple       ,      lowerCamelCase			:    Union[str, Any]       ,      lowerCamelCase			:    Optional[Any]       ,      lowerCamelCase			:    str							)      ->       Optional[Any]:
														__snake_case						:     List[str]									=  TFTransfoXLModel(lowerCamelCase							)

														__snake_case					,     __snake_case						:     str									=  model(lowerCamelCase							).to_tuple()

														__snake_case						:     Optional[Any]									=  {"input_ids": input_ids_a, "mems": mems_a}

														__snake_case					,     __snake_case						:     List[Any]									=  model(lowerCamelCase							).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        __snake_case   (				self			:    List[str]       ,      lowerCamelCase			:    Optional[int]       ,      lowerCamelCase			:    Tuple       ,      lowerCamelCase			:    List[Any]       ,      lowerCamelCase			:    int							)      ->       Dict:
														__snake_case						:     int									=  TFTransfoXLLMHeadModel(lowerCamelCase							)

														__snake_case					,     __snake_case						:     str									=  model(lowerCamelCase							).to_tuple()

														__snake_case						:     Tuple									=  {"input_ids": input_ids_a, "labels": lm_labels}
														__snake_case					,     __snake_case						:     List[Any]									=  model(lowerCamelCase							).to_tuple()

														__snake_case					,     __snake_case						:     Union[str, Any]									=  model([input_ids_a, mems_a]							).to_tuple()

														__snake_case						:     List[str]									=  {"input_ids": input_ids_a, "mems": mems_a, "labels": lm_labels}

														__snake_case					,     __snake_case						:     List[Any]									=  model(lowerCamelCase							).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        __snake_case   (				self			:    Union[str, Any]       ,      lowerCamelCase			:    str       ,      lowerCamelCase			:    Optional[int]       ,      lowerCamelCase			:    str       ,      lowerCamelCase			:    Optional[int]							)      ->       List[str]:
														__snake_case						:     str									=  TFTransfoXLForSequenceClassification(lowerCamelCase							)
														__snake_case						:     Optional[int]									=  model(lowerCamelCase							)
														self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)							)






							def        __snake_case   (				self			:    List[Any]							)      ->       Any:
														__snake_case						:     Union[str, Any]									=  self.prepare_config_and_inputs()
														((__snake_case)					,     (__snake_case)					,     (__snake_case)					,     (__snake_case))						:     Tuple									=  config_and_inputs
														__snake_case						:     Dict									=  {"input_ids": input_ids_a}
														return config, inputs_dict

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




							"""simple docstring"""


							__UpperCAmelCase       :				Any	    =    (
							    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
							)
							__UpperCAmelCase       :				List[Any]	    =    () if is_tf_available() else ()
							__UpperCAmelCase       :				Dict	    =    (
							    {
							        "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
							__UpperCAmelCase       :				Union[str, Any]	    =    False
							__UpperCAmelCase       :				List[Any]	    =    False
							__UpperCAmelCase       :				Optional[Any]	    =    False
							__UpperCAmelCase       :				int	    =    False

							def        __snake_case   (				self			:    Optional[int]       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    List[str]       ,      lowerCamelCase			:    List[str]       ,      lowerCamelCase			:    str       ,      lowerCamelCase			:    Any							)      ->       Optional[int]:
														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        __snake_case   (				self			:    int							)      ->       int:
														__snake_case						:     List[Any]									=  TFTransfoXLModelTester(self							)
														__snake_case						:     List[str]									=  ConfigTester(self       ,      config_class=lowerCamelCase       ,      d_embed=37							)

							def        __snake_case   (				self			:    int							)      ->       List[Any]:
														self.config_tester.run_common_tests()

							def        __snake_case   (				self			:    List[Any]							)      ->       Dict:
														self.model_tester.set_seed()
														__snake_case						:     Union[str, Any]									=  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_transfo_xl_model(*lowerCamelCase							)

							def        __snake_case   (				self			:    Optional[int]							)      ->       Optional[Any]:
														self.model_tester.set_seed()
														__snake_case						:     List[str]									=  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_transfo_xl_lm_head(*lowerCamelCase							)

							def        __snake_case   (				self			:    Optional[Any]							)      ->       Tuple:
														__snake_case						:     str									=  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowerCamelCase							)

							def        __snake_case   (				self			:    List[Any]							)      ->       Any:
														__snake_case					,     __snake_case						:     int									=  self.model_tester.prepare_config_and_inputs_for_common()
														__snake_case						:     int									=  [TFTransfoXLForSequenceClassification]

														for model_class in self.all_model_classes:
																					__snake_case						:     str									=  model_class(lowerCamelCase							)
																					assert isinstance(model.get_input_embeddings()       ,      tf.keras.layers.Layer							)
																					if model_class in list_other_models_with_output_ebd:
																												__snake_case						:     int									=  model.get_output_embeddings()
																												assert isinstance(lowerCamelCase       ,      tf.keras.layers.Layer							)
																												__snake_case						:     Optional[Any]									=  model.get_bias()
																												assert name is None
																					else:
																												__snake_case						:     Optional[int]									=  model.get_output_embeddings()
																												assert x is None
																												__snake_case						:     Tuple									=  model.get_bias()
																												assert name is None

							def        __snake_case   (				self			:    List[Any]							)      ->       List[Any]:
														# TODO JP: Make TransfoXL XLA compliant
														pass

							@slow
							def        __snake_case   (				self			:    Optional[int]							)      ->       Dict:
														for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					__snake_case						:     Union[str, Any]									=  TFTransfoXLModel.from_pretrained(lowerCamelCase							)
																					self.assertIsNotNone(lowerCamelCase							)






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

@require_tf
class 							a						(unittest.TestCase ):




							"""simple docstring"""


							@unittest.skip("Skip test until #12651 is resolved."							)
							@slow
							def        __snake_case   (				self			:    List[Any]							)      ->       Tuple:
														__snake_case						:     Dict									=  TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103"							)
														# fmt: off
														__snake_case						:     Union[str, Any]									=  tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]]       ,      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
														__snake_case						:     List[str]									=  [33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0]  # 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>

														__snake_case						:     Optional[int]									=  model.generate(lowerCamelCase       ,      max_length=200       ,      do_sample=lowerCamelCase							)
														self.assertListEqual(output_ids[0].numpy().tolist()       ,      lowerCamelCase							)





from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class 		__magic_name__						:





     '''simple docstring'''



     __lowercase				:							int	   =       BlenderbotConfig
     __lowercase				:							Any	   =       {}
     __lowercase				:							Optional[Any]	   =       'gelu'

     def __init__(					self:Tuple     ,		_a:Optional[Any]     ,		_a:Optional[Any]=13     ,		_a:Tuple=7     ,		_a:Union[str, Any]=True     ,		_a:int=False     ,		_a:int=99     ,		_a:Optional[int]=32     ,		_a:List[str]=2     ,		_a:List[str]=4     ,		_a:List[Any]=37     ,		_a:Any=0.1     ,		_a:int=0.1     ,		_a:List[Any]=20     ,		_a:List[str]=2     ,		_a:int=1     ,		_a:Dict=0     ,		):
       snake_case__						= parent
       snake_case__						= batch_size
       snake_case__						= seq_length
       snake_case__						= is_training
       snake_case__						= use_labels
       snake_case__						= vocab_size
       snake_case__						= hidden_size
       snake_case__						= num_hidden_layers
       snake_case__						= num_attention_heads
       snake_case__						= intermediate_size

       snake_case__						= hidden_dropout_prob
       snake_case__						= attention_probs_dropout_prob
       snake_case__						= max_position_embeddings
       snake_case__						= eos_token_id
       snake_case__						= pad_token_id
       snake_case__						= bos_token_id

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= ids_tensor([self.batch_size, self.seq_length - 1]     ,		self.vocab_size   )
       snake_case__						= tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )     ,		1   )
       snake_case__						= tf.concat([input_ids, eos_tensor]     ,		axis=1   )

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

       snake_case__						= self.config_cls(
           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_ids=[2]     ,		bos_token_id=self.bos_token_id     ,		pad_token_id=self.pad_token_id     ,		decoder_start_token_id=self.pad_token_id     ,		**self.config_updates     ,		)
       snake_case__						= prepare_blenderbot_inputs_dict(_a     ,		_a     ,		_a   )
       return config, inputs_dict







     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		_a:Optional[Any]     ,		_a:int   ):
       snake_case__						= TFBlenderbotModel(config=_a   ).get_decoder()
       snake_case__						= inputs_dict['''input_ids''']

       snake_case__						= input_ids[:1, :]
       snake_case__						= inputs_dict['''attention_mask'''][:1, :]
       snake_case__						= inputs_dict['''head_mask''']
       snake_case__						= 1

       # first forward pass
       snake_case__						= model(_a     ,		attention_mask=_a     ,		head_mask=_a     ,		use_cache=_a   )

       snake_case__				,				snake_case__						= outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       snake_case__						= ids_tensor((self.batch_size, 3)     ,		config.vocab_size   )
       snake_case__						= tf.cast(ids_tensor((self.batch_size, 3)     ,		2   )     ,		tf.inta   )

       # append to next input_ids and
       snake_case__						= tf.concat([input_ids, next_tokens]     ,		axis=-1   )
       snake_case__						= tf.concat([attention_mask, next_attn_mask]     ,		axis=-1   )

       snake_case__						= model(_a     ,		attention_mask=_a   )[0]
       snake_case__						= model(_a     ,		attention_mask=_a     ,		past_key_values=_a   )[0]

       self.parent.assertEqual(next_tokens.shape[1]     ,		output_from_past.shape[1]   )

       # select random slice
       snake_case__						= int(ids_tensor((1,)     ,		output_from_past.shape[-1]   )   )
       snake_case__						= output_from_no_past[:, -3:, random_slice_idx]
       snake_case__						= output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(_a     ,		_a     ,		rtol=1e-3   )







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase=None							,   __lowerCAmelCase=None							,   __lowerCAmelCase=None							,   __lowerCAmelCase=None							,   __lowerCAmelCase=None							,   )    ->				Tuple:
  if attention_mask is None:
    snake_case__						= tf.cast(tf.math.not_equal(__lowerCAmelCase							,   config.pad_token_id				)							,   tf.inta				)
  if decoder_attention_mask is None:
    snake_case__						= tf.concat(
        [
            tf.ones(decoder_input_ids[:, :1].shape							,   dtype=tf.inta				),
            tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]							,   config.pad_token_id				)							,   tf.inta				),
        ]							,   axis=-1							,   )
  if head_mask is None:
    snake_case__						= tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
  if decoder_head_mask is None:
    snake_case__						= tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
  if cross_attn_head_mask is None:
    snake_case__						= tf.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": decoder_attention_mask,
      "head_mask": head_mask,
      "decoder_head_mask": decoder_head_mask,
      "cross_attn_head_mask": cross_attn_head_mask,
  }
@require_tf
class 		__magic_name__						(snake_case_   ,snake_case_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							List[str]	   =       (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
     __lowercase				:							Any	   =       (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
     __lowercase				:							Tuple	   =       (
         {
             'conversational': TFBlenderbotForConditionalGeneration,
             'feature-extraction': TFBlenderbotModel,
             'summarization': TFBlenderbotForConditionalGeneration,
             'text2text-generation': TFBlenderbotForConditionalGeneration,
             'translation': TFBlenderbotForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     __lowercase				:							Any	   =       True
     __lowercase				:							int	   =       False
     __lowercase				:							int	   =       False

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= TFBlenderbotModelTester(self   )
       snake_case__						= ConfigTester(self     ,		config_class=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       self.config_tester.run_common_tests()







     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       snake_case__						= self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*_a   )




@require_tokenizers
@require_tf
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							Optional[int]	   =       ['My friends are cool but they eat too many carbs.']
     __lowercase				:							Optional[int]	   =       'facebook/blenderbot-400M-distill'

     @cached_property
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       return BlenderbotTokenizer.from_pretrained(self.model_name   )

     @cached_property
     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
       return model







     @slow
     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= self.tokenizer(self.src_text     ,		return_tensors='''tf'''   )
       snake_case__						= self.model.generate(
           model_inputs.input_ids     ,		)
       snake_case__						= self.tokenizer.batch_decode(generated_ids.numpy()     ,		skip_special_tokens=_a   )[0]
       assert (
           generated_words
           == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
       )

