






'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import RoFormerConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TFRoFormerForCausalLM,
       TFRoFormerForMaskedLM,
       TFRoFormerForMultipleChoice,
       TFRoFormerForQuestionAnswering,
       TFRoFormerForSequenceClassification,
       TFRoFormerForTokenClassification,
       TFRoFormerModel,
   )
   from transformers.models.roformer.modeling_tf_roformer import (
       TFRoFormerSelfAttention,
       TFRoFormerSinusoidalPositionalEmbedding,
   )


class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     def __init__(				self       :	int					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	Dict=13					, UpperCAmelCase_       :	Optional[int]=7					, UpperCAmelCase_       :	List[Any]=True					, UpperCAmelCase_       :	int=True					, UpperCAmelCase_       :	Optional[int]=True					, UpperCAmelCase_       :	Union[str, Any]=True					, UpperCAmelCase_       :	Dict=99					, UpperCAmelCase_       :	Any=32					, UpperCAmelCase_       :	int=2					, UpperCAmelCase_       :	Union[str, Any]=4					, UpperCAmelCase_       :	str=37					, UpperCAmelCase_       :	Tuple="gelu"					, UpperCAmelCase_       :	Optional[int]=0.1					, UpperCAmelCase_       :	str=0.1					, UpperCAmelCase_       :	Dict=512					, UpperCAmelCase_       :	Any=16					, UpperCAmelCase_       :	int=2					, UpperCAmelCase_       :	Dict=0.02					, UpperCAmelCase_       :	Optional[int]=3					, UpperCAmelCase_       :	Optional[Any]=4					, UpperCAmelCase_       :	Optional[int]=None					, ):
       SCREAMING_SNAKE_CASE       :      Dict							=    parent
       SCREAMING_SNAKE_CASE       :      int							=    13
       SCREAMING_SNAKE_CASE       :      List[str]							=    7
       SCREAMING_SNAKE_CASE       :      List[str]							=    True
       SCREAMING_SNAKE_CASE       :      str							=    True
       SCREAMING_SNAKE_CASE       :      List[str]							=    True
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    True
       SCREAMING_SNAKE_CASE       :      List[Any]							=    99
       SCREAMING_SNAKE_CASE       :      Tuple							=    32
       SCREAMING_SNAKE_CASE       :      int							=    2
       SCREAMING_SNAKE_CASE       :      List[Any]							=    4
       SCREAMING_SNAKE_CASE       :      Tuple							=    37
       SCREAMING_SNAKE_CASE       :      List[str]							=    "gelu"
       SCREAMING_SNAKE_CASE       :      int							=    0.1
       SCREAMING_SNAKE_CASE       :      List[str]							=    0.1
       SCREAMING_SNAKE_CASE       :      Any							=    512
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    16
       SCREAMING_SNAKE_CASE       :      List[Any]							=    2
       SCREAMING_SNAKE_CASE       :      List[str]							=    0.02
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    3
       SCREAMING_SNAKE_CASE       :      Dict							=    4
       SCREAMING_SNAKE_CASE       :      List[Any]							=    None


     def 				_A       (				self       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      int							=    ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size      )

       SCREAMING_SNAKE_CASE       :      int							=    None
       if self.use_input_mask:
         SCREAMING_SNAKE_CASE       :      str							=    random_attention_mask([self.batch_size, self.seq_length]      )

       SCREAMING_SNAKE_CASE       :      Any							=    None
       if self.use_token_type_ids:
         SCREAMING_SNAKE_CASE       :      Any							=    ids_tensor([self.batch_size, self.seq_length]					, self.type_vocab_size      )

       SCREAMING_SNAKE_CASE       :      Any							=    None
       SCREAMING_SNAKE_CASE       :      Tuple							=    None
       SCREAMING_SNAKE_CASE       :      Any							=    None
       if self.use_labels:
         SCREAMING_SNAKE_CASE       :      Tuple							=    ids_tensor([self.batch_size]					, self.type_sequence_label_size      )
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    ids_tensor([self.batch_size, self.seq_length]					, self.num_labels      )
         SCREAMING_SNAKE_CASE       :      List[str]							=    ids_tensor([self.batch_size]					, self.num_choices      )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    RoFormerConfig(
           vocab_size=self.vocab_size					, hidden_size=self.hidden_size					, num_hidden_layers=self.num_hidden_layers					, num_attention_heads=self.num_attention_heads					, intermediate_size=self.intermediate_size					, hidden_act=self.hidden_act					, hidden_dropout_prob=self.hidden_dropout_prob					, attention_probs_dropout_prob=self.attention_probs_dropout_prob					, max_position_embeddings=self.max_position_embeddings					, type_vocab_size=self.type_vocab_size					, initializer_range=self.initializer_range					, return_dict=UpperCAmelCase_					, )

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


     def 				_A       (				self       :	List[Any]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    TFRoFormerModel(config=UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

       SCREAMING_SNAKE_CASE       :      int							=    [input_ids, input_mask]
       SCREAMING_SNAKE_CASE       :      List[str]							=    model(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    model(UpperCAmelCase_      )

       self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.seq_length, self.hidden_size)      )


     def 				_A       (				self       :	Tuple					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      int							=    True
       SCREAMING_SNAKE_CASE       :      int							=    TFRoFormerForCausalLM(config=UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Any							=    {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }
       SCREAMING_SNAKE_CASE       :      List[str]							=    model(UpperCAmelCase_      )["logits"]
       self.parent.assertListEqual(
           list(prediction_scores.numpy().shape      )					, [self.batch_size, self.seq_length, self.vocab_size]      )


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      str							=    TFRoFormerForMaskedLM(config=UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Tuple							=    {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    model(UpperCAmelCase_      )
       self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.seq_length, self.vocab_size)      )


     def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    self.num_labels
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    TFRoFormerForSequenceClassification(config=UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      int							=    {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }

       SCREAMING_SNAKE_CASE       :      int							=    model(UpperCAmelCase_      )
       self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.num_labels)      )


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    self.num_choices
       SCREAMING_SNAKE_CASE       :      Dict							=    TFRoFormerForMultipleChoice(config=UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Any							=    tf.tile(tf.expand_dims(UpperCAmelCase_					, 1      )					, (1, self.num_choices, 1)      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    tf.tile(tf.expand_dims(UpperCAmelCase_					, 1      )					, (1, self.num_choices, 1)      )
       SCREAMING_SNAKE_CASE       :      Tuple							=    tf.tile(tf.expand_dims(UpperCAmelCase_					, 1      )					, (1, self.num_choices, 1)      )
       SCREAMING_SNAKE_CASE       :      List[str]							=    {
           "input_ids": multiple_choice_inputs_ids,
           "attention_mask": multiple_choice_input_mask,
           "token_type_ids": multiple_choice_token_type_ids,
       }
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    model(UpperCAmelCase_      )
       self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.num_choices)      )


     def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.num_labels
       SCREAMING_SNAKE_CASE       :      Any							=    TFRoFormerForTokenClassification(config=UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    model(UpperCAmelCase_      )
       self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.seq_length, self.num_labels)      )


     def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	int      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    TFRoFormerForQuestionAnswering(config=UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Any							=    {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }

       SCREAMING_SNAKE_CASE       :      int							=    model(UpperCAmelCase_      )
       self.parent.assertEqual(result.start_logits.shape					, (self.batch_size, self.seq_length)      )
       self.parent.assertEqual(result.end_logits.shape					, (self.batch_size, self.seq_length)      )






     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.prepare_config_and_inputs()
       (
           SCREAMING_SNAKE_CASE  
       )       :      Optional[Any]							=    config_and_inputs
       SCREAMING_SNAKE_CASE       :      List[Any]							=    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
       return config, inputs_dict





@require_tf
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		lowerCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Optional[int]			=	(
         (
             TFRoFormerModel,
             TFRoFormerForCausalLM,
             TFRoFormerForMaskedLM,
             TFRoFormerForQuestionAnswering,
             TFRoFormerForSequenceClassification,
             TFRoFormerForTokenClassification,
             TFRoFormerForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     UpperCamelCase_							:		List[str]			=	(
         {
             '''feature-extraction''': TFRoFormerModel,
             '''fill-mask''': TFRoFormerForMaskedLM,
             '''question-answering''': TFRoFormerForQuestionAnswering,
             '''text-classification''': TFRoFormerForSequenceClassification,
             '''text-generation''': TFRoFormerForCausalLM,
             '''token-classification''': TFRoFormerForTokenClassification,
             '''zero-shot''': TFRoFormerForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )

     UpperCamelCase_							:		List[str]			=	False
     UpperCamelCase_							:		Tuple			=	False


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	List[str]      ):
       if pipeline_test_casse_name == "TextGenerationPipelineTests":
         return True

       return False


     def 				_A       (				self       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    TFRoFormerModelTester(self      )
       SCREAMING_SNAKE_CASE       :      Dict							=    ConfigTester(self					, config_class=UpperCAmelCase_					, hidden_size=37      )


     def 				_A       (				self       :	Union[str, Any]      ):
       self.config_tester.run_common_tests()


     def 				_A       (				self       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCAmelCase_      )


     def 				_A       (				self       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_      )


     def 				_A       (				self       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_lm_head(*UpperCAmelCase_      )


     def 				_A       (				self       :	int      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase_      )


     def 				_A       (				self       :	int      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_      )


     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_      )


     def 				_A       (				self       :	Any      ):
       SCREAMING_SNAKE_CASE       :      int							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_      )






     @slow
     def 				_A       (				self       :	Any      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base"      )
       self.assertIsNotNone(UpperCAmelCase_      )





@require_tf
class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     @slow
     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"      )
       SCREAMING_SNAKE_CASE       :      str							=    tf.constant([[0, 1, 2, 3, 4, 5]]      )
       SCREAMING_SNAKE_CASE       :      str							=    model(UpperCAmelCase_      )[0]

       # TODO Replace vocab size
       SCREAMING_SNAKE_CASE       :      Tuple							=    5_0000

       SCREAMING_SNAKE_CASE       :      Tuple							=    [1, 6, vocab_size]
       self.assertEqual(output.shape					, UpperCAmelCase_      )

       print(output[:, :3, :3]      )

       # TODO Replace values below with what was printed above.
       SCREAMING_SNAKE_CASE       :      Any							=    tf.constant(
           [
               [
                   [-0.12_053_341, -1.0_264_901, 0.29_221_946],
                   [-1.5_133_783, 0.197_433, 0.15_190_607],
                   [-5.0_135_403, -3.900_256, -0.84_038_764],
               ]
           ]      )
       tf.debugging.assert_near(output[:, :3, :3]					, UpperCAmelCase_					, atol=1E-4      )





@require_tf
class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		int			=	1e-4


     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    tf.constant([[4, 10]]      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    TFRoFormerSinusoidalPositionalEmbedding(num_positions=6					, embedding_dim=6      )

       SCREAMING_SNAKE_CASE       :      Any							=    emba(input_ids.shape      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    tf.constant(
           [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]]      )

       tf.debugging.assert_near(UpperCAmelCase_					, UpperCAmelCase_					, atol=self.tolerance      )






     def 				_A       (				self       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      Any							=    tf.constant(
           [
               [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
               [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
               [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
           ]      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    TFRoFormerSinusoidalPositionalEmbedding(num_positions=512					, embedding_dim=512      )
       emba([2, 16, 512]      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    emba.weight[:3, :5]

       tf.debugging.assert_near(UpperCAmelCase_					, UpperCAmelCase_					, atol=self.tolerance      )





@require_tf
class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Dict			=	1e-4


     def 				_A       (				self       :	Union[str, Any]      ):
       # 2,12,16,64
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    tf.reshape(tf.range(2 * 12 * 16 * 64					, dtype=tf.floataa      )					, shape=(2, 12, 16, 64)      ) / 100

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    -tf.reshape(tf.range(2 * 12 * 16 * 64					, dtype=tf.floataa      )					, shape=(2, 12, 16, 64)      ) / 100

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    TFRoFormerSinusoidalPositionalEmbedding(num_positions=32					, embedding_dim=64      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    embed_positions([2, 16, 768]      )[None, None, :, :]

       SCREAMING_SNAKE_CASE       :      List[str]							=    TFRoFormerSelfAttention.apply_rotary_position_embeddings(
           UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      str							=    tf.constant(
           [
               [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
               [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
               [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
               [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
               [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
               [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
           ]      )
       SCREAMING_SNAKE_CASE       :      Dict							=    tf.constant(
           [
               [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
               [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
               [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
               [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
               [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
               [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
           ]      )

       tf.debugging.assert_near(query_layer[0, 0, :6, :8]					, UpperCAmelCase_					, atol=self.tolerance      )
       tf.debugging.assert_near(key_layer[0, 0, :6, :8]					, UpperCAmelCase_					, atol=self.tolerance      )














from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments


def   lowerCamelCase__			(     ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[Any]							=    HfArgumentParser(lowercase				)
  SCREAMING_SNAKE_CASE       :      Any							=    parser.parse_args_into_dataclasses()[0]
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    TensorFlowBenchmark(args=lowercase				)
  try:
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    parser.parse_args_into_dataclasses()[0]
  except ValueError as e:
    SCREAMING_SNAKE_CASE       :      int							=    "Arg --no_{0} is no longer used, please use --no-{0} instead."
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    " ".join(str(lowercase				).split(" "				)[:-1]				)
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    ""
    SCREAMING_SNAKE_CASE       :      Any							=    eval(str(lowercase				).split(" "				)[-1]				)
    SCREAMING_SNAKE_CASE       :      List[str]							=    []
    for arg in depreciated_args:
      # arg[2:] removes '--'
      if arg[2:] in TensorFlowBenchmark.deprecated_args:
        # arg[5:] removes '--no_'
        full_error_msg += arg_error_msg.format(arg[5:]				)
      else:
        wrong_args.append(lowercase				)
    if len(lowercase				) > 0:
      SCREAMING_SNAKE_CASE       :      Optional[int]							=    full_error_msg + begin_error_msg + str(lowercase				)
    raise ValueError(lowercase				)
  benchmark.run()


if __name__ == "__main__":
   main()






