



'''simple docstring'''





from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, 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 GPTaTokenizer, TFOPTForCausalLM, TFOPTModel
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase=None							,    UpperCamelCase=None			):



    """simple docstring"""





    if attention_mask is None:
        lowerCAmelCase__ :  Any							      =					tf.cast(tf.math.not_equal(UpperCamelCase							,    config.pad_token_id			)							,    tf.inta			)
    return {"input_ids": input_ids, "attention_mask": attention_mask}






@require_tf
class        lowerCAmelCase_:
  '''simple docstring'''




  __lowercase							:      str				=      OPTConfig
  __lowercase							:      List[Any]				=      {}
  __lowercase							:      List[str]				=      '''gelu'''




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=13  ,__UpperCAmelCase=7  ,__UpperCAmelCase=True  ,__UpperCAmelCase=False  ,__UpperCAmelCase=99  ,__UpperCAmelCase=16  ,__UpperCAmelCase=2  ,__UpperCAmelCase=4  ,__UpperCAmelCase=4  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=20  ,__UpperCAmelCase=2  ,__UpperCAmelCase=1  ,__UpperCAmelCase=0  ,__UpperCAmelCase=16  ,__UpperCAmelCase=16  ,)    ->		List[str]:
      lowerCAmelCase__ :  str							      =					parent
      lowerCAmelCase__ :  Dict							      =					batch_size
      lowerCAmelCase__ :  str							      =					seq_length
      lowerCAmelCase__ :  int							      =					is_training
      lowerCAmelCase__ :  Optional[Any]							      =					use_labels
      lowerCAmelCase__ :  Union[str, Any]							      =					vocab_size
      lowerCAmelCase__ :  Optional[Any]							      =					hidden_size
      lowerCAmelCase__ :  Dict							      =					num_hidden_layers
      lowerCAmelCase__ :  str							      =					num_attention_heads
      lowerCAmelCase__ :  Any							      =					intermediate_size
      lowerCAmelCase__ :  int							      =					hidden_act
      lowerCAmelCase__ :  List[Any]							      =					hidden_dropout_prob
      lowerCAmelCase__ :  Optional[Any]							      =					attention_probs_dropout_prob
      lowerCAmelCase__ :  Any							      =					max_position_embeddings
      lowerCAmelCase__ :  Union[str, Any]							      =					eos_token_id
      lowerCAmelCase__ :  Any							      =					pad_token_id
      lowerCAmelCase__ :  List[str]							      =					bos_token_id
      lowerCAmelCase__ :  List[str]							      =					embed_dim
      lowerCAmelCase__ :  int							      =					word_embed_proj_dim
      lowerCAmelCase__ :  Optional[int]							      =					False




  def 							UpperCAmelCase_	(    self      )    ->		Dict:
      lowerCAmelCase__ :  str							      =					ids_tensor([self.batch_size, self.seq_length - 1]  ,self.vocab_size      )
      lowerCAmelCase__ :  int							      =					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )  ,1      )
      lowerCAmelCase__ :  str							      =					tf.concat([input_ids, eos_tensor]  ,axis=1      )

      lowerCAmelCase__ :  Dict							      =					self.config_cls(
          vocab_size=self.vocab_size  ,hidden_size=self.hidden_size  ,num_hidden_layers=self.num_hidden_layers  ,num_attention_heads=self.num_attention_heads  ,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  ,embed_dim=self.embed_dim  ,word_embed_proj_dim=self.word_embed_proj_dim  ,is_encoder_decoder=__UpperCAmelCase  ,**self.config_updates  ,)
      lowerCAmelCase__ :  List[str]							      =					prepare_opt_inputs_dict(__UpperCAmelCase  ,__UpperCAmelCase      )
      return config, inputs_dict






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Optional[Any]:
      lowerCAmelCase__ :  Any							      =					TFOPTModel(config=__UpperCAmelCase      )
      lowerCAmelCase__ :  List[str]							      =					inputs_dict["""input_ids"""]

      lowerCAmelCase__ :  List[str]							      =					input_ids[:1, :]
      lowerCAmelCase__ :  str							      =					inputs_dict["""attention_mask"""][:1, :]
      lowerCAmelCase__ :  int							      =					1

      # first forward pass
      lowerCAmelCase__ :  int							      =					model(__UpperCAmelCase  ,attention_mask=__UpperCAmelCase  ,use_cache=__UpperCAmelCase      )

      lowerCAmelCase__      ,   lowerCAmelCase__ :  List[str]							      =					outputs.to_tuple()

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

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

      lowerCAmelCase__ :  Union[str, Any]							      =					model(__UpperCAmelCase  ,attention_mask=__UpperCAmelCase      )[0]
      lowerCAmelCase__ :  Optional[int]							      =					model(__UpperCAmelCase  ,attention_mask=__UpperCAmelCase  ,past_key_values=__UpperCAmelCase      )[0]

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

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

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







@require_tf
class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, unittest.TestCase     ):
  '''simple docstring'''




  __lowercase							:      Tuple				=      (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
  __lowercase							:      Dict				=      (TFOPTForCausalLM,) if is_tf_available() else ()
  __lowercase							:      List[str]				=      (
      {'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
  )
  __lowercase							:      int				=      False
  __lowercase							:      Any				=      False
  __lowercase							:      Union[str, Any]				=      False
  __lowercase							:      Union[str, Any]				=      1_0




  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      lowerCAmelCase__ :  Optional[Any]							      =					TFOPTModelTester(self      )
      lowerCAmelCase__ :  List[Any]							      =					ConfigTester(self  ,config_class=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      self.config_tester.run_common_tests()




  def 							UpperCAmelCase_	(    self      )    ->		int:
      lowerCAmelCase__ :  List[str]							      =					self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase      )






  def 							UpperCAmelCase_	(    self      )    ->		str:
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[Any]							      =					self.model_tester.prepare_config_and_inputs_for_common()

      def _get_word_embedding_weight(__UpperCAmelCase  ,__UpperCAmelCase      ):
          if hasattr(__UpperCAmelCase  ,"""weight"""      ):
              return embedding_layer.weight
          else:
              # Here we build the word embeddings weights if not exists.
              # And then we retry to get the attribute once built.
              model.build()
              if hasattr(__UpperCAmelCase  ,"""weight"""      ):
                  return embedding_layer.weight
              else:
                  return None

      for model_class in self.all_model_classes:
          for size in [config.vocab_size - 10, config.vocab_size + 10]:
              # build the embeddings
              lowerCAmelCase__ :  Optional[int]							      =					model_class(config=__UpperCAmelCase      )
              lowerCAmelCase__ :  Tuple							      =					_get_word_embedding_weight(__UpperCAmelCase  ,model.get_input_embeddings()      )
              lowerCAmelCase__ :  Optional[int]							      =					_get_word_embedding_weight(__UpperCAmelCase  ,model.get_output_embeddings()      )

              # reshape the embeddings
              model.resize_token_embeddings(__UpperCAmelCase      )
              lowerCAmelCase__ :  List[str]							      =					_get_word_embedding_weight(__UpperCAmelCase  ,model.get_input_embeddings()      )
              lowerCAmelCase__ :  Optional[int]							      =					_get_word_embedding_weight(__UpperCAmelCase  ,model.get_output_embeddings()      )

              # check that the resized embeddings size matches the desired size.
              lowerCAmelCase__ :  str							      =					size if size is not None else config.vocab_size

              self.assertEqual(new_input_embeddings.shape[0]  ,__UpperCAmelCase      )

              # check that weights remain the same after resizing
              lowerCAmelCase__ :  Dict							      =					True
              for pa, pa in zip(old_input_embeddings.value()  ,new_input_embeddings.value()      ):
                  if tf.math.reduce_sum(tf.math.abs(pa - pa      )      ) > 0:
                      lowerCAmelCase__ :  Any							      =					False
              self.assertTrue(__UpperCAmelCase      )

              if old_output_embeddings is not None and new_output_embeddings is not None:
                  self.assertEqual(new_output_embeddings.shape[0]  ,__UpperCAmelCase      )

                  lowerCAmelCase__ :  Union[str, Any]							      =					True
                  for pa, pa in zip(old_output_embeddings.value()  ,new_output_embeddings.value()      ):
                      if tf.math.reduce_sum(tf.math.abs(pa - pa      )      ) > 0:
                          lowerCAmelCase__ :  List[Any]							      =					False
                  self.assertTrue(__UpperCAmelCase      )







def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return tf.constant(UpperCamelCase							,    dtype=tf.intaa			)






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




  __lowercase							:      List[Any]				=      9_9




  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      lowerCAmelCase__ :  Optional[int]							      =					tf.ones((4, 1)  ,dtype=tf.intaa      ) * 2
      lowerCAmelCase__ :  List[Any]							      =					tf.concat([ids_tensor((4, 6)  ,self.vocab_size - 3      ) + 3, eos_column_vector]  ,axis=1      )
      lowerCAmelCase__ :  Dict							      =					input_ids.shape[0]
      lowerCAmelCase__ :  Tuple							      =					OPTConfig(
          vocab_size=self.vocab_size  ,hidden_size=24  ,num_hidden_layers=2  ,num_attention_heads=2  ,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







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




  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Any:
      lowerCAmelCase__ :  Optional[Any]							      =					TFOPTModel.from_pretrained("""facebook/opt-350m"""      )
      lowerCAmelCase__ :  Tuple							      =					_long_tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]      )
      lowerCAmelCase__ :  Optional[Any]							      =					tf.not_equal(__UpperCAmelCase  ,model.config.pad_token_id      )
      with tf.GradientTape():
          lowerCAmelCase__ :  str							      =					model(input_ids=__UpperCAmelCase  ,attention_mask=__UpperCAmelCase      ).last_hidden_state
      lowerCAmelCase__ :  Union[str, Any]							      =					(1, 11, 512)
      self.assertEqual(output.shape  ,__UpperCAmelCase      )
      lowerCAmelCase__ :  Tuple							      =					tf.constant(
          [[-0.2_8_7_3, -1.9_2_1_8, -0.3_0_3_3], [-1.2_7_1_0, -0.1_3_3_8, -0.1_9_0_2], [0.4_0_9_5, 0.1_2_1_4, -1.3_1_2_1]]      )
      self.assertTrue(np.allclose(output[:, :3, :3]  ,__UpperCAmelCase  ,atol=4E-3      )      )

      lowerCAmelCase__ :  str							      =					tf.function(__UpperCAmelCase  ,jit_compile=__UpperCAmelCase      )
      lowerCAmelCase__ :  List[str]							      =					xla_generate(__UpperCAmelCase  ,__UpperCAmelCase      )[0]
      self.assertTrue(np.allclose(output[:, :3, :3]  ,__UpperCAmelCase  ,atol=4E-2      )      )







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




  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      super().setUp()
      lowerCAmelCase__ :  Union[str, Any]							      =					"""facebook/opt-350m"""






  def 							UpperCAmelCase_	(    self      )    ->		int:
      lowerCAmelCase__ :  str							      =					TFOPTForCausalLM.from_pretrained(self.path_model      )
      lowerCAmelCase__ :  Tuple							      =					GPTaTokenizer.from_pretrained(self.path_model      )

      lowerCAmelCase__ :  List[Any]							      =					[
          """Today is a beautiful day and I want to""",
          """In the city of""",
          """Paris is the capital of France and""",
          """Computers and mobile phones have taken""",
      ]
      # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
      lowerCAmelCase__ :  List[str]							      =					tokenizer(__UpperCAmelCase  ,return_tensors="""tf"""  ,padding=__UpperCAmelCase  ,add_special_tokens=__UpperCAmelCase      )
      lowerCAmelCase__ :  int							      =					tf.math.reduce_mean(model(inputs.input_ids  ,attention_mask=inputs.attention_mask      )[0]  ,axis=-1      )
      lowerCAmelCase__ :  Tuple							      =					tf.constant(
          [
              [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
              [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
              [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
              [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
          ]      )
      self.assertTrue(np.allclose(__UpperCAmelCase  ,__UpperCAmelCase  ,atol=1E-4      )      )

      lowerCAmelCase__ :  Union[str, Any]							      =					tf.function(__UpperCAmelCase  ,jit_compile=__UpperCAmelCase      )
      lowerCAmelCase__ :  Dict							      =					tf.math.reduce_mean(xla_generate(inputs.input_ids  ,attention_mask=inputs.attention_mask      )[0]  ,axis=-1      )
      self.assertTrue(np.allclose(__UpperCAmelCase  ,__UpperCAmelCase  ,atol=1E-4      )      )







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




  @property
  def 							UpperCAmelCase_	(    self      )    ->		str:
      return [
          "Today is a beautiful day and I want",
          "In the city of",
          "Paris is the capital of France and",
          "Computers and mobile phones have taken",
      ]




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      lowerCAmelCase__ :  Optional[Any]							      =					"""facebook/opt-125m"""

      lowerCAmelCase__ :  int							      =					[
          """Today is a beautiful day and I want to""",
          """In the city of New York, the city""",
          """Paris is the capital of France and the capital""",
          """Computers and mobile phones have taken over the""",
      ]

      lowerCAmelCase__ :  int							      =					[]
      lowerCAmelCase__ :  str							      =					GPTaTokenizer.from_pretrained(__UpperCAmelCase      )
      lowerCAmelCase__ :  Optional[int]							      =					TFOPTForCausalLM.from_pretrained(__UpperCAmelCase      )

      for prompt in self.prompts:
          lowerCAmelCase__ :  Optional[int]							      =					tokenizer(__UpperCAmelCase  ,return_tensors="""tf"""      ).input_ids

          lowerCAmelCase__ :  int							      =					model.generate(__UpperCAmelCase  ,max_length=10      )

          lowerCAmelCase__ :  int							      =					tokenizer.batch_decode(__UpperCAmelCase  ,skip_special_tokens=__UpperCAmelCase      )
          predicted_outputs += generated_string

      self.assertListEqual(__UpperCAmelCase  ,__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		str:
      lowerCAmelCase__ :  str							      =					"""facebook/opt-350m"""

      lowerCAmelCase__ :  List[str]							      =					GPTaTokenizer.from_pretrained(__UpperCAmelCase      )
      lowerCAmelCase__ :  List[str]							      =					TFOPTForCausalLM.from_pretrained(__UpperCAmelCase      )

      lowerCAmelCase__ :  Any							      =					"""left"""

      # use different length sentences to test batching
      lowerCAmelCase__ :  Union[str, Any]							      =					[
          """Hello, my dog is a little""",
          """Today, I""",
      ]

      lowerCAmelCase__ :  List[str]							      =					tokenizer(__UpperCAmelCase  ,return_tensors="""tf"""  ,padding=__UpperCAmelCase      )
      lowerCAmelCase__ :  Tuple							      =					inputs["""input_ids"""]

      lowerCAmelCase__ :  Any							      =					model.generate(input_ids=__UpperCAmelCase  ,attention_mask=inputs["""attention_mask"""]      )

      lowerCAmelCase__ :  List[str]							      =					tokenizer(sentences[0]  ,return_tensors="""tf"""      ).input_ids
      lowerCAmelCase__ :  int							      =					model.generate(input_ids=__UpperCAmelCase      )

      lowerCAmelCase__ :  str							      =					inputs_non_padded.shape[-1] - tf.math.reduce_sum(
          tf.cast(inputs["""attention_mask"""][-1]  ,tf.intaa      )      )
      lowerCAmelCase__ :  List[str]							      =					tokenizer(sentences[1]  ,return_tensors="""tf"""      ).input_ids
      lowerCAmelCase__ :  Dict							      =					model.generate(input_ids=__UpperCAmelCase  ,max_length=model.config.max_length - num_paddings      )

      lowerCAmelCase__ :  List[Any]							      =					tokenizer.batch_decode(__UpperCAmelCase  ,skip_special_tokens=__UpperCAmelCase      )
      lowerCAmelCase__ :  int							      =					tokenizer.decode(output_non_padded[0]  ,skip_special_tokens=__UpperCAmelCase      )
      lowerCAmelCase__ :  Union[str, Any]							      =					tokenizer.decode(output_padded[0]  ,skip_special_tokens=__UpperCAmelCase      )

      lowerCAmelCase__ :  List[str]							      =					[
          """Hello, my dog is a little bit of a dork.\nI'm a little bit""",
          """Today, I was in the middle of a conversation with a friend about the""",
      ]
      self.assertListEqual(__UpperCAmelCase  ,__UpperCAmelCase      )
      self.assertListEqual(__UpperCAmelCase  ,[non_padded_sentence, padded_sentence]      )






  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      lowerCAmelCase__ :  Union[str, Any]							      =					"""facebook/opt-350m"""

      lowerCAmelCase__ :  Dict							      =					[
          """Today is a beautiful day and I want to""",
          """In the city of San Francisco, the city""",
          """Paris is the capital of France and the capital""",
          """Computers and mobile phones have taken over the""",
      ]

      lowerCAmelCase__ :  Dict							      =					[]
      lowerCAmelCase__ :  str							      =					GPTaTokenizer.from_pretrained(__UpperCAmelCase      )
      lowerCAmelCase__ :  str							      =					TFOPTForCausalLM.from_pretrained(__UpperCAmelCase      )

      for prompt in self.prompts:
          lowerCAmelCase__ :  int							      =					tokenizer(__UpperCAmelCase  ,return_tensors="""tf"""      ).input_ids

          lowerCAmelCase__ :  str							      =					model.generate(__UpperCAmelCase  ,max_length=10      )

          lowerCAmelCase__ :  Optional[Any]							      =					tokenizer.batch_decode(__UpperCAmelCase  ,skip_special_tokens=__UpperCAmelCase      )
          predicted_outputs += generated_string

      self.assertListEqual(__UpperCAmelCase  ,__UpperCAmelCase      )







'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


_lowerCAmelCase      						=	logging.get_logger(__name__)

_lowerCAmelCase      						=	{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      int				=      '''xlm-roberta-xl'''




  def __init__(    self  ,__UpperCAmelCase=25_0880  ,__UpperCAmelCase=2560  ,__UpperCAmelCase=36  ,__UpperCAmelCase=32  ,__UpperCAmelCase=1_0240  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=514  ,__UpperCAmelCase=1  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=1E-05  ,__UpperCAmelCase=1  ,__UpperCAmelCase=0  ,__UpperCAmelCase=2  ,__UpperCAmelCase="absolute"  ,__UpperCAmelCase=True  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,)    ->		str:
      super().__init__(pad_token_id=__UpperCAmelCase  ,bos_token_id=__UpperCAmelCase  ,eos_token_id=__UpperCAmelCase  ,**__UpperCAmelCase      )
      lowerCAmelCase__ :  List[Any]							      =					vocab_size
      lowerCAmelCase__ :  int							      =					hidden_size
      lowerCAmelCase__ :  int							      =					num_hidden_layers
      lowerCAmelCase__ :  str							      =					num_attention_heads
      lowerCAmelCase__ :  int							      =					hidden_act
      lowerCAmelCase__ :  Dict							      =					intermediate_size
      lowerCAmelCase__ :  List[Any]							      =					hidden_dropout_prob
      lowerCAmelCase__ :  str							      =					attention_probs_dropout_prob
      lowerCAmelCase__ :  Optional[int]							      =					max_position_embeddings
      lowerCAmelCase__ :  List[str]							      =					type_vocab_size
      lowerCAmelCase__ :  List[Any]							      =					initializer_range
      lowerCAmelCase__ :  Tuple							      =					layer_norm_eps
      lowerCAmelCase__ :  int							      =					position_embedding_type
      lowerCAmelCase__ :  Optional[Any]							      =					use_cache
      lowerCAmelCase__ :  Optional[Any]							      =					classifier_dropout







class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  @property
  def 							UpperCAmelCase_	(    self      )    ->		Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
          lowerCAmelCase__ :  Dict							      =					{0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
          lowerCAmelCase__ :  Any							      =					{0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ]      )


