
from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, 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, TFMBartForConditionalGeneration, TFMBartModel




@require_tf
class        UpperCAmelCase_			:


  '''simple docstring'''







  a__           =		MBartConfig
  a__           =		{}
  a__           =		"""gelu"""
  def __init__( self			: str			,							UpperCamelCase__			: Union[str, Any]			,							UpperCamelCase__			: List[str]=13			,							UpperCamelCase__			: Union[str, Any]=7			,							UpperCamelCase__			: Optional[Any]=True			,							UpperCamelCase__			: List[str]=False			,							UpperCamelCase__			: Optional[int]=99			,							UpperCamelCase__			: Optional[Any]=32			,							UpperCamelCase__			: Optional[Any]=2			,							UpperCamelCase__			: str=4			,							UpperCamelCase__			: List[str]=37			,							UpperCamelCase__			: List[Any]=0.1			,							UpperCamelCase__			: Optional[int]=0.1			,							UpperCamelCase__			: Tuple=20			,							UpperCamelCase__			: Optional[int]=2			,							UpperCamelCase__			: Optional[int]=1			,							UpperCamelCase__			: List[Any]=0			,							)			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       parent
    __magic_name__				     =       batch_size
    __magic_name__				     =       seq_length
    __magic_name__				     =       is_training
    __magic_name__				     =       use_labels
    __magic_name__				     =       vocab_size
    __magic_name__				     =       hidden_size
    __magic_name__				     =       num_hidden_layers
    __magic_name__				     =       num_attention_heads
    __magic_name__				     =       intermediate_size
    __magic_name__				     =       hidden_dropout_prob
    __magic_name__				     =       attention_probs_dropout_prob
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       eos_token_id
    __magic_name__				     =       pad_token_id
    __magic_name__				     =       bos_token_id
  def 							_lowercase							( self			: int    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       ids_tensor([self.batch_size, self.seq_length - 1]			,							self.vocab_size    )
    __magic_name__				     =       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )			,							1    )
    __magic_name__				     =       tf.concat([input_ids, eos_tensor]			,							axis=1    )

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

    __magic_name__				     =       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			,							)
    __magic_name__				     =       prepare_mbart_inputs_dict(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__    )
    return config, inputs_dict





  def 							_lowercase							( self			: str			,							UpperCamelCase__			: List[Any]			,							UpperCamelCase__			: Union[str, Any]    )			->	str:







    """simple docstring"""






    __magic_name__				     =       TFMBartModel(config=UpperCamelCase__    ).get_decoder()
    __magic_name__				     =       inputs_dict["""input_ids"""]

    __magic_name__				     =       input_ids[:1, :]
    __magic_name__				     =       inputs_dict["""attention_mask"""][:1, :]
    __magic_name__				     =       inputs_dict["""head_mask"""]
    __magic_name__				     =       1

    # first forward pass
    __magic_name__				     =       model(UpperCamelCase__			,							attention_mask=UpperCamelCase__			,							head_mask=UpperCamelCase__			,							use_cache=UpperCamelCase__    )

    __magic_name__     ,    __magic_name__				     =       outputs.to_tuple()
    __magic_name__				     =       past_key_values[1]







def a__    (       A_,     A_,     A_,     A_=None,     A_=None,     A_=None,     A_=None,     A_=None,     ):






  '''simple docstring'''





  if attention_mask is None:
    __magic_name__				     =       tf.cast(tf.math.not_equal(A_,     config.pad_token_id					),     tf.inta					)
  if decoder_attention_mask is None:
    __magic_name__				     =       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:
    __magic_name__				     =       tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
  if decoder_head_mask is None:
    __magic_name__				     =       tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
  if cross_attn_head_mask is None:
    __magic_name__				     =       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        UpperCAmelCase_			( _A  ,       _A  ,       unittest.TestCase     ):


  '''simple docstring'''







  a__           =		(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
  a__           =		(TFMBartForConditionalGeneration,) if is_tf_available() else ()
  a__           =		(
      {
          """conversational""": TFMBartForConditionalGeneration,
          """feature-extraction""": TFMBartModel,
          """summarization""": TFMBartForConditionalGeneration,
          """text2text-generation""": TFMBartForConditionalGeneration,
          """translation""": TFMBartForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  a__           =		True
  a__           =		False
  a__           =		False
  def 							_lowercase							( self			: int			,							UpperCamelCase__			: Optional[int]			,							UpperCamelCase__			: List[str]			,							UpperCamelCase__			: Tuple			,							UpperCamelCase__			: Tuple			,							UpperCamelCase__			: Tuple    )			->	Tuple:







    """simple docstring"""






    if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
      # Exception encountered when calling layer '...'
      return True

    return False
  def 							_lowercase							( self			: Optional[Any]    )			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       TFMBartModelTester(self    )
    __magic_name__				     =       ConfigTester(self			,							config_class=UpperCamelCase__    )
  def 							_lowercase							( self			: Dict    )			->	List[str]:







    """simple docstring"""






    self.config_tester.run_common_tests()





  def 							_lowercase							( self			: Tuple    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase__    )







@require_sentencepiece
@require_tokenizers
@require_tf
class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  a__           =		[
      """ UN Chief Says There Is No Military Solution in Syria""",
  ]
  a__           =		[
      """Şeful ONU declară că nu există o soluţie militară în Siria""",
  ]
  a__           =		"""facebook/mbart-large-en-ro"""
  @cached_property
  def 							_lowercase							( self			: List[str]    )			->	Dict:







    """simple docstring"""






    return AutoTokenizer.from_pretrained(self.model_name    )
  @cached_property
  def 							_lowercase							( self			: Union[str, Any]    )			->	int:







    """simple docstring"""






    __magic_name__				     =       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
    return model
  def 							_lowercase							( self			: Dict			,							**UpperCamelCase__			: Union[str, Any]    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       self.translate_src_text(**UpperCamelCase__    )
    self.assertListEqual(self.expected_text			,							UpperCamelCase__    )
  def 							_lowercase							( self			: Union[str, Any]			,							**UpperCamelCase__			: Optional[int]    )			->	str:







    """simple docstring"""






    __magic_name__				     =       self.tokenizer(self.src_text			,							**UpperCamelCase__			,							return_tensors="""tf"""    )
    __magic_name__				     =       self.model.generate(
        model_inputs.input_ids			,							attention_mask=model_inputs.attention_mask			,							num_beams=2    )
    __magic_name__				     =       self.tokenizer.batch_decode(UpperCamelCase__			,							skip_special_tokens=UpperCamelCase__    )
    return generated_words





  @slow
  def 							_lowercase							( self			: Optional[int]    )			->	Dict:







    """simple docstring"""






    self._assert_generated_batch_equal_expected()








"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

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


a_ =			logging.get_logger(__name__)

a_ =			{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
    """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
    """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
    """bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
    """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
    """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
    """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
    """bert-large-uncased-whole-word-masking""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
    """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
    """bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-cased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-uncased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
    ),
    """wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
    # See all BERT models at https://huggingface.co/models?filter=bert
}






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """bert"""

 def __init__(    self					,						__lowerCamelCase=3_0522					,						__lowerCamelCase=768					,						__lowerCamelCase=12					,						__lowerCamelCase=12					,						__lowerCamelCase=3072					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.1					,						__lowerCamelCase=0.1					,						__lowerCamelCase=512					,						__lowerCamelCase=2					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-1_2					,						__lowerCamelCase=0					,						__lowerCamelCase="absolute"					,						__lowerCamelCase=True					,						__lowerCamelCase=None					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__(pad_token_id=__lowerCamelCase					,						**__lowerCamelCase  )

     __A    :						Dict						 =     vocab_size
     __A    :						Any						 =     hidden_size
     __A    :						str						 =     num_hidden_layers
     __A    :						int						 =     num_attention_heads
     __A    :						Optional[int]						 =     hidden_act
     __A    :						List[Any]						 =     intermediate_size
     __A    :						Tuple						 =     hidden_dropout_prob
     __A    :						Optional[int]						 =     attention_probs_dropout_prob
     __A    :						Optional[int]						 =     max_position_embeddings
     __A    :						Optional[Any]						 =     type_vocab_size
     __A    :						Optional[Any]						 =     initializer_range
     __A    :						Dict						 =     layer_norm_eps
     __A    :						Any						 =     position_embedding_type
     __A    :						Optional[int]						 =     use_cache
     __A    :						str						 =     classifier_dropout






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     if self.task == "multiple-choice":
         __A    :						str						 =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         __A    :						Tuple						 =     {0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
             ('''token_type_ids''', dynamic_axis),
         ]  )




