




"""simple docstring"""




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       a					:

     '''simple docstring'''





     lowerCAmelCase    :	Any       		=  MBartConfig
     lowerCAmelCase    :	List[Any]       		=  {}
     lowerCAmelCase    :	List[str]       		=  'gelu'
     def __init__(     self       :      int ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      Union[str, Any]=13 ,   __snake_case       :      Any=7 ,   __snake_case       :      Tuple=True ,   __snake_case       :      List[str]=False ,   __snake_case       :      int=99 ,   __snake_case       :      Optional[Any]=32 ,   __snake_case       :      str=2 ,   __snake_case       :      int=4 ,   __snake_case       :      Tuple=37 ,   __snake_case       :      List[str]=0.1 ,   __snake_case       :      Optional[Any]=0.1 ,   __snake_case       :      str=20 ,   __snake_case       :      str=2 ,   __snake_case       :      Optional[int]=1 ,   __snake_case       :      Tuple=0 ,   ):
         UpperCAmelCase_										=					parent
         UpperCAmelCase_										=					batch_size
         UpperCAmelCase_										=					seq_length
         UpperCAmelCase_										=					is_training
         UpperCAmelCase_										=					use_labels
         UpperCAmelCase_										=					vocab_size
         UpperCAmelCase_										=					hidden_size
         UpperCAmelCase_										=					num_hidden_layers
         UpperCAmelCase_										=					num_attention_heads
         UpperCAmelCase_										=					intermediate_size
         UpperCAmelCase_										=					hidden_dropout_prob
         UpperCAmelCase_										=					attention_probs_dropout_prob
         UpperCAmelCase_										=					max_position_embeddings
         UpperCAmelCase_										=					eos_token_id
         UpperCAmelCase_										=					pad_token_id
         UpperCAmelCase_										=					bos_token_id
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					ids_tensor([self.batch_size, self.seq_length - 1] ,   self.vocab_size    )
         UpperCAmelCase_										=					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    ) ,   1    )
         UpperCAmelCase_										=					tf.concat([input_ids, eos_tensor] ,   axis=1    )

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

         UpperCAmelCase_										=					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 ,   )
         UpperCAmelCase_										=					prepare_mbart_inputs_dict(__snake_case ,   __snake_case ,   __snake_case    )
         return config, inputs_dict




     def   lowerCamelCase_       (     self       :      Optional[int] ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      Optional[int]    ):
         UpperCAmelCase_										=					TFMBartModel(config=__snake_case    ).get_decoder()
         UpperCAmelCase_										=					inputs_dict['''input_ids''']

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

         # first forward pass
         UpperCAmelCase_										=					model(__snake_case ,   attention_mask=__snake_case ,   head_mask=__snake_case ,   use_cache=__snake_case    )

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




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Any	,					__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						str	,					__UpperCamelCase      :						List[Any]=None	,					__UpperCamelCase      :						List[Any]=None	,					__UpperCamelCase      :						Dict=None	,					__UpperCamelCase      :						str=None	,					__UpperCamelCase      :						Any=None	,					)  ->							Union[str, Any]:
    if attention_mask is None:
        UpperCAmelCase_										=					tf.cast(tf.math.not_equal(__UpperCamelCase	,					config.pad_token_id				)	,					tf.inta				)
    if decoder_attention_mask is None:
        UpperCAmelCase_										=					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:
        UpperCAmelCase_										=					tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
    if decoder_head_mask is None:
        UpperCAmelCase_										=					tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
    if cross_attn_head_mask is None:
        UpperCAmelCase_										=					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       a					(       _A     ,       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	str       		=  (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
     lowerCAmelCase    :	Optional[int]       		=  (TFMBartForConditionalGeneration,) if is_tf_available() else ()
     lowerCAmelCase    :	str       		=  (
         {
             'conversational': TFMBartForConditionalGeneration,
             'feature-extraction': TFMBartModel,
             'summarization': TFMBartForConditionalGeneration,
             'text2text-generation': TFMBartForConditionalGeneration,
             'translation': TFMBartForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     lowerCAmelCase    :	str       		=  True
     lowerCAmelCase    :	Optional[Any]       		=  False
     lowerCAmelCase    :	Optional[Any]       		=  False
     def   lowerCamelCase_       (     self       :      Optional[int] ,   __snake_case       :      Optional[int] ,   __snake_case       :      List[str] ,   __snake_case       :      Optional[int] ,   __snake_case       :      str ,   __snake_case       :      int    ):
         if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
             # Exception encountered when calling layer '...'
             return True

         return False
     def   lowerCamelCase_       (     self       :      List[str]    ):
         UpperCAmelCase_										=					TFMBartModelTester(self    )
         UpperCAmelCase_										=					ConfigTester(self ,   config_class=__snake_case    )
     def   lowerCamelCase_       (     self       :      str    ):
         self.config_tester.run_common_tests()




     def   lowerCamelCase_       (     self       :      List[str]    ):
         UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*__snake_case    )



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

     '''simple docstring'''





     lowerCAmelCase    :	str       		=  [
         ' UN Chief Says There Is No Military Solution in Syria',
     ]
     lowerCAmelCase    :	Dict       		=  [
         'Şeful ONU declară că nu există o soluţie militară în Siria',
     ]
     lowerCAmelCase    :	List[Any]       		=  'facebook/mbart-large-en-ro'
     @cached_property
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         return AutoTokenizer.from_pretrained(self.model_name    )
     @cached_property
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         UpperCAmelCase_										=					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
         return model
     def   lowerCamelCase_       (     self       :      Optional[Any] ,   **__snake_case       :      List[str]    ):
         UpperCAmelCase_										=					self.translate_src_text(**__snake_case    )
         self.assertListEqual(self.expected_text ,   __snake_case    )
     def   lowerCamelCase_       (     self       :      Tuple ,   **__snake_case       :      Union[str, Any]    ):
         UpperCAmelCase_										=					self.tokenizer(self.src_text ,   **__snake_case ,   return_tensors='''tf'''    )
         UpperCAmelCase_										=					self.model.generate(
             model_inputs.input_ids ,   attention_mask=model_inputs.attention_mask ,   num_beams=2    )
         UpperCAmelCase_										=					self.tokenizer.batch_decode(__snake_case ,   skip_special_tokens=__snake_case    )
         return generated_words




     @slow
     def   lowerCamelCase_       (     self       :      str    ):
         self._assert_generated_batch_equal_expected()











import baseaa




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str				)  ->							bytes:
    return baseaa.baaencode(string.encode('''utf-8'''				)				)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						bytes				)  ->							str:
    return baseaa.baadecode(__UpperCamelCase				).decode('''utf-8'''				)


if __name__ == "__main__":
 _lowerCamelCase      			=						'Hello World!'
 _lowerCamelCase      			=						baseaa_encode(test)
 print(encoded)

 _lowerCamelCase      			=						baseaa_decode(encoded)
 print(decoded)





