

from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, 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, TFMarianModel, TFMarianMTModel







@require_tf
class 		__magic_name__     :

   '''simple docstring'''




   lowerCamelCase__					:      Any        =  MarianConfig
   lowerCamelCase__					:      Optional[Any]        =  {}
   lowerCamelCase__					:      Any        =  'gelu'



   def __init__(							self,       lowercase_,       lowercase_=13,       lowercase_=7,       lowercase_=True,       lowercase_=False,       lowercase_=99,       lowercase_=32,       lowercase_=2,       lowercase_=4,       lowercase_=37,       lowercase_=0.1,       lowercase_=0.1,       lowercase_=20,       lowercase_=2,       lowercase_=1,       lowercase_=0,       )				->       Tuple:






     """simple docstring"""






     a__    =parent
     a__    =batch_size
     a__    =seq_length
     a__    =is_training
     a__    =use_labels
     a__    =vocab_size
     a__    =hidden_size
     a__    =num_hidden_layers
     a__    =num_attention_heads
     a__    =intermediate_size

     a__    =hidden_dropout_prob
     a__    =attention_probs_dropout_prob
     a__    =max_position_embeddings
     a__    =eos_token_id
     a__    =pad_token_id
     a__    =bos_token_id



   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






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

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

     a__    =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,       )
     a__    =prepare_marian_inputs_dict(lowercase_,       lowercase_,       lowercase_			)
     return config, inputs_dict



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_			)				->       int:






     """simple docstring"""






     a__    =TFMarianModel(config=lowercase_			).get_decoder()
     a__    =inputs_dict['''input_ids''']

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

     # first forward pass
     a__    =model(lowercase_,       attention_mask=lowercase_,       head_mask=lowercase_,       use_cache=lowercase_			)

     a__, a__    =outputs.to_tuple()

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

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

     a__    =model(lowercase_,       attention_mask=lowercase_			)[0]
     a__    =model(lowercase_,       attention_mask=lowercase_,       past_key_values=lowercase_			)[0]

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

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

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





def      UpperCAmelCase__							(     _A :					Dict      , _A :					Union[str, Any]      , _A :					Dict      , _A :					Tuple=None      , _A :					str=None      , _A :					List[Any]=None      , _A :					Tuple=None      , _A :					Any=None      , ):





  '''simple docstring'''







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

   '''simple docstring'''




   lowerCamelCase__					:      Optional[int]        =  (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
   lowerCamelCase__					:      Optional[Any]        =  (TFMarianMTModel,) if is_tf_available() else ()
   lowerCamelCase__					:      List[Any]        =  (
       {
           'conversational': TFMarianMTModel,
           'feature-extraction': TFMarianModel,
           'summarization': TFMarianMTModel,
           'text2text-generation': TFMarianMTModel,
           'translation': TFMarianMTModel,
       }
       if is_tf_available()
       else {}
   )
   lowerCamelCase__					:      str        =  True
   lowerCamelCase__					:      int        =  False
   lowerCamelCase__					:      Union[str, Any]        =  False



   def 					_UpperCAmelCase					(							self			)				->       Dict:






     """simple docstring"""






     a__    =TFMarianModelTester(self			)
     a__    =ConfigTester(self,       config_class=lowercase_			)



   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






     self.config_tester.run_common_tests()



   def 					_UpperCAmelCase					(							self			)				->       Tuple:






     """simple docstring"""






     a__    =self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*lowercase_			)







@require_tf
class 		__magic_name__     (				unittest.TestCase				):

   '''simple docstring'''




   lowerCamelCase__					:      List[Any]        =  10_00  # show more chars for failing integration tests



   @classmethod
   def 					_UpperCAmelCase					(							cls			)				->       None:






     """simple docstring"""






     a__    =F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
     return cls



   @cached_property
   def 					_UpperCAmelCase					(							self			)				->       MarianTokenizer:






     """simple docstring"""






     return AutoTokenizer.from_pretrained(self.model_name			)



   @property
   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     return self.tokenizer.eos_token_id



   @cached_property
   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     warnings.simplefilter('''error'''			)
     a__    =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
     assert isinstance(lowercase_,       lowercase_			)
     a__    =model.config
     self.assertListEqual(c.bad_words_ids,       [[c.pad_token_id]]			)
     self.assertEqual(c.max_length,       512			)
     self.assertEqual(c.decoder_start_token_id,       c.pad_token_id			)
     return model



   def 					_UpperCAmelCase					(							self,       **lowercase_			)				->       str:






     """simple docstring"""






     a__    =self.translate_src_text(**lowercase_			)
     self.assertListEqual(self.expected_text,       lowercase_			)



   def 					_UpperCAmelCase					(							self,       **lowercase_			)				->       Any:






     """simple docstring"""






     a__    =self.tokenizer(self.src_text,       **lowercase_,       padding=lowercase_,       return_tensors='''tf'''			)
     a__    =self.model.generate(
         model_inputs.input_ids,       attention_mask=model_inputs.attention_mask,       num_beams=2,       max_length=128			)
     a__    =self.tokenizer.batch_decode(generated_ids.numpy(),       skip_special_tokens=lowercase_			)
     return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      Union[str, Any]        =  'mt'
   lowerCamelCase__					:      Union[str, Any]        =  'en'
   lowerCamelCase__					:      Any        =  ['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
   lowerCamelCase__					:      List[str]        =  ['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']



   @unittest.skip('''Skipping until #12647 is resolved.'''			)
   @slow
   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      List[str]        =  'en'
   lowerCamelCase__					:      Any        =  'zh'
   lowerCamelCase__					:      Tuple        =  ['My name is Wolfgang and I live in Berlin']
   lowerCamelCase__					:      List[Any]        =  ['我叫沃尔夫冈 我住在柏林']



   @unittest.skip('''Skipping until #12647 is resolved.'''			)
   @slow
   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






     self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      Dict        =  'en'
   lowerCamelCase__					:      Tuple        =  'ROMANCE'
   lowerCamelCase__					:      Any        =  [
       '>>fr<< Don\'t spend so much time watching TV.',
       '>>pt<< Your message has been sent.',
       '>>es<< He\'s two years older than me.',
   ]
   lowerCamelCase__					:      Optional[int]        =  [
       'Ne passez pas autant de temps à regarder la télé.',
       'A sua mensagem foi enviada.',
       'Es dos años más viejo que yo.',
   ]



   @unittest.skip('''Skipping until #12647 is resolved.'''			)
   @slow
   def 					_UpperCAmelCase					(							self			)				->       Optional[int]:






     """simple docstring"""






     self._assert_generated_batch_equal_expected()



   @unittest.skip('''Skipping until #12647 is resolved.'''			)
   @slow
   def 					_UpperCAmelCase					(							self			)				->       List[str]:






     """simple docstring"""






     a__    =TranslationPipeline(self.model,       self.tokenizer,       framework='''tf'''			)
     a__    =pipeline(self.src_text			)
     self.assertEqual(self.expected_text,       [x['''translation_text'''] for x in output]			)



def      UpperCAmelCase__							(     _A :					Optional[int]      , _A :					Optional[int]				):





  '''simple docstring'''







  sequence.append(_A				)

  a__    =0
  while sequence[index] != target:
    index += 1

  sequence.pop()

  if index == len(_A				):
    return None

  return index


if __name__ == "__main__":
       lowerCamelCase 	=   input('''Enter numbers separated by comma:\n''').strip()
       lowerCamelCase 	=   [int(item) for item in user_input.split(''',''')]

       lowerCamelCase 	=   input('''Enter a single number to be found in the list:\n''')
       lowerCamelCase 	=   int(target_input)
       lowerCamelCase 	=   sentinel_linear_search(sequence, target)
       if result is not None:
              print(f"""{target} found at positions: {result}""")
       else:
              print('''Not found''')
