


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       lowerCamelCase__   :
     SCREAMING_SNAKE_CASE__      			=			MBartConfig
     SCREAMING_SNAKE_CASE__      			=			{}
     SCREAMING_SNAKE_CASE__      			=			'''gelu'''





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase=1_3		,     UpperCAmelCase=7		,     UpperCAmelCase=True		,     UpperCAmelCase=False		,     UpperCAmelCase=9_9		,     UpperCAmelCase=3_2		,     UpperCAmelCase=2		,     UpperCAmelCase=4		,     UpperCAmelCase=3_7		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=2_0		,     UpperCAmelCase=2		,     UpperCAmelCase=1		,     UpperCAmelCase=0		,     )     ->   Optional[Any]:
      _lowercase							      =parent
      _lowercase							      =batch_size
      _lowercase							      =seq_length
      _lowercase							      =is_training
      _lowercase							      =use_labels
      _lowercase							      =vocab_size
      _lowercase							      =hidden_size
      _lowercase							      =num_hidden_layers
      _lowercase							      =num_attention_heads
      _lowercase							      =intermediate_size
      _lowercase							      =hidden_dropout_prob
      _lowercase							      =attention_probs_dropout_prob
      _lowercase							      =max_position_embeddings
      _lowercase							      =eos_token_id
      _lowercase							      =pad_token_id
      _lowercase							      =bos_token_id





     def      __A					(self       )     ->   Union[str, Any]:
      _lowercase							      =ids_tensor([self.batch_size, self.seq_length - 1]		,     self.vocab_size       )
      _lowercase							      =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )		,     1       )
      _lowercase							      =tf.concat([input_ids, eos_tensor]		,     axis=1       )

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

      _lowercase							      =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		,     )
      _lowercase							      =prepare_mbart_inputs_dict(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )
      return config, inputs_dict





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Tuple:
      _lowercase							      =TFMBartModel(config=UpperCAmelCase       ).get_decoder()
      _lowercase							      =inputs_dict['''input_ids''']

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

      # first forward pass
      _lowercase							      =model(UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     head_mask=UpperCAmelCase		,     use_cache=UpperCAmelCase       )

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



def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case=None     ,						__snake_case=None     ,						__snake_case=None     ,						__snake_case=None     ,						__snake_case=None     ,						)     ->			Dict:







 """simple docstring"""


 if attention_mask is None:
  _lowercase							      =tf.cast(tf.math.not_equal(__snake_case     ,						config.pad_token_id						)     ,						tf.inta						)
 if decoder_attention_mask is None:
  _lowercase							      =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:
  _lowercase							      =tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
 if decoder_head_mask is None:
  _lowercase							      =tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
 if cross_attn_head_mask is None:
  _lowercase							      =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       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
     SCREAMING_SNAKE_CASE__      			=			(TFMBartForConditionalGeneration,) if is_tf_available() else ()
     SCREAMING_SNAKE_CASE__      			=			(
         {
             '''conversational''': TFMBartForConditionalGeneration,
             '''feature-extraction''': TFMBartModel,
             '''summarization''': TFMBartForConditionalGeneration,
             '''text2text-generation''': TFMBartForConditionalGeneration,
             '''translation''': TFMBartForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     SCREAMING_SNAKE_CASE__      			=			True
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   List[str]:
      if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
       # Exception encountered when calling layer '...'
       return True

      return False





     def      __A					(self       )     ->   Tuple:
      _lowercase							      =TFMBartModelTester(self       )
      _lowercase							      =ConfigTester(self		,     config_class=UpperCAmelCase       )





     def      __A					(self       )     ->   str:
      self.config_tester.run_common_tests()





     def      __A					(self       )     ->   List[Any]:
      _lowercase							      =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       lowerCamelCase__   (       unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			[
         ''' UN Chief Says There Is No Military Solution in Syria''',
     ]
     SCREAMING_SNAKE_CASE__      			=			[
         '''Şeful ONU declară că nu există o soluţie militară în Siria''',
     ]
     SCREAMING_SNAKE_CASE__      			=			'''facebook/mbart-large-en-ro'''





     @cached_property
     def      __A					(self       )     ->   Optional[Any]:
      return AutoTokenizer.from_pretrained(self.model_name       )





     @cached_property
     def      __A					(self       )     ->   Optional[int]:
      _lowercase							      =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
      return model





     def      __A					(self		,     **UpperCAmelCase       )     ->   Dict:
      _lowercase							      =self.translate_src_text(**UpperCAmelCase       )
      self.assertListEqual(self.expected_text		,     UpperCAmelCase       )





     def      __A					(self		,     **UpperCAmelCase       )     ->   Tuple:
      _lowercase							      =self.tokenizer(self.src_text		,     **UpperCAmelCase		,     return_tensors='''tf'''       )
      _lowercase							      =self.model.generate(
          model_inputs.input_ids		,     attention_mask=model_inputs.attention_mask		,     num_beams=2       )
      _lowercase							      =self.tokenizer.batch_decode(UpperCAmelCase		,     skip_special_tokens=UpperCAmelCase       )
      return generated_words





     @slow
     def      __A					(self       )     ->   Optional[int]:
      self._assert_generated_batch_equal_expected()





def 				UpperCAmelCase_					(							__snake_case						)     ->			str:







 """simple docstring"""


 _lowercase							      =0

 # if input_string is "aba" than new_input_string become "a|b|a"
 _lowercase							      =''''''
 _lowercase							      =''''''

 # append each character + "|" in new_string for range(0, length-1)
 for i in input_string[: len(__snake_case						) - 1]:
  new_input_string += i + "|"
 # append last character
 new_input_string += input_string[-1]

 # we will store the starting and ending of previous furthest ending palindromic
 # substring
 _lowercase      ,    _lowercase							      =0, 0

 # length[i] shows the length of palindromic substring with center i
 _lowercase							      =[1 for i in range(len(__snake_case						)						)]

 # for each character in new_string find corresponding palindromic string
 _lowercase							      =0
 for j in range(len(__snake_case						)						):
  _lowercase							      =1 if j > r else min(length[l + r - j] // 2     ,						r - j + 1						)
  while (
      j - k >= 0
      and j + k < len(__snake_case						)
      and new_input_string[k + j] == new_input_string[j - k]
  ):
   k += 1

  _lowercase							      =2 * k - 1

  # does this string is ending after the previously explored end (that is r) ?
  # if yes the update the new r to the last index of this
  if j + k - 1 > r:
   _lowercase							      =j - k + 1  # noqa: E741
   _lowercase							      =j + k - 1

  # update max_length and start position
  if max_length < length[j]:
   _lowercase							      =length[j]
   _lowercase							      =j

    # create that string
 _lowercase							      =new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
 for i in s:
  if i != "|":
   output_string += i

 return output_string


if __name__ == "__main__":
  import doctest

  doctest.testmod()

