





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__       :
      _UpperCAmelCase     :List[Any]						   =       MBartConfig
      _UpperCAmelCase     :Dict						   =       {}
      _UpperCAmelCase     :Dict						   =       'gelu'





      def __init__(			self	,							A_	,							A_=13	,							A_=7	,							A_=True	,							A_=False	,							A_=99	,							A_=32	,							A_=2	,							A_=4	,							A_=37	,							A_=0.1	,							A_=0.1	,							A_=20	,							A_=2	,							A_=1	,							A_=0	,							):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       parent
       UpperCamelCase     : Optional[Any]	     =       batch_size
       UpperCamelCase     : Optional[int]	     =       seq_length
       UpperCamelCase     : Dict	     =       is_training
       UpperCamelCase     : int	     =       use_labels
       UpperCamelCase     : Union[str, Any]	     =       vocab_size
       UpperCamelCase     : Dict	     =       hidden_size
       UpperCamelCase     : Dict	     =       num_hidden_layers
       UpperCamelCase     : Optional[Any]	     =       num_attention_heads
       UpperCamelCase     : Dict	     =       intermediate_size
       UpperCamelCase     : List[str]	     =       hidden_dropout_prob
       UpperCamelCase     : List[str]	     =       attention_probs_dropout_prob
       UpperCamelCase     : Union[str, Any]	     =       max_position_embeddings
       UpperCamelCase     : List[Any]	     =       eos_token_id
       UpperCamelCase     : List[Any]	     =       pad_token_id
       UpperCamelCase     : List[str]	     =       bos_token_id





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       ids_tensor([self.batch_size, self.seq_length - 1]	,							self.vocab_size       )
       UpperCamelCase     : Optional[int]	     =       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )	,							1       )
       UpperCamelCase     : int	     =       tf.concat([input_ids, eos_tensor]	,							axis=1       )

       UpperCamelCase     : List[Any]	     =       ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size       )

       UpperCamelCase     : List[Any]	     =       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     : Optional[int]	     =       prepare_mbart_inputs_dict(A_	,							A_	,							A_       )
       return config, inputs_dict





      def 	__UpperCamelCase(			self	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       TFMBartModel(config=A_       ).get_decoder()
       UpperCamelCase     : Any	     =       inputs_dict["input_ids"]

       UpperCamelCase     : Union[str, Any]	     =       input_ids[:1, :]
       UpperCamelCase     : Dict	     =       inputs_dict["attention_mask"][:1, :]
       UpperCamelCase     : Optional[int]	     =       inputs_dict["head_mask"]
       UpperCamelCase     : Tuple	     =       1

       # first forward pass
       UpperCamelCase     : Union[str, Any]	     =       model(A_	,							attention_mask=A_	,							head_mask=A_	,							use_cache=A_       )

       UpperCamelCase     ,   UpperCamelCase     : Optional[int]	     =       outputs.to_tuple()
       UpperCamelCase     : str	     =       past_key_values[1]



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       )						->			Union[str, Any]:
 if attention_mask is None:
  UpperCamelCase     : Optional[Any]	     =       tf.cast(tf.math.not_equal(_lowerCAmelCase							,       config.pad_token_id					)							,       tf.inta					)
 if decoder_attention_mask is None:
  UpperCamelCase     : Union[str, Any]	     =       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     : Optional[Any]	     =       tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
 if decoder_head_mask is None:
  UpperCamelCase     : Optional[Any]	     =       tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
 if cross_attn_head_mask is None:
  UpperCamelCase     : List[Any]	     =       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__       (	__snake_case	,       __snake_case	,       unittest.TestCase      ):
      _UpperCAmelCase     :Optional[int]						   =       (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
      _UpperCAmelCase     :List[Any]						   =       (TFMBartForConditionalGeneration,) if is_tf_available() else ()
      _UpperCAmelCase     :List[str]						   =       (
          {
              'conversational': TFMBartForConditionalGeneration,
              'feature-extraction': TFMBartModel,
              'summarization': TFMBartForConditionalGeneration,
              'text2text-generation': TFMBartForConditionalGeneration,
              'translation': TFMBartForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      _UpperCAmelCase     :str						   =       True
      _UpperCAmelCase     :Optional[Any]						   =       False
      _UpperCAmelCase     :List[str]						   =       False





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




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

       return False





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       TFMBartModelTester(self       )
       UpperCamelCase     : Tuple	     =       ConfigTester(self	,							config_class=A_       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       self.config_tester.run_common_tests()





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*A_       )



@require_sentencepiece
@require_tokenizers
@require_tf
class  A__       (	unittest.TestCase      ):
      _UpperCAmelCase     :List[Any]						   =       [
          ' UN Chief Says There Is No Military Solution in Syria',
      ]
      _UpperCAmelCase     :int						   =       [
          'Şeful ONU declară că nu există o soluţie militară în Siria',
      ]
      _UpperCAmelCase     :str						   =       'facebook/mbart-large-en-ro'





      @cached_property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return AutoTokenizer.from_pretrained(self.model_name       )





      @cached_property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
       return model





      def 	__UpperCamelCase(			self	,							**A_       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[Any]	     =       self.translate_src_text(**A_       )
       self.assertListEqual(self.expected_text	,							A_       )





      def 	__UpperCamelCase(			self	,							**A_       ):
       '''simple docstring'''




       UpperCamelCase     : str	     =       self.tokenizer(self.src_text	,							**A_	,							return_tensors="tf"       )
       UpperCamelCase     : List[Any]	     =       self.model.generate(
           model_inputs.input_ids	,							attention_mask=model_inputs.attention_mask	,							num_beams=2       )
       UpperCamelCase     : int	     =       self.tokenizer.batch_decode(A_	,							skip_special_tokens=A_       )
       return generated_words





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       self._assert_generated_batch_equal_expected()







def 		A_    (					_lowerCAmelCase = "The quick brown fox jumps over the lazy dog"							,       )						->			bool:
 UpperCamelCase     : Union[str, Any]	     =       set()

 # Replace all the whitespace in our sentence
 UpperCamelCase     : Union[str, Any]	     =       input_str.replace(" "							,       ""					)
 for alpha in input_str:
  if "a" <= alpha.lower() <= "z":
   frequency.add(alpha.lower()					)
 return len(_lowerCAmelCase					) == 26



def 		A_    (					_lowerCAmelCase = "The quick brown fox jumps over the lazy dog"							,       )						->			bool:
 UpperCamelCase     : List[Any]	     =       [False] * 26
 for char in input_str:
  if char.islower():
   UpperCamelCase     : Tuple	     =       True
  elif char.isupper():
   UpperCamelCase     : str	     =       True
 return all(_lowerCAmelCase					)



def 		A_    (					_lowerCAmelCase = "The quick brown fox jumps over the lazy dog"							,       )						->			bool:
 return len({char for char in input_str.lower() if char.isalpha()}					) == 26



def 		A_    (					)						->			None:
 from timeit import timeit

 UpperCamelCase     : Tuple	     =       "from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
 print(timeit("is_pangram()"							,       setup=_lowerCAmelCase					)					)
 print(timeit("is_pangram_faster()"							,       setup=_lowerCAmelCase					)					)
 print(timeit("is_pangram_fastest()"							,       setup=_lowerCAmelCase					)					)
 # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
 # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()
