
"""simple docstring"""




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 	_lowercase    :


     '''simple docstring'''





     _A		    =				MarianConfig
     _A		    =				{}
     _A		    =				'gelu'



     def __init__(   self			,      __UpperCamelCase			,      __UpperCamelCase=13			,      __UpperCamelCase=7			,      __UpperCamelCase=True			,      __UpperCamelCase=False			,      __UpperCamelCase=99			,      __UpperCamelCase=32			,      __UpperCamelCase=2			,      __UpperCamelCase=4			,      __UpperCamelCase=37			,      __UpperCamelCase=0.1			,      __UpperCamelCase=0.1			,      __UpperCamelCase=20			,      __UpperCamelCase=2			,      __UpperCamelCase=1			,      __UpperCamelCase=0			,      )->       Union[str, Any]:
          UpperCAmelCase__				:			Optional[Any]							=    parent
          UpperCAmelCase__				:			Tuple							=    batch_size
          UpperCAmelCase__				:			Optional[int]							=    seq_length
          UpperCAmelCase__				:			Tuple							=    is_training
          UpperCAmelCase__				:			List[Any]							=    use_labels
          UpperCAmelCase__				:			int							=    vocab_size
          UpperCAmelCase__				:			Optional[int]							=    hidden_size
          UpperCAmelCase__				:			Union[str, Any]							=    num_hidden_layers
          UpperCAmelCase__				:			str							=    num_attention_heads
          UpperCAmelCase__				:			List[str]							=    intermediate_size

          UpperCAmelCase__				:			Union[str, Any]							=    hidden_dropout_prob
          UpperCAmelCase__				:			Optional[int]							=    attention_probs_dropout_prob
          UpperCAmelCase__				:			List[Any]							=    max_position_embeddings
          UpperCAmelCase__				:			str							=    eos_token_id
          UpperCAmelCase__				:			Tuple							=    pad_token_id
          UpperCAmelCase__				:			Optional[Any]							=    bos_token_id



     def 			lowerCAmelCase__							(   self	)->       Optional[Any]:
          UpperCAmelCase__				:			List[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__				:			Tuple							=    tf.concat([input_ids, eos_tensor]			,      axis=1	)

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

          UpperCAmelCase__				:			Optional[int]							=    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__				:			List[Any]							=    prepare_marian_inputs_dict(__UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase	)
          return config, inputs_dict



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase	)->       Tuple:
          UpperCAmelCase__				:			str							=    TFMarianModel(config=__UpperCamelCase	).get_decoder()
          UpperCAmelCase__				:			Optional[Any]							=    inputs_dict["input_ids"]

          UpperCAmelCase__				:			List[Any]							=    input_ids[:1, :]
          UpperCAmelCase__				:			Tuple							=    inputs_dict["attention_mask"][:1, :]
          UpperCAmelCase__				:			List[Any]							=    inputs_dict["head_mask"]
          UpperCAmelCase__				:			str							=    1

          # first forward pass
          UpperCAmelCase__				:			List[str]							=    model(__UpperCamelCase			,      attention_mask=__UpperCamelCase			,      head_mask=__UpperCamelCase			,      use_cache=__UpperCamelCase	)

          UpperCAmelCase__				,						UpperCAmelCase__				:			str							=    outputs.to_tuple()

          # create hypothetical next token and extent to next_input_ids
          UpperCAmelCase__				:			int							=    ids_tensor((self.batch_size, 3)			,      config.vocab_size	)
          UpperCAmelCase__				:			List[str]							=    tf.cast(ids_tensor((self.batch_size, 3)			,      2	)			,      tf.inta	)

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

          UpperCAmelCase__				:			Tuple							=    model(__UpperCamelCase			,      attention_mask=__UpperCamelCase	)[0]
          UpperCAmelCase__				:			str							=    model(__UpperCamelCase			,      attention_mask=__UpperCamelCase			,      past_key_values=__UpperCamelCase	)[0]

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

          # select random slice
          UpperCAmelCase__				:			List[str]							=    int(ids_tensor((1,)			,      output_from_past.shape[-1]	)	)
          UpperCAmelCase__				:			Optional[int]							=    output_from_no_past[:, -3:, random_slice_idx]
          UpperCAmelCase__				:			Tuple							=    output_from_past[:, :, random_slice_idx]

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





def 				a__			(					lowerCAmelCase						:							Dict							,     lowerCAmelCase						:							Optional[Any]							,     lowerCAmelCase						:							Union[str, Any]							,     lowerCAmelCase						:							str=None							,     lowerCAmelCase						:							Tuple=None							,     lowerCAmelCase						:							str=None							,     lowerCAmelCase						:							Any=None							,     lowerCAmelCase						:							List[Any]=None							,     ):






     '''simple docstring'''
     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__				:			Optional[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__				:			Tuple							=    tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
     if decoder_head_mask is None:
          UpperCAmelCase__				:			Tuple							=    tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
     if cross_attn_head_mask is None:
          UpperCAmelCase__				:			Optional[int]							=    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 	_lowercase    (							lowerCAmelCase_						, lowerCAmelCase_						, unittest.TestCase					):


     '''simple docstring'''





     _A		    =				(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
     _A		    =				(TFMarianMTModel,) if is_tf_available() else ()
     _A		    =				(
         {
             'conversational': TFMarianMTModel,
             'feature-extraction': TFMarianModel,
             'summarization': TFMarianMTModel,
             'text2text-generation': TFMarianMTModel,
             'translation': TFMarianMTModel,
         }
         if is_tf_available()
         else {}
     )
     _A		    =				True
     _A		    =				False
     _A		    =				False



     def 			lowerCAmelCase__							(   self	)->       Any:
          UpperCAmelCase__				:			Dict							=    TFMarianModelTester(self	)
          UpperCAmelCase__				:			List[Any]							=    ConfigTester(self			,      config_class=__UpperCamelCase	)



     def 			lowerCAmelCase__							(   self	)->       List[Any]:
          self.config_tester.run_common_tests()



     def 			lowerCAmelCase__							(   self	)->       Tuple:
          UpperCAmelCase__				:			List[Any]							=    self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*__UpperCamelCase	)







@require_tf
class 	_lowercase    (							unittest.TestCase					):


     '''simple docstring'''





     _A		    =				1000  # show more chars for failing integration tests



     @classmethod
     def 			lowerCAmelCase__							(   cls	)->       None:
          UpperCAmelCase__				:			Union[str, Any]							=    F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
          return cls



     @cached_property
     def 			lowerCAmelCase__							(   self	)->       MarianTokenizer:
          return AutoTokenizer.from_pretrained(self.model_name	)



     @property
     def 			lowerCAmelCase__							(   self	)->       int:
          return self.tokenizer.eos_token_id



     @cached_property
     def 			lowerCAmelCase__							(   self	)->       str:
          warnings.simplefilter("error"	)
          UpperCAmelCase__				:			TFMarianMTModel							=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
          assert isinstance(__UpperCamelCase			,      __UpperCamelCase	)
          UpperCAmelCase__				:			List[str]							=    model.config
          self.assertListEqual(c.bad_words_ids			,      [[c.pad_token_id]]	)
          self.assertEqual(c.max_length			,      5_12	)
          self.assertEqual(c.decoder_start_token_id			,      c.pad_token_id	)
          return model



     def 			lowerCAmelCase__							(   self			,      **__UpperCamelCase	)->       Optional[int]:
          UpperCAmelCase__				:			int							=    self.translate_src_text(**__UpperCamelCase	)
          self.assertListEqual(self.expected_text			,      __UpperCamelCase	)



     def 			lowerCAmelCase__							(   self			,      **__UpperCamelCase	)->       Dict:
          UpperCAmelCase__				:			Tuple							=    self.tokenizer(self.src_text			,      **__UpperCamelCase			,      padding=__UpperCamelCase			,      return_tensors="tf"	)
          UpperCAmelCase__				:			Optional[Any]							=    self.model.generate(
              model_inputs.input_ids			,      attention_mask=model_inputs.attention_mask			,      num_beams=2			,      max_length=1_28	)
          UpperCAmelCase__				:			Optional[int]							=    self.tokenizer.batch_decode(generated_ids.numpy()			,      skip_special_tokens=__UpperCamelCase	)
          return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class 	_lowercase    (							lowerCAmelCase_					):


     '''simple docstring'''





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



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







@require_sentencepiece
@require_tokenizers
@require_tf
class 	_lowercase    (							lowerCAmelCase_					):


     '''simple docstring'''





     _A		    =				'en'
     _A		    =				'zh'
     _A		    =				['My name is Wolfgang and I live in Berlin']
     _A		    =				['我叫沃尔夫冈 我住在柏林']



     @unittest.skip("Skipping until #12647 is resolved."	)
     @slow
     def 			lowerCAmelCase__							(   self	)->       Tuple:
          self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 	_lowercase    (							lowerCAmelCase_					):


     '''simple docstring'''





     _A		    =				'en'
     _A		    =				'ROMANCE'
     _A		    =				[
         '>>fr<< Don\'t spend so much time watching TV.',
         '>>pt<< Your message has been sent.',
         '>>es<< He\'s two years older than me.',
     ]
     _A		    =				[
         '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 			lowerCAmelCase__							(   self	)->       Dict:
          self._assert_generated_batch_equal_expected()



     @unittest.skip("Skipping until #12647 is resolved."	)
     @slow
     def 			lowerCAmelCase__							(   self	)->       Dict:
          UpperCAmelCase__				:			List[str]							=    TranslationPipeline(self.model			,      self.tokenizer			,      framework="tf"	)
          UpperCAmelCase__				:			List[Any]							=    pipeline(self.src_text	)
          self.assertEqual(self.expected_text			,      [x["translation_text"] for x in output]	)





"""simple docstring"""




def 				a__			(					lowerCAmelCase						:							int		):






     '''simple docstring'''
     if number != int(lowerCAmelCase		):
          raise ValueError("factorial() only accepts integral values"		)
     if number < 0:
          raise ValueError("factorial() not defined for negative values"		)
     UpperCAmelCase__				:			int							=    1
     for i in range(1							,     number + 1		):
          value *= i
     return value



def 				a__			(					lowerCAmelCase						:							int		):






     '''simple docstring'''
     if not isinstance(lowerCAmelCase							,     lowerCAmelCase		):
          raise ValueError("factorial() only accepts integral values"		)
     if n < 0:
          raise ValueError("factorial() not defined for negative values"		)
     return 1 if n == 0 or n == 1 else n * factorial(n - 1		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    A__   :						Any 							=     int(input("""Enter a positive integer: """).strip() or 0)
    print(f"""factorial{n} is {factorial(n)}""")



