





"""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 			a       :
 snake_case__								=					MarianConfig
 snake_case__								=					{}
 snake_case__								=					'''gelu'''



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







  """simple docstring"""






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

  lowerCAmelCase													=   hidden_dropout_prob
  lowerCAmelCase													=   attention_probs_dropout_prob
  lowerCAmelCase													=   max_position_embeddings
  lowerCAmelCase													=   eos_token_id
  lowerCAmelCase													=   pad_token_id
  lowerCAmelCase													=   bos_token_id



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






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

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

  lowerCAmelCase													=   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							,					)
  lowerCAmelCase													=   prepare_marian_inputs_dict(__lowerCamelCase							,					__lowerCamelCase							,					__lowerCamelCase    )
  return config, inputs_dict



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   TFMarianModel(config=__lowerCamelCase    ).get_decoder()
  lowerCAmelCase													=   inputs_dict["input_ids"]

  lowerCAmelCase													=   input_ids[:1, :]
  lowerCAmelCase													=   inputs_dict["attention_mask"][:1, :]
  lowerCAmelCase													=   inputs_dict["head_mask"]
  lowerCAmelCase													=   1

  # first forward pass
  lowerCAmelCase													=   model(__lowerCamelCase							,					attention_mask=__lowerCamelCase							,					head_mask=__lowerCamelCase							,					use_cache=__lowerCamelCase    )

  lowerCAmelCase													=   outputs.to_tuple()

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

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

  lowerCAmelCase													=   model(__lowerCamelCase							,					attention_mask=__lowerCamelCase    )[0]
  lowerCAmelCase													=   model(__lowerCamelCase							,					attention_mask=__lowerCamelCase							,					past_key_values=__lowerCamelCase    )[0]

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

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

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


def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						str     ,    _UpperCAmelCase				:						Tuple     ,    _UpperCAmelCase				:						str     ,    _UpperCAmelCase				:						List[Any]=None     ,    _UpperCAmelCase				:						Tuple=None     ,    _UpperCAmelCase				:						Optional[int]=None     ,    _UpperCAmelCase				:						List[str]=None     ,    _UpperCAmelCase				:						Any=None     ,    ):
 if attention_mask is None:
  lowerCAmelCase													=   tf.cast(tf.math.not_equal(UpperCamelCase__     ,    config.pad_token_id )     ,    tf.inta )
 if decoder_attention_mask is None:
  lowerCAmelCase													=   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:
  lowerCAmelCase													=   tf.ones((config.encoder_layers, config.encoder_attention_heads) )
 if decoder_head_mask is None:
  lowerCAmelCase													=   tf.ones((config.decoder_layers, config.decoder_attention_heads) )
 if cross_attn_head_mask is None:
  lowerCAmelCase													=   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     ):
 snake_case__								=					(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
 snake_case__								=					(TFMarianMTModel,) if is_tf_available() else ()
 snake_case__								=					(
     {
         '''conversational''': TFMarianMTModel,
         '''feature-extraction''': TFMarianModel,
         '''summarization''': TFMarianMTModel,
         '''text2text-generation''': TFMarianMTModel,
         '''translation''': TFMarianMTModel,
     }
     if is_tf_available()
     else {}
 )
 snake_case__								=					True
 snake_case__								=					False
 snake_case__								=					False



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   TFMarianModelTester(self    )
  lowerCAmelCase													=   ConfigTester(self							,					config_class=__lowerCamelCase    )



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  self.config_tester.run_common_tests()



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   self.model_tester.prepare_config_and_inputs_for_common()
  self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase    )







@require_tf
class 			a       (      unittest.TestCase     ):
 snake_case__								=					1_0_0_0  # show more chars for failing integration tests



 @classmethod
 def       UpperCamelCase__     (    cls    ):







  """simple docstring"""






  lowerCAmelCase													=   F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
  return cls



 @cached_property
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  return AutoTokenizer.from_pretrained(self.model_name    )



 @property
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  return self.tokenizer.eos_token_id



 @cached_property
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  warnings.simplefilter('error'    )
  lowerCAmelCase													=   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
  assert isinstance(__lowerCamelCase							,					__lowerCamelCase    )
  lowerCAmelCase													=   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       UpperCamelCase__     (    self							,					**_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   self.translate_src_text(**__lowerCamelCase    )
  self.assertListEqual(self.expected_text							,					__lowerCamelCase    )



 def       UpperCamelCase__     (    self							,					**_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   self.tokenizer(self.src_text							,					**__lowerCamelCase							,					padding=__lowerCamelCase							,					return_tensors='tf'    )
  lowerCAmelCase													=   self.model.generate(
      model_inputs.input_ids							,					attention_mask=model_inputs.attention_mask							,					num_beams=2							,					max_length=1_28    )
  lowerCAmelCase													=   self.tokenizer.batch_decode(generated_ids.numpy()							,					skip_special_tokens=__lowerCamelCase    )
  return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class 			a       (      a__     ):
 snake_case__								=					'''mt'''
 snake_case__								=					'''en'''
 snake_case__								=					['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
 snake_case__								=					['''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    ):







  """simple docstring"""






  self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 			a       (      a__     ):
 snake_case__								=					'''en'''
 snake_case__								=					'''zh'''
 snake_case__								=					['''My name is Wolfgang and I live in Berlin''']
 snake_case__								=					['''我叫沃尔夫冈 我住在柏林''']



 @unittest.skip('Skipping until #12647 is resolved.'    )
 @slow
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 			a       (      a__     ):
 snake_case__								=					'''en'''
 snake_case__								=					'''ROMANCE'''
 snake_case__								=					[
     '''>>fr<< Don\'t spend so much time watching TV.''',
     '''>>pt<< Your message has been sent.''',
     '''>>es<< He\'s two years older than me.''',
 ]
 snake_case__								=					[
     '''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    ):







  """simple docstring"""






  self._assert_generated_batch_equal_expected()



 @unittest.skip('Skipping until #12647 is resolved.'    )
 @slow
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






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












"""simple docstring"""
def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						int     ,    _UpperCAmelCase				:						int ):
 return (input_a, input_a).count(0 ) % 2

def 		_SCREAMING_SNAKE_CASE ():
 assert xor_gate(0     ,    0 ) == 0
 assert xor_gate(0     ,    1 ) == 1
 assert xor_gate(1     ,    0 ) == 1
 assert xor_gate(1     ,    1 ) == 0


if __name__ == "__main__":
       print(xor_gate(0, 0))
       print(xor_gate(0, 1))





