





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__     :
  __UpperCamelCase						  =	MarianConfig
  __UpperCamelCase						  =	{}
  __UpperCamelCase						  =	"""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     ,    ):
       lowerCAmelCase_       :							Any			 =		parent
       lowerCAmelCase_       :							Tuple			 =		batch_size
       lowerCAmelCase_       :							Optional[int]			 =		seq_length
       lowerCAmelCase_       :							List[Any]			 =		is_training
       lowerCAmelCase_       :							str			 =		use_labels
       lowerCAmelCase_       :							Any			 =		vocab_size
       lowerCAmelCase_       :							Tuple			 =		hidden_size
       lowerCAmelCase_       :							List[Any]			 =		num_hidden_layers
       lowerCAmelCase_       :							str			 =		num_attention_heads
       lowerCAmelCase_       :							List[Any]			 =		intermediate_size

       lowerCAmelCase_       :							Optional[Any]			 =		hidden_dropout_prob
       lowerCAmelCase_       :							int			 =		attention_probs_dropout_prob
       lowerCAmelCase_       :							Optional[Any]			 =		max_position_embeddings
       lowerCAmelCase_       :							Tuple			 =		eos_token_id
       lowerCAmelCase_       :							Dict			 =		pad_token_id
       lowerCAmelCase_       :							str			 =		bos_token_id







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							str			 =		ids_tensor([self.batch_size, self.seq_length - 1]     ,    self.vocab_size    )
       lowerCAmelCase_       :							Tuple			 =		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )     ,    1    )
       lowerCAmelCase_       :							Any			 =		tf.concat([input_ids, eos_tensor]     ,    axis=1    )

       lowerCAmelCase_       :							Optional[Any]			 =		ids_tensor([self.batch_size, self.seq_length]     ,    self.vocab_size    )

       lowerCAmelCase_       :							Dict			 =		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_       :							List[Any]			 =		prepare_marian_inputs_dict(_lowercase     ,    _lowercase     ,    _lowercase    )
       return config, inputs_dict







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase    ):
       lowerCAmelCase_       :							Any			 =		TFMarianModel(config=_lowercase    ).get_decoder()
       lowerCAmelCase_       :							List[str]			 =		inputs_dict["""input_ids"""]

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

       # first forward pass
       lowerCAmelCase_       :							Optional[Any]			 =		model(_lowercase     ,    attention_mask=_lowercase     ,    head_mask=_lowercase     ,    use_cache=_lowercase    )

       lowerCAmelCase_ ,	lowerCAmelCase_       :							Dict			 =		outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       lowerCAmelCase_       :							Optional[Any]			 =		ids_tensor((self.batch_size, 3)     ,    config.vocab_size    )
       lowerCAmelCase_       :							Optional[Any]			 =		tf.cast(ids_tensor((self.batch_size, 3)     ,    2    )     ,    tf.inta    )

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

       lowerCAmelCase_       :							Optional[Any]			 =		model(_lowercase     ,    attention_mask=_lowercase    )[0]
       lowerCAmelCase_       :							Optional[int]			 =		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
       lowerCAmelCase_       :							str			 =		int(ids_tensor((1,)     ,    output_from_past.shape[-1]    )    )
       lowerCAmelCase_       :							List[Any]			 =		output_from_no_past[:, -3:, random_slice_idx]
       lowerCAmelCase_       :							Optional[int]			 =		output_from_past[:, :, random_slice_idx]

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






def 				_lowerCAmelCase			(	_a :       Dict       , _a :       Optional[int]       , _a :       Dict       , _a :       Any=None       , _a :       Union[str, Any]=None       , _a :       Union[str, Any]=None       , _a :       int=None       , _a :       Tuple=None       , )						->      Optional[int]:
     if attention_mask is None:
          lowerCAmelCase_       :							Tuple			 =		tf.cast(tf.math.not_equal(_a       , config.pad_token_id      )       , tf.inta      )
     if decoder_attention_mask is None:
          lowerCAmelCase_       :							Tuple			 =		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_       :							int			 =		tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
     if decoder_head_mask is None:
          lowerCAmelCase_       :							Optional[int]			 =		tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
     if cross_attn_head_mask is None:
          lowerCAmelCase_       :							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   lowercase__     (				__A  ,       __A  ,       unittest.TestCase	):
  __UpperCamelCase						  =	(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
  __UpperCamelCase						  =	(TFMarianMTModel,) if is_tf_available() else ()
  __UpperCamelCase						  =	(
      {
          """conversational""": TFMarianMTModel,
          """feature-extraction""": TFMarianModel,
          """summarization""": TFMarianMTModel,
          """text2text-generation""": TFMarianMTModel,
          """translation""": TFMarianMTModel,
      }
      if is_tf_available()
      else {}
  )
  __UpperCamelCase						  =	True
  __UpperCamelCase						  =	False
  __UpperCamelCase						  =	False







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Tuple			 =		TFMarianModelTester(self    )
       lowerCAmelCase_       :							int			 =		ConfigTester(self     ,    config_class=_lowercase    )







  def 						UpperCAmelCase__		(						self    ):
       self.config_tester.run_common_tests()







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							str			 =		self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*_lowercase    )





@require_tf
class   lowercase__     (				unittest.TestCase	):
  __UpperCamelCase						  =	1000  # show more chars for failing integration tests







  @classmethod
  def 						UpperCAmelCase__		(						cls    ):
       lowerCAmelCase_       :							List[str]			 =		F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
       return cls







  @cached_property
  def 						UpperCAmelCase__		(						self    ):
       return AutoTokenizer.from_pretrained(self.model_name    )







  @property
  def 						UpperCAmelCase__		(						self    ):
       return self.tokenizer.eos_token_id







  @cached_property
  def 						UpperCAmelCase__		(						self    ):
       warnings.simplefilter("""error"""    )
       lowerCAmelCase_       :							TFMarianMTModel			 =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
       assert isinstance(_lowercase     ,    _lowercase    )
       lowerCAmelCase_       :							List[str]			 =		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    ):
       lowerCAmelCase_       :							Tuple			 =		self.translate_src_text(**_lowercase    )
       self.assertListEqual(self.expected_text     ,    _lowercase    )







  def 						UpperCAmelCase__		(						self     ,    **_lowercase    ):
       lowerCAmelCase_       :							List[Any]			 =		self.tokenizer(self.src_text     ,    **_lowercase     ,    padding=_lowercase     ,    return_tensors="""tf"""    )
       lowerCAmelCase_       :							int			 =		self.model.generate(
           model_inputs.input_ids     ,    attention_mask=model_inputs.attention_mask     ,    num_beams=2     ,    max_length=128    )
       lowerCAmelCase_       :							Tuple			 =		self.tokenizer.batch_decode(generated_ids.numpy()     ,    skip_special_tokens=_lowercase    )
       return generated_words





@require_sentencepiece
@require_tokenizers
@require_tf
class   lowercase__     (				__A	):
  __UpperCamelCase						  =	"""mt"""
  __UpperCamelCase						  =	"""en"""
  __UpperCamelCase						  =	["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
  __UpperCamelCase						  =	["""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    ):
       self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class   lowercase__     (				__A	):
  __UpperCamelCase						  =	"""en"""
  __UpperCamelCase						  =	"""zh"""
  __UpperCamelCase						  =	["""My name is Wolfgang and I live in Berlin"""]
  __UpperCamelCase						  =	["""我叫沃尔夫冈 我住在柏林"""]







  @unittest.skip("""Skipping until #12647 is resolved."""    )
  @slow
  def 						UpperCAmelCase__		(						self    ):
       self._assert_generated_batch_equal_expected()





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







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







import numpy as np






def 				_lowerCAmelCase			(	_a :       Optional[int]       , _a :       str      )						->      Union[str, Any]:
     lowerCAmelCase_       :							int			 =		np.array(_a      )
     lowerCAmelCase_       :							str			 =		np.array(_a      )

     lowerCAmelCase_       :							Optional[Any]			 =		abs(predict - actual      )
     lowerCAmelCase_       :							Dict			 =		difference.mean()

     return score






def 				_lowerCAmelCase			(	_a :       Union[str, Any]       , _a :       int      )						->      Tuple:
     lowerCAmelCase_       :							int			 =		np.array(_a      )
     lowerCAmelCase_       :							int			 =		np.array(_a      )

     lowerCAmelCase_       :							Union[str, Any]			 =		predict - actual
     lowerCAmelCase_       :							Tuple			 =		np.square(_a      )

     lowerCAmelCase_       :							Tuple			 =		square_diff.mean()
     return score






def 				_lowerCAmelCase			(	_a :       Optional[Any]       , _a :       Tuple      )						->      Optional[int]:
     lowerCAmelCase_       :							Optional[Any]			 =		np.array(_a      )
     lowerCAmelCase_       :							Optional[Any]			 =		np.array(_a      )

     lowerCAmelCase_       :							int			 =		predict - actual
     lowerCAmelCase_       :							Any			 =		np.square(_a      )
     lowerCAmelCase_       :							Union[str, Any]			 =		square_diff.mean()
     lowerCAmelCase_       :							Optional[Any]			 =		np.sqrt(_a      )
     return score






def 				_lowerCAmelCase			(	_a :       Optional[Any]       , _a :       Optional[Any]      )						->      List[Any]:
     lowerCAmelCase_       :							Dict			 =		np.array(_a      )
     lowerCAmelCase_       :							List[Any]			 =		np.array(_a      )

     lowerCAmelCase_       :							str			 =		np.log(predict + 1      )
     lowerCAmelCase_       :							Any			 =		np.log(actual + 1      )

     lowerCAmelCase_       :							List[str]			 =		log_predict - log_actual
     lowerCAmelCase_       :							Dict			 =		np.square(_a      )
     lowerCAmelCase_       :							Optional[Any]			 =		square_diff.mean()

     lowerCAmelCase_       :							int			 =		np.sqrt(_a      )

     return score






def 				_lowerCAmelCase			(	_a :       str       , _a :       List[Any]      )						->      int:
     lowerCAmelCase_       :							Any			 =		np.array(_a      )
     lowerCAmelCase_       :							Optional[int]			 =		np.array(_a      )

     lowerCAmelCase_       :							Optional[int]			 =		predict - actual
     lowerCAmelCase_       :							str			 =		np.sum(_a      ) / len(_a      )
     lowerCAmelCase_       :							List[str]			 =		np.sum(_a      ) / len(_a      )
     # print(numerator, denumerator)
     lowerCAmelCase_       :							int			 =		float(_a      ) / denumerator * 1_00

     return score







def 				_lowerCAmelCase			(	_a :       Any       , _a :       Union[str, Any]      )						->      str:
     return np.mean(np.array(_a      ) == np.array(_a      )      )
