

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'''


 __SCREAMING_SNAKE_CASE												=				MarianConfig
 __SCREAMING_SNAKE_CASE												=				{}
 __SCREAMING_SNAKE_CASE												=				"""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       ,       ) ->					str:






  """simple docstring"""





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

  UpperCAmelCase__							=				hidden_dropout_prob
  UpperCAmelCase__							=				attention_probs_dropout_prob
  UpperCAmelCase__							=				max_position_embeddings
  UpperCAmelCase__							=				eos_token_id
  UpperCAmelCase__							=				pad_token_id
  UpperCAmelCase__							=				bos_token_id




 def   UpperCamelCase__							(self ) ->					Union[str, Any]:






  """simple docstring"""





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

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

  UpperCAmelCase__							=				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__							=				prepare_marian_inputs_dict(__a       ,       __a       ,       __a )
  return config, inputs_dict




 def   UpperCamelCase__							(self       ,       __a       ,       __a ) ->					Any:






  """simple docstring"""





  UpperCAmelCase__							=				TFMarianModel(config=__a ).get_decoder()
  UpperCAmelCase__							=				inputs_dict['input_ids']

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

  # first forward pass
  UpperCAmelCase__							=				model(__a       ,       attention_mask=__a       ,       head_mask=__a       ,       use_cache=__a )

  UpperCAmelCase__				,    UpperCAmelCase__							=				outputs.to_tuple()

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

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

  UpperCAmelCase__							=				model(__a       ,       attention_mask=__a )[0]
  UpperCAmelCase__							=				model(__a       ,       attention_mask=__a       ,       past_key_values=__a )[0]

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

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

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







def 						UpperCamelCase_(			snake_case__:  Optional[int]  ,    snake_case__:  Dict  ,    snake_case__:  Any  ,    snake_case__:  List[Any]=None  ,    snake_case__:  Dict=None  ,    snake_case__:  Tuple=None  ,    snake_case__:  int=None  ,    snake_case__:  Optional[int]=None  ,    )       -> Tuple:
 if attention_mask is None:
  UpperCAmelCase__							=				tf.cast(tf.math.not_equal(snake_case__  ,    config.pad_token_id   )  ,    tf.inta   )
 if decoder_attention_mask is None:
  UpperCAmelCase__							=				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__							=				tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
 if decoder_head_mask is None:
  UpperCAmelCase__							=				tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
 if cross_attn_head_mask is None:
  UpperCAmelCase__							=				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						(       _UpperCamelCase ,				_UpperCamelCase ,				unittest.TestCase					):



 '''simple docstring'''


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




 def   UpperCamelCase__							(self ) ->					Union[str, Any]:






  """simple docstring"""





  UpperCAmelCase__							=				TFMarianModelTester(self )
  UpperCAmelCase__							=				ConfigTester(self       ,       config_class=__a )




 def   UpperCamelCase__							(self ) ->					int:






  """simple docstring"""





  self.config_tester.run_common_tests()




 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  UpperCAmelCase__							=				self.model_tester.prepare_config_and_inputs_for_common()
  self.model_tester.check_decoder_model_past_large_inputs(*__a )







@require_tf
class     lowercase						(       unittest.TestCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				1000  # show more chars for failing integration tests




 @classmethod
 def   UpperCamelCase__							(cls ) ->					None:






  """simple docstring"""





  UpperCAmelCase__							=				F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
  return cls




 @cached_property
 def   UpperCamelCase__							(self ) ->					MarianTokenizer:






  """simple docstring"""





  return AutoTokenizer.from_pretrained(self.model_name )




 @property
 def   UpperCamelCase__							(self ) ->					int:






  """simple docstring"""





  return self.tokenizer.eos_token_id




 @cached_property
 def   UpperCamelCase__							(self ) ->					List[Any]:






  """simple docstring"""





  warnings.simplefilter('error' )
  UpperCAmelCase__							=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
  assert isinstance(__a       ,       __a )
  UpperCAmelCase__							=				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       ,       **__a ) ->					Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				self.translate_src_text(**__a )
  self.assertListEqual(self.expected_text       ,       __a )




 def   UpperCamelCase__							(self       ,       **__a ) ->					List[str]:






  """simple docstring"""





  UpperCAmelCase__							=				self.tokenizer(self.src_text       ,       **__a       ,       padding=__a       ,       return_tensors='tf' )
  UpperCAmelCase__							=				self.model.generate(
      model_inputs.input_ids       ,       attention_mask=model_inputs.attention_mask       ,       num_beams=2       ,       max_length=128 )
  UpperCAmelCase__							=				self.tokenizer.batch_decode(generated_ids.numpy()       ,       skip_special_tokens=__a )
  return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class     lowercase						(       _UpperCamelCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				"""mt"""
 __SCREAMING_SNAKE_CASE												=				"""en"""
 __SCREAMING_SNAKE_CASE												=				["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
 __SCREAMING_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 ) ->					Dict:






  """simple docstring"""





  self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class     lowercase						(       _UpperCamelCase					):



 '''simple docstring'''


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




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






  """simple docstring"""





  self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class     lowercase						(       _UpperCamelCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				"""en"""
 __SCREAMING_SNAKE_CASE												=				"""ROMANCE"""
 __SCREAMING_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.""",
 ]
 __SCREAMING_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 ) ->					List[Any]:






  """simple docstring"""





  self._assert_generated_batch_equal_expected()




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






  """simple docstring"""





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






def 						UpperCamelCase_(			snake_case__:  list   )       -> list:
 UpperCAmelCase__							=				len(snake_case__   )

 # Set the gap value to be decreased by a factor of 1.3
 # after each iteration
 UpperCAmelCase__							=				1.3

 # Continue sorting until the gap is 1
 while gap > 1:
  # Decrease the gap value
  UpperCAmelCase__							=				int(gap / shrink   )

  # Sort the elements using insertion sort
  for i in range(snake_case__  ,    len(snake_case__   )   ):
   UpperCAmelCase__							=				collection[i]
   UpperCAmelCase__							=				i
   while j >= gap and collection[j - gap] > temp:
    UpperCAmelCase__							=				collection[j - gap]
    j -= gap
   UpperCAmelCase__							=				temp

 return collection


if __name__ == "__main__":
 import doctest

 doctest.testmod()



