
'''simple docstring'''





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     A							     = "platform"

     import jax
     import jax.numpy as jnp

     from transformers import MarianTokenizer
     from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right
def 	snake_case_							(				_a  :				Any			,       _a  :				List[Any]			,       _a  :				List[Any]=None			,       _a  :				str=None			,       _a  :				Union[str, Any]=None			,       _a  :				Optional[int]=None			,       _a  :				Tuple=None			,       _a  :				str=None			,       ):
      if attention_mask is None:
            snake_case_    							=				np.where(input_ids != config.pad_token_id			,       1			,       0      )
      if decoder_attention_mask is None:
            snake_case_    							=				np.where(decoder_input_ids != config.pad_token_id			,       1			,       0      )
      if head_mask is None:
            snake_case_    							=				np.ones((config.encoder_layers, config.encoder_attention_heads)      )
      if decoder_head_mask is None:
            snake_case_    							=				np.ones((config.decoder_layers, config.decoder_attention_heads)      )
      if cross_attn_head_mask is None:
            snake_case_    							=				np.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": attention_mask,
      }



class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			str						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			Optional[Any]=13						,			lowerCAmelCase_	:			int=7						,			lowerCAmelCase_	:			int=True						,			lowerCAmelCase_	:			str=False						,			lowerCAmelCase_	:			List[str]=99						,			lowerCAmelCase_	:			Optional[int]=16						,			lowerCAmelCase_	:			Tuple=2						,			lowerCAmelCase_	:			int=4						,			lowerCAmelCase_	:			Optional[Any]=4						,			lowerCAmelCase_	:			Optional[int]="gelu"						,			lowerCAmelCase_	:			Tuple=0.1						,			lowerCAmelCase_	:			Tuple=0.1						,			lowerCAmelCase_	:			Optional[int]=32						,			lowerCAmelCase_	:			List[str]=2						,			lowerCAmelCase_	:			Any=1						,			lowerCAmelCase_	:			Optional[Any]=0						,			lowerCAmelCase_	:			Union[str, Any]=0.02						,			) ->							Optional[int]:
        snake_case_    							=				parent
        snake_case_    							=				batch_size
        snake_case_    							=				seq_length
        snake_case_    							=				is_training
        snake_case_    							=				use_labels
        snake_case_    							=				vocab_size
        snake_case_    							=				hidden_size
        snake_case_    							=				num_hidden_layers
        snake_case_    							=				num_attention_heads
        snake_case_    							=				intermediate_size
        snake_case_    							=				hidden_act
        snake_case_    							=				hidden_dropout_prob
        snake_case_    							=				attention_probs_dropout_prob
        snake_case_    							=				max_position_embeddings
        snake_case_    							=				eos_token_id
        snake_case_    							=				pad_token_id
        snake_case_    							=				bos_token_id
        snake_case_    							=				initializer_range

  def 		_lowerCamelCase						(self	:			int     ) ->							Tuple:
        snake_case_    							=				np.clip(ids_tensor([self.batch_size, self.seq_length - 1]						,			self.vocab_size     )						,			3						,			self.vocab_size     )
        snake_case_    							=				np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)						,			dtype=np.intaa     ))						,			-1     )

        snake_case_    							=				shift_tokens_right(lowerCAmelCase_						,			1						,			2     )

        snake_case_    							=				MarianConfig(
            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_id=self.eos_token_id						,			bos_token_id=self.bos_token_id						,			pad_token_id=self.pad_token_id						,			initializer_range=self.initializer_range						,			use_cache=lowerCAmelCase_						,			)
        snake_case_    							=				prepare_marian_inputs_dict(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_     )
        return config, inputs_dict

  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							str:
        snake_case_							,						snake_case_    							=				self.prepare_config_and_inputs()
        return config, inputs_dict

  def 		_lowerCamelCase						(self	:			int						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			Dict						,			lowerCAmelCase_	:			List[Any]     ) ->							List[Any]:
        snake_case_    							=				20
        snake_case_    							=				model_class_name(lowerCAmelCase_     )

        snake_case_    							=				model.encode(inputs_dict["input_ids"]     )

        snake_case_							,						snake_case_    							=				(
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )

        snake_case_    							=				model.init_cache(decoder_input_ids.shape[0]						,			lowerCAmelCase_						,			lowerCAmelCase_     )
        snake_case_    							=				jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,			dtype="i4"     )

        snake_case_    							=				jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]						,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,			)
        snake_case_    							=				model.decode(
            decoder_input_ids[:, :-1]						,			lowerCAmelCase_						,			decoder_attention_mask=lowerCAmelCase_						,			past_key_values=lowerCAmelCase_						,			decoder_position_ids=lowerCAmelCase_						,			)

        snake_case_    							=				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,			dtype="i4"     )
        snake_case_    							=				model.decode(
            decoder_input_ids[:, -1:]						,			lowerCAmelCase_						,			decoder_attention_mask=lowerCAmelCase_						,			past_key_values=outputs_cache.past_key_values						,			decoder_position_ids=lowerCAmelCase_						,			)

        snake_case_    							=				model.decode(lowerCAmelCase_						,			lowerCAmelCase_     )

        snake_case_    							=				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
        self.parent.assertTrue(diff < 1E-3						,			msg=f'''Max diff is {diff}'''     )


  def 		_lowerCamelCase						(self	:			int						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			List[str]     ) ->							str:
        snake_case_    							=				20
        snake_case_    							=				model_class_name(lowerCAmelCase_     )

        snake_case_    							=				model.encode(inputs_dict["input_ids"]     )

        snake_case_							,						snake_case_    							=				(
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )

        snake_case_    							=				jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])     ),
            ]						,			axis=-1						,			)

        snake_case_    							=				model.init_cache(decoder_input_ids.shape[0]						,			lowerCAmelCase_						,			lowerCAmelCase_     )
        snake_case_    							=				jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]						,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,			)

        snake_case_    							=				model.decode(
            decoder_input_ids[:, :-1]						,			lowerCAmelCase_						,			decoder_attention_mask=lowerCAmelCase_						,			past_key_values=lowerCAmelCase_						,			decoder_position_ids=lowerCAmelCase_						,			)
        snake_case_    							=				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,			dtype="i4"     )
        snake_case_    							=				model.decode(
            decoder_input_ids[:, -1:]						,			lowerCAmelCase_						,			past_key_values=outputs_cache.past_key_values						,			decoder_attention_mask=lowerCAmelCase_						,			decoder_position_ids=lowerCAmelCase_						,			)

        snake_case_    							=				model.decode(lowerCAmelCase_						,			lowerCAmelCase_						,			decoder_attention_mask=lowerCAmelCase_     )

        snake_case_    							=				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
        self.parent.assertTrue(diff < 1E-3						,			msg=f'''Max diff is {diff}'''     )



@require_flax
class 			lowerCAmelCase__ (							UpperCamelCase       ,				unittest.TestCase       ,				UpperCamelCase							):



  '''simple docstring'''






  _UpperCamelCase							=			True
  _UpperCamelCase							=			(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
  _UpperCamelCase							=			(FlaxMarianMTModel,) if is_flax_available() else ()

  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							Union[str, Any]:
        snake_case_    							=				FlaxMarianModelTester(self     )

  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							Dict:
        snake_case_							,						snake_case_    							=				self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			int     ) ->							Optional[Any]:
        snake_case_							,						snake_case_    							=				self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			str     ) ->							int:
        snake_case_							,						snake_case_    							=				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__     ):
                    snake_case_    							=				self._prepare_for_class(lowerCAmelCase_						,			lowerCAmelCase_     )
                    snake_case_    							=				model_class(lowerCAmelCase_     )

                    @jax.jit
                    def encode_jitted(lowerCAmelCase_	:			List[Any]						,			lowerCAmelCase_	:			List[Any]=None						,			**lowerCAmelCase_	:			Tuple     ):
                          return model.encode(input_ids=lowerCAmelCase_						,			attention_mask=lowerCAmelCase_     )

                    with self.subTest("JIT Enabled"     ):
                          snake_case_    							=				encode_jitted(**lowerCAmelCase_     ).to_tuple()

                    with self.subTest("JIT Disabled"     ):
                          with jax.disable_jit():
                                snake_case_    							=				encode_jitted(**lowerCAmelCase_     ).to_tuple()

                    self.assertEqual(len(lowerCAmelCase_     )						,			len(lowerCAmelCase_     )     )
                    for jitted_output, output in zip(lowerCAmelCase_						,			lowerCAmelCase_     ):
                          self.assertEqual(jitted_output.shape						,			output.shape     )

  def 		_lowerCamelCase						(self	:			Tuple     ) ->							List[str]:
        snake_case_							,						snake_case_    							=				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__     ):
                    snake_case_    							=				model_class(lowerCAmelCase_     )
                    snake_case_    							=				model.encode(inputs_dict["input_ids"]						,			inputs_dict["attention_mask"]     )

                    snake_case_    							=				{
                        "decoder_input_ids": inputs_dict["decoder_input_ids"],
                        "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                        "encoder_outputs": encoder_outputs,
                    }

                    @jax.jit
                    def decode_jitted(lowerCAmelCase_	:			List[Any]						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			int     ):
                          return model.decode(
                              decoder_input_ids=lowerCAmelCase_						,			decoder_attention_mask=lowerCAmelCase_						,			encoder_outputs=lowerCAmelCase_						,			)

                    with self.subTest("JIT Enabled"     ):
                          snake_case_    							=				decode_jitted(**lowerCAmelCase_     ).to_tuple()

                    with self.subTest("JIT Disabled"     ):
                          with jax.disable_jit():
                                snake_case_    							=				decode_jitted(**lowerCAmelCase_     ).to_tuple()

                    self.assertEqual(len(lowerCAmelCase_     )						,			len(lowerCAmelCase_     )     )
                    for jitted_output, output in zip(lowerCAmelCase_						,			lowerCAmelCase_     ):
                          self.assertEqual(jitted_output.shape						,			output.shape     )


  @slow
  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							int:
        for model_class_name in self.all_model_classes:
              snake_case_    							=				model_class_name.from_pretrained("Helsinki-NLP/opus-mt-en-de"     )
              # FlaxMarianForSequenceClassification expects eos token in input_ids
              snake_case_    							=				np.ones((1, 1)     ) * model.config.eos_token_id
              snake_case_    							=				model(lowerCAmelCase_     )
              self.assertIsNotNone(lowerCAmelCase_     )



@require_flax
@require_sentencepiece
@require_tokenizers
class 			lowerCAmelCase__ (							unittest.TestCase							):



  '''simple docstring'''






  _UpperCamelCase							=			None
  _UpperCamelCase							=			None

  @classmethod
  def 		_lowerCamelCase						(cls	:			Optional[Any]     ) ->							None:
        snake_case_    							=				f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
        return cls

  @cached_property
  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							List[Any]:
        return MarianTokenizer.from_pretrained(self.model_name     )

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

  @cached_property
  def 		_lowerCamelCase						(self	:			int     ) ->							Dict:
        snake_case_    							=				FlaxMarianMTModel.from_pretrained(self.model_name     )
        self.assertEqual(model.config.decoder_start_token_id						,			model.config.pad_token_id     )
        return model

  def 		_lowerCamelCase						(self	:			Optional[Any]						,			**lowerCAmelCase_	:			Optional[Any]     ) ->							str:
        snake_case_    							=				self.translate_src_text(**lowerCAmelCase_     )
        self.assertListEqual(self.expected_text						,			lowerCAmelCase_     )


  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			**lowerCAmelCase_	:			Any     ) ->							Dict:
        snake_case_    							=				self.tokenizer(self.src_text						,			padding=lowerCAmelCase_						,			return_tensors="np"						,			**lowerCAmelCase_     )
        snake_case_    							=				self.model.generate(
            model_inputs.input_ids						,			attention_mask=model_inputs.attention_mask						,			num_beams=2						,			max_length=1_28						,			).sequences
        snake_case_    							=				self.tokenizer.batch_decode(lowerCAmelCase_						,			skip_special_tokens=lowerCAmelCase_     )
        return generated_words



@require_flax
@require_sentencepiece
@require_tokenizers
class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  _UpperCamelCase							=			'en'
  _UpperCamelCase							=			'fr'
  _UpperCamelCase							=			[
      'I am a small frog.',
      'Now I can forget the 100 words of german that I know.',
  ]
  _UpperCamelCase							=			[
      'Je suis une petite grenouille.',
      'Maintenant, je peux oublier les 100 mots d\'allemand que je connais.',
  ]

  @slow
  def 		_lowerCamelCase						(self	:			List[str]     ) ->							Union[str, Any]:
        self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  _UpperCamelCase							=			'fr'
  _UpperCamelCase							=			'en'
  _UpperCamelCase							=			[
      'Donnez moi le micro.',
      'Tom et Mary étaient assis à une table.',  # Accents
  ]
  _UpperCamelCase							=			[
      'Give me the microphone.',
      'Tom and Mary were sitting at a table.',
  ]

  @slow
  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							int:
        self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  _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.']

  @slow
  def 		_lowerCamelCase						(self	:			int     ) ->							List[str]:
        self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  _UpperCamelCase							=			'en'
  _UpperCamelCase							=			'de'
  _UpperCamelCase							=			[
      'I am a small frog.',
      'Now I can forget the 100 words of german that I know.',
      'Tom asked his teacher for advice.',
      'That\'s how I would do it.',
      'Tom really admired Mary\'s courage.',
      'Turn around and close your eyes.',
  ]
  _UpperCamelCase							=			[
      'Ich bin ein kleiner Frosch.',
      'Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.',
      'Tom bat seinen Lehrer um Rat.',
      'So würde ich das machen.',
      'Tom bewunderte Marias Mut wirklich.',
      'Drehen Sie sich um und schließen Sie die Augen.',
  ]

  @slow
  def 		_lowerCamelCase						(self	:			int     ) ->							str:
        self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  _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']

  @slow
  def 		_lowerCamelCase						(self	:			int     ) ->							Tuple:
        self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  _UpperCamelCase							=			'ru'
  _UpperCamelCase							=			'fr'
  _UpperCamelCase							=			['Он показал мне рукопись своей новой пьесы.']
  _UpperCamelCase							=			['Il m\'a montré le manuscrit de sa nouvelle pièce.']

  @slow
  def 		_lowerCamelCase						(self	:			Dict     ) ->							List[Any]:
        self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  _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.',
  ]

  @slow
  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							Union[str, Any]:
        self._assert_generated_batch_equal_expected()



'''simple docstring'''





from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap



@total_ordering
class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			Tuple						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			int     ) ->							None:
        snake_case_    							=				word
        snake_case_    							=				count

  def __eq__(self	:			List[Any]						,			lowerCAmelCase_	:			object     ) ->							bool:
        if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
              return NotImplemented
        return self.count == other.count


  def __lt__(self	:			Tuple						,			lowerCAmelCase_	:			object     ) ->							bool:
        if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
              return NotImplemented
        return self.count < other.count

def 	snake_case_							(				_a  :				list[str]			,       _a  :				int      ):
      snake_case_    							=				Heap()
      snake_case_    							=				Counter(_a      )
      heap.build_max_heap(
          [WordCount(_a			,       _a      ) for word, count in count_by_word.items()]      )
      return [heap.extract_max().word for _ in range(min(_a			,       len(_a      )      )      )]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

