




'''simple docstring'''






import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig




class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    str		  =     CustomConfig




      def __init__(    self    :				Optional[int]    ,    __snake_case    :				Tuple						)       ->   Optional[Any]:
            super().__init__(__snake_case						)
            _lowerCAmelCase							  =							torch.nn.Linear(config.hidden_size    ,    config.hidden_size						)




      def        lowercase__					(    self    :				Dict    ,    __snake_case    :				List[Any]						)       ->   Dict:
            return self.linear(__snake_case						)




      def        lowercase__					(    self    :				List[Any]    ,    __snake_case    :				int						)       ->   Any:
            pass




class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    List[str]		  =     NoSuperInitConfig




      def __init__(    self    :				List[str]    ,    __snake_case    :				List[str]						)       ->   int:
            super().__init__(__snake_case						)
            _lowerCAmelCase							  =							torch.nn.Linear(config.attribute    ,    config.attribute						)




      def        lowercase__					(    self    :				List[str]    ,    __snake_case    :				Dict						)       ->   Any:
            return self.linear(__snake_case						)




      def        lowercase__					(    self    :				Optional[int]    ,    __snake_case    :				List[Any]						)       ->   Dict:
            pass













'''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__       :     Optional[Any]          ='''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      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase=None							,			lowerCAmelCase=None							,			lowerCAmelCase=None							,			lowerCAmelCase=None							,			lowerCAmelCase=None							,			lowerCAmelCase=None							,			):





      """simple docstring"""


      if attention_mask is None:
            _lowerCAmelCase							  =							np.where(input_ids != config.pad_token_id							,			1							,			0						)
      if decoder_attention_mask is None:
            _lowerCAmelCase							  =							np.where(decoder_input_ids != config.pad_token_id							,			1							,			0						)
      if head_mask is None:
            _lowerCAmelCase							  =							np.ones((config.encoder_layers, config.encoder_attention_heads)						)
      if decoder_head_mask is None:
            _lowerCAmelCase							  =							np.ones((config.decoder_layers, config.decoder_attention_heads)						)
      if cross_attn_head_mask is None:
            _lowerCAmelCase							  =							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 				UpperCAmelCase  :




      def __init__(    self    :				Optional[int]    ,    __snake_case    :				str    ,    __snake_case    :				List[str]=13    ,    __snake_case    :				str=7    ,    __snake_case    :				Tuple=True    ,    __snake_case    :				Dict=False    ,    __snake_case    :				Dict=99    ,    __snake_case    :				Optional[int]=16    ,    __snake_case    :				Tuple=2    ,    __snake_case    :				int=4    ,    __snake_case    :				Any=4    ,    __snake_case    :				Any="gelu"    ,    __snake_case    :				Any=0.1    ,    __snake_case    :				List[str]=0.1    ,    __snake_case    :				List[str]=32    ,    __snake_case    :				Union[str, Any]=2    ,    __snake_case    :				List[str]=1    ,    __snake_case    :				Tuple=0    ,    __snake_case    :				int=0.02    ,    )       ->   int:
            _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_act
            _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
            _lowerCAmelCase							  =							initializer_range




      def        lowercase__					(    self    :				str						)       ->   Any:
            _lowerCAmelCase							  =							np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,    self.vocab_size						)    ,    3    ,    self.vocab_size						)
            _lowerCAmelCase							  =							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,    dtype=np.intaa						))    ,    -1						)

            _lowerCAmelCase							  =							shift_tokens_right(__snake_case    ,    1    ,    2						)

            _lowerCAmelCase							  =							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=__snake_case    ,    )
            _lowerCAmelCase							  =							prepare_marian_inputs_dict(__snake_case    ,    __snake_case    ,    __snake_case						)
            return config, inputs_dict




      def        lowercase__					(    self    :				Optional[int]						)       ->   Dict:
            _lowerCAmelCase  ,							_lowerCAmelCase							  =							self.prepare_config_and_inputs()
            return config, inputs_dict




      def        lowercase__					(    self    :				List[Any]    ,    __snake_case    :				int    ,    __snake_case    :				str    ,    __snake_case    :				Union[str, Any]						)       ->   List[str]:
            _lowerCAmelCase							  =							20
            _lowerCAmelCase							  =							model_class_name(__snake_case						)

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

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

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

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

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

            _lowerCAmelCase							  =							model.decode(__snake_case    ,    __snake_case						)

            _lowerCAmelCase							  =							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        lowercase__					(    self    :				List[str]    ,    __snake_case    :				List[Any]    ,    __snake_case    :				str    ,    __snake_case    :				Optional[int]						)       ->   str:
            _lowerCAmelCase							  =							20
            _lowerCAmelCase							  =							model_class_name(__snake_case						)

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

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

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

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

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

            _lowerCAmelCase							  =							model.decode(__snake_case    ,    __snake_case    ,    decoder_attention_mask=__snake_case						)

            _lowerCAmelCase							  =							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 				UpperCAmelCase  (    snake_case_							,	unittest.TestCase							,	snake_case_     ):
      _lowercase:    str		  =     True
      _lowercase:    List[str]		  =     (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
      _lowercase:    Optional[Any]		  =     (FlaxMarianMTModel,) if is_flax_available() else ()




      def        lowercase__					(    self    :				Union[str, Any]						)       ->   List[Any]:
            _lowerCAmelCase							  =							FlaxMarianModelTester(self						)




      def        lowercase__					(    self    :				List[Any]						)       ->   Dict:
            _lowerCAmelCase  ,							_lowerCAmelCase							  =							self.model_tester.prepare_config_and_inputs()
            for model_class in self.all_model_classes:
                  self.model_tester.check_use_cache_forward(__snake_case    ,    __snake_case    ,    __snake_case						)




      def        lowercase__					(    self    :				Union[str, Any]						)       ->   Union[str, Any]:
            _lowerCAmelCase  ,							_lowerCAmelCase							  =							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(__snake_case    ,    __snake_case    ,    __snake_case						)




      def        lowercase__					(    self    :				Optional[Any]						)       ->   Optional[int]:
            _lowerCAmelCase  ,							_lowerCAmelCase							  =							self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__						):
                        _lowerCAmelCase							  =							self._prepare_for_class(__snake_case    ,    __snake_case						)
                        _lowerCAmelCase							  =							model_class(__snake_case						)

                        @jax.jit
                        def encode_jitted(__snake_case    :				Optional[Any]    ,    __snake_case    :				Union[str, Any]=None    ,    **__snake_case    :				Dict						):
                              return model.encode(input_ids=__snake_case    ,    attention_mask=__snake_case						)

                        with self.subTest("""JIT Enabled"""						):
                              _lowerCAmelCase							  =							encode_jitted(**__snake_case						).to_tuple()

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

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




      def        lowercase__					(    self    :				Tuple						)       ->   Tuple:
            _lowerCAmelCase  ,							_lowerCAmelCase							  =							self.model_tester.prepare_config_and_inputs_for_common()

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

                        _lowerCAmelCase							  =							{
                            """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(__snake_case    :				Optional[Any]    ,    __snake_case    :				List[Any]    ,    __snake_case    :				Union[str, Any]						):
                              return model.decode(
                                  decoder_input_ids=__snake_case    ,    decoder_attention_mask=__snake_case    ,    encoder_outputs=__snake_case    ,    )

                        with self.subTest("""JIT Enabled"""						):
                              _lowerCAmelCase							  =							decode_jitted(**__snake_case						).to_tuple()

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

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




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




@require_flax
@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase  (    unittest.TestCase     ):
      _lowercase:    str		  =     None
      _lowercase:    Optional[int]		  =     None




      @classmethod
      def        lowercase__					(    cls    :				Optional[int]						)       ->   None:
            _lowerCAmelCase							  =							f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
            return cls




      @cached_property
      def        lowercase__					(    self    :				Dict						)       ->   Optional[int]:
            return MarianTokenizer.from_pretrained(self.model_name						)




      @property
      def        lowercase__					(    self    :				Union[str, Any]						)       ->   int:
            return self.tokenizer.eos_token_id




      @cached_property
      def        lowercase__					(    self    :				str						)       ->   int:
            _lowerCAmelCase							  =							FlaxMarianMTModel.from_pretrained(self.model_name						)
            self.assertEqual(model.config.decoder_start_token_id    ,    model.config.pad_token_id						)
            return model




      def        lowercase__					(    self    :				Optional[int]    ,    **__snake_case    :				Union[str, Any]						)       ->   List[str]:
            _lowerCAmelCase							  =							self.translate_src_text(**__snake_case						)
            self.assertListEqual(self.expected_text    ,    __snake_case						)




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




@require_flax
@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    str		  =     '''en'''
      _lowercase:    Dict		  =     '''fr'''
      _lowercase:    str		  =     [
          '''I am a small frog.''',
          '''Now I can forget the 100 words of german that I know.''',
      ]
      _lowercase:    str		  =     [
          '''Je suis une petite grenouille.''',
          '''Maintenant, je peux oublier les 100 mots d\'allemand que je connais.''',
      ]




      @slow
      def        lowercase__					(    self    :				Any						)       ->   str:
            self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    Optional[Any]		  =     '''fr'''
      _lowercase:    Optional[int]		  =     '''en'''
      _lowercase:    Optional[int]		  =     [
          '''Donnez moi le micro.''',
          '''Tom et Mary étaient assis à une table.''',  # Accents
      ]
      _lowercase:    Optional[Any]		  =     [
          '''Give me the microphone.''',
          '''Tom and Mary were sitting at a table.''',
      ]




      @slow
      def        lowercase__					(    self    :				Optional[Any]						)       ->   str:
            self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    Dict		  =     '''mt'''
      _lowercase:    int		  =     '''en'''
      _lowercase:    Any		  =     ['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
      _lowercase:    Optional[int]		  =     ['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']




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




@require_flax
@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    List[str]		  =     '''en'''
      _lowercase:    str		  =     '''de'''
      _lowercase:    Union[str, Any]		  =     [
          '''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.''',
      ]
      _lowercase:    Tuple		  =     [
          '''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        lowercase__					(    self    :				Optional[Any]						)       ->   Union[str, Any]:
            self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    Union[str, Any]		  =     '''en'''
      _lowercase:    Any		  =     '''zh'''
      _lowercase:    List[str]		  =     ['''My name is Wolfgang and I live in Berlin''']
      _lowercase:    Any		  =     ['''我叫沃尔夫冈 我住在柏林''']




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




@require_flax
@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    Any		  =     '''ru'''
      _lowercase:    Union[str, Any]		  =     '''fr'''
      _lowercase:    List[Any]		  =     ['''Он показал мне рукопись своей новой пьесы.''']
      _lowercase:    Tuple		  =     ['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']




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




@require_flax
@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    List[str]		  =     '''en'''
      _lowercase:    Union[str, Any]		  =     '''ROMANCE'''
      _lowercase:    Optional[int]		  =     [
          '''>>fr<< Don\'t spend so much time watching TV.''',
          '''>>pt<< Your message has been sent.''',
          '''>>es<< He\'s two years older than me.''',
      ]
      _lowercase:    Optional[Any]		  =     [
          '''Ne passez pas autant de temps à regarder la télé.''',
          '''A sua mensagem foi enviada.''',
          '''Es dos años más viejo que yo.''',
      ]




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






