

'''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
  UpperCAmelCase				: int         =  '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      a__ (						a__   ,					a__   ,					a__=None   ,					a__=None   ,					a__=None   ,					a__=None   ,					a__=None   ,					a__=None   ,					):




     """simple docstring"""


     if attention_mask is None:
          __SCREAMING_SNAKE_CASE						     =   np.where(input_ids != config.pad_token_id   ,					1   ,					0		)
     if decoder_attention_mask is None:
          __SCREAMING_SNAKE_CASE						     =   np.where(decoder_input_ids != config.pad_token_id   ,					1   ,					0		)
     if head_mask is None:
          __SCREAMING_SNAKE_CASE						     =   np.ones((config.encoder_layers, config.encoder_attention_heads)		)
     if decoder_head_mask is None:
          __SCREAMING_SNAKE_CASE						     =   np.ones((config.decoder_layers, config.decoder_attention_heads)		)
     if cross_attn_head_mask is None:
          __SCREAMING_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      : Optional[Any]      ,							__SCREAMING_SNAKE_CASE      : str      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=13      ,							__SCREAMING_SNAKE_CASE      : List[str]=7      ,							__SCREAMING_SNAKE_CASE      : List[str]=True      ,							__SCREAMING_SNAKE_CASE      : Any=False      ,							__SCREAMING_SNAKE_CASE      : Any=99      ,							__SCREAMING_SNAKE_CASE      : int=16      ,							__SCREAMING_SNAKE_CASE      : int=2      ,							__SCREAMING_SNAKE_CASE      : int=4      ,							__SCREAMING_SNAKE_CASE      : Tuple=4      ,							__SCREAMING_SNAKE_CASE      : str="gelu"      ,							__SCREAMING_SNAKE_CASE      : int=0.1      ,							__SCREAMING_SNAKE_CASE      : str=0.1      ,							__SCREAMING_SNAKE_CASE      : str=32      ,							__SCREAMING_SNAKE_CASE      : str=2      ,							__SCREAMING_SNAKE_CASE      : Dict=1      ,							__SCREAMING_SNAKE_CASE      : Any=0      ,							__SCREAMING_SNAKE_CASE      : Any=0.02      ,							)						->				Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   parent
      __SCREAMING_SNAKE_CASE						     =   batch_size
      __SCREAMING_SNAKE_CASE						     =   seq_length
      __SCREAMING_SNAKE_CASE						     =   is_training
      __SCREAMING_SNAKE_CASE						     =   use_labels
      __SCREAMING_SNAKE_CASE						     =   vocab_size
      __SCREAMING_SNAKE_CASE						     =   hidden_size
      __SCREAMING_SNAKE_CASE						     =   num_hidden_layers
      __SCREAMING_SNAKE_CASE						     =   num_attention_heads
      __SCREAMING_SNAKE_CASE						     =   intermediate_size
      __SCREAMING_SNAKE_CASE						     =   hidden_act
      __SCREAMING_SNAKE_CASE						     =   hidden_dropout_prob
      __SCREAMING_SNAKE_CASE						     =   attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE						     =   max_position_embeddings
      __SCREAMING_SNAKE_CASE						     =   eos_token_id
      __SCREAMING_SNAKE_CASE						     =   pad_token_id
      __SCREAMING_SNAKE_CASE						     =   bos_token_id
      __SCREAMING_SNAKE_CASE						     =   initializer_range
 def    UpperCAmelCase__	(	self      : List[Any]    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]      ,							self.vocab_size    )      ,							3      ,							self.vocab_size    )
      __SCREAMING_SNAKE_CASE						     =   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)      ,							dtype=np.intaa    ))      ,							-1    )

      __SCREAMING_SNAKE_CASE						     =   shift_tokens_right(__SCREAMING_SNAKE_CASE      ,							1      ,							2    )

      __SCREAMING_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=__SCREAMING_SNAKE_CASE      ,							)
      __SCREAMING_SNAKE_CASE						     =   prepare_marian_inputs_dict(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )
      return config, inputs_dict
 def    UpperCAmelCase__	(	self      : str    )						->				Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   self.prepare_config_and_inputs()
      return config, inputs_dict
 def    UpperCAmelCase__	(	self      : Tuple      ,							__SCREAMING_SNAKE_CASE      : int      ,							__SCREAMING_SNAKE_CASE      : Any      ,							__SCREAMING_SNAKE_CASE      : int    )						->				Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   20
      __SCREAMING_SNAKE_CASE						     =   model_class_name(__SCREAMING_SNAKE_CASE    )

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

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

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

      __SCREAMING_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)      ,							)
      __SCREAMING_SNAKE_CASE						     =   model.decode(
          decoder_input_ids[:, :-1]      ,							__SCREAMING_SNAKE_CASE      ,							decoder_attention_mask=__SCREAMING_SNAKE_CASE      ,							past_key_values=__SCREAMING_SNAKE_CASE      ,							decoder_position_ids=__SCREAMING_SNAKE_CASE      ,							)

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

      __SCREAMING_SNAKE_CASE						     =   model.decode(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )

      __SCREAMING_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    UpperCAmelCase__	(	self      : Optional[Any]      ,							__SCREAMING_SNAKE_CASE      : Any      ,							__SCREAMING_SNAKE_CASE      : Any      ,							__SCREAMING_SNAKE_CASE      : int    )						->				Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   20
      __SCREAMING_SNAKE_CASE						     =   model_class_name(__SCREAMING_SNAKE_CASE    )

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

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

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

      __SCREAMING_SNAKE_CASE						     =   model.init_cache(decoder_input_ids.shape[0]      ,							__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )
      __SCREAMING_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)      ,							)

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

      __SCREAMING_SNAKE_CASE						     =   model.decode(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							decoder_attention_mask=__SCREAMING_SNAKE_CASE    )

      __SCREAMING_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__     (   a				,      unittest.TestCase				,      a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   True
 lowerCAmelCase__						      =   (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
 lowerCAmelCase__						      =   (FlaxMarianMTModel,) if is_flax_available() else ()
 def    UpperCAmelCase__	(	self      : List[str]    )						->				Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   FlaxMarianModelTester(self    )
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : str    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_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(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Tuple    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_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__    ):
                __SCREAMING_SNAKE_CASE						     =   self._prepare_for_class(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )
                __SCREAMING_SNAKE_CASE						     =   model_class(__SCREAMING_SNAKE_CASE    )

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

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

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

                self.assertEqual(len(__SCREAMING_SNAKE_CASE    )      ,							len(__SCREAMING_SNAKE_CASE    )    )
                for jitted_output, output in zip(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    ):
                     self.assertEqual(jitted_output.shape      ,							output.shape    )
 def    UpperCAmelCase__	(	self      : List[str]    )						->				Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_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__    ):
                __SCREAMING_SNAKE_CASE						     =   model_class(__SCREAMING_SNAKE_CASE    )
                __SCREAMING_SNAKE_CASE						     =   model.encode(inputs_dict["""input_ids"""]      ,							inputs_dict["""attention_mask"""]    )

                __SCREAMING_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(__SCREAMING_SNAKE_CASE      : str      ,							__SCREAMING_SNAKE_CASE      : Any      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]    ):
                     return model.decode(
                         decoder_input_ids=__SCREAMING_SNAKE_CASE      ,							decoder_attention_mask=__SCREAMING_SNAKE_CASE      ,							encoder_outputs=__SCREAMING_SNAKE_CASE      ,							)

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

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

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


 @slow
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				int:


      """simple docstring"""




      for model_class_name in self.all_model_classes:
           __SCREAMING_SNAKE_CASE						     =   model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""    )
           # FlaxMarianForSequenceClassification expects eos token in input_ids
           __SCREAMING_SNAKE_CASE						     =   np.ones((1, 1)    ) * model.config.eos_token_id
           __SCREAMING_SNAKE_CASE						     =   model(__SCREAMING_SNAKE_CASE    )
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE    )







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



 """simple docstring"""



 lowerCAmelCase__						      =   None
 lowerCAmelCase__						      =   None
 @classmethod
 def    UpperCAmelCase__	(	cls      : Optional[Any]    )						->				None:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
      return cls
 @cached_property
 def    UpperCAmelCase__	(	self      : Dict    )						->				List[Any]:


      """simple docstring"""




      return MarianTokenizer.from_pretrained(self.model_name    )
 @property
 def    UpperCAmelCase__	(	self      : List[Any]    )						->				int:


      """simple docstring"""




      return self.tokenizer.eos_token_id
 @cached_property
 def    UpperCAmelCase__	(	self      : Dict    )						->				List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   FlaxMarianMTModel.from_pretrained(self.model_name    )
      self.assertEqual(model.config.decoder_start_token_id      ,							model.config.pad_token_id    )
      return model
 def    UpperCAmelCase__	(	self      : Any      ,							**__SCREAMING_SNAKE_CASE      : Optional[Any]    )						->				Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.translate_src_text(**__SCREAMING_SNAKE_CASE    )
      self.assertListEqual(self.expected_text      ,							__SCREAMING_SNAKE_CASE    )


 def    UpperCAmelCase__	(	self      : List[Any]      ,							**__SCREAMING_SNAKE_CASE      : Optional[Any]    )						->				Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.tokenizer(self.src_text      ,							padding=__SCREAMING_SNAKE_CASE      ,							return_tensors="""np"""      ,							**__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   self.model.generate(
          model_inputs.input_ids      ,							attention_mask=model_inputs.attention_mask      ,							num_beams=2      ,							max_length=128      ,							).sequences
      __SCREAMING_SNAKE_CASE						     =   self.tokenizer.batch_decode(__SCREAMING_SNAKE_CASE      ,							skip_special_tokens=__SCREAMING_SNAKE_CASE    )
      return generated_words







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



 """simple docstring"""



 lowerCAmelCase__						      =   "en"
 lowerCAmelCase__						      =   "fr"
 lowerCAmelCase__						      =   [
     "I am a small frog.",
     "Now I can forget the 100 words of german that I know.",
 ]
 lowerCAmelCase__						      =   [
     "Je suis une petite grenouille.",
     "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
 ]
 @slow
 def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Union[str, Any]:


      """simple docstring"""




      self._assert_generated_batch_equal_expected()







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



 """simple docstring"""



 lowerCAmelCase__						      =   "fr"
 lowerCAmelCase__						      =   "en"
 lowerCAmelCase__						      =   [
     "Donnez moi le micro.",
     "Tom et Mary étaient assis à une table.",  # Accents
 ]
 lowerCAmelCase__						      =   [
     "Give me the microphone.",
     "Tom and Mary were sitting at a table.",
 ]
 @slow
 def    UpperCAmelCase__	(	self      : List[Any]    )						->				Any:


      """simple docstring"""




      self._assert_generated_batch_equal_expected()







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



 """simple docstring"""



 lowerCAmelCase__						      =   "mt"
 lowerCAmelCase__						      =   "en"
 lowerCAmelCase__						      =   ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
 lowerCAmelCase__						      =   ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]
 @slow
 def    UpperCAmelCase__	(	self      : Optional[Any]    )						->				str:


      """simple docstring"""




      self._assert_generated_batch_equal_expected()







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



 """simple docstring"""



 lowerCAmelCase__						      =   "en"
 lowerCAmelCase__						      =   "de"
 lowerCAmelCase__						      =   [
     "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.",
 ]
 lowerCAmelCase__						      =   [
     "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    UpperCAmelCase__	(	self      : Any    )						->				Optional[int]:


      """simple docstring"""




      self._assert_generated_batch_equal_expected()







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



 """simple docstring"""



 lowerCAmelCase__						      =   "en"
 lowerCAmelCase__						      =   "zh"
 lowerCAmelCase__						      =   ["My name is Wolfgang and I live in Berlin"]
 lowerCAmelCase__						      =   ["我叫沃尔夫冈 我住在柏林"]
 @slow
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				List[str]:


      """simple docstring"""




      self._assert_generated_batch_equal_expected()







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



 """simple docstring"""



 lowerCAmelCase__						      =   "ru"
 lowerCAmelCase__						      =   "fr"
 lowerCAmelCase__						      =   ["Он показал мне рукопись своей новой пьесы."]
 lowerCAmelCase__						      =   ["Il m'a montré le manuscrit de sa nouvelle pièce."]
 @slow
 def    UpperCAmelCase__	(	self      : Dict    )						->				int:


      """simple docstring"""




      self._assert_generated_batch_equal_expected()







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



 """simple docstring"""



 lowerCAmelCase__						      =   "en"
 lowerCAmelCase__						      =   "ROMANCE"
 lowerCAmelCase__						      =   [
     ">>fr<< Don't spend so much time watching TV.",
     ">>pt<< Your message has been sent.",
     ">>es<< He's two years older than me.",
 ]
 lowerCAmelCase__						      =   [
     "Ne passez pas autant de temps à regarder la télé.",
     "A sua mensagem foi enviada.",
     "Es dos años más viejo que yo.",
 ]
 @slow
 def    UpperCAmelCase__	(	self      : Tuple    )						->				List[Any]:


      """simple docstring"""




      self._assert_generated_batch_equal_expected()









'''simple docstring'''



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCAmelCase				: Union[str, Any]         =  logging.get_logger(__name__)







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "mt5"
 lowerCAmelCase__						      =   ["past_key_values"]
 def __init__(	self      : str      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=250_112      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=512      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=64      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=1_024      ,							__SCREAMING_SNAKE_CASE      : List[str]=8      ,							__SCREAMING_SNAKE_CASE      : Any=None      ,							__SCREAMING_SNAKE_CASE      : List[str]=6      ,							__SCREAMING_SNAKE_CASE      : Any=32      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=128      ,							__SCREAMING_SNAKE_CASE      : List[str]=0.1      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=1E-6      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=1.0      ,							__SCREAMING_SNAKE_CASE      : Tuple="gated-gelu"      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=True      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=True      ,							__SCREAMING_SNAKE_CASE      : Tuple="T5Tokenizer"      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=False      ,							__SCREAMING_SNAKE_CASE      : Tuple=0      ,							__SCREAMING_SNAKE_CASE      : str=1      ,							__SCREAMING_SNAKE_CASE      : Dict=0      ,							**__SCREAMING_SNAKE_CASE      : str      ,							)						->				List[Any]:


      """simple docstring"""




      super().__init__(
          is_encoder_decoder=__SCREAMING_SNAKE_CASE      ,							tokenizer_class=__SCREAMING_SNAKE_CASE      ,							tie_word_embeddings=__SCREAMING_SNAKE_CASE      ,							pad_token_id=__SCREAMING_SNAKE_CASE      ,							eos_token_id=__SCREAMING_SNAKE_CASE      ,							decoder_start_token_id=__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE      ,							)
      __SCREAMING_SNAKE_CASE						     =   vocab_size
      __SCREAMING_SNAKE_CASE						     =   d_model
      __SCREAMING_SNAKE_CASE						     =   d_kv
      __SCREAMING_SNAKE_CASE						     =   d_ff
      __SCREAMING_SNAKE_CASE						     =   num_layers
      __SCREAMING_SNAKE_CASE						     =   (
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      __SCREAMING_SNAKE_CASE						     =   num_heads
      __SCREAMING_SNAKE_CASE						     =   relative_attention_num_buckets
      __SCREAMING_SNAKE_CASE						     =   relative_attention_max_distance
      __SCREAMING_SNAKE_CASE						     =   dropout_rate
      __SCREAMING_SNAKE_CASE						     =   layer_norm_epsilon
      __SCREAMING_SNAKE_CASE						     =   initializer_factor
      __SCREAMING_SNAKE_CASE						     =   feed_forward_proj
      __SCREAMING_SNAKE_CASE						     =   use_cache

      __SCREAMING_SNAKE_CASE						     =   self.feed_forward_proj.split("""-"""    )
      __SCREAMING_SNAKE_CASE						     =   act_info[-1]
      __SCREAMING_SNAKE_CASE						     =   act_info[0] == """gated"""

      if len(__SCREAMING_SNAKE_CASE    ) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE    ) > 2:
           raise ValueError(
               f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
               """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
               """'gated-gelu' or 'relu'"""    )

      # for backwards compatibility
      if feed_forward_proj == "gated-gelu":
           __SCREAMING_SNAKE_CASE						     =   """gelu_new"""
 @property
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				str:


      """simple docstring"""




      return self.d_model
 @property
 def    UpperCAmelCase__	(	self      : List[str]    )						->				int:


      """simple docstring"""




      return self.num_heads


 @property
 def    UpperCAmelCase__	(	self      : List[str]    )						->				Union[str, Any]:


      """simple docstring"""




      return self.num_layers







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Mapping[str, Mapping[int, str]]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   {
          """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
          """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
      }
      if self.use_past:
           __SCREAMING_SNAKE_CASE						     =   """past_encoder_sequence + sequence"""
           __SCREAMING_SNAKE_CASE						     =   {0: """batch"""}
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
      else:
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """decoder_sequence"""}
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """decoder_sequence"""}

      if self.use_past:
           self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE      ,							direction="""inputs"""    )

      return common_inputs
 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				int:


      """simple docstring"""




      return 13


 @property
 def    UpperCAmelCase__	(	self      : Tuple    )						->				float:


      """simple docstring"""




      return 5E-4






