

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, 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     =    '''platform'''

 import jax
 import jax.numpy as jnp

 from transformers import AutoTokenizer
 from transformers.models.mbart.modeling_flax_mbart import (
     FlaxMBartForConditionalGeneration,
     FlaxMBartForQuestionAnswering,
     FlaxMBartForSequenceClassification,
     FlaxMBartModel,
     shift_tokens_right,
 )
def 		UpperCamelCase_(    snake_case__:						int						,					snake_case__:						str						,					snake_case__:						str=None						,					snake_case__:						List[str]=None						,					snake_case__:						List[str]=None						,					snake_case__:						Any=None						,					snake_case__:						List[str]=None						,					snake_case__:						Optional[Any]=None						,					) ->				List[Any]:
 if attention_mask is None:
  UpperCAmelCase__           =       np.where(input_ids != config.pad_token_id						,					1						,					0     )
 if decoder_attention_mask is None:
  UpperCAmelCase__           =       np.where(decoder_input_ids != config.pad_token_id						,					1						,					0     )
 if head_mask is None:
  UpperCAmelCase__           =       np.ones((config.encoder_layers, config.encoder_attention_heads)     )
 if decoder_head_mask is None:
  UpperCAmelCase__           =       np.ones((config.decoder_layers, config.decoder_attention_heads)     )
 if cross_attn_head_mask is None:
  UpperCAmelCase__           =       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": decoder_attention_mask,
 }



class 					lowercase		( unittest.TestCase      ):



 '''simple docstring'''


 def __init__(self , __a , __a=13 , __a=7 , __a=True , __a=False , __a=99 , __a=16 , __a=2 , __a=4 , __a=4 , __a="gelu" , __a=0.1 , __a=0.1 , __a=32 , __a=2 , __a=1 , __a=0 , __a=2 , __a=0.02 , )				->      Dict:






  """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_act
  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
  UpperCAmelCase__           =       decoder_start_token_id
  UpperCAmelCase__           =       initializer_range




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






  """simple docstring"""





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

  UpperCAmelCase__           =       shift_tokens_right(__a , 1				)

  UpperCAmelCase__           =       MBartConfig(
      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 , decoder_start_token_id=self.decoder_start_token_id , initializer_range=self.initializer_range , use_cache=__a , )
  UpperCAmelCase__           =       prepare_mbart_inputs_dict(__a , __a , __a				)
  return config, inputs_dict




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.prepare_config_and_inputs()
  return config, inputs_dict




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






  """simple docstring"""





  UpperCAmelCase__           =       20
  UpperCAmelCase__           =       model_class_name(__a				)

  UpperCAmelCase__           =       model.encode(inputs_dict['input_ids']				)

  UpperCAmelCase__       ,					UpperCAmelCase__           =       (
      inputs_dict['decoder_input_ids'],
      inputs_dict['decoder_attention_mask'],
  )

  UpperCAmelCase__           =       model.init_cache(decoder_input_ids.shape[0] , __a , __a				)
  UpperCAmelCase__           =       jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='i4'				)

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

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

  UpperCAmelCase__           =       model.decode(__a , __a				)

  UpperCAmelCase__           =       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 , __a , __a , __a				)				->      List[Any]:






  """simple docstring"""





  UpperCAmelCase__           =       20
  UpperCAmelCase__           =       model_class_name(__a				)

  UpperCAmelCase__           =       model.encode(inputs_dict['input_ids']				)

  UpperCAmelCase__       ,					UpperCAmelCase__           =       (
      inputs_dict['decoder_input_ids'],
      inputs_dict['decoder_attention_mask'],
  )

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

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

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

  UpperCAmelCase__           =       model.decode(__a , __a , decoder_attention_mask=__a				)

  UpperCAmelCase__           =       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 					lowercase		( unittest.TestCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       99




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  UpperCAmelCase__           =       np.array(
      [
          [71, 82, 18, 33, 46, 91, 2],
          [68, 34, 26, 58, 30, 82, 2],
          [5, 97, 17, 39, 94, 40, 2],
          [76, 83, 94, 25, 70, 78, 2],
          [87, 59, 41, 35, 48, 66, 2],
          [55, 13, 16, 58, 5, 2, 1],  # note padding
          [64, 27, 31, 51, 12, 75, 2],
          [52, 64, 86, 17, 83, 39, 2],
          [48, 61, 9, 24, 71, 82, 2],
          [26, 1, 60, 48, 22, 13, 2],
          [21, 5, 62, 28, 14, 76, 2],
          [45, 98, 37, 86, 59, 48, 2],
          [70, 70, 50, 9, 28, 0, 2],
      ] , dtype=np.intaa , )

  UpperCAmelCase__           =       input_ids.shape[0]
  UpperCAmelCase__           =       MBartConfig(
      vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
  return config, input_ids, batch_size




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__           =       self._get_config_and_data()
  UpperCAmelCase__           =       FlaxMBartForSequenceClassification(__a				)
  UpperCAmelCase__           =       model(input_ids=__a , decoder_input_ids=__a				)
  UpperCAmelCase__           =       (batch_size, config.num_labels)
  self.assertEqual(outputs['logits'].shape , __a				)




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






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__           =       self._get_config_and_data()
  UpperCAmelCase__           =       FlaxMBartForQuestionAnswering(__a				)
  UpperCAmelCase__           =       model(input_ids=__a				)

  self.assertEqual(outputs['start_logits'].shape , input_ids.shape				)
  self.assertEqual(outputs['end_logits'].shape , input_ids.shape				)




 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__           =       self._get_config_and_data()
  UpperCAmelCase__           =       FlaxMBartForConditionalGeneration(__a				)
  UpperCAmelCase__           =       lm_model(input_ids=__a				)
  UpperCAmelCase__           =       (batch_size, input_ids.shape[1], config.vocab_size)
  self.assertEqual(outputs['logits'].shape , __a				)




 def 						UpperCamelCase__							(self				)				->      str:






  """simple docstring"""





  UpperCAmelCase__           =       MBartConfig(
      vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
  UpperCAmelCase__           =       FlaxMBartForConditionalGeneration(__a				)
  UpperCAmelCase__           =       np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa				)
  UpperCAmelCase__           =       np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa				)
  UpperCAmelCase__           =       lm_model(input_ids=__a , decoder_input_ids=__a				)
  UpperCAmelCase__           =       (*summary.shape, config.vocab_size)
  self.assertEqual(outputs['logits'].shape , __a				)




 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__           =       np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa				)
  UpperCAmelCase__           =       shift_tokens_right(__a , 1				)
  UpperCAmelCase__           =       np.equal(__a , 1				).astype(np.floataa				).sum()
  UpperCAmelCase__           =       np.equal(__a , 1				).astype(np.floataa				).sum()
  self.assertEqual(shifted.shape , input_ids.shape				)
  self.assertEqual(__a , n_pad_before - 1				)
  self.assertTrue(np.equal(shifted[:, 0] , 2				).all()				)







@require_flax
class 					lowercase		( _UpperCamelCase				,       unittest.TestCase				,       _UpperCamelCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       True
 __SCREAMING_SNAKE_CASE      							=       (
     (
         FlaxMBartModel,
         FlaxMBartForConditionalGeneration,
         FlaxMBartForSequenceClassification,
         FlaxMBartForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )
 __SCREAMING_SNAKE_CASE      							=       (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       FlaxMBartModelTester(self				)




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






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.model_tester.prepare_config_and_inputs()
  for model_class in self.all_model_classes:
   self.model_tester.check_use_cache_forward(__a , __a , __a				)




 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       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(__a , __a , __a				)




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






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   with self.subTest(model_class.__name__				):
    UpperCAmelCase__           =       self._prepare_for_class(__a , __a				)
    UpperCAmelCase__           =       model_class(__a				)

    @jax.jit
    def encode_jitted(__a , __a=None , **__a				):
     return model.encode(input_ids=__a , attention_mask=__a				)

    with self.subTest('JIT Enabled'				):
     UpperCAmelCase__           =       encode_jitted(**__a				).to_tuple()

    with self.subTest('JIT Disabled'				):
     with jax.disable_jit():
      UpperCAmelCase__           =       encode_jitted(**__a				).to_tuple()

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




 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   with self.subTest(model_class.__name__				):
    UpperCAmelCase__           =       model_class(__a				)
    UpperCAmelCase__           =       model.encode(inputs_dict['input_ids'] , inputs_dict['attention_mask']				)

    UpperCAmelCase__           =       {
        '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(__a , __a , __a				):
     return model.decode(
         decoder_input_ids=__a , decoder_attention_mask=__a , encoder_outputs=__a , )

    with self.subTest('JIT Enabled'				):
     UpperCAmelCase__           =       decode_jitted(**__a				).to_tuple()

    with self.subTest('JIT Disabled'				):
     with jax.disable_jit():
      UpperCAmelCase__           =       decode_jitted(**__a				).to_tuple()

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




 @slow
 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  for model_class_name in self.all_model_classes:
   UpperCAmelCase__           =       model_class_name.from_pretrained('facebook/mbart-large-cc25' , from_pt=__a				)
   # FlaxMBartForSequenceClassification expects eos token in input_ids
   UpperCAmelCase__           =       np.ones((1, 1)				) * model.config.eos_token_id
   UpperCAmelCase__           =       model(__a				)
   self.assertIsNotNone(__a				)







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



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       [
     """ UN Chief Says There Is No Military Solution in Syria""",
 ]
 __SCREAMING_SNAKE_CASE      							=       [
     """Şeful ONU declară că nu există o soluţie militară în Siria""",
 ]
 __SCREAMING_SNAKE_CASE      							=       """facebook/mbart-large-en-ro"""




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






  """simple docstring"""





  return AutoTokenizer.from_pretrained(self.model_name				)




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






  """simple docstring"""





  UpperCAmelCase__           =       FlaxMBartForConditionalGeneration.from_pretrained(self.model_name , from_pt=__a				)
  return model




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






  """simple docstring"""





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




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






  """simple docstring"""





  UpperCAmelCase__           =       self.tokenizer(self.src_text , **__a , return_tensors='np'				)
  UpperCAmelCase__           =       self.model.generate(
      model_inputs.input_ids , attention_mask=model_inputs.attention_mask , decoder_start_token_id=self.tokenizer.lang_code_to_id['ro_RO'] , early_stopping=__a , num_beams=2 , ).sequences
  UpperCAmelCase__           =       self.tokenizer.batch_decode(__a , skip_special_tokens=__a				)
  return generated_words




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






  """simple docstring"""





  self._assert_generated_batch_equal_expected()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase     =    {
    '''configuration_fsmt''': ['''FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FSMTConfig'''],
    '''tokenization_fsmt''': ['''FSMTTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _UpperCamelCase     =    ['''FSMTForConditionalGeneration''', '''FSMTModel''', '''PretrainedFSMTModel''']


if TYPE_CHECKING:
 from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
 from .tokenization_fsmt import FSMTTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
 import sys

 _UpperCamelCase     =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



