



'''simple docstring'''





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__    :     Tuple			  =   '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     (    a_      ,		a_      ,		a_=None      ,		a_=None      ,		a_=None      ,		a_=None      ,		a_=None      ,		a_=None      ,		)   ->       Optional[int]:



   """simple docstring"""






   if attention_mask is None:
      A_  :		List[Any]						=					np.where(input_ids != config.pad_token_id      ,		1      ,		0		)
   if decoder_attention_mask is None:
      A_  :		str						=					np.where(decoder_input_ids != config.pad_token_id      ,		1      ,		0		)
   if head_mask is None:
      A_  :		int						=					np.ones((config.encoder_layers, config.encoder_attention_heads)		)
   if decoder_head_mask is None:
      A_  :		int						=					np.ones((config.decoder_layers, config.decoder_attention_heads)		)
   if cross_attn_head_mask is None:
      A_  :		Any						=					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 							_lowerCAmelCase	(  unittest.TestCase							):


 """simple docstring"""

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







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

    A_  :		Any						=					shift_tokens_right(_lowerCamelCase					,    1  )

    A_  :		Tuple						=					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=_lowerCamelCase					,    )
    A_  :		Any						=					prepare_mbart_inputs_dict(_lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )
    return config, inputs_dict







 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    A_						,  A_  :		List[str]						=					self.prepare_config_and_inputs()
    return config, inputs_dict







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )    ->    Any:
    A_  :		List[str]						=					20
    A_  :		Optional[Any]						=					model_class_name(_lowerCamelCase  )

    A_  :		Optional[Any]						=					model.encode(inputs_dict["""input_ids"""]  )

    A_						,  A_  :		Optional[Any]						=					(
        inputs_dict["""decoder_input_ids"""],
        inputs_dict["""decoder_attention_mask"""],
    )

    A_  :		Any						=					model.init_cache(decoder_input_ids.shape[0]					,    _lowerCamelCase					,    _lowerCamelCase  )
    A_  :		Optional[Any]						=					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,    dtype="""i4"""  )

    A_  :		Dict						=					jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]					,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,    )
    A_  :		Tuple						=					model.decode(
        decoder_input_ids[:, :-1]					,    _lowerCamelCase					,    decoder_attention_mask=_lowerCamelCase					,    past_key_values=_lowerCamelCase					,    decoder_position_ids=_lowerCamelCase					,    )

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

    A_  :		Dict						=					model.decode(_lowerCamelCase					,    _lowerCamelCase  )

    A_  :		Union[str, Any]						=					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					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )    ->    int:
    A_  :		Union[str, Any]						=					20
    A_  :		Union[str, Any]						=					model_class_name(_lowerCamelCase  )

    A_  :		str						=					model.encode(inputs_dict["""input_ids"""]  )

    A_						,  A_  :		int						=					(
        inputs_dict["""decoder_input_ids"""],
        inputs_dict["""decoder_attention_mask"""],
    )

    A_  :		Optional[Any]						=					jnp.concatenate(
        [
            decoder_attention_mask,
            jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])  ),
        ]					,    axis=-1					,    )

    A_  :		Any						=					model.init_cache(decoder_input_ids.shape[0]					,    _lowerCamelCase					,    _lowerCamelCase  )
    A_  :		str						=					jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]					,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,    )

    A_  :		List[str]						=					model.decode(
        decoder_input_ids[:, :-1]					,    _lowerCamelCase					,    decoder_attention_mask=_lowerCamelCase					,    past_key_values=_lowerCamelCase					,    decoder_position_ids=_lowerCamelCase					,    )
    A_  :		List[Any]						=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,    dtype="""i4"""  )
    A_  :		Optional[int]						=					model.decode(
        decoder_input_ids[:, -1:]					,    _lowerCamelCase					,    past_key_values=outputs_cache.past_key_values					,    decoder_attention_mask=_lowerCamelCase					,    decoder_position_ids=_lowerCamelCase					,    )

    A_  :		Optional[int]						=					model.decode(_lowerCamelCase					,    _lowerCamelCase					,    decoder_attention_mask=_lowerCamelCase  )

    A_  :		Union[str, Any]						=					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	(  unittest.TestCase							):


 """simple docstring"""

 lowerCamelCase									=     99







 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    A_  :		str						=					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					,    )

    A_  :		Optional[Any]						=					input_ids.shape[0]
    A_  :		Dict						=					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  )    ->    int:
    A_						,  A_						,  A_  :		Optional[Any]						=					self._get_config_and_data()
    A_  :		Optional[Any]						=					FlaxMBartForSequenceClassification(_lowerCamelCase  )
    A_  :		Optional[int]						=					model(input_ids=_lowerCamelCase					,    decoder_input_ids=_lowerCamelCase  )
    A_  :		List[str]						=					(batch_size, config.num_labels)
    self.assertEqual(outputs["""logits"""].shape					,    _lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    A_						,  A_						,  A_  :		Optional[Any]						=					self._get_config_and_data()
    A_  :		int						=					FlaxMBartForQuestionAnswering(_lowerCamelCase  )
    A_  :		int						=					model(input_ids=_lowerCamelCase  )

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







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    A_						,  A_						,  A_  :		Union[str, Any]						=					self._get_config_and_data()
    A_  :		Dict						=					FlaxMBartForConditionalGeneration(_lowerCamelCase  )
    A_  :		str						=					lm_model(input_ids=_lowerCamelCase  )
    A_  :		int						=					(batch_size, input_ids.shape[1], config.vocab_size)
    self.assertEqual(outputs["""logits"""].shape					,    _lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    A_  :		Optional[Any]						=					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					,    )
    A_  :		int						=					FlaxMBartForConditionalGeneration(_lowerCamelCase  )
    A_  :		Optional[int]						=					np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]					,    dtype=np.intaa  )
    A_  :		int						=					np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]					,    dtype=np.intaa  )
    A_  :		int						=					lm_model(input_ids=_lowerCamelCase					,    decoder_input_ids=_lowerCamelCase  )
    A_  :		str						=					(*summary.shape, config.vocab_size)
    self.assertEqual(outputs["""logits"""].shape					,    _lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    A_  :		Optional[Any]						=					np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]					,    dtype=np.intaa  )
    A_  :		List[str]						=					shift_tokens_right(_lowerCamelCase					,    1  )
    A_  :		Optional[int]						=					np.equal(_lowerCamelCase					,    1  ).astype(np.floataa  ).sum()
    A_  :		List[str]						=					np.equal(_lowerCamelCase					,    1  ).astype(np.floataa  ).sum()
    self.assertEqual(shifted.shape					,    input_ids.shape  )
    self.assertEqual(_lowerCamelCase					,    n_pad_before - 1  )
    self.assertTrue(np.equal(shifted[:, 0]					,    2  ).all()  )


@require_flax
class 							_lowerCAmelCase	(  __A,							unittest.TestCase,							__A							):


 """simple docstring"""

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







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    A_  :		str						=					FlaxMBartModelTester(self  )







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_						,  A_  :		Dict						=					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       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_						,  A_  :		Any						=					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       UpperCAmelCase_       (      self  )    ->    int:
    A_						,  A_  :		int						=					self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__  ):
          A_  :		List[str]						=					self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase  )
          A_  :		int						=					model_class(_lowerCamelCase  )

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

          with self.subTest("""JIT Enabled"""  ):
             A_  :		Any						=					encode_jitted(**_lowerCamelCase  ).to_tuple()

          with self.subTest("""JIT Disabled"""  ):
             with jax.disable_jit():
                A_  :		Any						=					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       UpperCAmelCase_       (      self  )    ->    int:
    A_						,  A_  :		int						=					self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__  ):
          A_  :		Dict						=					model_class(_lowerCamelCase  )
          A_  :		Optional[Any]						=					model.encode(inputs_dict["""input_ids"""]					,    inputs_dict["""attention_mask"""]  )

          A_  :		Tuple						=					{
              """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					,    _lowerCamelCase					,    _lowerCamelCase  ):
             return model.decode(
                 decoder_input_ids=_lowerCamelCase					,    decoder_attention_mask=_lowerCamelCase					,    encoder_outputs=_lowerCamelCase					,    )

          with self.subTest("""JIT Enabled"""  ):
             A_  :		Union[str, Any]						=					decode_jitted(**_lowerCamelCase  ).to_tuple()

          with self.subTest("""JIT Disabled"""  ):
             with jax.disable_jit():
                A_  :		Union[str, Any]						=					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       UpperCAmelCase_       (      self  )    ->    List[str]:
    for model_class_name in self.all_model_classes:
       A_  :		Dict						=					model_class_name.from_pretrained("""facebook/mbart-large-cc25"""					,    from_pt=_lowerCamelCase  )
       # FlaxMBartForSequenceClassification expects eos token in input_ids
       A_  :		List[str]						=					np.ones((1, 1)  ) * model.config.eos_token_id
       A_  :		List[str]						=					model(_lowerCamelCase  )
       self.assertIsNotNone(_lowerCamelCase  )


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


 """simple docstring"""

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







 @cached_property
 def       UpperCAmelCase_       (      self  )    ->    str:
    return AutoTokenizer.from_pretrained(self.model_name  )







 @cached_property
 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    A_  :		Optional[int]						=					FlaxMBartForConditionalGeneration.from_pretrained(self.model_name					,    from_pt=_lowerCamelCase  )
    return model







 def       UpperCAmelCase_       (      self					,    **_lowerCamelCase  )    ->    int:
    A_  :		int						=					self.translate_src_text(**_lowerCamelCase  )
    self.assertListEqual(self.expected_text					,    _lowerCamelCase  )







 def       UpperCAmelCase_       (      self					,    **_lowerCamelCase  )    ->    List[str]:
    A_  :		Tuple						=					self.tokenizer(self.src_text					,    **_lowerCamelCase					,    return_tensors="""np"""  )
    A_  :		str						=					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=_lowerCamelCase					,    num_beams=2					,    ).sequences
    A_  :		Union[str, Any]						=					self.tokenizer.batch_decode(_lowerCamelCase					,    skip_special_tokens=_lowerCamelCase  )
    return generated_words







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





'''simple docstring'''





from ..utils import DummyObject, requires_backends


class 							_lowerCAmelCase	(  metaclass=__A							):


 """simple docstring"""

 lowerCamelCase									=     ['''torch''', '''librosa''']







 def __init__(      self					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    str:
    requires_backends(self					,    ["""torch""", """librosa"""]  )







 @classmethod
 def       UpperCAmelCase_       (      cls					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    Dict:
    requires_backends(cls					,    ["""torch""", """librosa"""]  )







 @classmethod
 def       UpperCAmelCase_       (      cls					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    Optional[int]:
    requires_backends(cls					,    ["""torch""", """librosa"""]  )


class 							_lowerCAmelCase	(  metaclass=__A							):


 """simple docstring"""

 lowerCamelCase									=     ['''torch''', '''librosa''']







 def __init__(      self					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    Dict:
    requires_backends(self					,    ["""torch""", """librosa"""]  )







 @classmethod
 def       UpperCAmelCase_       (      cls					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    str:
    requires_backends(cls					,    ["""torch""", """librosa"""]  )







 @classmethod
 def       UpperCAmelCase_       (      cls					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    Dict:
    requires_backends(cls					,    ["""torch""", """librosa"""]  )
