"""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
 lowerCAmelCase_:       Dict			     =       '''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        __a (		A     ,					A     ,					A=None     ,					A=None     ,					A=None     ,					A=None     ,					A=None     ,					A=None     ,					):






     '''simple docstring'''



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


   def __init__(  self,					_UpperCAmelCase,					_UpperCAmelCase=13,					_UpperCAmelCase=7,					_UpperCAmelCase=True,					_UpperCAmelCase=False,					_UpperCAmelCase=99,					_UpperCAmelCase=16,					_UpperCAmelCase=2,					_UpperCAmelCase=4,					_UpperCAmelCase=4,					_UpperCAmelCase="gelu",					_UpperCAmelCase=0.1,					_UpperCAmelCase=0.1,					_UpperCAmelCase=32,					_UpperCAmelCase=2,					_UpperCAmelCase=1,					_UpperCAmelCase=0,					_UpperCAmelCase=2,					_UpperCAmelCase=0.02,					):
        '''simple docstring'''



        lowercase__								=				parent
        lowercase__								=				batch_size
        lowercase__								=				seq_length
        lowercase__								=				is_training
        lowercase__								=				use_labels
        lowercase__								=				vocab_size
        lowercase__								=				hidden_size
        lowercase__								=				num_hidden_layers
        lowercase__								=				num_attention_heads
        lowercase__								=				intermediate_size
        lowercase__								=				hidden_act
        lowercase__								=				hidden_dropout_prob
        lowercase__								=				attention_probs_dropout_prob
        lowercase__								=				max_position_embeddings
        lowercase__								=				eos_token_id
        lowercase__								=				pad_token_id
        lowercase__								=				bos_token_id
        lowercase__								=				decoder_start_token_id
        lowercase__								=				initializer_range


   def 						snake_case__						(  self	):
        '''simple docstring'''



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

        lowercase__								=				shift_tokens_right(_SCREAMING_SNAKE_CASE,					1	)

        lowercase__								=				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=_SCREAMING_SNAKE_CASE,					)
        lowercase__								=				prepare_mbart_inputs_dict(_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE	)
        return config, inputs_dict


   def 						snake_case__						(  self	):
        '''simple docstring'''



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


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				20
        lowercase__								=				model_class_name(_SCREAMING_SNAKE_CASE	)

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

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

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

        lowercase__								=				jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :],					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),					)
        lowercase__								=				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,					)

        lowercase__								=				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],					dtype="i4"	)
        lowercase__								=				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,					)

        lowercase__								=				model.decode(_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE	)

        lowercase__								=				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 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				20
        lowercase__								=				model_class_name(_SCREAMING_SNAKE_CASE	)

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

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

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

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

        lowercase__								=				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,					)
        lowercase__								=				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],					dtype="i4"	)
        lowercase__								=				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,					)

        lowercase__								=				model.decode(_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE,					decoder_attention_mask=_SCREAMING_SNAKE_CASE	)

        lowercase__								=				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  a__     ( unittest.TestCase						):
   snake_case_				=   99


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				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,					)

        lowercase__								=				input_ids.shape[0]
        lowercase__								=				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 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__			,    lowercase__			,    lowercase__								=				self._get_config_and_data()
        lowercase__								=				FlaxMBartForSequenceClassification(_SCREAMING_SNAKE_CASE	)
        lowercase__								=				model(input_ids=_SCREAMING_SNAKE_CASE,					decoder_input_ids=_SCREAMING_SNAKE_CASE	)
        lowercase__								=				(batch_size, config.num_labels)
        self.assertEqual(outputs["logits"].shape,					_SCREAMING_SNAKE_CASE	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__			,    lowercase__			,    lowercase__								=				self._get_config_and_data()
        lowercase__								=				FlaxMBartForQuestionAnswering(_SCREAMING_SNAKE_CASE	)
        lowercase__								=				model(input_ids=_SCREAMING_SNAKE_CASE	)

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


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__			,    lowercase__			,    lowercase__								=				self._get_config_and_data()
        lowercase__								=				FlaxMBartForConditionalGeneration(_SCREAMING_SNAKE_CASE	)
        lowercase__								=				lm_model(input_ids=_SCREAMING_SNAKE_CASE	)
        lowercase__								=				(batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs["logits"].shape,					_SCREAMING_SNAKE_CASE	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				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,					)
        lowercase__								=				FlaxMBartForConditionalGeneration(_SCREAMING_SNAKE_CASE	)
        lowercase__								=				np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],					dtype=np.intaa	)
        lowercase__								=				np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],					dtype=np.intaa	)
        lowercase__								=				lm_model(input_ids=_SCREAMING_SNAKE_CASE,					decoder_input_ids=_SCREAMING_SNAKE_CASE	)
        lowercase__								=				(*summary.shape, config.vocab_size)
        self.assertEqual(outputs["logits"].shape,					_SCREAMING_SNAKE_CASE	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



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






@require_flax
class  a__     ( _a       ,       unittest.TestCase       ,       _a						):
   snake_case_				=   True
   snake_case_				=   (
       (
           FlaxMBartModel,
           FlaxMBartForConditionalGeneration,
           FlaxMBartForSequenceClassification,
           FlaxMBartForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )
   snake_case_				=   (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				FlaxMBartModelTester(self	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__			,    lowercase__								=				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 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__			,    lowercase__								=				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 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__			,    lowercase__								=				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__	):
                  lowercase__								=				self._prepare_for_class(_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE	)
                  lowercase__								=				model_class(_SCREAMING_SNAKE_CASE	)

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

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

                  with self.subTest("JIT Disabled"	):
                       with jax.disable_jit():
                            lowercase__								=				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 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__			,    lowercase__								=				self.model_tester.prepare_config_and_inputs_for_common()

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

                  lowercase__								=				{
                      "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(_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	):
                       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"	):
                       lowercase__								=				decode_jitted(**_SCREAMING_SNAKE_CASE	).to_tuple()

                  with self.subTest("JIT Disabled"	):
                       with jax.disable_jit():
                            lowercase__								=				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 						snake_case__						(  self	):
        '''simple docstring'''



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






@require_flax
@require_sentencepiece
@require_tokenizers
class  a__     ( unittest.TestCase						):
   snake_case_				=   [
       " UN Chief Says There Is No Military Solution in Syria",
   ]
   snake_case_				=   [
       "Şeful ONU declară că nu există o soluţie militară în Siria",
   ]
   snake_case_				=   "facebook/mbart-large-en-ro"


   @cached_property
   def 						snake_case__						(  self	):
        '''simple docstring'''



        return AutoTokenizer.from_pretrained(self.model_name	)


   @cached_property
   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				FlaxMBartForConditionalGeneration.from_pretrained(self.model_name,					from_pt=_SCREAMING_SNAKE_CASE	)
        return model


   def 						snake_case__						(  self,					**_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				self.translate_src_text(**_SCREAMING_SNAKE_CASE	)
        self.assertListEqual(self.expected_text,					_SCREAMING_SNAKE_CASE	)


   def 						snake_case__						(  self,					**_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				self.tokenizer(self.src_text,					**_SCREAMING_SNAKE_CASE,					return_tensors="np"	)
        lowercase__								=				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=_SCREAMING_SNAKE_CASE,					num_beams=2,					).sequences
        lowercase__								=				self.tokenizer.batch_decode(_SCREAMING_SNAKE_CASE,					skip_special_tokens=_SCREAMING_SNAKE_CASE	)
        return generated_words


   @slow
   def 						snake_case__						(  self	):
        '''simple docstring'''



        self._assert_generated_batch_equal_expected()





"""simple docstring"""







from ..utils import DummyObject, requires_backends






class      _SCREAMING_SNAKE_CASE							(			metaclass=UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Union[str, Any]    =    ["torch", "librosa"]







   def __init__(	self ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      requires_backends(self ,				['torch', 'librosa']	)







   @classmethod
   def    _A				(	cls ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      requires_backends(cls ,				['torch', 'librosa']	)







   @classmethod
   def    _A				(	cls ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      requires_backends(cls ,				['torch', 'librosa']	)






class      _SCREAMING_SNAKE_CASE							(			metaclass=UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Dict    =    ["torch", "librosa"]







   def __init__(	self ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      requires_backends(self ,				['torch', 'librosa']	)







   @classmethod
   def    _A				(	cls ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      requires_backends(cls ,				['torch', 'librosa']	)







   @classmethod
   def    _A				(	cls ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      requires_backends(cls ,				['torch', 'librosa']	)






