"""simple docstring"""






import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors


@require_tokenizers
class 					UpperCAmelCase		(a_					,unittest.TestCase						):



    """simple docstring"""

    _UpperCAmelCase			:List[Any]							=				BartTokenizer
    _UpperCAmelCase			:List[str]							=				BartTokenizerFast
    _UpperCAmelCase			:List[Any]							=				True
    _UpperCAmelCase			:Optional[Any]							=				filter_roberta_detectors






    def _snake_case    (     self							):
       super().setUp()
       lowercase__:     Optional[Any]      =   [
           '''l''',
           '''o''',
           '''w''',
           '''e''',
           '''r''',
           '''s''',
           '''t''',
           '''i''',
           '''d''',
           '''n''',
           '''\u0120''',
           '''\u0120l''',
           '''\u0120n''',
           '''\u0120lo''',
           '''\u0120low''',
           '''er''',
           '''\u0120lowest''',
           '''\u0120newer''',
           '''\u0120wider''',
           '''<unk>''',
       ]
       lowercase__:     int      =   dict(zip(_snake_case					,    range(len(_snake_case							)							)							)							)
       lowercase__:     Any      =   ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
       lowercase__:     Dict      =   {'''unk_token''': '''<unk>'''}

       lowercase__:     Optional[int]      =   os.path.join(self.tmpdirname					,    VOCAB_FILES_NAMES['''vocab_file''']							)
       lowercase__:     Optional[int]      =   os.path.join(self.tmpdirname					,    VOCAB_FILES_NAMES['''merges_file''']							)
       with open(self.vocab_file					,    '''w'''					,    encoding='''utf-8'''							) as fp:
          fp.write(json.dumps(_snake_case							) + '''\n'''							)
       with open(self.merges_file					,    '''w'''					,    encoding='''utf-8'''							) as fp:
          fp.write('''\n'''.join(_snake_case							)							)






    def _snake_case    (     self					,    **_UpperCAmelCase							):
       kwargs.update(self.special_tokens_map							)
       return self.tokenizer_class.from_pretrained(self.tmpdirname					,    **_snake_case							)






    def _snake_case    (     self					,    **_UpperCAmelCase							):
       kwargs.update(self.special_tokens_map							)
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname					,    **_snake_case							)






    def _snake_case    (     self					,    _UpperCAmelCase							):
       return "lower newer", "lower newer"






    @cached_property
    def _snake_case    (     self							):
       return BartTokenizer.from_pretrained('''facebook/bart-large'''							)






    @cached_property
    def _snake_case    (     self							):
       return BartTokenizerFast.from_pretrained('''facebook/bart-large'''							)






    @require_torch
    def _snake_case    (     self							):
       lowercase__:     Union[str, Any]      =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       lowercase__:     Optional[Any]      =   [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          lowercase__:     Dict      =   tokenizer(_snake_case					,    max_length=len(_snake_case							)					,    padding=_snake_case					,    return_tensors='''pt'''							)
          self.assertIsInstance(_snake_case					,    _snake_case							)

          self.assertEqual((2, 9)					,    batch.input_ids.shape							)
          self.assertEqual((2, 9)					,    batch.attention_mask.shape							)
          lowercase__:     List[Any]      =   batch.input_ids.tolist()[0]
          self.assertListEqual(_snake_case					,    _snake_case							)
          # Test that special tokens are reset






    @require_torch
    def _snake_case    (     self							):
       lowercase__:     Optional[int]      =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          lowercase__:     int      =   tokenizer(_snake_case					,    padding=_snake_case					,    return_tensors='''pt'''							)
          # check if input_ids are returned and no labels
          self.assertIn('''input_ids'''					,    _snake_case							)
          self.assertIn('''attention_mask'''					,    _snake_case							)
          self.assertNotIn('''labels'''					,    _snake_case							)
          self.assertNotIn('''decoder_attention_mask'''					,    _snake_case							)






    @require_torch
    def _snake_case    (     self							):
       lowercase__:     Tuple      =   [
           '''Summary of the text.''',
           '''Another summary.''',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          lowercase__:     Dict      =   tokenizer(text_target=_snake_case					,    max_length=32					,    padding='''max_length'''					,    return_tensors='''pt'''							)
          self.assertEqual(32					,    targets['''input_ids'''].shape[1]							)






    @require_torch
    def _snake_case    (     self							):
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          lowercase__:     Union[str, Any]      =   tokenizer(
              ['''I am a small frog''' * 1024, '''I am a small frog''']					,    padding=_snake_case					,    truncation=_snake_case					,    return_tensors='''pt'''							)
          self.assertIsInstance(_snake_case					,    _snake_case							)
          self.assertEqual(batch.input_ids.shape					,    (2, 1024)							)






    @require_torch
    def _snake_case    (     self							):
       lowercase__:     List[str]      =   ['''A long paragraph for summarization.''']
       lowercase__:     Optional[Any]      =   [
           '''Summary of the text.''',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          lowercase__:     Any      =   tokenizer(_snake_case					,    return_tensors='''pt'''							)
          lowercase__:     List[Any]      =   tokenizer(text_target=_snake_case					,    return_tensors='''pt'''							)
          lowercase__:     List[str]      =   inputs['''input_ids''']
          lowercase__:     str      =   targets['''input_ids''']
          self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()							)
          self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()							)
          self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()							)
          self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()							)






    def _snake_case    (     self							):
       pass






    def _snake_case    (     self							):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
             lowercase__:     Optional[int]      =   self.rust_tokenizer_class.from_pretrained(_snake_case					,    **_snake_case							)
             lowercase__:     List[str]      =   self.tokenizer_class.from_pretrained(_snake_case					,    **_snake_case							)
             lowercase__:     Optional[int]      =   '''A, <mask> AllenNLP sentence.'''
             lowercase__:     str      =   tokenizer_r.encode_plus(_snake_case					,    add_special_tokens=_snake_case					,    return_token_type_ids=_snake_case							)
             lowercase__:     Dict      =   tokenizer_p.encode_plus(_snake_case					,    add_special_tokens=_snake_case					,    return_token_type_ids=_snake_case							)

             # token_type_ids should put 0 everywhere
             self.assertEqual(sum(tokens_r['''token_type_ids''']							)					,    sum(tokens_p['''token_type_ids''']							)							)

             # attention_mask should put 1 everywhere, so sum over length should be 1
             self.assertEqual(
                 sum(tokens_r['''attention_mask''']							) / len(tokens_r['''attention_mask''']							)					,    sum(tokens_p['''attention_mask''']							) / len(tokens_p['''attention_mask''']							)					,    )

             lowercase__:     Any      =   tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']							)
             lowercase__:     Any      =   tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']							)

             # Rust correctly handles the space before the mask while python doesnt
             self.assertSequenceEqual(tokens_p['''input_ids''']					,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]							)
             self.assertSequenceEqual(tokens_r['''input_ids''']					,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]							)

             self.assertSequenceEqual(
                 _snake_case					,    ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']							)
             self.assertSequenceEqual(
                 _snake_case					,    ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']							)

"""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
       __A         =				"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 SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase=None ,					__UpperCAmelCase=None ,					__UpperCAmelCase=None ,					__UpperCAmelCase=None ,					__UpperCAmelCase=None ,					__UpperCAmelCase=None ,					)						->						Optional[Any]:
   if attention_mask is None:
      lowercase__:     Optional[int]      =   np.where(input_ids != config.pad_token_id ,					1 ,					0				)
   if decoder_attention_mask is None:
      lowercase__:     List[str]      =   np.where(decoder_input_ids != config.pad_token_id ,					1 ,					0				)
   if head_mask is None:
      lowercase__:     int      =   np.ones((config.encoder_layers, config.encoder_attention_heads)				)
   if decoder_head_mask is None:
      lowercase__:     List[Any]      =   np.ones((config.decoder_layers, config.decoder_attention_heads)				)
   if cross_attn_head_mask is None:
      lowercase__:     List[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 					UpperCAmelCase		(unittest.TestCase						):



    """simple docstring"""

    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					,    ):
       lowercase__:     Optional[int]      =   parent
       lowercase__:     Optional[int]      =   batch_size
       lowercase__:     Dict      =   seq_length
       lowercase__:     str      =   is_training
       lowercase__:     List[str]      =   use_labels
       lowercase__:     Optional[Any]      =   vocab_size
       lowercase__:     Optional[Any]      =   hidden_size
       lowercase__:     Dict      =   num_hidden_layers
       lowercase__:     Tuple      =   num_attention_heads
       lowercase__:     Tuple      =   intermediate_size
       lowercase__:     int      =   hidden_act
       lowercase__:     List[Any]      =   hidden_dropout_prob
       lowercase__:     Any      =   attention_probs_dropout_prob
       lowercase__:     Optional[Any]      =   max_position_embeddings
       lowercase__:     int      =   eos_token_id
       lowercase__:     int      =   pad_token_id
       lowercase__:     Any      =   bos_token_id
       lowercase__:     Tuple      =   decoder_start_token_id
       lowercase__:     Optional[int]      =   initializer_range






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

       lowercase__:     Optional[int]      =   shift_tokens_right(_UpperCAmelCase					,    1							)

       lowercase__:     List[str]      =   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=_UpperCAmelCase					,    )
       lowercase__:     Optional[Any]      =   prepare_mbart_inputs_dict(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase							)
       return config, inputs_dict






    def _snake_case    (     self							):
       lowercase__,				lowercase__:     Optional[int]      =   self.prepare_config_and_inputs()
       return config, inputs_dict






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase							):
       lowercase__:     str      =   20
       lowercase__:     str      =   model_class_name(_UpperCAmelCase							)

       lowercase__:     Tuple      =   model.encode(inputs_dict['''input_ids''']							)

       lowercase__,				lowercase__:     Optional[Any]      =   (
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       lowercase__:     List[Any]      =   model.init_cache(decoder_input_ids.shape[0]					,    _UpperCAmelCase					,    _UpperCAmelCase							)
       lowercase__:     Tuple      =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,    dtype='''i4'''							)

       lowercase__:     str      =   jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]					,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,    )
       lowercase__:     Optional[Any]      =   model.decode(
           decoder_input_ids[:, :-1]					,    _UpperCAmelCase					,    decoder_attention_mask=_UpperCAmelCase					,    past_key_values=_UpperCAmelCase					,    decoder_position_ids=_UpperCAmelCase					,    )

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

       lowercase__:     List[Any]      =   model.decode(_UpperCAmelCase					,    _UpperCAmelCase							)

       lowercase__:     List[str]      =   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							):
       lowercase__:     Dict      =   20
       lowercase__:     str      =   model_class_name(_UpperCAmelCase							)

       lowercase__:     str      =   model.encode(inputs_dict['''input_ids''']							)

       lowercase__,				lowercase__:     str      =   (
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

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

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

       lowercase__:     Union[str, Any]      =   model.decode(
           decoder_input_ids[:, :-1]					,    _UpperCAmelCase					,    decoder_attention_mask=_UpperCAmelCase					,    past_key_values=_UpperCAmelCase					,    decoder_position_ids=_UpperCAmelCase					,    )
       lowercase__:     Union[str, Any]      =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,    dtype='''i4'''							)
       lowercase__:     Optional[Any]      =   model.decode(
           decoder_input_ids[:, -1:]					,    _UpperCAmelCase					,    past_key_values=outputs_cache.past_key_values					,    decoder_attention_mask=_UpperCAmelCase					,    decoder_position_ids=_UpperCAmelCase					,    )

       lowercase__:     Dict      =   model.decode(_UpperCAmelCase					,    _UpperCAmelCase					,    decoder_attention_mask=_UpperCAmelCase							)

       lowercase__:     int      =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
       self.parent.assertTrue(diff < 1e-3					,    msg=F"""Max diff is {diff}"""							)



@require_flax
class 					UpperCAmelCase		(unittest.TestCase						):



    """simple docstring"""

    _UpperCAmelCase			:int							=				99






    def _snake_case    (     self							):
       lowercase__:     Any      =   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__:     List[Any]      =   input_ids.shape[0]
       lowercase__:     List[Any]      =   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							):
       lowercase__,				lowercase__,				lowercase__:     List[str]      =   self._get_config_and_data()
       lowercase__:     int      =   FlaxMBartForSequenceClassification(_UpperCAmelCase							)
       lowercase__:     str      =   model(input_ids=_UpperCAmelCase					,    decoder_input_ids=_UpperCAmelCase							)
       lowercase__:     Tuple      =   (batch_size, config.num_labels)
       self.assertEqual(outputs['''logits'''].shape					,    _UpperCAmelCase							)






    def _snake_case    (     self							):
       lowercase__,				lowercase__,				lowercase__:     int      =   self._get_config_and_data()
       lowercase__:     List[str]      =   FlaxMBartForQuestionAnswering(_UpperCAmelCase							)
       lowercase__:     Optional[Any]      =   model(input_ids=_UpperCAmelCase							)

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






    def _snake_case    (     self							):
       lowercase__,				lowercase__,				lowercase__:     Tuple      =   self._get_config_and_data()
       lowercase__:     Union[str, Any]      =   FlaxMBartForConditionalGeneration(_UpperCAmelCase							)
       lowercase__:     Optional[Any]      =   lm_model(input_ids=_UpperCAmelCase							)
       lowercase__:     Tuple      =   (batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape					,    _UpperCAmelCase							)






    def _snake_case    (     self							):
       lowercase__:     Union[str, 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					,    )
       lowercase__:     int      =   FlaxMBartForConditionalGeneration(_UpperCAmelCase							)
       lowercase__:     int      =   np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]					,    dtype=np.intaa							)
       lowercase__:     Union[str, Any]      =   np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]					,    dtype=np.intaa							)
       lowercase__:     Any      =   lm_model(input_ids=_UpperCAmelCase					,    decoder_input_ids=_UpperCAmelCase							)
       lowercase__:     int      =   (*summary.shape, config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape					,    _UpperCAmelCase							)






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



@require_flax
class 					UpperCAmelCase		(_UpperCAmelCase					,unittest.TestCase					,_UpperCAmelCase						):



    """simple docstring"""

    _UpperCAmelCase			:Any							=				True
    _UpperCAmelCase			:Union[str, Any]							=				(
        (
            FlaxMBartModel,
            FlaxMBartForConditionalGeneration,
            FlaxMBartForSequenceClassification,
            FlaxMBartForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )
    _UpperCAmelCase			:Any							=				(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()






    def _snake_case    (     self							):
       lowercase__:     Optional[Any]      =   FlaxMBartModelTester(self							)






    def _snake_case    (     self							):
       lowercase__,				lowercase__:     List[str]      =   self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase							)






    def _snake_case    (     self							):
       lowercase__,				lowercase__:     str      =   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(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase							)






    def _snake_case    (     self							):
       lowercase__,				lowercase__:     Optional[int]      =   self.model_tester.prepare_config_and_inputs_for_common()

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

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

             with self.subTest('''JIT Enabled'''							):
                lowercase__:     Any      =   encode_jitted(**_UpperCAmelCase							).to_tuple()

             with self.subTest('''JIT Disabled'''							):
                with jax.disable_jit():
                   lowercase__:     Optional[int]      =   encode_jitted(**_UpperCAmelCase							).to_tuple()

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






    def _snake_case    (     self							):
       lowercase__,				lowercase__:     Optional[int]      =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__							):
             lowercase__:     Tuple      =   model_class(_UpperCAmelCase							)
             lowercase__:     Optional[int]      =   model.encode(inputs_dict['''input_ids''']					,    inputs_dict['''attention_mask''']							)

             lowercase__:     Optional[int]      =   {
                 '''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=_UpperCAmelCase					,    decoder_attention_mask=_UpperCAmelCase					,    encoder_outputs=_UpperCAmelCase					,    )

             with self.subTest('''JIT Enabled'''							):
                lowercase__:     Union[str, Any]      =   decode_jitted(**_UpperCAmelCase							).to_tuple()

             with self.subTest('''JIT Disabled'''							):
                with jax.disable_jit():
                   lowercase__:     List[str]      =   decode_jitted(**_UpperCAmelCase							).to_tuple()

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






    @slow
    def _snake_case    (     self							):
       for model_class_name in self.all_model_classes:
          lowercase__:     Union[str, Any]      =   model_class_name.from_pretrained('''facebook/mbart-large-cc25'''					,    from_pt=_UpperCAmelCase							)
          # FlaxMBartForSequenceClassification expects eos token in input_ids
          lowercase__:     Tuple      =   np.ones((1, 1)							) * model.config.eos_token_id
          lowercase__:     Tuple      =   model(_UpperCAmelCase							)
          self.assertIsNotNone(_UpperCAmelCase							)



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



    """simple docstring"""

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






    @cached_property
    def _snake_case    (     self							):
       return AutoTokenizer.from_pretrained(self.model_name							)






    @cached_property
    def _snake_case    (     self							):
       lowercase__:     Dict      =   FlaxMBartForConditionalGeneration.from_pretrained(self.model_name					,    from_pt=_UpperCAmelCase							)
       return model






    def _snake_case    (     self					,    **_UpperCAmelCase							):
       lowercase__:     Union[str, Any]      =   self.translate_src_text(**_UpperCAmelCase							)
       self.assertListEqual(self.expected_text					,    _UpperCAmelCase							)






    def _snake_case    (     self					,    **_UpperCAmelCase							):
       lowercase__:     int      =   self.tokenizer(self.src_text					,    **_UpperCAmelCase					,    return_tensors='''np'''							)
       lowercase__:     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=_UpperCAmelCase					,    num_beams=2					,    ).sequences
       lowercase__:     List[str]      =   self.tokenizer.batch_decode(_UpperCAmelCase					,    skip_special_tokens=_UpperCAmelCase							)
       return generated_words






    @slow
    def _snake_case    (     self							):
       self._assert_generated_batch_equal_expected()
