





import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_SCREAMING_SNAKE_CASE			=							logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

_SCREAMING_SNAKE_CASE			=							{
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_SCREAMING_SNAKE_CASE			=							{
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_SCREAMING_SNAKE_CASE			=							sorted(arg_to_scheduler.keys())
_SCREAMING_SNAKE_CASE			=							"{" + ", ".join(arg_to_scheduler_choices) + "}"

class 				lowerCAmelCase_						(						pl.LightningModule						):




 def __init__(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase="base"     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=None     ,       **_lowerCAmelCase     ,       )      ->							Optional[Any]:
     super().__init__()
     # TODO: move to self.save_hyperparameters()
     # self.save_hyperparameters()
     # can also expand arguments into trainer signature for easier reading

     self.save_hyperparameters(_lowerCAmelCase						)
     _lowerCAmelCase		     = 0
     _lowerCAmelCase		     = Path(self.hparams.output_dir						)
     _lowerCAmelCase		     = self.hparams.cache_dir if self.hparams.cache_dir else None
     if config is None:
         _lowerCAmelCase		     = AutoConfig.from_pretrained(
             self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path     ,       **({"num_labels": num_labels} if num_labels is not None else {})     ,       cache_dir=_lowerCAmelCase     ,       **_lowerCAmelCase     ,       )
     else:
         _lowerCAmelCase		     = config

     _lowerCAmelCase		     = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
     for p in extra_model_params:
         if getattr(self.hparams     ,       _lowerCAmelCase     ,       _lowerCAmelCase						):
             assert hasattr(self.config     ,       _lowerCAmelCase						), f'''model config doesn\'t have a `{p}` attribute'''
             setattr(self.config     ,       _lowerCAmelCase     ,       getattr(self.hparams     ,       _lowerCAmelCase						)						)

     if tokenizer is None:
         _lowerCAmelCase		     = AutoTokenizer.from_pretrained(
             self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,       cache_dir=_lowerCAmelCase     ,       )
     else:
         _lowerCAmelCase		     = tokenizer
     _lowerCAmelCase		     = MODEL_MODES[mode]
     if model is None:
         _lowerCAmelCase		     = self.model_type.from_pretrained(
             self.hparams.model_name_or_path     ,       from_tf=bool(".ckpt" in self.hparams.model_name_or_path						)     ,       config=self.config     ,       cache_dir=_lowerCAmelCase     ,       )
     else:
         _lowerCAmelCase		     = model




 def 							_snake_case				(		self     ,       *_lowerCAmelCase     ,       **_lowerCAmelCase						)      ->							List[str]:
     _lowerCAmelCase		     = self.model_type.from_pretrained(*_lowerCAmelCase     ,       **_lowerCAmelCase						)




 def 							_snake_case				(		self						)      ->							Any:
     _lowerCAmelCase		     = arg_to_scheduler[self.hparams.lr_scheduler]
     _lowerCAmelCase		     = get_schedule_func(
         self.opt     ,       num_warmup_steps=self.hparams.warmup_steps     ,       num_training_steps=self.total_steps()						)
     _lowerCAmelCase		     = {"scheduler": scheduler, "interval": "step", "frequency": 1}
     return scheduler




 def 							_snake_case				(		self						)      ->							Any:
     _lowerCAmelCase		     = self.model
     _lowerCAmelCase		     = ["bias", "LayerNorm.weight"]
     _lowerCAmelCase		     = [
         {
             "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay						)],
             "weight_decay": self.hparams.weight_decay,
         },
         {
             "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay						)],
             "weight_decay": 0.0,
         },
     ]
     if self.hparams.adafactor:
         _lowerCAmelCase		     = Adafactor(
             _lowerCAmelCase     ,       lr=self.hparams.learning_rate     ,       scale_parameter=_lowerCAmelCase     ,       relative_step=_lowerCAmelCase						)

     else:
         _lowerCAmelCase		     = AdamW(
             _lowerCAmelCase     ,       lr=self.hparams.learning_rate     ,       eps=self.hparams.adam_epsilon						)
     _lowerCAmelCase		     = optimizer

     _lowerCAmelCase		     = self.get_lr_scheduler()

     return [optimizer], [scheduler]




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							int:
     return self.validation_step(_lowerCAmelCase     ,       _lowerCAmelCase						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							str:
     return self.validation_end(_lowerCAmelCase						)




 def 							_snake_case				(		self						)      ->							int:
     _lowerCAmelCase		     = max(1     ,       self.hparams.gpus						)  # TODO: consider num_tpu_cores
     _lowerCAmelCase		     = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
     return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							List[Any]:






     if mode == "test":
         _lowerCAmelCase		     = len(self.test_dataloader().dataset						)
     else:
         _lowerCAmelCase		     = self.get_dataloader("train"     ,       self.hparams.train_batch_size     ,       shuffle=_lowerCAmelCase						)
         _lowerCAmelCase		     = len(self.train_dataloader().dataset						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = False						)      ->							Dict:
     raise NotImplementedError("You must implement this for your task"						)




 def 							_snake_case				(		self						)      ->							Union[str, Any]:
     return self.train_loader




 def 							_snake_case				(		self						)      ->							int:
     return self.get_dataloader("dev"     ,       self.hparams.eval_batch_size     ,       shuffle=_lowerCAmelCase						)




 def 							_snake_case				(		self						)      ->							Union[str, Any]:
     return self.get_dataloader("test"     ,       self.hparams.eval_batch_size     ,       shuffle=_lowerCAmelCase						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Tuple:
     return os.path.join(
         self.hparams.data_dir     ,       "cached_{}_{}_{}".format(
             _lowerCAmelCase     ,       list(filter(_lowerCAmelCase     ,       self.hparams.model_name_or_path.split("/"						)						)						).pop()     ,       str(self.hparams.max_seq_length						)     ,       )     ,       )




 @pl.utilities.rank_zero_only
 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							None:
     _lowerCAmelCase		     = self.output_dir.joinpath("best_tfmr"						)
     _lowerCAmelCase		     = self.step_count
     self.model.save_pretrained(_lowerCAmelCase						)
     self.tokenizer.save_pretrained(_lowerCAmelCase						)





 @staticmethod
 def 							_snake_case				(		_lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Optional[Any]:
     parser.add_argument(
         "--model_name_or_path"     ,       default=_lowerCAmelCase     ,       type=_lowerCAmelCase     ,       required=_lowerCAmelCase     ,       help="Path to pretrained model or model identifier from huggingface.co/models"     ,       )
     parser.add_argument(
         "--config_name"     ,       default=""     ,       type=_lowerCAmelCase     ,       help="Pretrained config name or path if not the same as model_name"						)
     parser.add_argument(
         "--tokenizer_name"     ,       default=_lowerCAmelCase     ,       type=_lowerCAmelCase     ,       help="Pretrained tokenizer name or path if not the same as model_name"     ,       )
     parser.add_argument(
         "--cache_dir"     ,       default=""     ,       type=_lowerCAmelCase     ,       help="Where do you want to store the pre-trained models downloaded from huggingface.co"     ,       )
     parser.add_argument(
         "--encoder_layerdrop"     ,       type=_lowerCAmelCase     ,       help="Encoder layer dropout probability (Optional). Goes into model.config"     ,       )
     parser.add_argument(
         "--decoder_layerdrop"     ,       type=_lowerCAmelCase     ,       help="Decoder layer dropout probability (Optional). Goes into model.config"     ,       )
     parser.add_argument(
         "--dropout"     ,       type=_lowerCAmelCase     ,       help="Dropout probability (Optional). Goes into model.config"     ,       )
     parser.add_argument(
         "--attention_dropout"     ,       type=_lowerCAmelCase     ,       help="Attention dropout probability (Optional). Goes into model.config"     ,       )
     parser.add_argument("--learning_rate"     ,       default=5E-5     ,       type=_lowerCAmelCase     ,       help="The initial learning rate for Adam."						)
     parser.add_argument(
         "--lr_scheduler"     ,       default="linear"     ,       choices=_lowerCAmelCase     ,       metavar=_lowerCAmelCase     ,       type=_lowerCAmelCase     ,       help="Learning rate scheduler"     ,       )
     parser.add_argument("--weight_decay"     ,       default=0.0     ,       type=_lowerCAmelCase     ,       help="Weight decay if we apply some."						)
     parser.add_argument("--adam_epsilon"     ,       default=1E-8     ,       type=_lowerCAmelCase     ,       help="Epsilon for Adam optimizer."						)
     parser.add_argument("--warmup_steps"     ,       default=0     ,       type=_lowerCAmelCase     ,       help="Linear warmup over warmup_steps."						)
     parser.add_argument("--num_workers"     ,       default=4     ,       type=_lowerCAmelCase     ,       help="kwarg passed to DataLoader"						)
     parser.add_argument("--num_train_epochs"     ,       dest="max_epochs"     ,       default=3     ,       type=_lowerCAmelCase						)
     parser.add_argument("--train_batch_size"     ,       default=32     ,       type=_lowerCAmelCase						)
     parser.add_argument("--eval_batch_size"     ,       default=32     ,       type=_lowerCAmelCase						)
     parser.add_argument("--adafactor"     ,       action="store_true"						)







class 				lowerCAmelCase_						(						pl.Callback						):




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Any:
     _lowerCAmelCase		     = trainer.lr_schedulers[0]["scheduler"]
     _lowerCAmelCase		     = {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()						)}
     pl_module.logger.log_metrics(_lowerCAmelCase						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Any:
     rank_zero_info("***** Validation results *****"						)
     _lowerCAmelCase		     = trainer.callback_metrics
     # Log results
     for key in sorted(_lowerCAmelCase						):
         if key not in ["log", "progress_bar"]:
             rank_zero_info("{} = {}\n".format(_lowerCAmelCase     ,       str(metrics[key]						)						)						)





 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Tuple:
     rank_zero_info("***** Test results *****"						)
     _lowerCAmelCase		     = trainer.callback_metrics
     # Log and save results to file
     _lowerCAmelCase		     = os.path.join(pl_module.hparams.output_dir     ,       "test_results.txt"						)
     with open(_lowerCAmelCase     ,       "w"						) as writer:
         for key in sorted(_lowerCAmelCase						):
             if key not in ["log", "progress_bar"]:
                 rank_zero_info("{} = {}\n".format(_lowerCAmelCase     ,       str(metrics[key]						)						)						)
                 writer.write("{} = {}\n".format(_lowerCAmelCase     ,       str(metrics[key]						)						)						)







def 		__a(SCREAMING_SNAKE_CASE_					:   List[str]						,    SCREAMING_SNAKE_CASE_					:   Dict      ):



    '''simple docstring'''





    parser.add_argument(
        "--output_dir"						,    default=SCREAMING_SNAKE_CASE_						,    type=SCREAMING_SNAKE_CASE_						,    required=SCREAMING_SNAKE_CASE_						,    help="The output directory where the model predictions and checkpoints will be written."						,    )
    parser.add_argument(
        "--fp16"						,    action="store_true"						,    help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"						,    )

    parser.add_argument(
        "--fp16_opt_level"						,    type=SCREAMING_SNAKE_CASE_						,    default="O2"						,    help=(
            "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
            "See details at https://nvidia.github.io/apex/amp.html"
        )						,    )
    parser.add_argument("--n_tpu_cores"						,    dest="tpu_cores"						,    type=SCREAMING_SNAKE_CASE_      )
    parser.add_argument("--max_grad_norm"						,    dest="gradient_clip_val"						,    default=1.0						,    type=SCREAMING_SNAKE_CASE_						,    help="Max gradient norm"      )
    parser.add_argument("--do_train"						,    action="store_true"						,    help="Whether to run training."      )
    parser.add_argument("--do_predict"						,    action="store_true"						,    help="Whether to run predictions on the test set."      )
    parser.add_argument(
        "--gradient_accumulation_steps"						,    dest="accumulate_grad_batches"						,    type=SCREAMING_SNAKE_CASE_						,    default=1						,    help="Number of updates steps to accumulate before performing a backward/update pass."						,    )
    parser.add_argument("--seed"						,    type=SCREAMING_SNAKE_CASE_						,    default=42						,    help="random seed for initialization"      )
    parser.add_argument(
        "--data_dir"						,    default=SCREAMING_SNAKE_CASE_						,    type=SCREAMING_SNAKE_CASE_						,    required=SCREAMING_SNAKE_CASE_						,    help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."						,    )





def 		__a(SCREAMING_SNAKE_CASE_					:   BaseTransformer						,    SCREAMING_SNAKE_CASE_					:   argparse.Namespace						,    SCREAMING_SNAKE_CASE_					:   List[str]=None						,    SCREAMING_SNAKE_CASE_					:   Any=True						,    SCREAMING_SNAKE_CASE_					:   Any=[]						,    SCREAMING_SNAKE_CASE_					:   int=None						,    SCREAMING_SNAKE_CASE_					:   List[Any]=None						,    **SCREAMING_SNAKE_CASE_					:   str						,    ):



    '''simple docstring'''





    pl.seed_everything(args.seed      )

    # init model
    _lowerCAmelCase		     = Path(model.hparams.output_dir      )
    odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_      )

    # add custom checkpoints
    if checkpoint_callback is None:
        _lowerCAmelCase		     = pl.callbacks.ModelCheckpoint(
            filepath=args.output_dir						,    prefix="checkpoint"						,    monitor="val_loss"						,    mode="min"						,    save_top_k=1      )
    if early_stopping_callback:
        extra_callbacks.append(SCREAMING_SNAKE_CASE_      )
    if logging_callback is None:
        _lowerCAmelCase		     = LoggingCallback()

    _lowerCAmelCase		     = {}

    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fpaa:
        _lowerCAmelCase		     = 16
        _lowerCAmelCase		     = args.fpaa_opt_level

    if args.gpus > 1:
        _lowerCAmelCase		     = "ddp"

    _lowerCAmelCase		     = args.accumulate_grad_batches
    _lowerCAmelCase		     = extra_train_kwargs.get("accelerator"						,    SCREAMING_SNAKE_CASE_      )
    _lowerCAmelCase		     = extra_train_kwargs.get("profiler"						,    SCREAMING_SNAKE_CASE_      )

    _lowerCAmelCase		     = pl.Trainer.from_argparse_args(
        SCREAMING_SNAKE_CASE_						,    weights_summary=SCREAMING_SNAKE_CASE_						,    callbacks=[logging_callback] + extra_callbacks						,    logger=SCREAMING_SNAKE_CASE_						,    checkpoint_callback=SCREAMING_SNAKE_CASE_						,    **SCREAMING_SNAKE_CASE_						,    )

    if args.do_train:
        trainer.fit(SCREAMING_SNAKE_CASE_      )

    return trainer









'''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
       _SCREAMING_SNAKE_CASE			=							"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(SCREAMING_SNAKE_CASE_					:   Tuple						,    SCREAMING_SNAKE_CASE_					:   Union[str, Any]						,    SCREAMING_SNAKE_CASE_					:   Optional[int]=None						,    SCREAMING_SNAKE_CASE_					:   int=None						,    SCREAMING_SNAKE_CASE_					:   int=None						,    SCREAMING_SNAKE_CASE_					:   Union[str, Any]=None						,    SCREAMING_SNAKE_CASE_					:   Any=None						,    SCREAMING_SNAKE_CASE_					:   Tuple=None						,    ):



    '''simple docstring'''





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




 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     ,       )      ->							List[Any]:
     _lowerCAmelCase		     = parent
     _lowerCAmelCase		     = batch_size
     _lowerCAmelCase		     = seq_length
     _lowerCAmelCase		     = is_training
     _lowerCAmelCase		     = use_labels
     _lowerCAmelCase		     = vocab_size
     _lowerCAmelCase		     = hidden_size
     _lowerCAmelCase		     = num_hidden_layers
     _lowerCAmelCase		     = num_attention_heads
     _lowerCAmelCase		     = intermediate_size
     _lowerCAmelCase		     = hidden_act
     _lowerCAmelCase		     = hidden_dropout_prob
     _lowerCAmelCase		     = attention_probs_dropout_prob
     _lowerCAmelCase		     = max_position_embeddings
     _lowerCAmelCase		     = eos_token_id
     _lowerCAmelCase		     = pad_token_id
     _lowerCAmelCase		     = bos_token_id
     _lowerCAmelCase		     = decoder_start_token_id
     _lowerCAmelCase		     = initializer_range




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

     _lowerCAmelCase		     = shift_tokens_right(_lowerCAmelCase     ,       1						)

     _lowerCAmelCase		     = 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     ,       )
     _lowerCAmelCase		     = prepare_mbart_inputs_dict(_lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)
     return config, inputs_dict




 def 							_snake_case				(		self						)      ->							int:
     _lowerCAmelCase     ,     _lowerCAmelCase		     = self.prepare_config_and_inputs()
     return config, inputs_dict




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Optional[Any]:
     _lowerCAmelCase		     = 20
     _lowerCAmelCase		     = model_class_name(_lowerCAmelCase						)

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

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

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

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

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

     _lowerCAmelCase		     = model.decode(_lowerCAmelCase     ,       _lowerCAmelCase						)

     _lowerCAmelCase		     = 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     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Union[str, Any]:
     _lowerCAmelCase		     = 20
     _lowerCAmelCase		     = model_class_name(_lowerCAmelCase						)

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

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

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

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

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

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

     _lowerCAmelCase		     = 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						):
 __lowerCamelCase  :							Optional[int]    					=   99




 def 							_snake_case				(		self						)      ->							Tuple:
     _lowerCAmelCase		     = 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     ,       )

     _lowerCAmelCase		     = input_ids.shape[0]
     _lowerCAmelCase		     = 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						)      ->							Any:
     _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase		     = self._get_config_and_data()
     _lowerCAmelCase		     = FlaxMBartForSequenceClassification(_lowerCAmelCase						)
     _lowerCAmelCase		     = model(input_ids=_lowerCAmelCase     ,       decoder_input_ids=_lowerCAmelCase						)
     _lowerCAmelCase		     = (batch_size, config.num_labels)
     self.assertEqual(outputs["logits"].shape     ,       _lowerCAmelCase						)




 def 							_snake_case				(		self						)      ->							Optional[int]:
     _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase		     = self._get_config_and_data()
     _lowerCAmelCase		     = FlaxMBartForQuestionAnswering(_lowerCAmelCase						)
     _lowerCAmelCase		     = model(input_ids=_lowerCAmelCase						)

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




 def 							_snake_case				(		self						)      ->							List[Any]:
     _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase		     = self._get_config_and_data()
     _lowerCAmelCase		     = FlaxMBartForConditionalGeneration(_lowerCAmelCase						)
     _lowerCAmelCase		     = lm_model(input_ids=_lowerCAmelCase						)
     _lowerCAmelCase		     = (batch_size, input_ids.shape[1], config.vocab_size)
     self.assertEqual(outputs["logits"].shape     ,       _lowerCAmelCase						)




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





 def 							_snake_case				(		self						)      ->							Any:
     _lowerCAmelCase		     = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]     ,       dtype=np.intaa						)
     _lowerCAmelCase		     = shift_tokens_right(_lowerCAmelCase     ,       1						)
     _lowerCAmelCase		     = np.equal(_lowerCAmelCase     ,       1						).astype(np.floataa						).sum()
     _lowerCAmelCase		     = 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_						(						__magic_name__  ,unittest.TestCase  ,__magic_name__						):
 __lowerCamelCase  :							List[Any]    					=   True
 __lowerCamelCase  :							Tuple    					=   (
     (
         FlaxMBartModel,
         FlaxMBartForConditionalGeneration,
         FlaxMBartForSequenceClassification,
         FlaxMBartForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )
 __lowerCamelCase  :							str    					=   (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




 def 							_snake_case				(		self						)      ->							Tuple:
     _lowerCAmelCase		     = FlaxMBartModelTester(self						)




 def 							_snake_case				(		self						)      ->							Optional[int]:
     _lowerCAmelCase     ,     _lowerCAmelCase		     = 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 							_snake_case				(		self						)      ->							str:
     _lowerCAmelCase     ,     _lowerCAmelCase		     = 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 							_snake_case				(		self						)      ->							Optional[int]:
     _lowerCAmelCase     ,     _lowerCAmelCase		     = self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__						):
             _lowerCAmelCase		     = self._prepare_for_class(_lowerCAmelCase     ,       _lowerCAmelCase						)
             _lowerCAmelCase		     = 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"						):
                 _lowerCAmelCase		     = encode_jitted(**_lowerCAmelCase						).to_tuple()

             with self.subTest("JIT Disabled"						):
                 with jax.disable_jit():
                     _lowerCAmelCase		     = 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 							_snake_case				(		self						)      ->							str:
     _lowerCAmelCase     ,     _lowerCAmelCase		     = self.model_tester.prepare_config_and_inputs_for_common()

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

             _lowerCAmelCase		     = {
                 "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"						):
                 _lowerCAmelCase		     = decode_jitted(**_lowerCAmelCase						).to_tuple()

             with self.subTest("JIT Disabled"						):
                 with jax.disable_jit():
                     _lowerCAmelCase		     = 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 							_snake_case				(		self						)      ->							int:
     for model_class_name in self.all_model_classes:
         _lowerCAmelCase		     = model_class_name.from_pretrained("facebook/mbart-large-cc25"     ,       from_pt=_lowerCAmelCase						)
         # FlaxMBartForSequenceClassification expects eos token in input_ids
         _lowerCAmelCase		     = np.ones((1, 1)						) * model.config.eos_token_id
         _lowerCAmelCase		     = model(_lowerCAmelCase						)
         self.assertIsNotNone(_lowerCAmelCase						)







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




 @cached_property
 def 							_snake_case				(		self						)      ->							Union[str, Any]:
     return AutoTokenizer.from_pretrained(self.model_name						)




 @cached_property
 def 							_snake_case				(		self						)      ->							Union[str, Any]:
     _lowerCAmelCase		     = FlaxMBartForConditionalGeneration.from_pretrained(self.model_name     ,       from_pt=_lowerCAmelCase						)
     return model




 def 							_snake_case				(		self     ,       **_lowerCAmelCase						)      ->							int:
     _lowerCAmelCase		     = self.translate_src_text(**_lowerCAmelCase						)
     self.assertListEqual(self.expected_text     ,       _lowerCAmelCase						)




 def 							_snake_case				(		self     ,       **_lowerCAmelCase						)      ->							Optional[Any]:
     _lowerCAmelCase		     = self.tokenizer(self.src_text     ,       **_lowerCAmelCase     ,       return_tensors="np"						)
     _lowerCAmelCase		     = 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
     _lowerCAmelCase		     = self.tokenizer.batch_decode(_lowerCAmelCase     ,       skip_special_tokens=_lowerCAmelCase						)
     return generated_words





 @slow
 def 							_snake_case				(		self						)      ->							Optional[Any]:
     self._assert_generated_batch_equal_expected()


