





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


_UpperCamelCase   :							int     =	logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

_UpperCamelCase   :							Dict     =	{
    """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
_UpperCamelCase   :							Optional[int]     =	{
    """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
}
_UpperCamelCase   :							Optional[Any]     =	sorted(arg_to_scheduler.keys())
_UpperCamelCase   :							int     =	"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""







class 						_lowerCAmelCase(						pl.LightningModule):

  """simple docstring"""







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

     self.save_hyperparameters(_A     )
     __A				 =       0
     __A				 =       Path(self.hparams.output_dir     )
     __A				 =       self.hparams.cache_dir if self.hparams.cache_dir else None
     if config is None:
        __A				 =       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=_A  ,    **_A  ,    )
     else:
        __A				 =       config

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

     if tokenizer is None:
        __A				 =       AutoTokenizer.from_pretrained(
            self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path  ,    cache_dir=_A  ,    )
     else:
        __A				 =       tokenizer
     __A				 =       MODEL_MODES[mode]
     if model is None:
        __A				 =       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=_A  ,    )
     else:
        __A				 =       model

  def 		SCREAMING_SNAKE_CASE__	( self  ,    *UpperCAmelCase  ,    **UpperCAmelCase     )-> Dict:
     __A				 =       self.model_type.from_pretrained(*_A  ,    **_A     )

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

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[int]:
     __A				 =       self.model
     __A				 =       ['''bias''', '''LayerNorm.weight''']
     __A				 =       [
         {
             '''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:
        __A				 =       Adafactor(
            _A  ,    lr=self.hparams.learning_rate  ,    scale_parameter=_A  ,    relative_step=_A     )

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

     __A				 =       self.get_lr_scheduler()

     return [optimizer], [scheduler]

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase     )-> List[str]:
     return self.validation_step(_A  ,    _A     )

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase     )-> Any:
     return self.validation_end(_A     )

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

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase     )-> int:




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

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase = False     )-> Any:
     raise NotImplementedError('''You must implement this for your task'''     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[int]:
     return self.train_loader

  def 		SCREAMING_SNAKE_CASE__	( self     )-> str:
     return self.get_dataloader('''dev'''  ,    self.hparams.eval_batch_size  ,    shuffle=_A     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     return self.get_dataloader('''test'''  ,    self.hparams.eval_batch_size  ,    shuffle=_A     )

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase     )-> Optional[int]:
     return os.path.join(
         self.hparams.data_dir  ,    '''cached_{}_{}_{}'''.format(
             _A  ,    list(filter(_A  ,    self.hparams.model_name_or_path.split('''/'''     )     )     ).pop()  ,    str(self.hparams.max_seq_length     )  ,    )  ,    )

  @pl.utilities.rank_zero_only
  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase     )-> List[Any]:
     __A				 =       self.output_dir.joinpath('''best_tfmr'''     )
     __A				 =       self.step_count
     self.model.save_pretrained(_A     )
     self.tokenizer.save_pretrained(_A     )





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







class 						_lowerCAmelCase(						pl.Callback):

  """simple docstring"""







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

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





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


   '''simple docstring'''







   parser.add_argument(
       '''--output_dir'''    ,	default=snake_case    ,	type=snake_case    ,	required=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=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=snake_case     )
   parser.add_argument('''--max_grad_norm'''    ,	dest='''gradient_clip_val'''    ,	default=1.0    ,	type=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=snake_case    ,	default=1    ,	help='''Number of updates steps to accumulate before performing a backward/update pass.'''    ,	)
   parser.add_argument('''--seed'''    ,	type=snake_case    ,	default=4_2    ,	help='''random seed for initialization'''     )
   parser.add_argument(
       '''--data_dir'''    ,	default=snake_case    ,	type=snake_case    ,	required=snake_case    ,	help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''    ,	)
def 				__UpperCamelCase  (		snake_case    ,	snake_case    ,	snake_case=None    ,	snake_case=True    ,	snake_case=[]    ,	snake_case=None    ,	snake_case=None    ,	**snake_case    ,	)  ->						Optional[Any]:


   '''simple docstring'''







   pl.seed_everything(args.seed     )

   # init model
   __A				 =       Path(model.hparams.output_dir     )
   odir.mkdir(exist_ok=snake_case     )

   # add custom checkpoints
   if checkpoint_callback is None:
      __A				 =       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(snake_case     )
   if logging_callback is None:
      __A				 =       LoggingCallback()

   __A				 =       {}

   # TODO: remove with PyTorch 1.6 since pl uses native amp
   if args.fpaa:
      __A				 =       1_6
      __A				 =       args.fpaa_opt_level

   if args.gpus > 1:
      __A				 =       '''ddp'''

   __A				 =       args.accumulate_grad_batches
   __A				 =       extra_train_kwargs.get('''accelerator'''    ,	snake_case     )
   __A				 =       extra_train_kwargs.get('''profiler'''    ,	snake_case     )

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

   if args.do_train:
      trainer.fit(snake_case     )

   return trainer








import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
    import os

    # The slow tests are often failing with OOM error on GPU
    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
    _UpperCamelCase   :							Dict     =	"""platform"""

    import jax
    import jax.numpy as jnp

    from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
def 				__UpperCamelCase  (		snake_case    ,	snake_case    ,	snake_case=None    ,	snake_case=None     )  ->						str:


   '''simple docstring'''







   if attention_mask is None:
      __A				 =       np.where(input_ids != config.pad_token_id    ,	1    ,	0     )
   return {
       "input_ids": input_ids,
       "attention_mask": attention_mask,
   }







@require_flax
class 						_lowerCAmelCase:

  """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=20  ,    UpperCAmelCase=2  ,    UpperCAmelCase=1  ,    UpperCAmelCase=0  ,    UpperCAmelCase=16  ,    UpperCAmelCase=16  ,    UpperCAmelCase=0.02  ,    )-> List[str]:
     __A				 =       parent
     __A				 =       batch_size
     __A				 =       seq_length
     __A				 =       is_training
     __A				 =       use_labels
     __A				 =       vocab_size
     __A				 =       hidden_size
     __A				 =       num_hidden_layers
     __A				 =       num_attention_heads
     __A				 =       intermediate_size
     __A				 =       hidden_act
     __A				 =       hidden_dropout_prob
     __A				 =       attention_probs_dropout_prob
     __A				 =       max_position_embeddings
     __A				 =       eos_token_id
     __A				 =       pad_token_id
     __A				 =       bos_token_id
     __A				 =       embed_dim
     __A				 =       word_embed_proj_dim
     __A				 =       initializer_range
     __A				 =       False

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

     __A				 =       OPTConfig(
         vocab_size=self.vocab_size  ,    hidden_size=self.hidden_size  ,    num_hidden_layers=self.num_hidden_layers  ,    num_attention_heads=self.num_attention_heads  ,    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  ,    embed_dim=self.embed_dim  ,    is_encoder_decoder=UpperCAmelCase  ,    word_embed_proj_dim=self.word_embed_proj_dim  ,    initializer_range=self.initializer_range  ,    use_cache=UpperCAmelCase  ,    )
     __A				 =       prepare_opt_inputs_dict(UpperCAmelCase  ,    UpperCAmelCase     )
     return config, inputs_dict

  def 		SCREAMING_SNAKE_CASE__	( self     )-> int:
     __A ,						__A				 =       self.prepare_config_and_inputs()
     return config, inputs_dict

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase     )-> Any:
     __A				 =       20
     __A				 =       model_class_name(UpperCAmelCase     )

     __A				 =       inputs_dict['''input_ids''']
     __A				 =       inputs_dict['''attention_mask''']

     __A				 =       model.init_cache(input_ids.shape[0]  ,    UpperCAmelCase     )
     __A				 =       jnp.ones((input_ids.shape[0], max_length)  ,    dtype='''i4'''     )

     __A				 =       jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1     )[None, :]  ,    (input_ids.shape[0], input_ids.shape[-1] - 1)  ,    )
     __A				 =       model(
         input_ids[:, :-1]  ,    attention_mask=UpperCAmelCase  ,    past_key_values=UpperCAmelCase  ,    position_ids=UpperCAmelCase  ,    )

     __A				 =       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,    dtype='''i4'''     )
     __A				 =       model(
         input_ids[:, -1:]  ,    attention_mask=UpperCAmelCase  ,    past_key_values=outputs_cache.past_key_values  ,    position_ids=UpperCAmelCase  ,    )

     __A				 =       model(UpperCAmelCase     )

     __A				 =       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 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase     )-> str:
     __A				 =       20
     __A				 =       model_class_name(UpperCAmelCase     )

     __A ,						__A				 =       (
         inputs_dict['''input_ids'''],
         inputs_dict['''attention_mask'''],
     )

     __A				 =       jnp.concatenate(
         [
             attention_mask,
             jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])     ),
         ]  ,    axis=-1  ,    )

     __A				 =       model.init_cache(input_ids.shape[0]  ,    UpperCAmelCase     )
     __A				 =       jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1     )[None, :]  ,    (input_ids.shape[0], input_ids.shape[-1] - 1)  ,    )

     __A				 =       model(
         input_ids[:, :-1]  ,    attention_mask=UpperCAmelCase  ,    past_key_values=UpperCAmelCase  ,    position_ids=UpperCAmelCase  ,    )
     __A				 =       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,    dtype='''i4'''     )
     __A				 =       model(
         input_ids[:, -1:]  ,    past_key_values=outputs_cache.past_key_values  ,    attention_mask=UpperCAmelCase  ,    position_ids=UpperCAmelCase  ,    )

     __A				 =       model(UpperCAmelCase  ,    attention_mask=UpperCAmelCase     )

     __A				 =       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(						_a		,   unittest.TestCase		,   _a):

  """simple docstring"""







  lowerCamelCase__      					=   (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
  lowerCamelCase__      					=   () if is_flax_available() else ()

  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[Any]:
     __A				 =       FlaxOPTModelTester(self     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[Any]:
     __A ,						__A				 =       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 		SCREAMING_SNAKE_CASE__	( self     )-> int:
     __A ,						__A				 =       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     )





  @slow
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Union[str, Any]:
     for model_class_name in self.all_model_classes:
        __A				 =       model_class_name.from_pretrained('''facebook/opt-125m'''     )
        __A				 =       np.ones((1, 1)     ) * model.config.eos_token_id
        __A				 =       model(UpperCAmelCase     )
        self.assertIsNotNone(UpperCAmelCase     )







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

  """simple docstring"""







  @slow
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Tuple:
     __A				 =       FlaxOPTModel.from_pretrained('''facebook/opt-350m'''     )
     __A				 =       jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]     )
     __A				 =       model(input_ids=UpperCAmelCase     ).last_hidden_state
     __A				 =       (1, 11, 5_12)
     self.assertEqual(output.shape  ,    UpperCAmelCase     )
     __A				 =       jnp.array(
         [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]     )
     self.assertTrue(jnp.allclose(output[:, :3, :3]  ,    UpperCAmelCase  ,    atol=4e-2     )     )







@require_flax
@slow
class 						_lowerCAmelCase(						unittest.TestCase):

  """simple docstring"""







  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[str]:
     super().setUp()
     __A				 =       '''facebook/opt-350m'''





  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     __A				 =       FlaxOPTForCausalLM.from_pretrained(self.path_model     )
     __A				 =       GPTaTokenizer.from_pretrained(self.path_model     )

     __A				 =       [
         '''Today is a beautiful day and I want to''',
         '''In the city of''',
         '''Paris is the capital of France and''',
         '''Computers and mobile phones have taken''',
     ]
     # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
     __A				 =       tokenizer(UpperCAmelCase  ,    return_tensors='''jax'''  ,    padding=UpperCAmelCase  ,    add_special_tokens=UpperCAmelCase     )
     __A				 =       model(inputs.input_ids  ,    attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
     __A				 =       jnp.array(
         [
             [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
             [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
             [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
             [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
         ]     )
     self.assertTrue(jnp.allclose(UpperCAmelCase  ,    UpperCAmelCase  ,    atol=4e-2     )     )

     __A				 =       jax.jit(UpperCAmelCase     )
     __A				 =       model(inputs.input_ids  ,    attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
     self.assertTrue(jnp.allclose(UpperCAmelCase  ,    UpperCAmelCase  ,    atol=4e-2     )     )







@require_flax
@slow
class 						_lowerCAmelCase(						unittest.TestCase):

  """simple docstring"""







  @property
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Union[str, Any]:
     return [
         "Today is a beautiful day and I want",
         "In the city of",
         "Paris is the capital of France and",
         "Computers and mobile phones have taken",
     ]

  def 		SCREAMING_SNAKE_CASE__	( self     )-> str:
     __A				 =       '''facebook/opt-125m'''

     __A				 =       [
         '''Today is a beautiful day and I want to''',
         '''In the city of New York, the city''',
         '''Paris is the capital of France and the capital''',
         '''Computers and mobile phones have taken over the''',
     ]

     __A				 =       []

     __A				 =       FlaxOPTForCausalLM.from_pretrained(UpperCAmelCase     )
     __A				 =       GPTaTokenizer.from_pretrained(UpperCAmelCase     )

     for prompt in self.prompts:
        __A				 =       tokenizer(UpperCAmelCase  ,    return_tensors='''jax'''     ).input_ids

        __A				 =       model.generate(UpperCAmelCase  ,    max_length=10     )
        __A				 =       generated_ids[0]

        __A				 =       tokenizer.batch_decode(UpperCAmelCase  ,    skip_special_tokens=UpperCAmelCase     )
        predicted_outputs += generated_string

     self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[Any]:
     __A				 =       '''facebook/opt-350m'''

     __A				 =       [
         '''Today is a beautiful day and I want to''',
         '''In the city of San Francisco, the city''',
         '''Paris is the capital of France and the capital''',
         '''Computers and mobile phones have taken over the''',
     ]

     __A				 =       []
     __A				 =       FlaxOPTForCausalLM.from_pretrained(UpperCAmelCase     )
     __A				 =       GPTaTokenizer.from_pretrained(UpperCAmelCase     )

     for prompt in self.prompts:
        __A				 =       tokenizer(UpperCAmelCase  ,    return_tensors='''jax'''     ).input_ids

        __A				 =       model.generate(UpperCAmelCase  ,    max_length=10     )
        __A				 =       generated_ids[0]

        __A				 =       tokenizer.batch_decode(UpperCAmelCase  ,    skip_special_tokens=UpperCAmelCase     )
        predicted_outputs += generated_string

     self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     __A				 =       '''facebook/opt-125m'''
     __A				 =       [
         '''Today is a beautiful day and I want to thank''',
         '''In the city of Rome Canaver Canaver Canaver Canaver''',
     ]
     __A				 =       FlaxOPTForCausalLM.from_pretrained(UpperCAmelCase     )
     __A				 =       GPTaTokenizer.from_pretrained(UpperCAmelCase     )
     __A				 =       tokenizer(
         [
             '''Today is a beautiful day and I want to''',
             '''In the city of''',
         ]  ,    return_tensors='''jax'''  ,    padding=UpperCAmelCase  ,    )

     __A				 =       jax.jit(model.generate     )

     __A				 =       jit_generate(inputs['''input_ids''']  ,    attention_mask=inputs['''attention_mask''']     ).sequences

     __A				 =       tokenizer.batch_decode(UpperCAmelCase  ,    skip_special_tokens=UpperCAmelCase     )

     self.assertIsNotNone(UpperCAmelCase  ,    UpperCAmelCase     )





  def 		SCREAMING_SNAKE_CASE__	( self     )-> Any:
     __A				 =       '''facebook/opt-350m'''

     __A				 =       GPTaTokenizer.from_pretrained(UpperCAmelCase     )
     __A				 =       FlaxOPTForCausalLM.from_pretrained(UpperCAmelCase     )

     __A				 =       '''left'''

     # use different length sentences to test batching
     __A				 =       [
         '''Hello, my dog is a little''',
         '''Today, I''',
     ]

     __A				 =       tokenizer(UpperCAmelCase  ,    return_tensors='''jax'''  ,    padding=UpperCAmelCase     )
     __A				 =       inputs['''input_ids''']

     __A				 =       model.generate(input_ids=UpperCAmelCase  ,    attention_mask=inputs['''attention_mask''']  ,    trace=UpperCAmelCase     )

     __A				 =       tokenizer(sentences[0]  ,    return_tensors='''jax'''     ).input_ids
     __A				 =       model.generate(input_ids=UpperCAmelCase     )

     __A				 =       inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
     __A				 =       tokenizer(sentences[1]  ,    return_tensors='''jax'''     ).input_ids
     __A				 =       model.generate(input_ids=UpperCAmelCase  ,    max_length=model.config.max_length - num_paddings     )

     __A				 =       tokenizer.batch_decode(outputs[0]  ,    skip_special_tokens=UpperCAmelCase     )
     __A				 =       tokenizer.decode(output_non_padded[0][0]  ,    skip_special_tokens=UpperCAmelCase     )
     __A				 =       tokenizer.decode(output_padded[0][0]  ,    skip_special_tokens=UpperCAmelCase     )

     __A				 =       [
         '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
         '''Today, I was in the middle of a conversation with a friend about the''',
     ]
     self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )
     self.assertListEqual(UpperCAmelCase  ,    [non_padded_sentence, padded_sentence]     )

