





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_lowerCAmelCase							:      Dict						=	logging.getLogger(__name__)


@dataclass
class 					__magic_name__  :
     __UpperCamelCase					    =     field(
         metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}							)
     __UpperCamelCase					    =     field(
         default=UpperCamelCase_    ,				metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}							)
     __UpperCamelCase					    =     field(
         default=UpperCamelCase_    ,				metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}							)
     __UpperCamelCase					    =     field(
         default=UpperCamelCase_    ,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}    ,				)




@dataclass
class 					__magic_name__  :
     __UpperCamelCase					    =     field(
         metadata={'''help''': '''The name of the task to train selected in the list: ''' + ''', '''.join(hans_processors.keys()							)}							)
     __UpperCamelCase					    =     field(
         metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}							)
     __UpperCamelCase					    =     field(
         default=1_28    ,				metadata={
             '''help''': (
                 '''The maximum total input sequence length after tokenization. Sequences longer '''
                 '''than this will be truncated, sequences shorter will be padded.'''
             )
         }    ,				)
     __UpperCamelCase					    =     field(
         default=UpperCamelCase_    ,				metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}							)




def      __snake_case						(					_lowerCAmelCase : Optional[Any]	) ->						Dict[str, torch.Tensor]:
   A_					:				str          =			default_data_collator(snake_case_	)
   A_					:				Optional[Any]          =			batch.pop("pairID"				,				snake_case_	)
   return batch

def      __snake_case						(					) ->						Optional[int]:
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   A_					:				Union[str, Any]          =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
   A_					:				Tuple          =			parser.parse_args_into_dataclasses()

   if (
       os.path.exists(training_args.output_dir	)
       and os.listdir(training_args.output_dir	)
       and training_args.do_train
       and not training_args.overwrite_output_dir
   ):
      raise ValueError(
          f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
          " --overwrite_output_dir to overcome."	)

   # Setup logging
   logging.basicConfig(
       format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,				datefmt="%m/%d/%Y %H:%M:%S"				,				level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN				,				)
   logger.warning(
       "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"				,				training_args.local_rank				,				training_args.device				,				training_args.n_gpu				,				bool(training_args.local_rank != -1	)				,				training_args.fpaa				,				)
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank	):
      transformers.utils.logging.set_verbosity_info()
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()
   logger.info("Training/evaluation parameters %s"				,				snake_case_	)

   # Set seed
   set_seed(training_args.seed	)

   try:
      A_					:				Optional[int]          =			hans_tasks_num_labels[data_args.task_name]
   except KeyError:
      raise ValueError("Task not found: %s" % (data_args.task_name)	)

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.

   A_					:				str          =			AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path				,				num_labels=snake_case_				,				finetuning_task=data_args.task_name				,				cache_dir=model_args.cache_dir				,				)
   A_					:				Union[str, Any]          =			AutoTokenizer.from_pretrained(
       model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,				cache_dir=model_args.cache_dir				,				)
   A_					:				int          =			AutoModelForSequenceClassification.from_pretrained(
       model_args.model_name_or_path				,				from_tf=bool(".ckpt" in model_args.model_name_or_path	)				,				config=snake_case_				,				cache_dir=model_args.cache_dir				,				)

   # Get datasets
   A_					:				List[Any]          =			(
       HansDataset(
           data_dir=data_args.data_dir				,				tokenizer=snake_case_				,				task=data_args.task_name				,				max_seq_length=data_args.max_seq_length				,				overwrite_cache=data_args.overwrite_cache				,				)
       if training_args.do_train
       else None
   )
   A_					:				List[str]          =			(
       HansDataset(
           data_dir=data_args.data_dir				,				tokenizer=snake_case_				,				task=data_args.task_name				,				max_seq_length=data_args.max_seq_length				,				overwrite_cache=data_args.overwrite_cache				,				evaluate=snake_case_				,				)
       if training_args.do_eval
       else None
   )

   # Initialize our Trainer
   A_					:				Optional[Any]          =			Trainer(
       model=snake_case_				,				args=snake_case_				,				train_dataset=snake_case_				,				eval_dataset=snake_case_				,				data_collator=snake_case_				,				)

   # Training
   if training_args.do_train:
      trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
      trainer.save_model()
      # For convenience, we also re-save the tokenizer to the same directory,
      # so that you can share your model easily on huggingface.co/models =)
      if trainer.is_world_master():
         tokenizer.save_pretrained(training_args.output_dir	)

    # Evaluation
   if training_args.do_eval:
      logger.info("*** Evaluate ***"	)

      A_					:				Optional[Any]          =			trainer.predict(snake_case_	)
      A_					:				Dict          =			output.predictions
      A_					:				Optional[int]          =			np.argmax(snake_case_				,				axis=1	)

      A_					:				Dict          =			[ex.pairID for ex in eval_dataset]
      A_					:				Tuple          =			os.path.join(training_args.output_dir				,				"hans_predictions.txt"	)
      A_					:				List[Any]          =			eval_dataset.get_labels()
      if trainer.is_world_master():
         with open(snake_case_				,				"w"	) as writer:
            writer.write("pairID,gold_label\n"	)
            for pid, pred in zip(snake_case_				,				snake_case_	):
               writer.write("ex" + str(snake_case_	) + "," + label_list[int(snake_case_	)] + "\n"	)

      trainer._log(output.metrics	)

def      __snake_case						(					_lowerCAmelCase : int	) ->						Union[str, Any]:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
 main()







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
 _lowerCAmelCase							:      List[Any]						=	'''platform'''

 import jax
 import jax.numpy as jnp

 from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer

def      __snake_case						(					_lowerCAmelCase : List[str]				,				_lowerCAmelCase : int				,				_lowerCAmelCase : Dict=None				,				_lowerCAmelCase : Dict=None	) ->						Tuple:
   if attention_mask is None:
      A_					:				Tuple          =			np.where(input_ids != config.pad_token_id				,				1				,				0	)
   return {
       "input_ids": input_ids,
       "attention_mask": attention_mask,
   }


@require_flax
class 					__magic_name__  :





     """simple docstring"""



     def __init__(							self       :Dict			,      snake_case       :List[Any]			,      snake_case       :str=13			,      snake_case       :Dict=7			,      snake_case       :List[Any]=True			,      snake_case       :Dict=False			,      snake_case       :List[Any]=99			,      snake_case       :Dict=16			,      snake_case       :Optional[Any]=2			,      snake_case       :Tuple=4			,      snake_case       :List[Any]=4			,      snake_case       :List[Any]="gelu"			,      snake_case       :List[str]=0.1			,      snake_case       :Optional[int]=0.1			,      snake_case       :Union[str, Any]=20			,      snake_case       :Dict=2			,      snake_case       :Any=1			,      snake_case       :Tuple=0			,      snake_case       :Optional[Any]=16			,      snake_case       :Union[str, Any]=16			,      snake_case       :Optional[Any]=0.02			,      ):





        '''simple docstring'''




        A_					:				str          =			parent
        A_					:				Tuple          =			batch_size
        A_					:				Dict          =			seq_length
        A_					:				Any          =			is_training
        A_					:				Optional[Any]          =			use_labels
        A_					:				Optional[Any]          =			vocab_size
        A_					:				Optional[int]          =			hidden_size
        A_					:				Dict          =			num_hidden_layers
        A_					:				Dict          =			num_attention_heads
        A_					:				Tuple          =			intermediate_size
        A_					:				Tuple          =			hidden_act
        A_					:				Union[str, Any]          =			hidden_dropout_prob
        A_					:				Tuple          =			attention_probs_dropout_prob
        A_					:				Any          =			max_position_embeddings
        A_					:				Tuple          =			eos_token_id
        A_					:				Any          =			pad_token_id
        A_					:				Tuple          =			bos_token_id
        A_					:				str          =			embed_dim
        A_					:				List[str]          =			word_embed_proj_dim
        A_					:				Dict          =			initializer_range
        A_					:				Any          =			False



     def 						SCREAMING_SNAKE_CASE		(							self       :List[Any]  ):





        '''simple docstring'''




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

        A_					:				Any          =			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=snake_case			,      word_embed_proj_dim=self.word_embed_proj_dim			,      initializer_range=self.initializer_range			,      use_cache=snake_case			,      )
        A_					:				List[str]          =			prepare_opt_inputs_dict(snake_case			,      snake_case  )
        return config, inputs_dict



     def 						SCREAMING_SNAKE_CASE		(							self       :Dict  ):





        '''simple docstring'''




        A_      ,						A_					:				List[Any]          =			self.prepare_config_and_inputs()
        return config, inputs_dict



     def 						SCREAMING_SNAKE_CASE		(							self       :List[str]			,      snake_case       :Tuple			,      snake_case       :List[Any]			,      snake_case       :List[str]  ):





        '''simple docstring'''




        A_					:				int          =			20
        A_					:				int          =			model_class_name(snake_case  )

        A_					:				Any          =			inputs_dict["input_ids"]
        A_					:				Union[str, Any]          =			inputs_dict["attention_mask"]

        A_					:				List[Any]          =			model.init_cache(input_ids.shape[0]			,      snake_case  )
        A_					:				Dict          =			jnp.ones((input_ids.shape[0], max_length)			,      dtype="i4"  )

        A_					:				str          =			jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1  )[None, :]			,      (input_ids.shape[0], input_ids.shape[-1] - 1)			,      )
        A_					:				Union[str, Any]          =			model(
            input_ids[:, :-1]			,      attention_mask=snake_case			,      past_key_values=snake_case			,      position_ids=snake_case			,      )

        A_					:				Union[str, Any]          =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,      dtype="i4"  )
        A_					:				Dict          =			model(
            input_ids[:, -1:]			,      attention_mask=snake_case			,      past_key_values=outputs_cache.past_key_values			,      position_ids=snake_case			,      )

        A_					:				Union[str, Any]          =			model(snake_case  )

        A_					:				Dict          =			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       :Union[str, Any]			,      snake_case       :int			,      snake_case       :Dict			,      snake_case       :Any  ):





        '''simple docstring'''




        A_					:				Optional[int]          =			20
        A_					:				Any          =			model_class_name(snake_case  )

        A_      ,						A_					:				List[str]          =			(
            inputs_dict["input_ids"],
            inputs_dict["attention_mask"],
        )

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

        A_					:				Optional[int]          =			model.init_cache(input_ids.shape[0]			,      snake_case  )
        A_					:				Any          =			jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1  )[None, :]			,      (input_ids.shape[0], input_ids.shape[-1] - 1)			,      )

        A_					:				int          =			model(
            input_ids[:, :-1]			,      attention_mask=snake_case			,      past_key_values=snake_case			,      position_ids=snake_case			,      )
        A_					:				List[Any]          =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,      dtype="i4"  )
        A_					:				Union[str, Any]          =			model(
            input_ids[:, -1:]			,      past_key_values=outputs_cache.past_key_values			,      attention_mask=snake_case			,      position_ids=snake_case			,      )

        A_					:				str          =			model(snake_case			,      attention_mask=snake_case  )

        A_					:				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}"  )




@require_flax
class 					__magic_name__  (							lowerCamelCase__    ,				unittest.TestCase    ,				lowerCamelCase__							):





     """simple docstring"""



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



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        A_					:				Tuple          =			FlaxOPTModelTester(self  )



     def 						SCREAMING_SNAKE_CASE		(							self       :List[str]  ):





        '''simple docstring'''




        A_      ,						A_					:				List[Any]          =			self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(snake_case			,      snake_case			,      snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]  ):





        '''simple docstring'''




        A_      ,						A_					:				Any          =			self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward_with_attn_mask(snake_case			,      snake_case			,      snake_case  )





     @slow
     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        for model_class_name in self.all_model_classes:
           A_					:				Dict          =			model_class_name.from_pretrained("facebook/opt-125m"  )
           A_					:				str          =			np.ones((1, 1)  ) * model.config.eos_token_id
           A_					:				List[str]          =			model(snake_case  )
           self.assertIsNotNone(snake_case  )




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





     """simple docstring"""



     @slow
     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]  ):





        '''simple docstring'''




        A_					:				str          =			FlaxOPTModel.from_pretrained("facebook/opt-350m"  )
        A_					:				int          =			jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  )
        A_					:				int          =			model(input_ids=snake_case  ).last_hidden_state
        A_					:				int          =			(1, 11, 512)
        self.assertEqual(output.shape			,      snake_case  )
        A_					:				List[Any]          =			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]			,      snake_case			,      atol=4e-2  )  )




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





     """simple docstring"""



     def 						SCREAMING_SNAKE_CASE		(							self       :str  ):





        '''simple docstring'''




        super().setUp()
        A_					:				Dict          =			"facebook/opt-350m"





     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        A_					:				Any          =			FlaxOPTForCausalLM.from_pretrained(self.path_model  )
        A_					:				List[Any]          =			GPTaTokenizer.from_pretrained(self.path_model  )

        A_					:				int          =			[
            "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_					:				Any          =			tokenizer(snake_case			,      return_tensors="jax"			,      padding=snake_case			,      add_special_tokens=snake_case  )
        A_					:				int          =			model(inputs.input_ids			,      attention_mask=inputs.attention_mask  )[0].mean(axis=-1  )
        A_					:				Dict          =			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(snake_case			,      snake_case			,      atol=4e-2  )  )

        A_					:				Any          =			jax.jit(snake_case  )
        A_					:				List[str]          =			model(inputs.input_ids			,      attention_mask=inputs.attention_mask  )[0].mean(axis=-1  )
        self.assertTrue(jnp.allclose(snake_case			,      snake_case			,      atol=4e-2  )  )




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





     """simple docstring"""



     @property
     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        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       :int  ):





        '''simple docstring'''




        A_					:				Optional[Any]          =			"facebook/opt-125m"

        A_					:				Optional[int]          =			[
            "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_					:				str          =			[]

        A_					:				str          =			FlaxOPTForCausalLM.from_pretrained(snake_case  )
        A_					:				Any          =			GPTaTokenizer.from_pretrained(snake_case  )

        for prompt in self.prompts:
           A_					:				List[str]          =			tokenizer(snake_case			,      return_tensors="jax"  ).input_ids

           A_					:				List[Any]          =			model.generate(snake_case			,      max_length=10  )
           A_					:				Optional[int]          =			generated_ids[0]

           A_					:				str          =			tokenizer.batch_decode(snake_case			,      skip_special_tokens=snake_case  )
           predicted_outputs += generated_string

        self.assertListEqual(snake_case			,      snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Tuple  ):





        '''simple docstring'''




        A_					:				Dict          =			"facebook/opt-350m"

        A_					:				int          =			[
            "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_					:				List[Any]          =			[]
        A_					:				Tuple          =			FlaxOPTForCausalLM.from_pretrained(snake_case  )
        A_					:				Dict          =			GPTaTokenizer.from_pretrained(snake_case  )

        for prompt in self.prompts:
           A_					:				Optional[int]          =			tokenizer(snake_case			,      return_tensors="jax"  ).input_ids

           A_					:				Optional[int]          =			model.generate(snake_case			,      max_length=10  )
           A_					:				Tuple          =			generated_ids[0]

           A_					:				Tuple          =			tokenizer.batch_decode(snake_case			,      skip_special_tokens=snake_case  )
           predicted_outputs += generated_string

        self.assertListEqual(snake_case			,      snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Dict  ):





        '''simple docstring'''




        A_					:				str          =			"facebook/opt-125m"
        A_					:				Tuple          =			[
            "Today is a beautiful day and I want to thank",
            "In the city of Rome Canaver Canaver Canaver Canaver",
        ]
        A_					:				str          =			FlaxOPTForCausalLM.from_pretrained(snake_case  )
        A_					:				Optional[int]          =			GPTaTokenizer.from_pretrained(snake_case  )
        A_					:				Optional[Any]          =			tokenizer(
            [
                "Today is a beautiful day and I want to",
                "In the city of",
            ]			,      return_tensors="jax"			,      padding=snake_case			,      )

        A_					:				Any          =			jax.jit(model.generate  )

        A_					:				Tuple          =			jit_generate(inputs["input_ids"]			,      attention_mask=inputs["attention_mask"]  ).sequences

        A_					:				Tuple          =			tokenizer.batch_decode(snake_case			,      skip_special_tokens=snake_case  )

        self.assertIsNotNone(snake_case			,      snake_case  )





     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]  ):





        '''simple docstring'''




        A_					:				Optional[int]          =			"facebook/opt-350m"

        A_					:				Any          =			GPTaTokenizer.from_pretrained(snake_case  )
        A_					:				int          =			FlaxOPTForCausalLM.from_pretrained(snake_case  )

        A_					:				Tuple          =			"left"

        # use different length sentences to test batching
        A_					:				Tuple          =			[
            "Hello, my dog is a little",
            "Today, I",
        ]

        A_					:				List[Any]          =			tokenizer(snake_case			,      return_tensors="jax"			,      padding=snake_case  )
        A_					:				Any          =			inputs["input_ids"]

        A_					:				int          =			model.generate(input_ids=snake_case			,      attention_mask=inputs["attention_mask"]			,      trace=snake_case  )

        A_					:				Optional[int]          =			tokenizer(sentences[0]			,      return_tensors="jax"  ).input_ids
        A_					:				Dict          =			model.generate(input_ids=snake_case  )

        A_					:				int          =			inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
        A_					:				Union[str, Any]          =			tokenizer(sentences[1]			,      return_tensors="jax"  ).input_ids
        A_					:				Optional[int]          =			model.generate(input_ids=snake_case			,      max_length=model.config.max_length - num_paddings  )

        A_					:				List[Any]          =			tokenizer.batch_decode(outputs[0]			,      skip_special_tokens=snake_case  )
        A_					:				Dict          =			tokenizer.decode(output_non_padded[0][0]			,      skip_special_tokens=snake_case  )
        A_					:				Any          =			tokenizer.decode(output_padded[0][0]			,      skip_special_tokens=snake_case  )

        A_					:				Optional[Any]          =			[
            "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(snake_case			,      snake_case  )
        self.assertListEqual(snake_case			,      [non_padded_sentence, padded_sentence]  )
