"""simple docstring"""


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, 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
    __UpperCamelCase								=    '''platform'''

    import jax
    import jax.numpy as jnp

    from transformers import MarianTokenizer
    from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase=None		,      UpperCAmelCase=None		,      UpperCAmelCase=None		,      UpperCAmelCase=None		,      UpperCAmelCase=None		,      UpperCAmelCase=None		,      )       ->   List[str]:
     if attention_mask is None:
          snake_case_       =      np.where(input_ids != config.pad_token_id		,      1		,      0      )
     if decoder_attention_mask is None:
          snake_case_       =      np.where(decoder_input_ids != config.pad_token_id		,      1		,      0      )
     if head_mask is None:
          snake_case_       =      np.ones((config.encoder_layers, config.encoder_attention_heads)      )
     if decoder_head_mask is None:
          snake_case_       =      np.ones((config.decoder_layers, config.decoder_attention_heads)      )
     if cross_attn_head_mask is None:
          snake_case_       =      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": attention_mask,
     }




class 				UpperCamelCase      :







       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__=0.02,							)   ->   Tuple:
            snake_case_       =      parent
            snake_case_       =      batch_size
            snake_case_       =      seq_length
            snake_case_       =      is_training
            snake_case_       =      use_labels
            snake_case_       =      vocab_size
            snake_case_       =      hidden_size
            snake_case_       =      num_hidden_layers
            snake_case_       =      num_attention_heads
            snake_case_       =      intermediate_size
            snake_case_       =      hidden_act
            snake_case_       =      hidden_dropout_prob
            snake_case_       =      attention_probs_dropout_prob
            snake_case_       =      max_position_embeddings
            snake_case_       =      eos_token_id
            snake_case_       =      pad_token_id
            snake_case_       =      bos_token_id
            snake_case_       =      initializer_range







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

            snake_case_       =      shift_tokens_right(lowerCAmelCase__,							1,							2)

            snake_case_       =      MarianConfig(
                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,							initializer_range=self.initializer_range,							use_cache=lowerCAmelCase__,							)
            snake_case_       =      prepare_marian_inputs_dict(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)
            return config, inputs_dict







       def 	a_		(				self)   ->   Optional[int]:
            snake_case_ ,			snake_case_       =      self.prepare_config_and_inputs()
            return config, inputs_dict







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)   ->   List[Any]:
            snake_case_       =      20
            snake_case_       =      model_class_name(lowerCAmelCase__)

            snake_case_       =      model.encode(inputs_dict['input_ids'])

            snake_case_ ,			snake_case_       =      (
                inputs_dict['decoder_input_ids'],
                inputs_dict['decoder_attention_mask'],
            )

            snake_case_       =      model.init_cache(decoder_input_ids.shape[0],							lowerCAmelCase__,							lowerCAmelCase__)
            snake_case_       =      jnp.ones((decoder_input_ids.shape[0], max_decoder_length),							dtype='i4')

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

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

            snake_case_       =      model.decode(lowerCAmelCase__,							lowerCAmelCase__)

            snake_case_       =      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 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)   ->   int:
            snake_case_       =      20
            snake_case_       =      model_class_name(lowerCAmelCase__)

            snake_case_       =      model.encode(inputs_dict['input_ids'])

            snake_case_ ,			snake_case_       =      (
                inputs_dict['decoder_input_ids'],
                inputs_dict['decoder_attention_mask'],
            )

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

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

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

            snake_case_       =      model.decode(lowerCAmelCase__,							lowerCAmelCase__,							decoder_attention_mask=lowerCAmelCase__)

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




@require_flax
class 				UpperCamelCase      (       lowerCAmelCase__					,	unittest.TestCase					,	lowerCAmelCase__     ):
       SCREAMING_SNAKE_CASE_				=   True
       SCREAMING_SNAKE_CASE_				=   (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
       SCREAMING_SNAKE_CASE_				=   (FlaxMarianMTModel,) if is_flax_available() else ()







       def 	a_		(				self)   ->   List[str]:
            snake_case_       =      FlaxMarianModelTester(self)







       def 	a_		(				self)   ->   Tuple:
            snake_case_ ,			snake_case_       =      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 	a_		(				self)   ->   Optional[int]:
            snake_case_ ,			snake_case_       =      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 	a_		(				self)   ->   int:
            snake_case_ ,			snake_case_       =      self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__):
                      snake_case_       =      self._prepare_for_class(lowerCAmelCase__,							lowerCAmelCase__)
                      snake_case_       =      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'):
                           snake_case_       =      encode_jitted(**lowerCAmelCase__).to_tuple()

                      with self.subTest('JIT Disabled'):
                           with jax.disable_jit():
                                snake_case_       =      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 	a_		(				self)   ->   Union[str, Any]:
            snake_case_ ,			snake_case_       =      self.model_tester.prepare_config_and_inputs_for_common()

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

                      snake_case_       =      {
                          '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'):
                           snake_case_       =      decode_jitted(**lowerCAmelCase__).to_tuple()

                      with self.subTest('JIT Disabled'):
                           with jax.disable_jit():
                                snake_case_       =      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 	a_		(				self)   ->   Optional[int]:
            for model_class_name in self.all_model_classes:
                 snake_case_       =      model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de')
                 # FlaxMarianForSequenceClassification expects eos token in input_ids
                 snake_case_       =      np.ones((1, 1)) * model.config.eos_token_id
                 snake_case_       =      model(lowerCAmelCase__)
                 self.assertIsNotNone(lowerCAmelCase__)




@require_flax
@require_sentencepiece
@require_tokenizers
class 				UpperCamelCase      (       unittest.TestCase     ):
       SCREAMING_SNAKE_CASE_				=   None
       SCREAMING_SNAKE_CASE_				=   None







       @classmethod
       def 	a_		(				cls)   ->   None:
            snake_case_       =      f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
            return cls







       @cached_property
       def 	a_		(				self)   ->   Dict:
            return MarianTokenizer.from_pretrained(self.model_name)







       @property
       def 	a_		(				self)   ->   int:
            return self.tokenizer.eos_token_id







       @cached_property
       def 	a_		(				self)   ->   Optional[int]:
            snake_case_       =      FlaxMarianMTModel.from_pretrained(self.model_name)
            self.assertEqual(model.config.decoder_start_token_id,							model.config.pad_token_id)
            return model







       def 	a_		(				self,							**lowerCAmelCase__)   ->   List[Any]:
            snake_case_       =      self.translate_src_text(**lowerCAmelCase__)
            self.assertListEqual(self.expected_text,							lowerCAmelCase__)







       def 	a_		(				self,							**lowerCAmelCase__)   ->   Tuple:
            snake_case_       =      self.tokenizer(self.src_text,							padding=lowerCAmelCase__,							return_tensors='np',							**lowerCAmelCase__)
            snake_case_       =      self.model.generate(
                model_inputs.input_ids,							attention_mask=model_inputs.attention_mask,							num_beams=2,							max_length=128,							).sequences
            snake_case_       =      self.tokenizer.batch_decode(lowerCAmelCase__,							skip_special_tokens=lowerCAmelCase__)
            return generated_words




@require_flax
@require_sentencepiece
@require_tokenizers
class 				UpperCamelCase      (       lowerCAmelCase__     ):
       SCREAMING_SNAKE_CASE_				=   "en"
       SCREAMING_SNAKE_CASE_				=   "fr"
       SCREAMING_SNAKE_CASE_				=   [
           "I am a small frog.",
           "Now I can forget the 100 words of german that I know.",
       ]
       SCREAMING_SNAKE_CASE_				=   [
           "Je suis une petite grenouille.",
           "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
       ]







       @slow
       def 	a_		(				self)   ->   int:
            self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 				UpperCamelCase      (       lowerCAmelCase__     ):
       SCREAMING_SNAKE_CASE_				=   "fr"
       SCREAMING_SNAKE_CASE_				=   "en"
       SCREAMING_SNAKE_CASE_				=   [
           "Donnez moi le micro.",
           "Tom et Mary étaient assis à une table.",  # Accents
       ]
       SCREAMING_SNAKE_CASE_				=   [
           "Give me the microphone.",
           "Tom and Mary were sitting at a table.",
       ]







       @slow
       def 	a_		(				self)   ->   Union[str, Any]:
            self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 				UpperCamelCase      (       lowerCAmelCase__     ):
       SCREAMING_SNAKE_CASE_				=   "mt"
       SCREAMING_SNAKE_CASE_				=   "en"
       SCREAMING_SNAKE_CASE_				=   ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
       SCREAMING_SNAKE_CASE_				=   ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]







       @slow
       def 	a_		(				self)   ->   List[str]:
            self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 				UpperCamelCase      (       lowerCAmelCase__     ):
       SCREAMING_SNAKE_CASE_				=   "en"
       SCREAMING_SNAKE_CASE_				=   "de"
       SCREAMING_SNAKE_CASE_				=   [
           "I am a small frog.",
           "Now I can forget the 100 words of german that I know.",
           "Tom asked his teacher for advice.",
           "That's how I would do it.",
           "Tom really admired Mary's courage.",
           "Turn around and close your eyes.",
       ]
       SCREAMING_SNAKE_CASE_				=   [
           "Ich bin ein kleiner Frosch.",
           "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
           "Tom bat seinen Lehrer um Rat.",
           "So würde ich das machen.",
           "Tom bewunderte Marias Mut wirklich.",
           "Drehen Sie sich um und schließen Sie die Augen.",
       ]







       @slow
       def 	a_		(				self)   ->   int:
            self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 				UpperCamelCase      (       lowerCAmelCase__     ):
       SCREAMING_SNAKE_CASE_				=   "en"
       SCREAMING_SNAKE_CASE_				=   "zh"
       SCREAMING_SNAKE_CASE_				=   ["My name is Wolfgang and I live in Berlin"]
       SCREAMING_SNAKE_CASE_				=   ["我叫沃尔夫冈 我住在柏林"]







       @slow
       def 	a_		(				self)   ->   Optional[int]:
            self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 				UpperCamelCase      (       lowerCAmelCase__     ):
       SCREAMING_SNAKE_CASE_				=   "ru"
       SCREAMING_SNAKE_CASE_				=   "fr"
       SCREAMING_SNAKE_CASE_				=   ["Он показал мне рукопись своей новой пьесы."]
       SCREAMING_SNAKE_CASE_				=   ["Il m'a montré le manuscrit de sa nouvelle pièce."]







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




@require_flax
@require_sentencepiece
@require_tokenizers
class 				UpperCamelCase      (       lowerCAmelCase__     ):
       SCREAMING_SNAKE_CASE_				=   "en"
       SCREAMING_SNAKE_CASE_				=   "ROMANCE"
       SCREAMING_SNAKE_CASE_				=   [
           ">>fr<< Don't spend so much time watching TV.",
           ">>pt<< Your message has been sent.",
           ">>es<< He's two years older than me.",
       ]
       SCREAMING_SNAKE_CASE_				=   [
           "Ne passez pas autant de temps à regarder la télé.",
           "A sua mensagem foi enviada.",
           "Es dos años más viejo que yo.",
       ]







       @slow
       def 	a_		(				self)   ->   Tuple:
            self._assert_generated_batch_equal_expected()


"""simple docstring"""


from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
    from .data.data_collator import DataCollator
    from .modeling_utils import PreTrainedModel
    from .tokenization_utils_base import PreTrainedTokenizerBase
    from .trainer_callback import TrainerCallback
    from .trainer_utils import EvalPrediction, PredictionOutput
    from .training_args import TrainingArguments


__UpperCamelCase								=    logging.get_logger(__name__)




class 				UpperCamelCase      (       lowerCAmelCase__     ):







       def __init__(				self,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = (None, None),							lowerCAmelCase__ = None,							)   ->   Dict:
            super().__init__(
                model=lowerCAmelCase__,							args=lowerCAmelCase__,							data_collator=lowerCAmelCase__,							train_dataset=lowerCAmelCase__,							eval_dataset=lowerCAmelCase__,							tokenizer=lowerCAmelCase__,							model_init=lowerCAmelCase__,							compute_metrics=lowerCAmelCase__,							callbacks=lowerCAmelCase__,							optimizers=lowerCAmelCase__,							preprocess_logits_for_metrics=lowerCAmelCase__,							)

            # Override self.model.generation_config if a GenerationConfig is specified in args.
            # Priority: args.generation_config > model.generation_config > default GenerationConfig.
            if self.args.generation_config is not None:
                 snake_case_       =      self.load_generation_config(self.args.generation_config)
                 snake_case_       =      gen_config







       @staticmethod
       def 	a_		(				lowerCAmelCase__)   ->   GenerationConfig:

            # GenerationConfig provided, nothing to do
            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 return deepcopy(lowerCAmelCase__)

            # str or Path
            snake_case_       =      Path(lowerCAmelCase__) if isinstance(lowerCAmelCase__,							lowerCAmelCase__) else gen_config_arg
            snake_case_       =      None

            # Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
            # This step is required in order to determine config_file_name
            if pretrained_model_name.is_file():
                 snake_case_       =      pretrained_model_name.name
                 snake_case_       =      pretrained_model_name.parent
            # dir path
            elif pretrained_model_name.is_dir():
                 pass
            # model id or URL
            else:
                 snake_case_       =      gen_config_arg

            snake_case_       =      GenerationConfig.from_pretrained(lowerCAmelCase__,							lowerCAmelCase__)
            return gen_config







       def 	a_		(				self,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = "eval",							**lowerCAmelCase__,							)   ->   Dict[str, float]:
            snake_case_       =      gen_kwargs.copy()
            if gen_kwargs.get('max_length') is None and gen_kwargs.get('max_new_tokens') is None:
                 snake_case_       =      self.args.generation_max_length
            snake_case_       =      (
                gen_kwargs['num_beams'] if gen_kwargs.get('num_beams') is not None else self.args.generation_num_beams
            )
            snake_case_       =      gen_kwargs

            return super().evaluate(lowerCAmelCase__,							ignore_keys=lowerCAmelCase__,							metric_key_prefix=lowerCAmelCase__)







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__ = None,							lowerCAmelCase__ = "test",							**lowerCAmelCase__,							)   ->   "PredictionOutput":
            snake_case_       =      gen_kwargs.copy()
            if gen_kwargs.get('max_length') is None and gen_kwargs.get('max_new_tokens') is None:
                 snake_case_       =      self.args.generation_max_length
            snake_case_       =      (
                gen_kwargs['num_beams'] if gen_kwargs.get('num_beams') is not None else self.args.generation_num_beams
            )
            snake_case_       =      gen_kwargs

            return super().predict(lowerCAmelCase__,							ignore_keys=lowerCAmelCase__,							metric_key_prefix=lowerCAmelCase__)







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = None,							**lowerCAmelCase__,							)   ->   Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:

            if not self.args.predict_with_generate or prediction_loss_only:
                 return super().prediction_step(
                     lowerCAmelCase__,							lowerCAmelCase__,							prediction_loss_only=lowerCAmelCase__,							ignore_keys=lowerCAmelCase__)

            snake_case_       =      'labels' in inputs
            snake_case_       =      self._prepare_inputs(lowerCAmelCase__)

            # XXX: adapt synced_gpus for fairscale as well
            # Priority (handled in generate):
            # gen_kwargs > model.generation_config > default GenerationConfig()

            if len(lowerCAmelCase__) == 0 and hasattr(self,							'_gen_kwargs'):
                 snake_case_       =      self._gen_kwargs.copy()

            if gen_kwargs.get('max_length') is None and gen_kwargs.get('max_new_tokens') is None:
                 snake_case_       =      self.model.config.max_length
            snake_case_       =      (
                gen_kwargs['num_beams'] if gen_kwargs.get('num_beams') is not None else self.model.config.num_beams
            )
            snake_case_       =      True if is_deepspeed_zeroa_enabled() else False
            snake_case_       =      (
                gen_kwargs['synced_gpus'] if gen_kwargs.get('synced_gpus') is not None else default_synced_gpus
            )

            # If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
            # (otherwise, it would continue generating from the padded `decoder_input_ids`)
            if (
                "labels" in inputs
                and "decoder_input_ids" in inputs
                and inputs["labels"].shape == inputs["decoder_input_ids"].shape
            ):
                 snake_case_       =      {k: v for k, v in inputs.items() if k != 'decoder_input_ids'}
            snake_case_       =      self.model.generate(**lowerCAmelCase__,							**lowerCAmelCase__)

            # Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
            # TODO: remove this hack when the legacy code that initializes generation_config from a model config is
            # removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
            if self.model.generation_config._from_model_config:
                 snake_case_       =      False

            # Retrieves GenerationConfig from model.generation_config
            snake_case_       =      self.model.generation_config
            # in case the batch is shorter than max length, the output should be padded
            if generated_tokens.shape[-1] < gen_config.max_length:
                 snake_case_       =      self._pad_tensors_to_max_len(lowerCAmelCase__,							gen_config.max_length)
            elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
                 snake_case_       =      self._pad_tensors_to_max_len(lowerCAmelCase__,							gen_config.max_new_tokens + 1)

            with torch.no_grad():
                 if has_labels:
                      with self.compute_loss_context_manager():
                           snake_case_       =      model(**lowerCAmelCase__)
                      if self.label_smoother is not None:
                           snake_case_       =      self.label_smoother(lowerCAmelCase__,							inputs['labels']).mean().detach()
                      else:
                           snake_case_       =      (outputs['loss'] if isinstance(lowerCAmelCase__,							lowerCAmelCase__) else outputs[0]).mean().detach()
                 else:
                      snake_case_       =      None

            if self.args.prediction_loss_only:
                 return loss, None, None

            if has_labels:
                 snake_case_       =      inputs['labels']
                 if labels.shape[-1] < gen_config.max_length:
                      snake_case_       =      self._pad_tensors_to_max_len(lowerCAmelCase__,							gen_config.max_length)
                 elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
                      snake_case_       =      self._pad_tensors_to_max_len(lowerCAmelCase__,							gen_config.max_new_tokens + 1)
            else:
                 snake_case_       =      None

            return loss, generated_tokens, labels







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__)   ->   Any:
            if self.tokenizer is not None and hasattr(self.tokenizer,							'pad_token_id'):
                 # If PAD token is not defined at least EOS token has to be defined
                 snake_case_       =      (
                     self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
                 )
            else:
                 if self.model.config.pad_token_id is not None:
                      snake_case_       =      self.model.config.pad_token_id
                 else:
                      raise ValueError('Pad_token_id must be set in the configuration of the model, in order to pad tensors')

            snake_case_       =      pad_token_id * torch.ones(
                (tensor.shape[0], max_length),							dtype=tensor.dtype,							device=tensor.device)
            snake_case_       =      tensor
            return padded_tensor

