





import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


_A :   str  			=  get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
       from transformers.models.mbart.modeling_mbart import shift_tokens_right

_A :   List[Any]  			=  25_00_04
_A :   Dict  			=  25_00_20


@require_sentencepiece
@require_tokenizers
class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,unittest.TestCase ):






       """simple docstring"""




       _UpperCAmelCase				:  Dict			   =   MBartTokenizer
       _UpperCAmelCase				:  str			   =   MBartTokenizerFast
       _UpperCAmelCase				:  List[Any]			   =   True
       _UpperCAmelCase				:  Dict			   =   True







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->List[str]:
          super().setUp()

          # We have a SentencePiece fixture for testing
          lowerCamelCase__       :						Dict        =							MBartTokenizer(A		, keep_accents=A      )
          tokenizer.save_pretrained(self.tmpdirname      )







       def 						__lowerCamelCase   (				self					:     Any      )       ->List[Any]:
          lowerCamelCase__       :						Dict        =							MBartTokenizer(A		, keep_accents=A      )

          lowerCamelCase__       :						Dict        =							tokenizer.tokenize('''This is a test'''      )
          self.assertListEqual(A		, ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']      )

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(A      )		, [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]		, )

          lowerCamelCase__       :						int        =							tokenizer.tokenize('''I was born in 92000, and this is falsé.'''      )
          self.assertListEqual(
              A		, [
                  SPIECE_UNDERLINE + '''I''',
                  SPIECE_UNDERLINE + '''was''',
                  SPIECE_UNDERLINE + '''b''',
                  '''or''',
                  '''n''',
                  SPIECE_UNDERLINE + '''in''',
                  SPIECE_UNDERLINE + '''''',
                  '''9''',
                  '''2''',
                  '''0''',
                  '''0''',
                  '''0''',
                  ''',''',
                  SPIECE_UNDERLINE + '''and''',
                  SPIECE_UNDERLINE + '''this''',
                  SPIECE_UNDERLINE + '''is''',
                  SPIECE_UNDERLINE + '''f''',
                  '''al''',
                  '''s''',
                  '''é''',
                  '''.''',
              ]		, )
          lowerCamelCase__       :						int        =							tokenizer.convert_tokens_to_ids(A      )
          self.assertListEqual(
              A		, [
                  value + tokenizer.fairseq_offset
                  for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
                  #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
              ]		, )

          lowerCamelCase__       :						Optional[int]        =							tokenizer.convert_ids_to_tokens(A      )
          self.assertListEqual(
              A		, [
                  SPIECE_UNDERLINE + '''I''',
                  SPIECE_UNDERLINE + '''was''',
                  SPIECE_UNDERLINE + '''b''',
                  '''or''',
                  '''n''',
                  SPIECE_UNDERLINE + '''in''',
                  SPIECE_UNDERLINE + '''''',
                  '''<unk>''',
                  '''2''',
                  '''0''',
                  '''0''',
                  '''0''',
                  ''',''',
                  SPIECE_UNDERLINE + '''and''',
                  SPIECE_UNDERLINE + '''this''',
                  SPIECE_UNDERLINE + '''is''',
                  SPIECE_UNDERLINE + '''f''',
                  '''al''',
                  '''s''',
                  '''<unk>''',
                  '''.''',
              ]		, )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->List[str]:
          if not self.test_slow_tokenizer:
             # as we don't have a slow version, we can't compare the outputs between slow and fast versions
             return

          lowerCamelCase__       :						Any        =							(self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart''', {})
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
                lowerCamelCase__       :						Tuple        =							self.rust_tokenizer_class.from_pretrained(A		, **A      )
                lowerCamelCase__       :						str        =							self.tokenizer_class.from_pretrained(A		, **A      )

                lowerCamelCase__       :						Dict        =							tempfile.mkdtemp()

                lowerCamelCase__       :						str        =							tokenizer_r.save_pretrained(A      )
                lowerCamelCase__       :						Optional[Any]        =							tokenizer_p.save_pretrained(A      )

                # Checks it save with the same files + the tokenizer.json file for the fast one
                self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files      )      )
                lowerCamelCase__       :						Union[str, Any]        =							tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f      )
                self.assertSequenceEqual(A		, A      )

                # Checks everything loads correctly in the same way
                lowerCamelCase__       :						Optional[int]        =							tokenizer_r.from_pretrained(A      )
                lowerCamelCase__       :						List[Any]        =							tokenizer_p.from_pretrained(A      )

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                   self.assertTrue(hasattr(A		, A      )      )
                   # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                   # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                shutil.rmtree(A      )

                # Save tokenizer rust, legacy_format=True
                lowerCamelCase__       :						Optional[int]        =							tempfile.mkdtemp()

                lowerCamelCase__       :						Optional[int]        =							tokenizer_r.save_pretrained(A		, legacy_format=A      )
                lowerCamelCase__       :						int        =							tokenizer_p.save_pretrained(A      )

                # Checks it save with the same files
                self.assertSequenceEqual(A		, A      )

                # Checks everything loads correctly in the same way
                lowerCamelCase__       :						List[str]        =							tokenizer_r.from_pretrained(A      )
                lowerCamelCase__       :						Optional[int]        =							tokenizer_p.from_pretrained(A      )

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                   self.assertTrue(hasattr(A		, A      )      )

                shutil.rmtree(A      )

                # Save tokenizer rust, legacy_format=False
                lowerCamelCase__       :						Tuple        =							tempfile.mkdtemp()

                lowerCamelCase__       :						Optional[Any]        =							tokenizer_r.save_pretrained(A		, legacy_format=A      )
                lowerCamelCase__       :						List[Any]        =							tokenizer_p.save_pretrained(A      )

                # Checks it saved the tokenizer.json file
                self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files      )      )

                # Checks everything loads correctly in the same way
                lowerCamelCase__       :						Any        =							tokenizer_r.from_pretrained(A      )
                lowerCamelCase__       :						List[Any]        =							tokenizer_p.from_pretrained(A      )

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                   self.assertTrue(hasattr(A		, A      )      )

                shutil.rmtree(A      )


@require_torch
@require_sentencepiece
@require_tokenizers
class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):






       """simple docstring"""




       _UpperCAmelCase				:  List[Any]			   =   "facebook/mbart-large-en-ro"
       _UpperCAmelCase				:  str			   =   [
           " UN Chief Says There Is No Military Solution in Syria",
           " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
       ]
       _UpperCAmelCase				:  Tuple			   =   [
           "Şeful ONU declară că nu există o soluţie militară în Siria",
           "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
           " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
           " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
       ]
       _UpperCAmelCase				:  Any			   =   [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]







       @classmethod
       def 						__lowerCamelCase   (				cls					:     Dict      )       ->Tuple:
          lowerCamelCase__       :						MBartTokenizer        =							MBartTokenizer.from_pretrained(
              cls.checkpoint_name		, src_lang='''en_XX'''		, tgt_lang='''ro_RO'''      )
          lowerCamelCase__       :						Dict        =							1
          return cls







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Optional[int]:
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']		, 2_5_0_0_0_1      )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']		, 2_5_0_0_0_4      )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']		, 2_5_0_0_2_0      )







       def 						__lowerCamelCase   (				self					:     Any      )       ->Dict:
          lowerCamelCase__       :						int        =							self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
          self.assertListEqual(self.expected_src_tokens		, A      )







       def 						__lowerCamelCase   (				self					:     Any      )       ->Dict:
          self.assertIn(A		, self.tokenizer.all_special_ids      )
          lowerCamelCase__       :						List[str]        =							[RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
          lowerCamelCase__       :						Dict        =							self.tokenizer.decode(A		, skip_special_tokens=A      )
          lowerCamelCase__       :						Union[str, Any]        =							self.tokenizer.decode(generated_ids[1:]		, skip_special_tokens=A      )
          self.assertEqual(A		, A      )
          self.assertNotIn(self.tokenizer.eos_token		, A      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Tuple:
          lowerCamelCase__       :						List[str]        =							['''this is gunna be a long sentence ''' * 2_0]
          assert isinstance(src_text[0]		, A      )
          lowerCamelCase__       :						Optional[int]        =							1_0
          lowerCamelCase__       :						Optional[int]        =							self.tokenizer(A		, max_length=A		, truncation=A      ).input_ids[0]
          self.assertEqual(ids[-2]		, 2      )
          self.assertEqual(ids[-1]		, A      )
          self.assertEqual(len(A      )		, A      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Dict:
          self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']      )		, [2_5_0_0_2_6, 2_5_0_0_0_1]      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Tuple:
          lowerCamelCase__       :						Union[str, Any]        =							tempfile.mkdtemp()
          lowerCamelCase__       :						Dict        =							self.tokenizer.fairseq_tokens_to_ids
          self.tokenizer.save_pretrained(A      )
          lowerCamelCase__       :						int        =							MBartTokenizer.from_pretrained(A      )
          self.assertDictEqual(new_tok.fairseq_tokens_to_ids		, A      )







       @require_torch
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->str:
          lowerCamelCase__       :						int        =							self.tokenizer(self.src_text		, text_target=self.tgt_text		, padding=A		, return_tensors='''pt'''      )
          lowerCamelCase__       :						int        =							shift_tokens_right(batch['''labels''']		, self.tokenizer.pad_token_id      )

          # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
          assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
          assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
          assert batch.decoder_input_ids[1][-1] == 2
          assert batch.labels[1][-2:].tolist() == [2, RO_CODE]







       @require_torch
       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->Optional[int]:
          lowerCamelCase__       :						List[str]        =							self.tokenizer(
              self.src_text		, text_target=self.tgt_text		, padding=A		, truncation=A		, max_length=len(self.expected_src_tokens      )		, return_tensors='''pt'''		, )

          lowerCamelCase__       :						Optional[Any]        =							shift_tokens_right(batch['''labels''']		, self.tokenizer.pad_token_id      )

          self.assertIsInstance(A		, A      )

          self.assertEqual((2, 1_4)		, batch.input_ids.shape      )
          self.assertEqual((2, 1_4)		, batch.attention_mask.shape      )
          lowerCamelCase__       :						str        =							batch.input_ids.tolist()[0]
          self.assertListEqual(self.expected_src_tokens		, A      )
          self.assertEqual(2		, batch.decoder_input_ids[0, -1]      )  # EOS
          # Test that special tokens are reset
          self.assertEqual(self.tokenizer.prefix_tokens		, []      )
          self.assertEqual(self.tokenizer.suffix_tokens		, [self.tokenizer.eos_token_id, EN_CODE]      )







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Dict:
          lowerCamelCase__       :						Any        =							self.tokenizer(self.src_text		, padding=A		, truncation=A		, max_length=3		, return_tensors='''pt'''      )
          lowerCamelCase__       :						Tuple        =							self.tokenizer(
              text_target=self.tgt_text		, padding=A		, truncation=A		, max_length=1_0		, return_tensors='''pt'''      )
          lowerCamelCase__       :						Optional[int]        =							targets['''input_ids''']
          lowerCamelCase__       :						List[str]        =							shift_tokens_right(A		, self.tokenizer.pad_token_id      )

          self.assertEqual(batch.input_ids.shape[1]		, 3      )
          self.assertEqual(batch.decoder_input_ids.shape[1]		, 1_0      )







       @require_torch
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->str:
          lowerCamelCase__       :						str        =							self.tokenizer._build_translation_inputs(
              '''A test'''		, return_tensors='''pt'''		, src_lang='''en_XX'''		, tgt_lang='''ar_AR'''      )

          self.assertEqual(
              nested_simplify(A      )		, {
                  # A, test, EOS, en_XX
                  '''input_ids''': [[6_2, 3_0_3_4, 2, 2_5_0_0_0_4]],
                  '''attention_mask''': [[1, 1, 1, 1]],
                  # ar_AR
                  '''forced_bos_token_id''': 2_5_0_0_0_1,
              }		, )










import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
       import tensorflow as tf
       from tensorflow.python.framework.errors_impl import ResourceExhaustedError

       from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
       import pyanvml.pyanvml as nvml

_A :   Any  			=  logging.get_logger(__name__)



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    List[str]:







   """simple docstring"""
   def run_func(UpperCAmelCase					):
      @wraps(UpperCAmelCase					)
      def run_in_eager_mode(*UpperCAmelCase      ,  **UpperCAmelCase					):
         return func(*UpperCAmelCase      ,  **UpperCAmelCase					)

      @wraps(UpperCAmelCase					)
      @tf.function(experimental_compile=UpperCAmelCase					)
      def run_in_graph_mode(*UpperCAmelCase      ,  **UpperCAmelCase					):
         return func(*UpperCAmelCase      ,  **UpperCAmelCase					)

      if do_eager_mode is True:
         if use_xla is not False:
            raise ValueError(
                '''Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.'''					)
         return run_in_eager_mode
      else:
         return run_in_graph_mode

   return run_func



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    ["tf.Tensor"]:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							random.Random()
   lowerCamelCase__       :						str        =							[rng.randint(0      ,  vocab_size - 1					) for i in range(batch_size * sequence_length					)]
   return tf.constant(UpperCAmelCase      ,  shape=(batch_size, sequence_length)      ,  dtype=tf.intaa					)




class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  TensorFlowBenchmarkArguments
       _UpperCAmelCase				:  PretrainedConfig
       _UpperCAmelCase				:  str			   =   "TensorFlow"







       @property
       def 						__lowerCamelCase   (				self					:     int      )       ->Optional[int]:
          return tf.__version__







       def 						__lowerCamelCase   (				self					:     Optional[int]		, A					:     str		, A					:     int		, A					:     int      )       ->float:
          # initialize GPU on separate process
          lowerCamelCase__       :						Dict        =							self.args.strategy
          if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''      )
          lowerCamelCase__       :						int        =							self._prepare_inference_func(A		, A		, A      )
          return self._measure_speed(_inference      )







       def 						__lowerCamelCase   (				self					:     str		, A					:     str		, A					:     int		, A					:     int      )       ->float:
          lowerCamelCase__       :						Optional[int]        =							self.args.strategy
          if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''      )
          lowerCamelCase__       :						List[Any]        =							self._prepare_train_func(A		, A		, A      )
          return self._measure_speed(_train      )







       def 						__lowerCamelCase   (				self					:     int		, A					:     str		, A					:     int		, A					:     int      )       ->[Memory, Optional[MemorySummary]]:
          # initialize GPU on separate process
          if self.args.is_gpu:
             tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]		, A      )
          lowerCamelCase__       :						int        =							self.args.strategy
          if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''      )
          lowerCamelCase__       :						str        =							self._prepare_inference_func(A		, A		, A      )
          return self._measure_memory(_inference      )







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     str		, A					:     int		, A					:     int      )       ->[Memory, Optional[MemorySummary]]:
          if self.args.is_gpu:
             tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]		, A      )
          lowerCamelCase__       :						List[Any]        =							self.args.strategy
          if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''      )

          lowerCamelCase__       :						str        =							self._prepare_train_func(A		, A		, A      )
          return self._measure_memory(_train      )







       def 						__lowerCamelCase   (				self					:     Dict		, A					:     str		, A					:     int		, A					:     int      )       ->Callable[[], None]:
          lowerCamelCase__       :						Tuple        =							self.config_dict[model_name]

          if self.args.fpaa:
             raise NotImplementedError('''Mixed precision is currently not supported.'''      )

          lowerCamelCase__       :						Tuple        =							(
              hasattr(A		, '''architectures'''      )
              and isinstance(config.architectures		, A      )
              and len(config.architectures      ) > 0
          )
          if not self.args.only_pretrain_model and has_model_class_in_config:
             try:
                lowerCamelCase__       :						Any        =							'''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                lowerCamelCase__       :						List[Any]        =							__import__('''transformers'''		, fromlist=[model_class]      )
                lowerCamelCase__       :						int        =							getattr(A		, A      )
                lowerCamelCase__       :						int        =							model_cls(A      )
             except ImportError:
                raise ImportError(
                    F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
                    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''      )
          else:
             lowerCamelCase__       :						Union[str, Any]        =							TF_MODEL_MAPPING[config.__class__](A      )

          # encoder-decoder has vocab size saved differently
          lowerCamelCase__       :						Tuple        =							config.vocab_size if hasattr(A		, '''vocab_size'''      ) else config.encoder.vocab_size
          lowerCamelCase__       :						Optional[Any]        =							random_input_ids(A		, A		, A      )

          @run_with_tf_optimizations(self.args.eager_mode		, self.args.use_xla      )
          def encoder_decoder_forward():
             return model(A		, decoder_input_ids=A		, training=A      )

          @run_with_tf_optimizations(self.args.eager_mode		, self.args.use_xla      )
          def encoder_forward():
             return model(A		, training=A      )

          lowerCamelCase__       :						int        =							encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

          return _inference







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     str		, A					:     int		, A					:     int      )       ->Callable[[], None]:
          lowerCamelCase__       :						Tuple        =							self.config_dict[model_name]

          if self.args.eager_mode is not False:
             raise ValueError('''Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.'''      )

          if self.args.fpaa:
             raise NotImplementedError('''Mixed precision is currently not supported.'''      )

          lowerCamelCase__       :						Optional[int]        =							(
              hasattr(A		, '''architectures'''      )
              and isinstance(config.architectures		, A      )
              and len(config.architectures      ) > 0
          )
          if not self.args.only_pretrain_model and has_model_class_in_config:
             try:
                lowerCamelCase__       :						Any        =							'''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                lowerCamelCase__       :						List[str]        =							__import__('''transformers'''		, fromlist=[model_class]      )
                lowerCamelCase__       :						Optional[int]        =							getattr(A		, A      )
                lowerCamelCase__       :						Optional[Any]        =							model_cls(A      )
             except ImportError:
                raise ImportError(
                    F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
                    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''      )
          else:
             lowerCamelCase__       :						List[str]        =							TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](A      )

          # encoder-decoder has vocab size saved differently
          lowerCamelCase__       :						Optional[int]        =							config.vocab_size if hasattr(A		, '''vocab_size'''      ) else config.encoder.vocab_size
          lowerCamelCase__       :						Dict        =							random_input_ids(A		, A		, A      )

          @run_with_tf_optimizations(self.args.eager_mode		, self.args.use_xla      )
          def encoder_decoder_train():
             lowerCamelCase__       :						int        =							model(A		, decoder_input_ids=A		, labels=A		, training=A      )[0]
             lowerCamelCase__       :						List[Any]        =							tf.gradients(A		, model.trainable_variables      )
             return gradients

          @run_with_tf_optimizations(self.args.eager_mode		, self.args.use_xla      )
          def encoder_train():
             lowerCamelCase__       :						Optional[int]        =							model(A		, labels=A		, training=A      )[0]
             lowerCamelCase__       :						List[str]        =							tf.gradients(A		, model.trainable_variables      )
             return gradients

          lowerCamelCase__       :						Tuple        =							encoder_decoder_train if config.is_encoder_decoder else encoder_train

          return _train







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     Any      )       ->float:
          with self.args.strategy.scope():
             try:
                if self.args.is_tpu or self.args.use_xla:
                   # run additional 10 times to stabilize compilation for tpu
                   logger.info('''Do inference on TPU. Running model 5 times to stabilize compilation'''      )
                   timeit.repeat(A		, repeat=1		, number=5      )

                # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
                lowerCamelCase__       :						Optional[Any]        =							timeit.repeat(
                    A		, repeat=self.args.repeat		, number=1_0		, )

                return min(A      ) / 10.0
             except ResourceExhaustedError as e:
                self.print_fn(F"Doesn't fit on GPU. {e}"      )







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     Callable[[], None]      )       ->[Memory, MemorySummary]:
          logger.info(
              '''Note that TensorFlow allocates more memory than '''
              '''it might need to speed up computation. '''
              '''The memory reported here corresponds to the memory '''
              '''reported by `nvidia-smi`, which can vary depending '''
              '''on total available memory on the GPU that is used.'''      )
          with self.args.strategy.scope():
             try:
                if self.args.trace_memory_line_by_line:
                   if not self.args.eager_mode:
                      raise ValueError(
                          '''`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'''
                          ''' consumption line by line.'''      )
                   lowerCamelCase__       :						Union[str, Any]        =							start_memory_tracing('''transformers'''      )

                if self.args.is_tpu:
                   # tpu
                   raise NotImplementedError(
                       '''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'''
                       ''' with `args.memory=False`'''      )
                elif self.args.is_gpu:
                   # gpu
                   if not is_pyanvml_available():
                      logger.warning(
                          '''py3nvml not installed, we won\'t log GPU memory usage. '''
                          '''Install py3nvml (pip install py3nvml) to log information about GPU.'''      )
                      lowerCamelCase__       :						Union[str, Any]        =							'''N/A'''
                   else:
                      logger.info(
                          '''Measuring total GPU usage on GPU device. Make sure to not have additional processes'''
                          ''' running on the same GPU.'''      )
                      # init nvml
                      nvml.nvmlInit()
                      func()
                      lowerCamelCase__       :						Any        =							nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx      )
                      lowerCamelCase__       :						Optional[int]        =							nvml.nvmlDeviceGetMemoryInfo(A      )
                      lowerCamelCase__       :						List[Any]        =							meminfo.used
                      lowerCamelCase__       :						Union[str, Any]        =							Memory(A      )
                      # shutdown nvml
                      nvml.nvmlShutdown()
                else:
                   # cpu
                   if self.args.trace_memory_line_by_line:
                      logger.info(
                          '''When enabling line by line tracing, the max peak memory for CPU is inaccurate in'''
                          ''' TensorFlow.'''      )
                      lowerCamelCase__       :						Tuple        =							None
                   else:
                      lowerCamelCase__       :						Dict        =							measure_peak_memory_cpu(A      )
                      lowerCamelCase__       :						Optional[Any]        =							Memory(A      ) if isinstance(A		, A      ) else memory_bytes
                if self.args.trace_memory_line_by_line:
                   lowerCamelCase__       :						Union[str, Any]        =							stop_memory_tracing(A      )
                   if memory is None:
                      lowerCamelCase__       :						Dict        =							summary.total
                else:
                   lowerCamelCase__       :						Optional[int]        =							None

                return memory, summary
             except ResourceExhaustedError as e:
                self.print_fn(F"Doesn't fit on GPU. {e}"      )
                return "N/A", None



