


"""simple docstring"""







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


__lowercase    					=      get_tests_dir("""fixtures/test_sentencepiece.model""")


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

__lowercase    					=      250004
__lowercase    					=      250020






@require_sentencepiece
@require_tokenizers
class 						_A  (  _a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							Optional[Any]	=       MBartTokenizer
    UpperCAmelCase       :							Dict	=       MBartTokenizerFast
    UpperCAmelCase       :							Optional[Any]	=       True
    UpperCAmelCase       :							str	=       True






    def 	__snake_case							(					self				:    List[Any]):
      super().setUp()

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






    def 	__snake_case							(					self				:    List[Any]):
      a						:		str      							=			MBartTokenizer(__UpperCAmelCase				, keep_accents=__UpperCAmelCase)

      a						:		Union[str, Any]      							=			tokenizer.tokenize("This is a test")
      self.assertListEqual(__UpperCAmelCase				, ["▁This", "▁is", "▁a", "▁t", "est"])

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(__UpperCAmelCase)				, [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]				, )

      a						:		int      							=			tokenizer.tokenize("I was born in 92000, and this is falsé.")
      self.assertListEqual(
          __UpperCAmelCase				, [
              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",
              ".",
          ]				, )
      a						:		Optional[Any]      							=			tokenizer.convert_tokens_to_ids(__UpperCAmelCase)
      self.assertListEqual(
          __UpperCAmelCase				, [
              value + tokenizer.fairseq_offset
              for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
              #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
          ]				, )

      a						:		Tuple      							=			tokenizer.convert_ids_to_tokens(__UpperCAmelCase)
      self.assertListEqual(
          __UpperCAmelCase				, [
              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 	__snake_case							(					self				:    Any):
      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

      a						:		Optional[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})'''):
          a						:		Dict      							=			self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase				, **__UpperCAmelCase)
          a						:		List[Any]      							=			self.tokenizer_class.from_pretrained(__UpperCAmelCase				, **__UpperCAmelCase)

          a						:		Optional[int]      							=			tempfile.mkdtemp()

          a						:		Optional[int]      							=			tokenizer_r.save_pretrained(__UpperCAmelCase)
          a						:		List[Any]      							=			tokenizer_p.save_pretrained(__UpperCAmelCase)

          # 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))
          a						:		Optional[int]      							=			tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
          self.assertSequenceEqual(__UpperCAmelCase				, __UpperCAmelCase)

          # Checks everything loads correctly in the same way
          a						:		Dict      							=			tokenizer_r.from_pretrained(__UpperCAmelCase)
          a						:		int      							=			tokenizer_p.from_pretrained(__UpperCAmelCase)

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

          shutil.rmtree(__UpperCAmelCase)

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

          a						:		Tuple      							=			tokenizer_r.save_pretrained(__UpperCAmelCase				, legacy_format=__UpperCAmelCase)
          a						:		List[str]      							=			tokenizer_p.save_pretrained(__UpperCAmelCase)

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

          # Checks everything loads correctly in the same way
          a						:		Dict      							=			tokenizer_r.from_pretrained(__UpperCAmelCase)
          a						:		Optional[Any]      							=			tokenizer_p.from_pretrained(__UpperCAmelCase)

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

          shutil.rmtree(__UpperCAmelCase)

          # Save tokenizer rust, legacy_format=False
          a						:		Dict      							=			tempfile.mkdtemp()

          a						:		List[str]      							=			tokenizer_r.save_pretrained(__UpperCAmelCase				, legacy_format=__UpperCAmelCase)
          a						:		Optional[int]      							=			tokenizer_p.save_pretrained(__UpperCAmelCase)

          # 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
          a						:		Any      							=			tokenizer_r.from_pretrained(__UpperCAmelCase)
          a						:		Any      							=			tokenizer_p.from_pretrained(__UpperCAmelCase)

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

          shutil.rmtree(__UpperCAmelCase)






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







    """simple docstring"""




    UpperCAmelCase       :							List[str]	=       """facebook/mbart-large-en-ro"""
    UpperCAmelCase       :							Optional[Any]	=       [
        """ 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       :							str	=       [
        """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       :							str	=       [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 	__snake_case							(					cls				:    str):
      a						:		MBartTokenizer      							=			MBartTokenizer.from_pretrained(
          cls.checkpoint_name				, src_lang="en_XX"				, tgt_lang="ro_RO")
      a						:		Optional[int]      							=			1
      return cls






    def 	__snake_case							(					self				:    Optional[Any]):
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]				, 250001)
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]				, 250004)
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]				, 250020)






    def 	__snake_case							(					self				:    Union[str, Any]):
      a						:		Union[str, Any]      							=			self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
      self.assertListEqual(self.expected_src_tokens				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    int):
      self.assertIn(__UpperCAmelCase				, self.tokenizer.all_special_ids)
      a						:		Tuple      							=			[RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
      a						:		List[Any]      							=			self.tokenizer.decode(__UpperCAmelCase				, skip_special_tokens=__UpperCAmelCase)
      a						:		List[str]      							=			self.tokenizer.decode(generated_ids[1:]				, skip_special_tokens=__UpperCAmelCase)
      self.assertEqual(__UpperCAmelCase				, __UpperCAmelCase)
      self.assertNotIn(self.tokenizer.eos_token				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    List[str]):
      a						:		List[Any]      							=			["this is gunna be a long sentence " * 20]
      assert isinstance(src_text[0]				, __UpperCAmelCase)
      a						:		Tuple      							=			10
      a						:		Dict      							=			self.tokenizer(__UpperCAmelCase				, max_length=__UpperCAmelCase				, truncation=__UpperCAmelCase).input_ids[0]
      self.assertEqual(ids[-2]				, 2)
      self.assertEqual(ids[-1]				, __UpperCAmelCase)
      self.assertEqual(len(__UpperCAmelCase)				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    List[str]):
      self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"])				, [250026, 250001])






    def 	__snake_case							(					self				:    str):
      a						:		List[str]      							=			tempfile.mkdtemp()
      a						:		Optional[int]      							=			self.tokenizer.fairseq_tokens_to_ids
      self.tokenizer.save_pretrained(__UpperCAmelCase)
      a						:		Dict      							=			MBartTokenizer.from_pretrained(__UpperCAmelCase)
      self.assertDictEqual(new_tok.fairseq_tokens_to_ids				, __UpperCAmelCase)






    @require_torch
    def 	__snake_case							(					self				:    Dict):
      a						:		Union[str, Any]      							=			self.tokenizer(self.src_text				, text_target=self.tgt_text				, padding=__UpperCAmelCase				, return_tensors="pt")
      a						:		Tuple      							=			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 	__snake_case							(					self				:    List[Any]):
      a						:		List[Any]      							=			self.tokenizer(
          self.src_text				, text_target=self.tgt_text				, padding=__UpperCAmelCase				, truncation=__UpperCAmelCase				, max_length=len(self.expected_src_tokens)				, return_tensors="pt"				, )

      a						:		Dict      							=			shift_tokens_right(batch["labels"]				, self.tokenizer.pad_token_id)

      self.assertIsInstance(__UpperCAmelCase				, __UpperCAmelCase)

      self.assertEqual((2, 14)				, batch.input_ids.shape)
      self.assertEqual((2, 14)				, batch.attention_mask.shape)
      a						:		Tuple      							=			batch.input_ids.tolist()[0]
      self.assertListEqual(self.expected_src_tokens				, __UpperCAmelCase)
      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 	__snake_case							(					self				:    Optional[int]):
      a						:		List[str]      							=			self.tokenizer(self.src_text				, padding=__UpperCAmelCase				, truncation=__UpperCAmelCase				, max_length=3				, return_tensors="pt")
      a						:		List[str]      							=			self.tokenizer(
          text_target=self.tgt_text				, padding=__UpperCAmelCase				, truncation=__UpperCAmelCase				, max_length=10				, return_tensors="pt")
      a						:		str      							=			targets["input_ids"]
      a						:		Optional[Any]      							=			shift_tokens_right(__UpperCAmelCase				, self.tokenizer.pad_token_id)

      self.assertEqual(batch.input_ids.shape[1]				, 3)
      self.assertEqual(batch.decoder_input_ids.shape[1]				, 10)






    @require_torch
    def 	__snake_case							(					self				:    Any):
      a						:		Tuple      							=			self.tokenizer._build_translation_inputs(
          "A test"				, return_tensors="pt"				, src_lang="en_XX"				, tgt_lang="ar_AR")

      self.assertEqual(
          nested_simplify(__UpperCAmelCase)				, {
              # A, test, EOS, en_XX
              "input_ids": [[62, 3034, 2, 250004]],
              "attention_mask": [[1, 1, 1, 1]],
              # ar_AR
              "forced_bos_token_id": 250001,
          }				, )












from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
					import tensorflow as tf


UpperCamelCase__    			=			logging.get_logger(__name__)
@dataclass
class 			a__    (					snake_case__      ):
						_a							:    List[str]					=    [
						    """no_inference""",
						    """no_cuda""",
						    """no_tpu""",
						    """no_speed""",
						    """no_memory""",
						    """no_env_print""",
						    """no_multi_process""",
						]






						def __init__(   self		,			**_A			):




											"""simple docstring"""



											for deprecated_arg in self.deprecated_args:
																if deprecated_arg in kwargs:
																					__lowerCAmelCase				   =       deprecated_arg[3:]
																					__lowerCAmelCase				   =       not kwargs.pop(_A			)
																					logger.warning(
																					    f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
																					    f""" {positive_arg}={kwargs[positive_arg]}"""			)
											__lowerCAmelCase				   =       kwargs.pop("tpu_name"		,			self.tpu_name			)
											__lowerCAmelCase				   =       kwargs.pop("device_idx"		,			self.device_idx			)
											__lowerCAmelCase				   =       kwargs.pop("eager_mode"		,			self.eager_mode			)
											__lowerCAmelCase				   =       kwargs.pop("use_xla"		,			self.use_xla			)
											super().__init__(**_A			)

						_a							:    str					=    field(
						    default=snake_case__ ,				metadata={"""help""": """Name of TPU"""} ,				)
						_a							:    int					=    field(
						    default=0 ,				metadata={"""help""": """CPU / GPU device index. Defaults to 0."""} ,				)
						_a							:    bool					=    field(default=snake_case__ ,				metadata={"""help""": """Benchmark models in eager model."""}      )
						_a							:    bool					=    field(
						    default=snake_case__ ,				metadata={
						        """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
						    } ,				)






						@cached_property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											requires_backends(self		,			["tf"]			)
											__lowerCAmelCase				   =       None
											if self.tpu:
																try:
																					if self.tpu_name:
																										__lowerCAmelCase				   =       tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name			)
																					else:
																										__lowerCAmelCase				   =       tf.distribute.cluster_resolver.TPUClusterResolver()
																except ValueError:
																					__lowerCAmelCase				   =       None
											return tpu






						@cached_property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											requires_backends(self		,			["tf"]			)
											if self.is_tpu:
																tf.config.experimental_connect_to_cluster(self._setup_tpu			)
																tf.tpu.experimental.initialize_tpu_system(self._setup_tpu			)

																__lowerCAmelCase				   =       tf.distribute.TPUStrategy(self._setup_tpu			)
											else:
																# currently no multi gpu is allowed
																if self.is_gpu:
																					# TODO: Currently only single GPU is supported
																					tf.config.set_visible_devices(self.gpu_list[self.device_idx]		,			"GPU"			)
																					__lowerCAmelCase				   =       tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}"""			)
																else:
																					tf.config.set_visible_devices([]		,			"GPU"			)  # disable GPU
																					__lowerCAmelCase				   =       tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}"""			)

											return strategy






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											requires_backends(self		,			["tf"]			)
											return self._setup_tpu is not None






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											requires_backends(self		,			["tf"]			)
											return self._setup_strategy






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											requires_backends(self		,			["tf"]			)
											return tf.config.list_physical_devices("GPU"			)






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											requires_backends(self		,			["tf"]			)
											if self.cuda:
																return len(self.gpu_list			)
											return 0






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return self.n_gpu > 0




