



"""simple docstring"""





# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


lowerCamelCase       	=						Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowerCamelCase       	=						{"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

lowerCamelCase       	=						"""zero2"""
lowerCamelCase       	=						"""zero3"""
lowerCamelCase       	=						[ZEROa, ZEROa]
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				):
      # customize the test name generator function as we want both params to appear in the sub-test
      # name, as by default it shows only the first param
      UpperCAmelCase_      			=							parameterized.to_safe_name("_".join(str(lowerCAmelCase__				) for x in param.args				)				)
      return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
lowerCamelCase       	=						list(itertools.product(stages, models.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       @parameterized.expand(_UpperCAmelCase		,    name_func=_UpperCAmelCase   )
       def 					lowercase__    (							self      :  Dict		,    _UpperCAmelCase      :  int		,    _UpperCAmelCase      :  Any   )			->						Optional[int]:


             '''simple docstring'''




             self.run_and_check(
                 stage=_UpperCAmelCase		,    model=_UpperCAmelCase		,    distributed=_UpperCAmelCase		,    fpaa=_UpperCAmelCase		,    )



       @require_torch_multi_gpu
       @parameterized.expand(_UpperCAmelCase		,    name_func=_UpperCAmelCase   )
       def 					lowercase__    (							self      :  Any		,    _UpperCAmelCase      :  Optional[Any]		,    _UpperCAmelCase      :  str   )			->						Any:


             '''simple docstring'''




             self.run_and_check(
                 stage=_UpperCAmelCase		,    model=_UpperCAmelCase		,    distributed=_UpperCAmelCase		,    fpaa=_UpperCAmelCase		,    )



       @parameterized.expand(_UpperCAmelCase		,    name_func=_UpperCAmelCase   )
       def 					lowercase__    (							self      :  Optional[int]		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  List[str]   )			->						Union[str, Any]:


             '''simple docstring'''




             self.run_and_check(
                 stage=_UpperCAmelCase		,    model=_UpperCAmelCase		,    distributed=_UpperCAmelCase		,    fpaa=_UpperCAmelCase		,    )



       @require_torch_multi_gpu
       @parameterized.expand(_UpperCAmelCase		,    name_func=_UpperCAmelCase   )
       def 					lowercase__    (							self      :  int		,    _UpperCAmelCase      :  int		,    _UpperCAmelCase      :  Optional[int]   )			->						Optional[int]:


             '''simple docstring'''




             self.run_and_check(
                 stage=_UpperCAmelCase		,    model=_UpperCAmelCase		,    distributed=_UpperCAmelCase		,    fpaa=_UpperCAmelCase		,    )



       def 					lowercase__    (							self      :  Optional[Any]		,    _UpperCAmelCase      :  Tuple   )			->						Optional[int]:


             '''simple docstring'''




             pass



       def 					lowercase__    (							self      :  Optional[Any]		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  int = 10		,    _UpperCAmelCase      :  bool = True		,    _UpperCAmelCase      :  bool = True		,    _UpperCAmelCase      :  bool = True		,    )			->						Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							models[model]

             UpperCAmelCase_      			=							self.run_trainer(
                 stage=_UpperCAmelCase		,    model_name=_UpperCAmelCase		,    eval_steps=_UpperCAmelCase		,    num_train_epochs=1		,    distributed=_UpperCAmelCase		,    fpaa=_UpperCAmelCase		,    )

             self.do_checks(_UpperCAmelCase   )

             return output_dir



       def 					lowercase__    (							self      :  Union[str, Any]		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  int = 10		,    _UpperCAmelCase      :  int = 1		,    _UpperCAmelCase      :  bool = True		,    _UpperCAmelCase      :  bool = True		,    )			->						Tuple:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.get_auto_remove_tmp_dir("./xxx"		,    after=_UpperCAmelCase   )
             UpperCAmelCase_      			=							F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(_UpperCAmelCase   )}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

             if fpaa:
                   args.extend(["--fp16"]   )

             # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
             # hence the separate config files
             UpperCAmelCase_      			=							F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
             UpperCAmelCase_      			=							[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
             UpperCAmelCase_      			=							self.get_launcher(_UpperCAmelCase   )

             UpperCAmelCase_      			=							launcher + script + args + ds_args
             # keep for quick debug
             # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
             execute_subprocess_async(_UpperCAmelCase		,    env=self.get_env()   )

             return output_dir







       def 					lowercase__    (							self      :  List[Any]		,    _UpperCAmelCase      :  int=False   )			->						Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							min(2		,    get_gpu_count()   ) if distributed else 1
             return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()









"""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


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


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

lowerCamelCase       	=						250_004
lowerCamelCase       	=						250_020
@require_sentencepiece
@require_tokenizers
class 				lowercase__       (   SCREAMING_SNAKE_CASE    , unittest.TestCase				):







       '''simple docstring'''


       UpperCamelCase						=					MBartTokenizer
       UpperCamelCase						=					MBartTokenizerFast
       UpperCamelCase						=					True
       UpperCamelCase						=					True



       def 					lowercase__    (							self      :  int   )			->						Union[str, Any]:


             '''simple docstring'''




             super().setUp()

             # We have a SentencePiece fixture for testing
             UpperCAmelCase_      			=							MBartTokenizer(_UpperCAmelCase		,    keep_accents=_UpperCAmelCase   )
             tokenizer.save_pretrained(self.tmpdirname   )



       def 					lowercase__    (							self      :  Any   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							MBartTokenizer(_UpperCAmelCase		,    keep_accents=_UpperCAmelCase   )

             UpperCAmelCase_      			=							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]]		,    )

             UpperCAmelCase_      			=							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",
                     ".",
                 ]		,    )
             UpperCAmelCase_      			=							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 ^
                 ]		,    )

             UpperCAmelCase_      			=							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 					lowercase__    (							self      :  Optional[Any]   )			->						Dict:


             '''simple docstring'''




             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

             UpperCAmelCase_      			=							(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})"""   ):
                         UpperCAmelCase_      			=							self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase		,    **_UpperCAmelCase   )
                         UpperCAmelCase_      			=							self.tokenizer_class.from_pretrained(_UpperCAmelCase		,    **_UpperCAmelCase   )

                         UpperCAmelCase_      			=							tempfile.mkdtemp()

                         UpperCAmelCase_      			=							tokenizer_r.save_pretrained(_UpperCAmelCase   )
                         UpperCAmelCase_      			=							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   )   )
                         UpperCAmelCase_      			=							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
                         UpperCAmelCase_      			=							tokenizer_r.from_pretrained(_UpperCAmelCase   )
                         UpperCAmelCase_      			=							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
                         UpperCAmelCase_      			=							tempfile.mkdtemp()

                         UpperCAmelCase_      			=							tokenizer_r.save_pretrained(_UpperCAmelCase		,    legacy_format=_UpperCAmelCase   )
                         UpperCAmelCase_      			=							tokenizer_p.save_pretrained(_UpperCAmelCase   )

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

                         # Checks everything loads correctly in the same way
                         UpperCAmelCase_      			=							tokenizer_r.from_pretrained(_UpperCAmelCase   )
                         UpperCAmelCase_      			=							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
                         UpperCAmelCase_      			=							tempfile.mkdtemp()

                         UpperCAmelCase_      			=							tokenizer_r.save_pretrained(_UpperCAmelCase		,    legacy_format=_UpperCAmelCase   )
                         UpperCAmelCase_      			=							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
                         UpperCAmelCase_      			=							tokenizer_r.from_pretrained(_UpperCAmelCase   )
                         UpperCAmelCase_      			=							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 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


       UpperCamelCase						=					'''facebook/mbart-large-en-ro'''
       UpperCamelCase						=					[
           ''' 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						=					[
           '''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						=					[82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]



       @classmethod
       def 					lowercase__    (							cls      :  Optional[Any]   )			->						List[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							MBartTokenizer.from_pretrained(
                 cls.checkpoint_name		,    src_lang="en_XX"		,    tgt_lang="ro_RO"   )
             UpperCAmelCase_      			=							1
             return cls



       def 					lowercase__    (							self      :  List[Any]   )			->						Union[str, Any]:


             '''simple docstring'''




             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 					lowercase__    (							self      :  Any   )			->						Tuple:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
             self.assertListEqual(self.expected_src_tokens		,    _UpperCAmelCase   )



       def 					lowercase__    (							self      :  Any   )			->						str:


             '''simple docstring'''




             self.assertIn(_UpperCAmelCase		,    self.tokenizer.all_special_ids   )
             UpperCAmelCase_      			=							[RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
             UpperCAmelCase_      			=							self.tokenizer.decode(_UpperCAmelCase		,    skip_special_tokens=_UpperCAmelCase   )
             UpperCAmelCase_      			=							self.tokenizer.decode(generated_ids[1:]		,    skip_special_tokens=_UpperCAmelCase   )
             self.assertEqual(_UpperCAmelCase		,    _UpperCAmelCase   )
             self.assertNotIn(self.tokenizer.eos_token		,    _UpperCAmelCase   )



       def 					lowercase__    (							self      :  Tuple   )			->						Any:


             '''simple docstring'''




             UpperCAmelCase_      			=							["this is gunna be a long sentence " * 20]
             assert isinstance(src_text[0]		,    _UpperCAmelCase   )
             UpperCAmelCase_      			=							10
             UpperCAmelCase_      			=							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 					lowercase__    (							self      :  int   )			->						int:


             '''simple docstring'''




             self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]   )		,    [250026, 250001]   )



       def 					lowercase__    (							self      :  Any   )			->						Any:


             '''simple docstring'''




             UpperCAmelCase_      			=							tempfile.mkdtemp()
             UpperCAmelCase_      			=							self.tokenizer.fairseq_tokens_to_ids
             self.tokenizer.save_pretrained(_UpperCAmelCase   )
             UpperCAmelCase_      			=							MBartTokenizer.from_pretrained(_UpperCAmelCase   )
             self.assertDictEqual(new_tok.fairseq_tokens_to_ids		,    _UpperCAmelCase   )



       @require_torch
       def 					lowercase__    (							self      :  List[Any]   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.tokenizer(self.src_text		,    text_target=self.tgt_text		,    padding=_UpperCAmelCase		,    return_tensors="pt"   )
             UpperCAmelCase_      			=							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 					lowercase__    (							self      :  Any   )			->						Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.tokenizer(
                 self.src_text		,    text_target=self.tgt_text		,    padding=_UpperCAmelCase		,    truncation=_UpperCAmelCase		,    max_length=len(self.expected_src_tokens   )		,    return_tensors="pt"		,    )

             UpperCAmelCase_      			=							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   )
             UpperCAmelCase_      			=							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 					lowercase__    (							self      :  Union[str, Any]   )			->						Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.tokenizer(self.src_text		,    padding=_UpperCAmelCase		,    truncation=_UpperCAmelCase		,    max_length=3		,    return_tensors="pt"   )
             UpperCAmelCase_      			=							self.tokenizer(
                 text_target=self.tgt_text		,    padding=_UpperCAmelCase		,    truncation=_UpperCAmelCase		,    max_length=10		,    return_tensors="pt"   )
             UpperCAmelCase_      			=							targets["input_ids"]
             UpperCAmelCase_      			=							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 					lowercase__    (							self      :  Optional[int]   )			->						Any:


             '''simple docstring'''




             UpperCAmelCase_      			=							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,
                 }		,    )




