



"""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


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


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

__a      =				25_00_04
__a      =				25_00_20
@require_sentencepiece
@require_tokenizers
class 				lowerCamelCase  (			_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			List[str]						=     MBartTokenizer
					_A		:			str						=     MBartTokenizerFast
					_A		:			Union[str, Any]						=     True
					_A		:			List[Any]						=     True






					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	str:
											super().setUp()

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






					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	List[str]:
											snake_case_ :str						=			MBartTokenizer(snake_case						,     keep_accents=snake_case						)

											snake_case_ :int						=			tokenizer.tokenize("""This is a test"""						)
											self.assertListEqual(snake_case						,     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]						)

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

											snake_case_ :Union[str, Any]						=			tokenizer.tokenize("""I was born in 92000, and this is falsé."""						)
											self.assertListEqual(
											    snake_case						,     [
											        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""",
											        """.""",
											    ]						,     )
											snake_case_ :Union[str, Any]						=			tokenizer.convert_tokens_to_ids(snake_case						)
											self.assertListEqual(
											    snake_case						,     [
											        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 ^
											    ]						,     )

											snake_case_ :Dict						=			tokenizer.convert_ids_to_tokens(snake_case						)
											self.assertListEqual(
											    snake_case						,     [
											        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:							Optional[int]						)   ->	int:
											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

											snake_case_ :List[str]						=			(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})"""						):
																							snake_case_ :Optional[int]						=			self.rust_tokenizer_class.from_pretrained(snake_case						,     **snake_case						)
																							snake_case_ :Optional[Any]						=			self.tokenizer_class.from_pretrained(snake_case						,     **snake_case						)

																							snake_case_ :Dict						=			tempfile.mkdtemp()

																							snake_case_ :str						=			tokenizer_r.save_pretrained(snake_case						)
																							snake_case_ :int						=			tokenizer_p.save_pretrained(snake_case						)

																							# 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						)						)
																							snake_case_ :int						=			tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f						)
																							self.assertSequenceEqual(snake_case						,     snake_case						)

																							# Checks everything loads correctly in the same way
																							snake_case_ :Dict						=			tokenizer_r.from_pretrained(snake_case						)
																							snake_case_ :Optional[int]						=			tokenizer_p.from_pretrained(snake_case						)

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

																							shutil.rmtree(snake_case						)

																							# Save tokenizer rust, legacy_format=True
																							snake_case_ :str						=			tempfile.mkdtemp()

																							snake_case_ :Union[str, Any]						=			tokenizer_r.save_pretrained(snake_case						,     legacy_format=snake_case						)
																							snake_case_ :Any						=			tokenizer_p.save_pretrained(snake_case						)

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

																							# Checks everything loads correctly in the same way
																							snake_case_ :Union[str, Any]						=			tokenizer_r.from_pretrained(snake_case						)
																							snake_case_ :Dict						=			tokenizer_p.from_pretrained(snake_case						)

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

																							shutil.rmtree(snake_case						)

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

																							snake_case_ :Tuple						=			tokenizer_r.save_pretrained(snake_case						,     legacy_format=snake_case						)
																							snake_case_ :int						=			tokenizer_p.save_pretrained(snake_case						)

																							# 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
																							snake_case_ :Tuple						=			tokenizer_r.from_pretrained(snake_case						)
																							snake_case_ :List[Any]						=			tokenizer_p.from_pretrained(snake_case						)

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

																							shutil.rmtree(snake_case						)

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






					'''simple docstring'''





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






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[Any]:
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]						,     250_001						)
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]						,     250_004						)
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]						,     250_020						)






					def    lowerCAmelCase_				(       self:							Dict						)   ->	Dict:
											snake_case_ :List[Any]						=			self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
											self.assertListEqual(self.expected_src_tokens						,     snake_case						)






					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	Optional[int]:
											self.assertIn(snake_case						,     self.tokenizer.all_special_ids						)
											snake_case_ :int						=			[RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
											snake_case_ :Any						=			self.tokenizer.decode(snake_case						,     skip_special_tokens=snake_case						)
											snake_case_ :Optional[int]						=			self.tokenizer.decode(generated_ids[1:]						,     skip_special_tokens=snake_case						)
											self.assertEqual(snake_case						,     snake_case						)
											self.assertNotIn(self.tokenizer.eos_token						,     snake_case						)






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






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Tuple:
											self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]						)						,     [250_026, 250_001]						)






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[Any]:
											snake_case_ :List[Any]						=			tempfile.mkdtemp()
											snake_case_ :int						=			self.tokenizer.fairseq_tokens_to_ids
											self.tokenizer.save_pretrained(snake_case						)
											snake_case_ :Dict						=			MBartTokenizer.from_pretrained(snake_case						)
											self.assertDictEqual(new_tok.fairseq_tokens_to_ids						,     snake_case						)






					@require_torch
					def    lowerCAmelCase_				(       self:							List[str]						)   ->	List[str]:
											snake_case_ :str						=			self.tokenizer(self.src_text						,     text_target=self.tgt_text						,     padding=snake_case						,     return_tensors="""pt"""						)
											snake_case_ :str						=			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]						)   ->	Any:
											snake_case_ :Union[str, Any]						=			self.tokenizer(
											    self.src_text						,     text_target=self.tgt_text						,     padding=snake_case						,     truncation=snake_case						,     max_length=len(self.expected_src_tokens						)						,     return_tensors="""pt"""						,     )

											snake_case_ :int						=			shift_tokens_right(batch["""labels"""]						,     self.tokenizer.pad_token_id						)

											self.assertIsInstance(snake_case						,     snake_case						)

											self.assertEqual((2, 14)						,     batch.input_ids.shape						)
											self.assertEqual((2, 14)						,     batch.attention_mask.shape						)
											snake_case_ :List[Any]						=			batch.input_ids.tolist()[0]
											self.assertListEqual(self.expected_src_tokens						,     snake_case						)
											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:							Dict						)   ->	List[str]:
											snake_case_ :Dict						=			self.tokenizer(self.src_text						,     padding=snake_case						,     truncation=snake_case						,     max_length=3						,     return_tensors="""pt"""						)
											snake_case_ :Dict						=			self.tokenizer(
											    text_target=self.tgt_text						,     padding=snake_case						,     truncation=snake_case						,     max_length=10						,     return_tensors="""pt"""						)
											snake_case_ :Any						=			targets["""input_ids"""]
											snake_case_ :str						=			shift_tokens_right(snake_case						,     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    lowerCAmelCase_				(       self:							Dict						)   ->	Tuple:
											snake_case_ :Optional[Any]						=			self.tokenizer._build_translation_inputs(
											    """A test"""						,     return_tensors="""pt"""						,     src_lang="""en_XX"""						,     tgt_lang="""ar_AR"""						)

											self.assertEqual(
											    nested_simplify(snake_case						)						,     {
											        # A, test, EOS, en_XX
											        """input_ids""": [[62, 3_034, 2, 250_004]],
											        """attention_mask""": [[1, 1, 1, 1]],
											        # ar_AR
											        """forced_bos_token_id""": 250_001,
											    }						,     )





"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
						from sagemaker import Session, TrainingJobAnalytics
						from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER"""    ,						"""False"""				)				) is not True    ,						reason="""Skipping test because should only be run when releasing minor transformers version"""    ,						)
@pytest.mark.usefixtures("""sm_env"""				)
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue_model_parallelism.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]				)
class 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							Any						)   ->	str:
											if self.framework == "pytorch":
																	subprocess.run(
																	    f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()						,     encoding="""utf-8"""						,     check=snake_case						,     )
											assert hasattr(self						,     """env"""						)






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							Dict						)   ->	List[Any]:
											# configuration for running training on smdistributed Model Parallel
											snake_case_ :Tuple						=			{
											    """enabled""": True,
											    """processes_per_host""": 8,
											}
											snake_case_ :List[Any]						=			{
											    """enabled""": True,
											    """parameters""": {
											        """microbatches""": 4,
											        """placement_strategy""": """spread""",
											        """pipeline""": """interleaved""",
											        """optimize""": """speed""",
											        """partitions""": 4,
											        """ddp""": True,
											    },
											}

											snake_case_ :Tuple						=			{"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

											snake_case_ :Any						=			"""trainer""" if self.script == """run_glue.py""" else """smtrainer"""
											# creates estimator
											return HuggingFace(
											    entry_point=self.script						,     source_dir=self.env.test_path						,     role=self.env.role						,     image_uri=self.env.image_uri						,     base_job_name=f"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}"""						,     instance_count=snake_case						,     instance_type=self.instance_type						,     debugger_hook_config=snake_case						,     hyperparameters={
											        **self.env.hyperparameters,
											        """model_name_or_path""": self.model_name_or_path,
											        """max_steps""": 500,
											    }						,     metric_definitions=self.env.metric_definitions						,     distribution=snake_case						,     py_version="""py36"""						,     )






					def    lowerCAmelCase_				(       self:							Any						,     snake_case:							Tuple						)   ->	List[str]:
											TrainingJobAnalytics(snake_case						).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""						)






					@parameterized.expand([(1,)]						)
					def    lowerCAmelCase_				(       self:							Dict						,     snake_case:							Dict						)   ->	List[Any]:
											# create estimator
											snake_case_ :List[Any]						=			self.create_estimator(snake_case						)

											# run training
											estimator.fit()

											# result dataframe
											snake_case_ :Any						=			TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

											# extract kpis
											snake_case_ :Tuple						=			list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]						)
											snake_case_ :Dict						=			list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]						)
											# get train time from SageMaker job, this includes starting, preprocessing, stopping
											snake_case_ :int						=			(
											    Session().describe_training_job(estimator.latest_training_job.name						).get("""TrainingTimeInSeconds"""						,     999_999						)
											)

											# assert kpis
											assert train_runtime <= self.results["train_runtime"]
											assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy						)
											assert all(t <= self.results["""eval_loss"""] for t in eval_loss						)

											# dump tests result into json file to share in PR
											with open(f"""{estimator.latest_training_job.name}.json"""						,     """w"""						) as outfile:
																	json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss}						,     snake_case						)
