






'''simple docstring'''





import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


a_		:						List[Any]             =    get_tests_dir('fixtures/test_sentencepiece.model')

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

a_		:						str             =    25_00_04
a_		:						Optional[Any]             =    25_00_20

@require_sentencepiece
@require_tokenizers
class 		_snake_case  (  A__   ,    unittest.TestCase					):
			_lowercase		:    List[str]			     =							MBartaaTokenizer
			_lowercase		:    Optional[Any]			     =							MBartaaTokenizerFast
			_lowercase		:    Optional[int]			     =							True
			_lowercase		:    Dict			     =							True






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						super().setUp()

						# We have a SentencePiece fixture for testing
						SCREAMING_SNAKE_CASE						= MBartaaTokenizer(a		,					src_lang='en_XX'		,					tgt_lang='ro_RO'		,					keep_accents=a)
						tokenizer.save_pretrained(self.tmpdirname)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= '<s>'
						SCREAMING_SNAKE_CASE						= 0

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(a)		,					a)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(a)		,					a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= list(self.get_tokenizer().get_vocab().keys())

						self.assertEqual(vocab_keys[0]		,					'<s>')
						self.assertEqual(vocab_keys[1]		,					'<pad>')
						self.assertEqual(vocab_keys[-1]		,					'<mask>')
						self.assertEqual(len(a)		,					1054)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						self.assertEqual(self.get_tokenizer().vocab_size		,					1054)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= MBartaaTokenizer(a		,					src_lang='en_XX'		,					tgt_lang='ro_RO'		,					keep_accents=a)

						SCREAMING_SNAKE_CASE						= 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 [285, 46, 10, 170, 382]]		,					)

						SCREAMING_SNAKE_CASE						= 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', '.']		,					)
						SCREAMING_SNAKE_CASE						= tokenizer.convert_tokens_to_ids(a)
						self.assertListEqual(
						    a		,					[
						        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]
						    ]		,					)

						SCREAMING_SNAKE_CASE						= 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>', '.']		,					)






			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						# fmt: off
						SCREAMING_SNAKE_CASE						= {'input_ids': [[25_0004, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [25_0004, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_0004, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=a		,					model_name='facebook/mbart-large-50'		,					revision='d3913889c59cd5c9e456b269c376325eabad57e2'		,					)







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						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

						SCREAMING_SNAKE_CASE						= (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart50', {})
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
												SCREAMING_SNAKE_CASE						= self.rust_tokenizer_class.from_pretrained(a		,					**a)
												SCREAMING_SNAKE_CASE						= self.tokenizer_class.from_pretrained(a		,					**a)

												SCREAMING_SNAKE_CASE						= tempfile.mkdtemp()

												SCREAMING_SNAKE_CASE						= tokenizer_r.save_pretrained(a)
												SCREAMING_SNAKE_CASE						= 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))
												SCREAMING_SNAKE_CASE						= 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
												SCREAMING_SNAKE_CASE						= tokenizer_r.from_pretrained(a)
												SCREAMING_SNAKE_CASE						= 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
												SCREAMING_SNAKE_CASE						= tempfile.mkdtemp()

												SCREAMING_SNAKE_CASE						= tokenizer_r.save_pretrained(a		,					legacy_format=a)
												SCREAMING_SNAKE_CASE						= tokenizer_p.save_pretrained(a)

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

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

												SCREAMING_SNAKE_CASE						= tokenizer_r.save_pretrained(a		,					legacy_format=a)
												SCREAMING_SNAKE_CASE						= 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
												SCREAMING_SNAKE_CASE						= tokenizer_r.from_pretrained(a)
												SCREAMING_SNAKE_CASE						= 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 		_snake_case  (  unittest.TestCase					):
			_lowercase		:    List[Any]			     =							'''facebook/mbart-large-50-one-to-many-mmt'''
			_lowercase		:    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.''',
			]
			_lowercase		:    Optional[int]			     =							[
			    '''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.''',
			]
			_lowercase		:    Any			     =							[EN_CODE, 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]






			@classmethod
			def    SCREAMING_SNAKE_CASE__    (       cls)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= MBartaaTokenizer.from_pretrained(
						    cls.checkpoint_name		,					src_lang='en_XX'		,					tgt_lang='ro_RO')
						SCREAMING_SNAKE_CASE						= 1
						return cls






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']		,					25_0001)
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']		,					25_0004)
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']		,					25_0020)
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']		,					25_0038)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
						self.assertListEqual(self.expected_src_tokens		,					a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						self.assertIn(a		,					self.tokenizer.all_special_ids)
						SCREAMING_SNAKE_CASE						= [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
						SCREAMING_SNAKE_CASE						= self.tokenizer.decode(a		,					skip_special_tokens=a)
						SCREAMING_SNAKE_CASE						= self.tokenizer.decode(generated_ids[1:]		,					skip_special_tokens=a)
						self.assertEqual(a		,					a)
						self.assertNotIn(self.tokenizer.eos_token		,					a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= ['this is gunna be a long sentence ' * 20]
						assert isinstance(src_text[0]		,					a)
						SCREAMING_SNAKE_CASE						= 10
						SCREAMING_SNAKE_CASE						= self.tokenizer(a		,					max_length=a		,					truncation=a).input_ids[0]
						self.assertEqual(ids[0]		,					a)
						self.assertEqual(ids[-1]		,					2)
						self.assertEqual(len(a)		,					a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'])		,					[25_0053, 25_0001])






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= tempfile.mkdtemp()
						SCREAMING_SNAKE_CASE						= self.tokenizer.fairseq_tokens_to_ids
						self.tokenizer.save_pretrained(a)
						SCREAMING_SNAKE_CASE						= MBartaaTokenizer.from_pretrained(a)
						self.assertDictEqual(new_tok.fairseq_tokens_to_ids		,					a)






			@require_torch
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= self.tokenizer(self.src_text		,					text_target=self.tgt_text		,					padding=a		,					return_tensors='pt')
						SCREAMING_SNAKE_CASE						= shift_tokens_right(batch['labels']		,					self.tokenizer.pad_token_id)

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






			@require_torch
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						SCREAMING_SNAKE_CASE						= self.tokenizer(
						    self.src_text		,					text_target=self.tgt_text		,					padding=a		,					truncation=a		,					max_length=len(self.expected_src_tokens)		,					return_tensors='pt'		,					)
						SCREAMING_SNAKE_CASE						= shift_tokens_right(batch['labels']		,					self.tokenizer.pad_token_id)

						self.assertIsInstance(a		,					a)

						self.assertEqual((2, 14)		,					batch.input_ids.shape)
						self.assertEqual((2, 14)		,					batch.attention_mask.shape)
						SCREAMING_SNAKE_CASE						= batch.input_ids.tolist()[0]
						self.assertListEqual(self.expected_src_tokens		,					a)
						self.assertEqual(2		,					batch.decoder_input_ids[0, 0])  # decoder_start_token_id
						# Test that special tokens are reset
						self.assertEqual(self.tokenizer.prefix_tokens		,					[EN_CODE])
						self.assertEqual(self.tokenizer.suffix_tokens		,					[self.tokenizer.eos_token_id])






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self.tokenizer(self.src_text		,					padding=a		,					truncation=a		,					max_length=3		,					return_tensors='pt')
						SCREAMING_SNAKE_CASE						= self.tokenizer(
						    text_target=self.tgt_text		,					padding=a		,					truncation=a		,					max_length=10		,					return_tensors='pt')
						SCREAMING_SNAKE_CASE						= targets['input_ids']
						SCREAMING_SNAKE_CASE						= 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]		,					10)







			@require_torch
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= self.tokenizer._build_translation_inputs(
						    'A test'		,					return_tensors='pt'		,					src_lang='en_XX'		,					tgt_lang='ar_AR')

						self.assertEqual(
						    nested_simplify(a)		,					{
						        # en_XX, A, test, EOS
						        'input_ids': [[25_0004, 62, 3034, 2]],
						        'attention_mask': [[1, 1, 1, 1]],
						        # ar_AR
						        'forced_bos_token_id': 25_0001,
						    }		,					)












import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/text-classification/requirements.txt')

a_		:						Union[str, Any]             =    logging.getLogger(__name__)

@dataclass
class 		_snake_case  :
			_lowercase		:    Optional[int]			     =							field(
			    default=1_28   ,    metadata={
			        '''help''': (
			            '''The maximum total input sequence length after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded.'''
			        )
			    }   ,    )
			_lowercase		:    bool			     =							field(
			    default=A__   ,    metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}					)
			_lowercase		:    bool			     =							field(
			    default=A__   ,    metadata={
			        '''help''': (
			            '''Whether to pad all samples to `max_seq_length`. '''
			            '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=A__   ,    metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
			            '''value if set.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=A__   ,    metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
			            '''value if set.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=A__   ,    metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
			            '''value if set.'''
			        )
			    }   ,    )



@dataclass
class 		_snake_case  :
			_lowercase		:    str			     =							field(
			    default=A__   ,    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}					)
			_lowercase		:    str			     =							field(
			    default=A__   ,    metadata={'''help''': '''Evaluation language. Also train language if `train_language` is set to None.'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default=A__   ,    metadata={'''help''': '''Train language if it is different from the evaluation language.'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default=A__   ,    metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default=A__   ,    metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default=A__   ,    metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}   ,    )
			_lowercase		:    Optional[bool]			     =							field(
			    default=A__   ,    metadata={'''help''': '''arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'''}   ,    )
			_lowercase		:    bool			     =							field(
			    default=A__   ,    metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}   ,    )
			_lowercase		:    str			     =							field(
			    default='''main'''   ,    metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}   ,    )
			_lowercase		:    bool			     =							field(
			    default=A__   ,    metadata={
			        '''help''': (
			            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
			            '''with private models).'''
			        )
			    }   ,    )
			_lowercase		:    bool			     =							field(
			    default=A__   ,    metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}   ,    )






def 				lowerCamelCase__				():
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			SCREAMING_SNAKE_CASE						= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
			SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= parser.parse_args_into_dataclasses()

			# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
			# information sent is the one passed as arguments along with your Python/PyTorch versions.
			send_example_telemetry('run_xnli'							,       _UpperCAmelCase)

			# Setup logging
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,       datefmt='%m/%d/%Y %H:%M:%S'							,       handlers=[logging.StreamHandler(sys.stdout)]							,       )

			if training_args.should_log:
						# The default of training_args.log_level is passive, so we set log level at info here to have that default.
						transformers.utils.logging.set_verbosity_info()

			SCREAMING_SNAKE_CASE						= training_args.get_process_log_level()
			logger.setLevel(_UpperCAmelCase)
			datasets.utils.logging.set_verbosity(_UpperCAmelCase)
			transformers.utils.logging.set_verbosity(_UpperCAmelCase)
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()

			# Log on each process the small summary:
			logger.warning(
			    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
			    + F'''distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fpaa}''')
			logger.info(F'''Training/evaluation parameters {training_args}''')

			# Detecting last checkpoint.
			SCREAMING_SNAKE_CASE						= None
			if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
						SCREAMING_SNAKE_CASE						= get_last_checkpoint(training_args.output_dir)
						if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
									raise ValueError(
									    F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
									    'Use --overwrite_output_dir to overcome.')
						elif last_checkpoint is not None:
									logger.info(
									    F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
									    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.')

    # Set seed before initializing model.
			set_seed(training_args.seed)

			# In distributed training, the load_dataset function guarantees that only one local process can concurrently
			# download the dataset.
			# Downloading and loading xnli dataset from the hub.
			if training_args.do_train:
						if model_args.train_language is None:
									SCREAMING_SNAKE_CASE						= load_dataset(
									    'xnli'							,       model_args.language							,       split='train'							,       cache_dir=model_args.cache_dir							,       use_auth_token=True if model_args.use_auth_token else None							,       )
						else:
									SCREAMING_SNAKE_CASE						= load_dataset(
									    'xnli'							,       model_args.train_language							,       split='train'							,       cache_dir=model_args.cache_dir							,       use_auth_token=True if model_args.use_auth_token else None							,       )
						SCREAMING_SNAKE_CASE						= train_dataset.features['label'].names

			if training_args.do_eval:
						SCREAMING_SNAKE_CASE						= load_dataset(
						    'xnli'							,       model_args.language							,       split='validation'							,       cache_dir=model_args.cache_dir							,       use_auth_token=True if model_args.use_auth_token else None							,       )
						SCREAMING_SNAKE_CASE						= eval_dataset.features['label'].names

			if training_args.do_predict:
						SCREAMING_SNAKE_CASE						= load_dataset(
						    'xnli'							,       model_args.language							,       split='test'							,       cache_dir=model_args.cache_dir							,       use_auth_token=True if model_args.use_auth_token else None							,       )
						SCREAMING_SNAKE_CASE						= predict_dataset.features['label'].names

			# Labels
			SCREAMING_SNAKE_CASE						= len(_UpperCAmelCase)

			# Load pretrained model and tokenizer
			# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			SCREAMING_SNAKE_CASE						= AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path							,       num_labels=_UpperCAmelCase							,       idalabel={str(_UpperCAmelCase): label for i, label in enumerate(_UpperCAmelCase)}							,       labelaid={label: i for i, label in enumerate(_UpperCAmelCase)}							,       finetuning_task='xnli'							,       cache_dir=model_args.cache_dir							,       revision=model_args.model_revision							,       use_auth_token=True if model_args.use_auth_token else None							,       )
			SCREAMING_SNAKE_CASE						= AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,       do_lower_case=model_args.do_lower_case							,       cache_dir=model_args.cache_dir							,       use_fast=model_args.use_fast_tokenizer							,       revision=model_args.model_revision							,       use_auth_token=True if model_args.use_auth_token else None							,       )
			SCREAMING_SNAKE_CASE						= AutoModelForSequenceClassification.from_pretrained(
			    model_args.model_name_or_path							,       from_tf=bool('.ckpt' in model_args.model_name_or_path)							,       config=_UpperCAmelCase							,       cache_dir=model_args.cache_dir							,       revision=model_args.model_revision							,       use_auth_token=True if model_args.use_auth_token else None							,       ignore_mismatched_sizes=model_args.ignore_mismatched_sizes							,       )

			# Preprocessing the datasets
			# Padding strategy
			if data_args.pad_to_max_length:
						SCREAMING_SNAKE_CASE						= 'max_length'
			else:
						# We will pad later, dynamically at batch creation, to the max sequence length in each batch
						SCREAMING_SNAKE_CASE						= False

			def preprocess_function(_UpperCAmelCase):
						# Tokenize the texts
						return tokenizer(
						    examples['premise']							,       examples['hypothesis']							,       padding=_UpperCAmelCase							,       max_length=data_args.max_seq_length							,       truncation=_UpperCAmelCase							,       )

			if training_args.do_train:
						if data_args.max_train_samples is not None:
									SCREAMING_SNAKE_CASE						= min(len(_UpperCAmelCase)							,       data_args.max_train_samples)
									SCREAMING_SNAKE_CASE						= train_dataset.select(range(_UpperCAmelCase))
						with training_args.main_process_first(desc='train dataset map pre-processing'):
									SCREAMING_SNAKE_CASE						= train_dataset.map(
									    _UpperCAmelCase							,       batched=_UpperCAmelCase							,       load_from_cache_file=not data_args.overwrite_cache							,       desc='Running tokenizer on train dataset'							,       )
						# Log a few random samples from the training set:
						for index in random.sample(range(len(_UpperCAmelCase))							,       3):
									logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''')

			if training_args.do_eval:
						if data_args.max_eval_samples is not None:
									SCREAMING_SNAKE_CASE						= min(len(_UpperCAmelCase)							,       data_args.max_eval_samples)
									SCREAMING_SNAKE_CASE						= eval_dataset.select(range(_UpperCAmelCase))
						with training_args.main_process_first(desc='validation dataset map pre-processing'):
									SCREAMING_SNAKE_CASE						= eval_dataset.map(
									    _UpperCAmelCase							,       batched=_UpperCAmelCase							,       load_from_cache_file=not data_args.overwrite_cache							,       desc='Running tokenizer on validation dataset'							,       )

			if training_args.do_predict:
						if data_args.max_predict_samples is not None:
									SCREAMING_SNAKE_CASE						= min(len(_UpperCAmelCase)							,       data_args.max_predict_samples)
									SCREAMING_SNAKE_CASE						= predict_dataset.select(range(_UpperCAmelCase))
						with training_args.main_process_first(desc='prediction dataset map pre-processing'):
									SCREAMING_SNAKE_CASE						= predict_dataset.map(
									    _UpperCAmelCase							,       batched=_UpperCAmelCase							,       load_from_cache_file=not data_args.overwrite_cache							,       desc='Running tokenizer on prediction dataset'							,       )

    # Get the metric function
			SCREAMING_SNAKE_CASE						= evaluate.load('xnli')

			# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
			# predictions and label_ids field) and has to return a dictionary string to float.
			def compute_metrics(_UpperCAmelCase):
						SCREAMING_SNAKE_CASE						= p.predictions[0] if isinstance(p.predictions							,       _UpperCAmelCase) else p.predictions
						SCREAMING_SNAKE_CASE						= np.argmax(_UpperCAmelCase							,       axis=1)
						return metric.compute(predictions=_UpperCAmelCase							,       references=p.label_ids)

			# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
			if data_args.pad_to_max_length:
						SCREAMING_SNAKE_CASE						= default_data_collator
			elif training_args.fpaa:
						SCREAMING_SNAKE_CASE						= DataCollatorWithPadding(_UpperCAmelCase							,       pad_to_multiple_of=8)
			else:
						SCREAMING_SNAKE_CASE						= None

			# Initialize our Trainer
			SCREAMING_SNAKE_CASE						= Trainer(
			    model=_UpperCAmelCase							,       args=_UpperCAmelCase							,       train_dataset=train_dataset if training_args.do_train else None							,       eval_dataset=eval_dataset if training_args.do_eval else None							,       compute_metrics=_UpperCAmelCase							,       tokenizer=_UpperCAmelCase							,       data_collator=_UpperCAmelCase							,       )

			# Training
			if training_args.do_train:
						SCREAMING_SNAKE_CASE						= None
						if training_args.resume_from_checkpoint is not None:
									SCREAMING_SNAKE_CASE						= training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									SCREAMING_SNAKE_CASE						= last_checkpoint
						SCREAMING_SNAKE_CASE						= trainer.train(resume_from_checkpoint=_UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= train_result.metrics
						SCREAMING_SNAKE_CASE						= (
						    data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCAmelCase)
						)
						SCREAMING_SNAKE_CASE						= min(_UpperCAmelCase							,       len(_UpperCAmelCase))

						trainer.save_model()  # Saves the tokenizer too for easy upload

						trainer.log_metrics('train'							,       _UpperCAmelCase)
						trainer.save_metrics('train'							,       _UpperCAmelCase)
						trainer.save_state()

			# Evaluation
			if training_args.do_eval:
						logger.info('*** Evaluate ***')
						SCREAMING_SNAKE_CASE						= trainer.evaluate(eval_dataset=_UpperCAmelCase)

						SCREAMING_SNAKE_CASE						= data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= min(_UpperCAmelCase							,       len(_UpperCAmelCase))

						trainer.log_metrics('eval'							,       _UpperCAmelCase)
						trainer.save_metrics('eval'							,       _UpperCAmelCase)

			# Prediction
			if training_args.do_predict:
						logger.info('*** Predict ***')
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= trainer.predict(_UpperCAmelCase							,       metric_key_prefix='predict')

						SCREAMING_SNAKE_CASE						= (
						    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_UpperCAmelCase)
						)
						SCREAMING_SNAKE_CASE						= min(_UpperCAmelCase							,       len(_UpperCAmelCase))

						trainer.log_metrics('predict'							,       _UpperCAmelCase)
						trainer.save_metrics('predict'							,       _UpperCAmelCase)

						SCREAMING_SNAKE_CASE						= np.argmax(_UpperCAmelCase							,       axis=1)
						SCREAMING_SNAKE_CASE						= os.path.join(training_args.output_dir							,       'predictions.txt')
						if trainer.is_world_process_zero():
									with open(_UpperCAmelCase							,       'w') as writer:
												writer.write('index\tprediction\n')
												for index, item in enumerate(_UpperCAmelCase):
															SCREAMING_SNAKE_CASE						= label_list[item]
															writer.write(F'''{index}\t{item}\n''')


if __name__ == "__main__":
				main()




