



import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    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.mam_aaa.modeling_mam_aaa import shift_tokens_right

lowerCAmelCase__					  =  2_5_6_0_4_7
lowerCAmelCase__					  =  2_5_6_1_4_5

@require_sentencepiece
@require_tokenizers
class 				snake_case__(_UpperCamelCase    ,      unittest.TestCase      ):

					"""simple docstring"""






					lowercase_								=		NllbTokenizer
					lowercase_								=		NllbTokenizerFast
					lowercase_								=		True
					lowercase_								=		True
					lowercase_								=		{}







					def 			snake_case					(							self :     str					):
									super().setUp()

									# We have a SentencePiece fixture for testing
									lowercase__					:   Union[str, Any]													=					NllbTokenizer(SCREAMING_SNAKE_CASE				,       keep_accents=SCREAMING_SNAKE_CASE					)
									tokenizer.save_pretrained(self.tmpdirname					)







					def 			snake_case					(							self :     Tuple					):
									lowercase__					:   int													=					NllbTokenizer(SCREAMING_SNAKE_CASE				,       keep_accents=SCREAMING_SNAKE_CASE					)

									lowercase__					:   List[str]													=					tokenizer.tokenize("This is a test"					)
									self.assertListEqual(SCREAMING_SNAKE_CASE				,       ["▁This", "▁is", "▁a", "▁t", "est"]					)

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

									lowercase__					:   int													=					tokenizer.tokenize("I was born in 92000, and this is falsé."					)
									self.assertListEqual(
									    SCREAMING_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",
									        ".",
									    ]				,       )
									lowercase__					:   Any													=					tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE					)
									self.assertListEqual(
									    SCREAMING_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]
									    ]				,       )

									lowercase__					:   Optional[Any]													=					tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE					)
									self.assertListEqual(
									    SCREAMING_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 			snake_case					(							self :     str					):
									lowercase__					:   Optional[Any]													=					(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
																	lowercase__					:   Optional[int]													=					self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)
																	lowercase__					:   Optional[int]													=					self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)

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

																	lowercase__					:   Tuple													=					tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE					)
																	lowercase__					:   List[str]													=					tokenizer_p.save_pretrained(SCREAMING_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					)					)
																	lowercase__					:   Any													=					tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f					)
																	self.assertSequenceEqual(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)

																	# Checks everything loads correctly in the same way
																	lowercase__					:   Union[str, Any]													=					tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE					)
																	lowercase__					:   Optional[int]													=					tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE					)

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

																	shutil.rmtree(SCREAMING_SNAKE_CASE					)

																	# Save tokenizer rust, legacy_format=True
																	lowercase__					:   Union[str, Any]													=					tempfile.mkdtemp()

																	lowercase__					:   Union[str, Any]													=					tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE				,       legacy_format=SCREAMING_SNAKE_CASE					)
																	lowercase__					:   List[str]													=					tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE					)

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

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

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

																	shutil.rmtree(SCREAMING_SNAKE_CASE					)

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

																	lowercase__					:   str													=					tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE				,       legacy_format=SCREAMING_SNAKE_CASE					)
																	lowercase__					:   Dict													=					tokenizer_p.save_pretrained(SCREAMING_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
																	lowercase__					:   Optional[Any]													=					tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE					)
																	lowercase__					:   Optional[int]													=					tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE					)

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

																	shutil.rmtree(SCREAMING_SNAKE_CASE					)







					@require_torch
					def 			snake_case					(							self :     int					):
									if not self.test_seqaseq:
													return

									lowercase__					:   List[str]													=					self.get_tokenizers()
									for tokenizer in tokenizers:
													with self.subTest(f"""{tokenizer.__class__.__name__}"""					):
																	# Longer text that will definitely require truncation.
																	lowercase__					:   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.",
																	]
																	lowercase__					:   List[Any]													=					[
																	    "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.",
																	]
																	try:
																					lowercase__					:   Tuple													=					tokenizer.prepare_seqaseq_batch(
																					    src_texts=SCREAMING_SNAKE_CASE				,       tgt_texts=SCREAMING_SNAKE_CASE				,       max_length=3				,       max_target_length=10				,       return_tensors="pt"				,       src_lang="eng_Latn"				,       tgt_lang="ron_Latn"				,       )
																	except NotImplementedError:
																					return
																	self.assertEqual(batch.input_ids.shape[1]				,       3					)
																	self.assertEqual(batch.labels.shape[1]				,       10					)
																	# max_target_length will default to max_length if not specified
																	lowercase__					:   str													=					tokenizer.prepare_seqaseq_batch(
																	    SCREAMING_SNAKE_CASE				,       tgt_texts=SCREAMING_SNAKE_CASE				,       max_length=3				,       return_tensors="pt"					)
																	self.assertEqual(batch.input_ids.shape[1]				,       3					)
																	self.assertEqual(batch.labels.shape[1]				,       3					)

																	lowercase__					:   List[Any]													=					tokenizer.prepare_seqaseq_batch(
																	    src_texts=SCREAMING_SNAKE_CASE				,       max_length=3				,       max_target_length=10				,       return_tensors="pt"					)
																	self.assertEqual(batch_encoder_only.input_ids.shape[1]				,       3					)
																	self.assertEqual(batch_encoder_only.attention_mask.shape[1]				,       3					)
																	self.assertNotIn("decoder_input_ids"				,       SCREAMING_SNAKE_CASE					)







					@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece."					)
					def 			snake_case					(							self :     Optional[int]					):
									pass







					def 			snake_case					(							self :     List[Any]					):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
																	lowercase__					:   List[Any]													=					[AddedToken("<special>"				,       lstrip=SCREAMING_SNAKE_CASE					)]

																	lowercase__					:   List[str]													=					self.rust_tokenizer_class.from_pretrained(
																	    SCREAMING_SNAKE_CASE				,       additional_special_tokens=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)
																	lowercase__					:   Dict													=					tokenizer_r.encode("Hey this is a <special> token"					)

																	lowercase__					:   Dict													=					tokenizer_r.encode("<special>"				,       add_special_tokens=SCREAMING_SNAKE_CASE					)[0]

																	self.assertTrue(special_token_id in r_output					)

																	if self.test_slow_tokenizer:
																					lowercase__					:   List[Any]													=					self.rust_tokenizer_class.from_pretrained(
																					    SCREAMING_SNAKE_CASE				,       additional_special_tokens=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE				,       )
																					lowercase__					:   Dict													=					self.tokenizer_class.from_pretrained(
																					    SCREAMING_SNAKE_CASE				,       additional_special_tokens=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)

																					lowercase__					:   int													=					tokenizer_p.encode("Hey this is a <special> token"					)

																					lowercase__					:   List[str]													=					tokenizer_cr.encode("Hey this is a <special> token"					)

																					self.assertEqual(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
																					self.assertEqual(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
																					self.assertTrue(special_token_id in p_output					)
																					self.assertTrue(special_token_id in cr_output					)






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

					"""simple docstring"""






					lowercase_								=		"""facebook/nllb-200-distilled-600M"""
					lowercase_								=		[
					    """ 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_								=		[
					    """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_								=		[
					    2_5_6_0_4_7,
					    1_6_2_9_7,
					    1_3_4_4_0_8,
					    8_1_6_5,
					    2_4_8_0_6_6,
					    1_4_7_3_4,
					    9_5_0,
					    1_1_3_5,
					    1_0_5_7_2_1,
					    3_5_7_3,
					    8_3,
					    2_7_3_5_2,
					    1_0_8,
					    4_9_4_8_6,
					    2,
					]







					@classmethod
					def 			snake_case					(							cls :     Tuple					):
									lowercase__					:   NllbTokenizer													=					NllbTokenizer.from_pretrained(
									    cls.checkpoint_name				,       src_lang="eng_Latn"				,       tgt_lang="ron_Latn"					)
									lowercase__					:   Dict													=					1
									return cls







					def 			snake_case					(							self :     List[Any]					):
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]				,       256_001					)
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]				,       256_002					)
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]				,       256_057					)







					def 			snake_case					(							self :     Tuple					):
									lowercase__					:   Tuple													=					self.tokenizer.batch_encode_plus(self.src_text					).input_ids[0]
									self.assertListEqual(self.expected_src_tokens				,       SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Optional[Any]					):
									self.assertIn(SCREAMING_SNAKE_CASE				,       self.tokenizer.all_special_ids					)
									# fmt: off
									lowercase__					:   str													=					[RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
									# fmt: on

									lowercase__					:   Union[str, Any]													=					self.tokenizer.decode(SCREAMING_SNAKE_CASE				,       skip_special_tokens=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Tuple													=					self.tokenizer.decode(generated_ids[1:]				,       skip_special_tokens=SCREAMING_SNAKE_CASE					)
									self.assertEqual(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
									self.assertNotIn(self.tokenizer.eos_token				,       SCREAMING_SNAKE_CASE					)







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







					def 			snake_case					(							self :     Dict					):
									self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]					)				,       [256_203, 3]					)







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   Any													=					tempfile.mkdtemp()
									lowercase__					:   Optional[int]													=					self.tokenizer.fairseq_tokens_to_ids
									self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE					)
									lowercase__					:   List[Any]													=					NllbTokenizer.from_pretrained(SCREAMING_SNAKE_CASE					)
									self.assertDictEqual(new_tok.fairseq_tokens_to_ids				,       SCREAMING_SNAKE_CASE					)







					@require_torch
					def 			snake_case					(							self :     int					):
									lowercase__					:   Tuple													=					self.tokenizer(
									    self.src_text				,       text_target=self.tgt_text				,       padding=SCREAMING_SNAKE_CASE				,       truncation=SCREAMING_SNAKE_CASE				,       max_length=len(self.expected_src_tokens					)				,       return_tensors="pt"				,       )
									lowercase__					:   List[str]													=					shift_tokens_right(
									    batch["labels"]				,       self.tokenizer.pad_token_id				,       self.tokenizer.lang_code_to_id["ron_Latn"]					)

									self.assertIsInstance(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)

									self.assertEqual((2, 15)				,       batch.input_ids.shape					)
									self.assertEqual((2, 15)				,       batch.attention_mask.shape					)
									lowercase__					:   Union[str, Any]													=					batch.input_ids.tolist()[0]
									self.assertListEqual(self.expected_src_tokens				,       SCREAMING_SNAKE_CASE					)
									self.assertEqual(SCREAMING_SNAKE_CASE				,       batch.decoder_input_ids[0, 0]					)  # EOS
									# 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 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   Dict													=					self.tokenizer(self.src_text				,       padding=SCREAMING_SNAKE_CASE				,       truncation=SCREAMING_SNAKE_CASE				,       max_length=3				,       return_tensors="pt"					)
									lowercase__					:   Optional[Any]													=					self.tokenizer(
									    text_target=self.tgt_text				,       padding=SCREAMING_SNAKE_CASE				,       truncation=SCREAMING_SNAKE_CASE				,       max_length=10				,       return_tensors="pt"					)
									lowercase__					:   List[Any]													=					targets["input_ids"]
									lowercase__					:   int													=					shift_tokens_right(
									    SCREAMING_SNAKE_CASE				,       self.tokenizer.pad_token_id				,       decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]				,       )

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







					@require_torch
					def 			snake_case					(							self :     List[Any]					):
									lowercase__					:   Dict													=					self.tokenizer._build_translation_inputs(
									    "A test"				,       return_tensors="pt"				,       src_lang="eng_Latn"				,       tgt_lang="fra_Latn"					)

									self.assertEqual(
									    nested_simplify(SCREAMING_SNAKE_CASE					)				,       {
									        # A, test, EOS, en_XX
									        "input_ids": [[256_047, 70, 7_356, 2]],
									        "attention_mask": [[1, 1, 1, 1]],
									        # ar_AR
									        "forced_bos_token_id": 256_057,
									    }				,       )







					@require_torch
					def 			snake_case					(							self :     Union[str, Any]					):
									lowercase__					:   Dict													=					True
									lowercase__					:   str													=					self.tokenizer(
									    "UN Chief says there is no military solution in Syria"				,       src_lang="eng_Latn"				,       tgt_lang="fra_Latn"					)
									self.assertEqual(
									    inputs.input_ids				,       [16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047]					)

									lowercase__					:   Any													=					False
									lowercase__					:   str													=					self.tokenizer(
									    "UN Chief says there is no military solution in Syria"				,       src_lang="eng_Latn"				,       tgt_lang="fra_Latn"					)
									self.assertEqual(
									    inputs.input_ids				,       [256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2]					)









import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
				raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
lowerCAmelCase__					  =  False
lowerCAmelCase__					  =  False


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				return TrainCommand(lowerCamelCase__						)

class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					@staticmethod
					def 			snake_case					(							SCREAMING_SNAKE_CASE :     ArgumentParser					):
									lowercase__					:   Optional[int]													=					parser.add_parser("train"				,       help="CLI tool to train a model on a task."					)

									train_parser.add_argument(
									    "--train_data"				,       type=SCREAMING_SNAKE_CASE				,       required=SCREAMING_SNAKE_CASE				,       help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."				,       )
									train_parser.add_argument(
									    "--column_label"				,       type=SCREAMING_SNAKE_CASE				,       default=0				,       help="Column of the dataset csv file with example labels."					)
									train_parser.add_argument(
									    "--column_text"				,       type=SCREAMING_SNAKE_CASE				,       default=1				,       help="Column of the dataset csv file with example texts."					)
									train_parser.add_argument(
									    "--column_id"				,       type=SCREAMING_SNAKE_CASE				,       default=2				,       help="Column of the dataset csv file with example ids."					)
									train_parser.add_argument(
									    "--skip_first_row"				,       action="store_true"				,       help="Skip the first row of the csv file (headers)."					)

									train_parser.add_argument("--validation_data"				,       type=SCREAMING_SNAKE_CASE				,       default=""				,       help="path to validation dataset."					)
									train_parser.add_argument(
									    "--validation_split"				,       type=SCREAMING_SNAKE_CASE				,       default=0.1				,       help="if validation dataset is not provided, fraction of train dataset to use as validation dataset."				,       )

									train_parser.add_argument("--output"				,       type=SCREAMING_SNAKE_CASE				,       default="./"				,       help="path to saved the trained model."					)

									train_parser.add_argument(
									    "--task"				,       type=SCREAMING_SNAKE_CASE				,       default="text_classification"				,       help="Task to train the model on."					)
									train_parser.add_argument(
									    "--model"				,       type=SCREAMING_SNAKE_CASE				,       default="bert-base-uncased"				,       help="Model's name or path to stored model."					)
									train_parser.add_argument("--train_batch_size"				,       type=SCREAMING_SNAKE_CASE				,       default=32				,       help="Batch size for training."					)
									train_parser.add_argument("--valid_batch_size"				,       type=SCREAMING_SNAKE_CASE				,       default=64				,       help="Batch size for validation."					)
									train_parser.add_argument("--learning_rate"				,       type=SCREAMING_SNAKE_CASE				,       default=3E-5				,       help="Learning rate."					)
									train_parser.add_argument("--adam_epsilon"				,       type=SCREAMING_SNAKE_CASE				,       default=1E-0_8				,       help="Epsilon for Adam optimizer."					)
									train_parser.set_defaults(func=SCREAMING_SNAKE_CASE					)







					def __init__(							self :     int				,       SCREAMING_SNAKE_CASE :     Namespace					):
									lowercase__					:   int													=					logging.get_logger("transformers-cli/training"					)

									lowercase__					:   List[Any]													=					"tf" if is_tf_available() else "torch"

									os.makedirs(args.output				,       exist_ok=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Optional[Any]													=					args.output

									lowercase__					:   Union[str, Any]													=					args.column_label
									lowercase__					:   Optional[int]													=					args.column_text
									lowercase__					:   Optional[int]													=					args.column_id

									self.logger.info(f"""Loading {args.task} pipeline for {args.model}"""					)
									if args.task == "text_classification":
													lowercase__					:   int													=					TextClassificationPipeline.from_pretrained(args.model					)
									elif args.task == "token_classification":
													raise NotImplementedError
									elif args.task == "question_answering":
													raise NotImplementedError

									self.logger.info(f"""Loading dataset from {args.train_data}"""					)
									lowercase__					:   List[str]													=					Processor.create_from_csv(
									    args.train_data				,       column_label=args.column_label				,       column_text=args.column_text				,       column_id=args.column_id				,       skip_first_row=args.skip_first_row				,       )
									lowercase__					:   Union[str, Any]													=					None
									if args.validation_data:
													self.logger.info(f"""Loading validation dataset from {args.validation_data}"""					)
													lowercase__					:   Optional[int]													=					Processor.create_from_csv(
													    args.validation_data				,       column_label=args.column_label				,       column_text=args.column_text				,       column_id=args.column_id				,       skip_first_row=args.skip_first_row				,       )

									lowercase__					:   Dict													=					args.validation_split
									lowercase__					:   List[str]													=					args.train_batch_size
									lowercase__					:   Any													=					args.valid_batch_size
									lowercase__					:   Optional[int]													=					args.learning_rate
									lowercase__					:   int													=					args.adam_epsilon







					def 			snake_case					(							self :     Dict					):
									if self.framework == "tf":
													return self.run_tf()
									return self.run_torch()







					def 			snake_case					(							self :     Union[str, Any]					):
									raise NotImplementedError







					def 			snake_case					(							self :     Union[str, Any]					):
									self.pipeline.fit(
									    self.train_dataset				,       validation_data=self.valid_dataset				,       validation_split=self.validation_split				,       learning_rate=self.learning_rate				,       adam_epsilon=self.adam_epsilon				,       train_batch_size=self.train_batch_size				,       valid_batch_size=self.valid_batch_size				,       )

									# Save trained pipeline
									self.pipeline.save_pretrained(self.output					)




