




from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


__A						:							Any							=  logging.get_logger(__name__)
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''






			try:
						with open(_lowercase					,	"""rb"""   ) as flax_state_f:
									SCREAMING_SNAKE_CASE           =					from_bytes(_lowercase					,	flax_state_f.read()   )
			except UnpicklingError as e:
						try:
									with open(_lowercase   ) as f:
												if f.read().startswith("""version"""   ):
															raise OSError(
															    """You seem to have cloned a repository without having git-lfs installed. Please"""
															    """ install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
															    """ folder you cloned."""   )
												else:
															raise ValueError from e
						except (UnicodeDecodeError, ValueError):
									raise EnvironmentError(f"""Unable to convert {model_file} to Flax deserializable object. """   )

			return load_flax_weights_in_pytorch_model(_lowercase					,	_lowercase   )




def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''






			try:
						import torch  # noqa: F401
			except ImportError:
						logger.error(
						    """Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
						    """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
						    """ instructions."""   )
						raise

			# check if we have bf16 weights
			SCREAMING_SNAKE_CASE           =					flatten_dict(jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE   : x.dtype == jnp.bfloataa					,	_lowercase   )   ).values()
			if any(_lowercase   ):
						# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

						# and bf16 is not fully supported in PT yet.
						logger.warning(
						    """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
						    """before loading those in PyTorch model."""   )
						SCREAMING_SNAKE_CASE           =					jax.tree_util.tree_map(
						    lambda SCREAMING_SNAKE_CASE   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params					,	_lowercase   )

			SCREAMING_SNAKE_CASE           =					""

			SCREAMING_SNAKE_CASE           =					flatten_dict(_lowercase					,	sep="""."""   )
			SCREAMING_SNAKE_CASE           =					pt_model.state_dict()

			# keep track of unexpected & missing keys
			SCREAMING_SNAKE_CASE           =					[]
			SCREAMING_SNAKE_CASE           =					set(pt_model_dict.keys()   )

			for flax_key_tuple, flax_tensor in flax_state_dict.items():
						SCREAMING_SNAKE_CASE           =					flax_key_tuple.split("""."""   )

						if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
									SCREAMING_SNAKE_CASE           =					flax_key_tuple_array[:-1] + ["weight"]
									SCREAMING_SNAKE_CASE           =					jnp.transpose(_lowercase					,	(3, 2, 0, 1)   )
						elif flax_key_tuple_array[-1] == "kernel":
									SCREAMING_SNAKE_CASE           =					flax_key_tuple_array[:-1] + ["weight"]
									SCREAMING_SNAKE_CASE           =					flax_tensor.T
						elif flax_key_tuple_array[-1] == "scale":
									SCREAMING_SNAKE_CASE           =					flax_key_tuple_array[:-1] + ["weight"]

						if "time_embedding" not in flax_key_tuple_array:
									for i, flax_key_tuple_string in enumerate(_lowercase   ):
												SCREAMING_SNAKE_CASE           =					(
												    flax_key_tuple_string.replace("""_0"""					,	""".0"""   )
												    .replace("""_1"""					,	""".1"""   )
												    .replace("""_2"""					,	""".2"""   )
												    .replace("""_3"""					,	""".3"""   )
												    .replace("""_4"""					,	""".4"""   )
												    .replace("""_5"""					,	""".5"""   )
												    .replace("""_6"""					,	""".6"""   )
												    .replace("""_7"""					,	""".7"""   )
												    .replace("""_8"""					,	""".8"""   )
												    .replace("""_9"""					,	""".9"""   )
												)

						SCREAMING_SNAKE_CASE           =					".".join(_lowercase   )

						if flax_key in pt_model_dict:
									if flax_tensor.shape != pt_model_dict[flax_key].shape:
												raise ValueError(
												    f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
												    f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""   )
									else:
												# add weight to pytorch dict
												SCREAMING_SNAKE_CASE           =					np.asarray(_lowercase   ) if not isinstance(_lowercase					,	np.ndarray   ) else flax_tensor
												SCREAMING_SNAKE_CASE           =					torch.from_numpy(_lowercase   )
												# remove from missing keys
												missing_keys.remove(_lowercase   )
						else:
									# weight is not expected by PyTorch model
									unexpected_keys.append(_lowercase   )

			pt_model.load_state_dict(_lowercase   )

			# re-transform missing_keys to list
			SCREAMING_SNAKE_CASE           =					list(_lowercase   )

			if len(_lowercase   ) > 0:
						logger.warning(
						    """Some weights of the Flax model were not used when initializing the PyTorch model"""
						    f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
						    f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
						    """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
						    f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
						    """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
						    """ FlaxBertForSequenceClassification model)."""   )
			if len(_lowercase   ) > 0:
						logger.warning(
						    f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
						    f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
						    """ use it for predictions and inference."""   )

			return pt_model












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


__A						:							Any							=  get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
		from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__A						:							Optional[int]							=  2_5_6_0_4_7
__A						:							Dict							=  2_5_6_1_4_5




@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase_      (   A		,      unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     NllbTokenizer
							a__						  =     NllbTokenizerFast
							a__						  =     True
							a__						  =     True
							a__						  =     {}





							def    _UpperCAmelCase     (					self       :	Any			)			->					int:
										super().setUp()

										# We have a SentencePiece fixture for testing
										SCREAMING_SNAKE_CASE           =					NllbTokenizer(a       ,     keep_accents=a			)
										tokenizer.save_pretrained(self.tmpdirname			)





							def    _UpperCAmelCase     (					self       :	int			)			->					str:
										SCREAMING_SNAKE_CASE           =					NllbTokenizer(a       ,     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>""",
										        """.""",
										    ]       ,     )





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Any:
										SCREAMING_SNAKE_CASE           =					(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})"""			):
																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			)			)

																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
							def    _UpperCAmelCase     (					self       :	Dict			)			->					List[str]:
										if not self.test_seqaseq:
													return

										SCREAMING_SNAKE_CASE           =					self.get_tokenizers()
										for tokenizer in tokenizers:
													with self.subTest(f"""{tokenizer.__class__.__name__}"""			):
																# Longer text that will definitely require truncation.
																SCREAMING_SNAKE_CASE           =					[
																    """ 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.""",
																]
																SCREAMING_SNAKE_CASE           =					[
																    """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:
																			SCREAMING_SNAKE_CASE           =					tokenizer.prepare_seqaseq_batch(
																			    src_texts=a       ,     tgt_texts=a       ,     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
																SCREAMING_SNAKE_CASE           =					tokenizer.prepare_seqaseq_batch(
																    a       ,     tgt_texts=a       ,     max_length=3       ,     return_tensors="""pt"""			)
																self.assertEqual(batch.input_ids.shape[1]       ,     3			)
																self.assertEqual(batch.labels.shape[1]       ,     3			)

																SCREAMING_SNAKE_CASE           =					tokenizer.prepare_seqaseq_batch(
																    src_texts=a       ,     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"""       ,     a			)





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







							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Optional[int]:
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
																SCREAMING_SNAKE_CASE           =					[AddedToken("""<special>"""       ,     lstrip=a			)]

																SCREAMING_SNAKE_CASE           =					self.rust_tokenizer_class.from_pretrained(
																    a       ,     additional_special_tokens=a       ,     **a			)
																SCREAMING_SNAKE_CASE           =					tokenizer_r.encode("""Hey this is a <special> token"""			)

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

																self.assertTrue(special_token_id in r_output			)

																if self.test_slow_tokenizer:
																			SCREAMING_SNAKE_CASE           =					self.rust_tokenizer_class.from_pretrained(
																			    a       ,     additional_special_tokens=a       ,     **a       ,     )
																			SCREAMING_SNAKE_CASE           =					self.tokenizer_class.from_pretrained(
																			    a       ,     additional_special_tokens=a       ,     **a			)

																			SCREAMING_SNAKE_CASE           =					tokenizer_p.encode("""Hey this is a <special> token"""			)

																			SCREAMING_SNAKE_CASE           =					tokenizer_cr.encode("""Hey this is a <special> token"""			)

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





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




							'''simple docstring'''



							a__						  =     '''facebook/nllb-200-distilled-600M'''
							a__						  =     [
							    ''' 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__						  =     [
							    '''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__						  =     [
							    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    _UpperCAmelCase     (					cls       :	List[Any]			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					NllbTokenizer.from_pretrained(
										    cls.checkpoint_name       ,     src_lang="""eng_Latn"""       ,     tgt_lang="""ron_Latn"""			)
										SCREAMING_SNAKE_CASE           =					1
										return cls





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





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Tuple:
										self.assertIn(a       ,     self.tokenizer.all_special_ids			)
										# fmt: off
										SCREAMING_SNAKE_CASE           =					[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

										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    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					List[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[-1]       ,     2			)
										self.assertEqual(ids[0]       ,     a			)
										self.assertEqual(len(a			)       ,     a			)





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





							def    _UpperCAmelCase     (					self       :	str			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					tempfile.mkdtemp()
										SCREAMING_SNAKE_CASE           =					self.tokenizer.fairseq_tokens_to_ids
										self.tokenizer.save_pretrained(a			)
										SCREAMING_SNAKE_CASE           =					NllbTokenizer.from_pretrained(a			)
										self.assertDictEqual(new_tok.fairseq_tokens_to_ids       ,     a			)





							@require_torch
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Union[str, 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.tokenizer.lang_code_to_id["""ron_Latn"""]			)

										self.assertIsInstance(a       ,     a			)

										self.assertEqual((2, 15)       ,     batch.input_ids.shape			)
										self.assertEqual((2, 15)       ,     batch.attention_mask.shape			)
										SCREAMING_SNAKE_CASE           =					batch.input_ids.tolist()[0]
										self.assertListEqual(self.expected_src_tokens       ,     a			)
										self.assertEqual(a       ,     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    _UpperCAmelCase     (					self       :	str			)			->					Dict:
										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       ,     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    _UpperCAmelCase     (					self       :	int			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					self.tokenizer._build_translation_inputs(
										    """A test"""       ,     return_tensors="""pt"""       ,     src_lang="""eng_Latn"""       ,     tgt_lang="""fra_Latn"""			)

										self.assertEqual(
										    nested_simplify(a			)       ,     {
										        # 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    _UpperCAmelCase     (					self       :	str			)			->					int:
										SCREAMING_SNAKE_CASE           =					True
										SCREAMING_SNAKE_CASE           =					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]			)

										SCREAMING_SNAKE_CASE           =					False
										SCREAMING_SNAKE_CASE           =					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]			)






