



"""simple docstring"""






import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def   snake_case__     (	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					List[str]			):
			"""simple docstring"""
			lowerCamelCase__	:	Tuple				=old_name

			if "patch_embed" in old_name:
						lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	str				=old_name.split('''.'''			)

						if layer == "0":
									lowerCamelCase__	:	List[Any]				=old_name.replace('''0'''							,	'''convolution1'''			)
						elif layer == "1":
									lowerCamelCase__	:	Union[str, Any]				=old_name.replace('''1'''							,	'''batchnorm_before'''			)
						elif layer == "3":
									lowerCamelCase__	:	Any				=old_name.replace('''3'''							,	'''convolution2'''			)
						else:
									lowerCamelCase__	:	List[str]				=old_name.replace('''4'''							,	'''batchnorm_after'''			)

			if "network" in old_name and re.search(R'''\d\.\d'''							,	__lowerCamelCase			):
						lowerCamelCase__	:	Optional[int]				=R'''\b\d{2}\b'''
						if bool(re.search(__lowerCamelCase							,	__lowerCamelCase			)			):
									lowerCamelCase__	:	List[Any]				=re.search(R'''\d\.\d\d.'''							,	__lowerCamelCase			).group()
						else:
									lowerCamelCase__	:	Any				=re.search(R'''\d\.\d.'''							,	__lowerCamelCase			).group()
						if int(match[0]			) < 6:
									lowerCamelCase__	:	Any				=old_name.replace(__lowerCamelCase							,	''''''			)
									lowerCamelCase__	:	int				=trimmed_name.replace('''network'''							,	match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]			)
									lowerCamelCase__	:	str				='''intermediate_stages.''' + trimmed_name
						else:
									lowerCamelCase__	:	Optional[Any]				=old_name.replace(__lowerCamelCase							,	''''''			)
									if int(match[2]			) < num_meta4D_last_stage:
												lowerCamelCase__	:	Any				=trimmed_name.replace('''network'''							,	'''meta4D_layers.blocks.''' + match[2]			)
									else:
												lowerCamelCase__	:	Optional[int]				=str(int(match[2]			) - num_meta4D_last_stage			)
												lowerCamelCase__	:	Union[str, Any]				=trimmed_name.replace('''network'''							,	'''meta3D_layers.blocks.''' + layer_index			)
												if "norm1" in old_name:
															lowerCamelCase__	:	Any				=trimmed_name.replace('''norm1'''							,	'''layernorm1'''			)
												elif "norm2" in old_name:
															lowerCamelCase__	:	str				=trimmed_name.replace('''norm2'''							,	'''layernorm2'''			)
												elif "fc1" in old_name:
															lowerCamelCase__	:	List[Any]				=trimmed_name.replace('''fc1'''							,	'''linear_in'''			)
												elif "fc2" in old_name:
															lowerCamelCase__	:	Dict				=trimmed_name.replace('''fc2'''							,	'''linear_out'''			)

									lowerCamelCase__	:	List[Any]				='''last_stage.''' + trimmed_name

			elif "network" in old_name and re.search(R'''.\d.'''							,	__lowerCamelCase			):
						lowerCamelCase__	:	Dict				=old_name.replace('''network'''							,	'''intermediate_stages'''			)

			if "fc" in new_name:
						lowerCamelCase__	:	Dict				=new_name.replace('''fc'''							,	'''convolution'''			)
			elif ("norm1" in new_name) and ("layernorm1" not in new_name):
						lowerCamelCase__	:	Tuple				=new_name.replace('''norm1'''							,	'''batchnorm_before'''			)
			elif ("norm2" in new_name) and ("layernorm2" not in new_name):
						lowerCamelCase__	:	Dict				=new_name.replace('''norm2'''							,	'''batchnorm_after'''			)
			if "proj" in new_name:
						lowerCamelCase__	:	Any				=new_name.replace('''proj'''							,	'''projection'''			)
			if "dist_head" in new_name:
						lowerCamelCase__	:	int				=new_name.replace('''dist_head'''							,	'''distillation_classifier'''			)
			elif "head" in new_name:
						lowerCamelCase__	:	List[Any]				=new_name.replace('''head'''							,	'''classifier'''			)
			elif "patch_embed" in new_name:
						lowerCamelCase__	:	int				='''efficientformer.''' + new_name
			elif new_name == "norm.weight" or new_name == "norm.bias":
						lowerCamelCase__	:	Optional[int]				=new_name.replace('''norm'''							,	'''layernorm'''			)
						lowerCamelCase__	:	List[str]				='''efficientformer.''' + new_name
			else:
						lowerCamelCase__	:	Union[str, Any]				='''efficientformer.encoder.''' + new_name

			return new_name






def   snake_case__     (	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					Optional[Any]			):
			"""simple docstring"""
			for key in checkpoint.copy().keys():
						lowerCamelCase__	:	List[Any]				=checkpoint.pop(__lowerCamelCase			)
						lowerCamelCase__	:	Dict				=val

			return checkpoint






def   snake_case__     (	):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[Any]				='''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowerCamelCase__	:	Optional[Any]				=Image.open(requests.get(__lowerCamelCase							,	stream=__lowerCamelCase			).raw			)

			return image






def   snake_case__     (	__lowerCamelCase       :					Path							,	__lowerCamelCase       :					Path							,	__lowerCamelCase       :					Path							,	__lowerCamelCase       :					bool			):
			"""simple docstring"""
			lowerCamelCase__	:	List[str]				=torch.load(__lowerCamelCase							,	map_location='''cpu'''			)['''model''']
			lowerCamelCase__	:	Optional[Any]				=EfficientFormerConfig.from_json_file(__lowerCamelCase			)
			lowerCamelCase__	:	Dict				=EfficientFormerForImageClassificationWithTeacher(__lowerCamelCase			)
			lowerCamelCase__	:	int				='''_'''.join(checkpoint_path.split('''/'''			)[-1].split('''.'''			)[0].split('''_'''			)[:-1]			)

			lowerCamelCase__	:	Union[str, Any]				=config.depths[-1] - config.num_metaad_blocks + 1
			lowerCamelCase__	:	List[str]				=convert_torch_checkpoint(__lowerCamelCase							,	__lowerCamelCase			)

			model.load_state_dict(__lowerCamelCase			)
			model.eval()

			lowerCamelCase__	:	str				={
			    '''bilinear''': PILImageResampling.BILINEAR,
			    '''bicubic''': PILImageResampling.BICUBIC,
			    '''nearest''': PILImageResampling.NEAREST,
			}

			# prepare image
			lowerCamelCase__	:	Optional[int]				=prepare_img()
			lowerCamelCase__	:	Optional[int]				=256
			lowerCamelCase__	:	Optional[int]				=224
			lowerCamelCase__	:	int				=EfficientFormerImageProcessor(
			    size={'''shortest_edge''': image_size}							,	crop_size={'''height''': crop_size, '''width''': crop_size}							,	resample=pillow_resamplings['''bicubic''']							,	)
			lowerCamelCase__	:	Tuple				=processor(images=__lowerCamelCase							,	return_tensors='''pt'''			).pixel_values

			# original processing pipeline
			lowerCamelCase__	:	int				=Compose(
			    [
			        Resize(__lowerCamelCase							,	interpolation=pillow_resamplings['''bicubic''']			),
			        CenterCrop(__lowerCamelCase			),
			        ToTensor(),
			        Normalize(__lowerCamelCase							,	__lowerCamelCase			),
			    ]			)
			lowerCamelCase__	:	List[Any]				=image_transforms(__lowerCamelCase			).unsqueeze(0			)

			assert torch.allclose(__lowerCamelCase							,	__lowerCamelCase			)

			lowerCamelCase__	:	Union[str, Any]				=model(__lowerCamelCase			)
			lowerCamelCase__	:	Tuple				=outputs.logits

			lowerCamelCase__	:	Tuple				=(1, 1000)

			if "l1" in model_name:
						lowerCamelCase__	:	Optional[Any]				=torch.Tensor(
						    [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28]			)
						assert torch.allclose(logits[0, :10]							,	__lowerCamelCase							,	atol=1e-3			)
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						lowerCamelCase__	:	List[str]				=torch.Tensor(
						    [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27]			)
						assert torch.allclose(logits[0, :10]							,	__lowerCamelCase							,	atol=1e-3			)
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						lowerCamelCase__	:	str				=torch.Tensor(
						    [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78]			)
						assert logits.shape == expected_shape
			else:
						raise ValueError(
						    f'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'''			)

			# Save Checkpoints
			Path(__lowerCamelCase			).mkdir(exist_ok=__lowerCamelCase			)
			model.save_pretrained(__lowerCamelCase			)
			print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''			)
			processor.save_pretrained(__lowerCamelCase			)
			print(f'''Processor successfuly saved at {pytorch_dump_path}'''			)

			if push_to_hub:
						print('''Pushing model to the hub...'''			)

						model.push_to_hub(
						    repo_id=f'''Bearnardd/{pytorch_dump_path}'''							,	commit_message='''Add model'''							,	use_temp_dir=__lowerCamelCase							,	)
						processor.push_to_hub(
						    repo_id=f'''Bearnardd/{pytorch_dump_path}'''							,	commit_message='''Add image processor'''							,	use_temp_dir=__lowerCamelCase							,	)


if __name__ == "__main__":
		_lowercase				:				Dict   		=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--pytorch_model_path",
		    default=None,
		    type=str,
		    required=True,
		    help="Path to EfficientFormer pytorch checkpoint.",
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help="The json file for EfficientFormer model config.",
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)

		parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
		parser.add_argument(
		    "--no-push_to_hub",
		    dest="push_to_hub",
		    action="store_false",
		    help="Do not push model and image processor to the hub",
		)
		parser.set_defaults(push_to_hub=True)

		_lowercase				:				List[str]   		=		parser.parse_args()
		convert_efficientformer_checkpoint(
		    checkpoint_path=args.pytorch_model_path,
		    efficientformer_config_file=args.config_file,
		    pytorch_dump_path=args.pytorch_dump_path,
		    push_to_hub=args.push_to_hub,
		)









"""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


_lowercase				:				List[str]   		=		get_tests_dir("fixtures/test_sentencepiece.model")


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

_lowercase				:				List[str]   		=		2_5_0_0_0_4
_lowercase				:				Optional[Any]   		=		2_5_0_0_2_0

@require_sentencepiece
@require_tokenizers
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							MBartTokenizer
					_a         =							MBartTokenizerFast
					_a         =							True
					_a         =							True



					def 	snake_case ( self							:						Tuple  )->      Union[str, Any]:
								super().setUp()

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



					def 	snake_case ( self							:						Dict  )->      Union[str, Any]:
								lowerCamelCase__	:	Any				=MBartTokenizer(lowerCamelCase,						keep_accents=lowerCamelCase  )

								lowerCamelCase__	:	List[Any]				=tokenizer.tokenize('''This is a test'''  )
								self.assertListEqual(lowerCamelCase,						['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']  )

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

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

								lowerCamelCase__	:	str				=tokenizer.convert_ids_to_tokens(lowerCamelCase  )
								self.assertListEqual(
								    lowerCamelCase,						[
								        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							:						Tuple  )->      List[Any]:
								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

								lowerCamelCase__	:	int				=(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})'''  ):
														lowerCamelCase__	:	Union[str, Any]				=self.rust_tokenizer_class.from_pretrained(lowerCamelCase,						**lowerCamelCase  )
														lowerCamelCase__	:	str				=self.tokenizer_class.from_pretrained(lowerCamelCase,						**lowerCamelCase  )

														lowerCamelCase__	:	List[str]				=tempfile.mkdtemp()

														lowerCamelCase__	:	Union[str, Any]				=tokenizer_r.save_pretrained(lowerCamelCase  )
														lowerCamelCase__	:	Optional[int]				=tokenizer_p.save_pretrained(lowerCamelCase  )

														# 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  )  )
														lowerCamelCase__	:	List[str]				=tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f  )
														self.assertSequenceEqual(lowerCamelCase,						lowerCamelCase  )

														# Checks everything loads correctly in the same way
														lowerCamelCase__	:	Any				=tokenizer_r.from_pretrained(lowerCamelCase  )
														lowerCamelCase__	:	Dict				=tokenizer_p.from_pretrained(lowerCamelCase  )

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

														shutil.rmtree(lowerCamelCase  )

														# Save tokenizer rust, legacy_format=True
														lowerCamelCase__	:	Dict				=tempfile.mkdtemp()

														lowerCamelCase__	:	List[str]				=tokenizer_r.save_pretrained(lowerCamelCase,						legacy_format=lowerCamelCase  )
														lowerCamelCase__	:	Tuple				=tokenizer_p.save_pretrained(lowerCamelCase  )

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

														# Checks everything loads correctly in the same way
														lowerCamelCase__	:	Optional[int]				=tokenizer_r.from_pretrained(lowerCamelCase  )
														lowerCamelCase__	:	Any				=tokenizer_p.from_pretrained(lowerCamelCase  )

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

														shutil.rmtree(lowerCamelCase  )

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

														lowerCamelCase__	:	int				=tokenizer_r.save_pretrained(lowerCamelCase,						legacy_format=lowerCamelCase  )
														lowerCamelCase__	:	Dict				=tokenizer_p.save_pretrained(lowerCamelCase  )

														# 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
														lowerCamelCase__	:	Dict				=tokenizer_r.from_pretrained(lowerCamelCase  )
														lowerCamelCase__	:	int				=tokenizer_p.from_pretrained(lowerCamelCase  )

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

														shutil.rmtree(lowerCamelCase  )





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


					'''simple docstring'''





					_a         =							'facebook/mbart-large-en-ro'
					_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         =							[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 	snake_case ( cls							:						List[Any]  )->      Optional[int]:
								lowerCamelCase__	:	MBartTokenizer				=MBartTokenizer.from_pretrained(
								    cls.checkpoint_name,						src_lang='''en_XX''',						tgt_lang='''ro_RO'''  )
								lowerCamelCase__	:	Optional[int]				=1
								return cls



					def 	snake_case ( self							:						Optional[Any]  )->      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  )



					def 	snake_case ( self							:						Optional[int]  )->      List[Any]:
								lowerCamelCase__	:	Union[str, Any]				=self.tokenizer.batch_encode_plus(self.src_text  ).input_ids[0]
								self.assertListEqual(self.expected_src_tokens,						lowerCamelCase  )



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



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



					def 	snake_case ( self							:						int  )->      Any:
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']  ),						[25_0026, 25_0001]  )



					def 	snake_case ( self							:						Tuple  )->      Optional[Any]:
								lowerCamelCase__	:	int				=tempfile.mkdtemp()
								lowerCamelCase__	:	Optional[int]				=self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=MBartTokenizer.from_pretrained(lowerCamelCase  )
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids,						lowerCamelCase  )



					@require_torch
					def 	snake_case ( self							:						Optional[Any]  )->      Tuple:
								lowerCamelCase__	:	Optional[Any]				=self.tokenizer(self.src_text,						text_target=self.tgt_text,						padding=lowerCamelCase,						return_tensors='''pt'''  )
								lowerCamelCase__	:	Dict				=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 	snake_case ( self							:						Optional[Any]  )->      Any:
								lowerCamelCase__	:	str				=self.tokenizer(
								    self.src_text,						text_target=self.tgt_text,						padding=lowerCamelCase,						truncation=lowerCamelCase,						max_length=len(self.expected_src_tokens  ),						return_tensors='''pt''',						)

								lowerCamelCase__	:	List[Any]				=shift_tokens_right(batch['''labels'''],						self.tokenizer.pad_token_id  )

								self.assertIsInstance(lowerCamelCase,						lowerCamelCase  )

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

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




