



"""simple docstring"""






import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def   snake_case__     (	__lowerCamelCase       :					Tuple			):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[int]				=[2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
			lowerCamelCase__	:	Union[str, Any]				=True if '''large''' in model_name or '''huge''' in model_name else False
			lowerCamelCase__	:	Optional[Any]				=True if '''large''' in model_name or '''huge''' in model_name else False
			lowerCamelCase__	:	Optional[int]				=True if '''large''' in model_name or '''huge''' in model_name else False

			if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
						if "fl3" in model_name:
									lowerCamelCase__	:	Union[str, Any]				=[3, 3, 3, 3]
									lowerCamelCase__	:	Dict				=[5, 5, 5, 5]
						elif "fl4" in model_name:
									lowerCamelCase__	:	List[str]				=[4, 4, 4, 4]
									lowerCamelCase__	:	int				=[3, 3, 3, 3]

			if "tiny" in model_name or "small" in model_name or "base" in model_name:
						lowerCamelCase__	:	List[str]				=[3, 3, 3, 3]
						if "lrf" in model_name:
									lowerCamelCase__	:	Dict				=[3, 3, 3, 3]
						else:
									lowerCamelCase__	:	Optional[int]				=[2, 2, 2, 2]

			if "tiny" in model_name:
						lowerCamelCase__	:	Dict				=96
			elif "small" in model_name:
						lowerCamelCase__	:	Any				=96
			elif "base" in model_name:
						lowerCamelCase__	:	str				=128
			elif "large" in model_name:
						lowerCamelCase__	:	Tuple				=192
			elif "xlarge" in model_name:
						lowerCamelCase__	:	Optional[Any]				=256
			elif "huge" in model_name:
						lowerCamelCase__	:	Optional[Any]				=352

			# set label information
			lowerCamelCase__	:	List[Any]				='''huggingface/label-files'''
			if "large" in model_name or "huge" in model_name:
						lowerCamelCase__	:	int				='''imagenet-22k-id2label.json'''
			else:
						lowerCamelCase__	:	Optional[int]				='''imagenet-1k-id2label.json'''

			lowerCamelCase__	:	List[Any]				=json.load(open(hf_hub_download(__lowerCamelCase							,	__lowerCamelCase							,	repo_type='''dataset'''			)							,	'''r'''			)			)
			lowerCamelCase__	:	Dict				={int(__lowerCamelCase			): v for k, v in idalabel.items()}
			lowerCamelCase__	:	int				={v: k for k, v in idalabel.items()}

			lowerCamelCase__	:	int				=FocalNetConfig(
			    embed_dim=__lowerCamelCase							,	depths=__lowerCamelCase							,	focal_levels=__lowerCamelCase							,	focal_windows=__lowerCamelCase							,	use_conv_embed=__lowerCamelCase							,	idalabel=__lowerCamelCase							,	labelaid=__lowerCamelCase							,	use_post_layernorm=__lowerCamelCase							,	use_layerscale=__lowerCamelCase							,	)

			return config






def   snake_case__     (	__lowerCamelCase       :					str			):
			"""simple docstring"""
			if "patch_embed.proj" in name:
						lowerCamelCase__	:	Union[str, Any]				=name.replace('''patch_embed.proj'''							,	'''embeddings.patch_embeddings.projection'''			)
			if "patch_embed.norm" in name:
						lowerCamelCase__	:	Tuple				=name.replace('''patch_embed.norm'''							,	'''embeddings.norm'''			)
			if "layers" in name:
						lowerCamelCase__	:	str				='''encoder.''' + name
			if "encoder.layers" in name:
						lowerCamelCase__	:	Optional[int]				=name.replace('''encoder.layers'''							,	'''encoder.stages'''			)
			if "downsample.proj" in name:
						lowerCamelCase__	:	Optional[Any]				=name.replace('''downsample.proj'''							,	'''downsample.projection'''			)
			if "blocks" in name:
						lowerCamelCase__	:	Optional[Any]				=name.replace('''blocks'''							,	'''layers'''			)
			if "modulation.f.weight" in name or "modulation.f.bias" in name:
						lowerCamelCase__	:	Optional[Any]				=name.replace('''modulation.f'''							,	'''modulation.projection_in'''			)
			if "modulation.h.weight" in name or "modulation.h.bias" in name:
						lowerCamelCase__	:	Union[str, Any]				=name.replace('''modulation.h'''							,	'''modulation.projection_context'''			)
			if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
						lowerCamelCase__	:	str				=name.replace('''modulation.proj'''							,	'''modulation.projection_out'''			)

			if name == "norm.weight":
						lowerCamelCase__	:	int				='''layernorm.weight'''
			if name == "norm.bias":
						lowerCamelCase__	:	int				='''layernorm.bias'''

			if "head" in name:
						lowerCamelCase__	:	Optional[int]				=name.replace('''head'''							,	'''classifier'''			)
			else:
						lowerCamelCase__	:	Union[str, Any]				='''focalnet.''' + name

			return name






def   snake_case__     (	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Optional[int]							,	__lowerCamelCase       :					Optional[Any]=False			):
			"""simple docstring"""
			# fmt: off
			lowerCamelCase__	:	Dict				={
			    '''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
			    '''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
			    '''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
			    '''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
			    '''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
			    '''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
			    '''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
			    '''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
			    '''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
			    '''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
			}
			# fmt: on

			lowerCamelCase__	:	Optional[Any]				=model_name_to_url[model_name]
			print('''Checkpoint URL: '''							,	__lowerCamelCase			)
			lowerCamelCase__	:	List[str]				=torch.hub.load_state_dict_from_url(__lowerCamelCase							,	map_location='''cpu'''			)['''model''']

			# rename keys
			for key in state_dict.copy().keys():
						lowerCamelCase__	:	List[Any]				=state_dict.pop(__lowerCamelCase			)
						lowerCamelCase__	:	Any				=val

			lowerCamelCase__	:	Tuple				=get_focalnet_config(__lowerCamelCase			)
			lowerCamelCase__	:	Optional[Any]				=FocalNetForImageClassification(__lowerCamelCase			)
			model.eval()

			# load state dict
			model.load_state_dict(__lowerCamelCase			)

			# verify conversion
			lowerCamelCase__	:	Any				='''http://images.cocodataset.org/val2017/000000039769.jpg'''

			lowerCamelCase__	:	Dict				=BitImageProcessor(
			    do_resize=__lowerCamelCase							,	size={'''shortest_edge''': 256}							,	resample=PILImageResampling.BILINEAR							,	do_center_crop=__lowerCamelCase							,	crop_size=224							,	do_normalize=__lowerCamelCase							,	image_mean=__lowerCamelCase							,	image_std=__lowerCamelCase							,	)
			lowerCamelCase__	:	Any				=Image.open(requests.get(__lowerCamelCase							,	stream=__lowerCamelCase			).raw			)
			lowerCamelCase__	:	Optional[int]				=processor(images=__lowerCamelCase							,	return_tensors='''pt'''			)

			lowerCamelCase__	:	List[str]				=transforms.Compose(
			    [
			        transforms.Resize(256			),
			        transforms.CenterCrop(224			),
			        transforms.ToTensor(),
			        transforms.Normalize(mean=[0.4_85, 0.4_56, 0.4_06]							,	std=[0.2_29, 0.2_24, 0.2_25]			),
			    ]			)

			lowerCamelCase__	:	Dict				=image_transforms(__lowerCamelCase			).unsqueeze(0			)

			# verify pixel_values
			assert torch.allclose(inputs.pixel_values							,	__lowerCamelCase							,	atol=1e-4			)

			lowerCamelCase__	:	Any				=model(**__lowerCamelCase			)

			lowerCamelCase__	:	List[str]				=outputs.logits.argmax(-1			).item()
			print('''Predicted class:'''							,	model.config.idalabel[predicted_class_idx]			)

			print('''First values of logits:'''							,	outputs.logits[0, :3]			)

			if model_name == "focalnet-tiny":
						lowerCamelCase__	:	Tuple				=torch.tensor([0.21_66, -0.43_68, 0.21_91]			)
			elif model_name == "focalnet-tiny-lrf":
						lowerCamelCase__	:	Union[str, Any]				=torch.tensor([1.16_69, 0.01_25, -0.16_95]			)
			elif model_name == "focalnet-small":
						lowerCamelCase__	:	Optional[Any]				=torch.tensor([0.49_17, -0.04_30, 0.13_41]			)
			elif model_name == "focalnet-small-lrf":
						lowerCamelCase__	:	Any				=torch.tensor([-0.25_88, -0.53_42, -0.23_31]			)
			elif model_name == "focalnet-base":
						lowerCamelCase__	:	Any				=torch.tensor([-0.16_55, -0.40_90, -0.17_30]			)
			elif model_name == "focalnet-base-lrf":
						lowerCamelCase__	:	Tuple				=torch.tensor([0.53_06, -0.04_83, -0.39_28]			)
			assert torch.allclose(outputs.logits[0, :3]							,	__lowerCamelCase							,	atol=1e-4			)
			print('''Looks ok!'''			)

			if pytorch_dump_folder_path is not None:
						print(f'''Saving model and processor of {model_name} to {pytorch_dump_folder_path}'''			)
						model.save_pretrained(__lowerCamelCase			)
						processor.save_pretrained(__lowerCamelCase			)

			if push_to_hub:
						print(f'''Pushing model and processor of {model_name} to the hub...'''			)
						model.push_to_hub(f'''{model_name}'''			)
						processor.push_to_hub(f'''{model_name}'''			)


if __name__ == "__main__":
		_lowercase				:				str   		=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--model_name",
		    default="focalnet-tiny",
		    type=str,
		    help="Name of the FocalNet model you'd like to convert.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
		)
		parser.add_argument(
		    "--push_to_hub",
		    action="store_true",
		    help="Whether to push the model and processor to the hub.",
		)

		_lowercase				:				Any   		=		parser.parse_args()
		convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, 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,
								    },						)




