
"""simple docstring"""






import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil

class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					def __init__( self							:						Union[str, Any],						lowerCamelCase							:						List[Any] = "cpu",						lowerCamelCase							:						List[str] = "openai/clip-vit-large-patch14"  )->      None:
								lowerCamelCase__	:	Optional[int]				=device
								lowerCamelCase__	:	Optional[Any]				=CLIPTokenizerFast.from_pretrained(lowerCAmelCase_  )
								lowerCamelCase__	:	int				=[0.48_145_466, 0.4_578_275, 0.40_821_073]
								lowerCamelCase__	:	List[Any]				=[0.26_862_954, 0.26_130_258, 0.27_577_711]
								lowerCamelCase__	:	Optional[Any]				=torchvision.transforms.Normalize(self.image_mean,						self.image_std  )
								lowerCamelCase__	:	Optional[Any]				=torchvision.transforms.Resize(224  )
								lowerCamelCase__	:	str				=torchvision.transforms.CenterCrop(224  )



					def 	snake_case ( self							:						Tuple,						lowerCamelCase							:						Optional[int]  )->      Optional[Any]:
								lowerCamelCase__	:	Tuple				=self.resize(lowerCAmelCase_  )
								lowerCamelCase__	:	Any				=self.center_crop(lowerCAmelCase_  )
								lowerCamelCase__	:	str				=self.normalize(lowerCAmelCase_  )
								return images




					def __call__( self							:						str,						lowerCamelCase							:						Any=None,						lowerCamelCase							:						Optional[int]=None,						**lowerCamelCase							:						str  )->      Optional[Any]:
								lowerCamelCase__	:	Optional[int]				=self.tokenizer(text=lowerCAmelCase_,						**lowerCAmelCase_  )
								lowerCamelCase__	:	Optional[Any]				=self.preprocess_img(lowerCAmelCase_  )
								lowerCamelCase__	:	List[Any]				={key: value.to(self.device  ) for (key, value) in encoding.items()}
								return encoding





class  __SCREAMING_SNAKE_CASE			(   nn.Module     ):


					'''simple docstring'''





					def __init__( self							:						List[str],						lowerCamelCase							:						Any=10,						lowerCamelCase							:						str=0.01,						lowerCamelCase							:						Optional[int]=None,						lowerCamelCase							:						Dict=None,						lowerCamelCase							:						Union[str, Any]=None,						lowerCamelCase							:						Tuple=None,						lowerCamelCase							:						Any=None,						lowerCamelCase							:						Dict=None,						lowerCamelCase							:						List[Any]=False,						lowerCamelCase							:						List[str]=True,						lowerCamelCase							:						Dict="image",						lowerCamelCase							:						Dict=True,						lowerCamelCase							:						Union[str, Any]=False,						lowerCamelCase							:						int=False,						lowerCamelCase							:						Any=False,						)->      None:
								super().__init__()
								lowerCamelCase__	:	str				=None
								lowerCamelCase__	:	Union[str, Any]				=device if device else get_device()
								if vqgan:
											lowerCamelCase__	:	Dict				=vqgan
								else:
											lowerCamelCase__	:	Dict				=load_vqgan(self.device,						conf_path=lowerCAmelCase_,						ckpt_path=lowerCAmelCase_  )
								self.vqgan.eval()
								if clip:
											lowerCamelCase__	:	Any				=clip
								else:
											lowerCamelCase__	:	Any				=CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''  )
								self.clip.to(self.device  )
								lowerCamelCase__	:	List[str]				=ProcessorGradientFlow(device=self.device  )

								lowerCamelCase__	:	str				=iterations
								lowerCamelCase__	:	int				=lr
								lowerCamelCase__	:	Tuple				=log
								lowerCamelCase__	:	Optional[int]				=make_grid
								lowerCamelCase__	:	Tuple				=return_val
								lowerCamelCase__	:	List[str]				=quantize
								lowerCamelCase__	:	str				=self.vqgan.decoder.z_shape



					def 	snake_case ( self							:						Union[str, Any],						lowerCamelCase							:						Optional[int]=None,						lowerCamelCase							:						Optional[Any]=None,						lowerCamelCase							:						Dict=5,						lowerCamelCase							:						Dict=True  )->      Any:
								lowerCamelCase__	:	Tuple				=[]
								if output_path is None:
											lowerCamelCase__	:	Optional[int]				='''./animation.gif'''
								if input_path is None:
											lowerCamelCase__	:	int				=self.save_path
								lowerCamelCase__	:	Optional[Any]				=sorted(glob(input_path + '''/*'''  )  )
								if not len(lowerCAmelCase_  ):
											raise ValueError(
											    '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
											    ''' function?)'''  )
								if len(lowerCAmelCase_  ) == 1:
											print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'''  )
								lowerCamelCase__	:	Any				=total_duration / len(lowerCAmelCase_  )
								lowerCamelCase__	:	List[Any]				=[frame_duration] * len(lowerCAmelCase_  )
								if extend_frames:
											lowerCamelCase__	:	Tuple				=1.5
											lowerCamelCase__	:	Dict				=3
								for file_name in paths:
											if file_name.endswith('''.png'''  ):
														images.append(imageio.imread(lowerCAmelCase_  )  )
								imageio.mimsave(lowerCAmelCase_,						lowerCAmelCase_,						duration=lowerCAmelCase_  )
								print(F'''gif saved to {output_path}'''  )



					def 	snake_case ( self							:						Optional[Any],						lowerCamelCase							:						List[Any]=None,						lowerCamelCase							:						int=None  )->      str:







								if not (path or img):
											raise ValueError('''Input either path or tensor'''  )
								if img is not None:
											raise NotImplementedError
								lowerCamelCase__	:	Dict				=preprocess(Image.open(lowerCAmelCase_  ),						target_image_size=256  ).to(self.device  )
								lowerCamelCase__	:	Any				=preprocess_vqgan(lowerCAmelCase_  )
								lowerCamelCase__			,     *lowerCamelCase__	:	Union[str, Any]				=self.vqgan.encode(lowerCAmelCase_  )
								return z



					def 	snake_case ( self							:						Tuple,						lowerCamelCase							:						Optional[int]  )->      Union[str, Any]:
								lowerCamelCase__	:	Union[str, Any]				=self.latent.detach().requires_grad_()
								lowerCamelCase__	:	List[Any]				=base_latent + transform_vector
								if self.quantize:
											lowerCamelCase__			,     *lowerCamelCase__	:	str				=self.vqgan.quantize(lowerCAmelCase_  )
								else:
											lowerCamelCase__	:	Any				=trans_latent
								return self.vqgan.decode(lowerCAmelCase_  )



					def 	snake_case ( self							:						str,						lowerCamelCase							:						List[str],						lowerCamelCase							:						Any,						lowerCamelCase							:						Optional[int]=None  )->      Tuple:
								lowerCamelCase__	:	Union[str, Any]				=self.clip_preprocessor(text=lowerCAmelCase_,						images=lowerCAmelCase_,						return_tensors='''pt''',						padding=lowerCAmelCase_  )
								lowerCamelCase__	:	int				=self.clip(**lowerCAmelCase_  )
								lowerCamelCase__	:	Optional[Any]				=clip_outputs.logits_per_image
								if weights is not None:
											lowerCamelCase__	:	Optional[int]				=similarity_logits * weights
								return similarity_logits.sum()



					def 	snake_case ( self							:						str,						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						Any  )->      int:
								lowerCamelCase__	:	Tuple				=self._get_clip_similarity(pos_prompts['''prompts'''],						lowerCAmelCase_,						weights=(1 / pos_prompts['''weights'''])  )
								if neg_prompts:
											lowerCamelCase__	:	Tuple				=self._get_clip_similarity(neg_prompts['''prompts'''],						lowerCAmelCase_,						weights=neg_prompts['''weights''']  )
								else:
											lowerCamelCase__	:	List[Any]				=torch.tensor([1],						device=self.device  )
								lowerCamelCase__	:	Any				=-torch.log(lowerCAmelCase_  ) + torch.log(lowerCAmelCase_  )
								return loss



					def 	snake_case ( self							:						str,						lowerCamelCase							:						str,						lowerCamelCase							:						List[Any],						lowerCamelCase							:						List[str]  )->      Any:
								lowerCamelCase__	:	Dict				=torch.randn_like(self.latent,						requires_grad=lowerCAmelCase_,						device=self.device  )
								lowerCamelCase__	:	Optional[int]				=torch.optim.Adam([vector],						lr=self.lr  )

								for i in range(self.iterations  ):
											optim.zero_grad()
											lowerCamelCase__	:	Union[str, Any]				=self._add_vector(lowerCAmelCase_  )
											lowerCamelCase__	:	List[Any]				=loop_post_process(lowerCAmelCase_  )
											lowerCamelCase__	:	List[Any]				=self._get_CLIP_loss(lowerCAmelCase_,						lowerCAmelCase_,						lowerCAmelCase_  )
											print('''CLIP loss''',						lowerCAmelCase_  )
											if self.log:
														wandb.log({'''CLIP Loss''': clip_loss}  )
											clip_loss.backward(retain_graph=lowerCAmelCase_  )
											optim.step()
											if self.return_val == "image":
														yield custom_to_pil(transformed_img[0]  )
											else:
														yield vector



					def 	snake_case ( self							:						Dict,						lowerCamelCase							:						Optional[int],						lowerCamelCase							:						Optional[int],						lowerCamelCase							:						List[str]  )->      List[str]:
								wandb.init(reinit=lowerCAmelCase_,						project='''face-editor'''  )
								wandb.config.update({'''Positive Prompts''': positive_prompts}  )
								wandb.config.update({'''Negative Prompts''': negative_prompts}  )
								wandb.config.update({'''lr''': self.lr, '''iterations''': self.iterations}  )
								if image_path:
											lowerCamelCase__	:	Dict				=Image.open(lowerCAmelCase_  )
											lowerCamelCase__	:	str				=image.resize((256, 256)  )
											wandb.log('''Original Image''',						wandb.Image(lowerCAmelCase_  )  )



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







								if not prompts:
											return []
								lowerCamelCase__	:	int				=[]
								lowerCamelCase__	:	Dict				=[]
								if isinstance(lowerCAmelCase_,						lowerCAmelCase_  ):
											lowerCamelCase__	:	int				=[prompt.strip() for prompt in prompts.split('''|'''  )]
								for prompt in prompts:
											if isinstance(lowerCAmelCase_,						(tuple, list)  ):
														lowerCamelCase__	:	Tuple				=prompt[0]
														lowerCamelCase__	:	Optional[Any]				=float(prompt[1]  )
											elif ":" in prompt:
														lowerCamelCase__			,     lowerCamelCase__	:	List[str]				=prompt.split(''':'''  )
														lowerCamelCase__	:	List[str]				=float(lowerCAmelCase_  )
											else:
														lowerCamelCase__	:	List[str]				=prompt
														lowerCamelCase__	:	Optional[Any]				=1.0
											processed_prompts.append(lowerCAmelCase_  )
											weights.append(lowerCAmelCase_  )
								return {
								    "prompts": processed_prompts,
								    "weights": torch.tensor(lowerCAmelCase_,						device=self.device  ),
								}




					def 	snake_case ( self							:						Optional[int],						lowerCamelCase							:						Dict,						lowerCamelCase							:						Optional[Any]=None,						lowerCamelCase							:						Optional[Any]=None,						lowerCamelCase							:						Any=True,						lowerCamelCase							:						List[str]=False,						lowerCamelCase							:						Tuple=True,						lowerCamelCase							:						Optional[Any]=True,						lowerCamelCase							:						Union[str, Any]=None,						)->      str:







								if image_path:
											lowerCamelCase__	:	Tuple				=self._get_latent(lowerCAmelCase_  )
								else:
											lowerCamelCase__	:	Optional[Any]				=torch.randn(self.latent_dim,						device=self.device  )
								if self.log:
											self._init_logging(lowerCAmelCase_,						lowerCAmelCase_,						lowerCAmelCase_  )

								assert pos_prompts, "You must provide at least one positive prompt."
								lowerCamelCase__	:	Any				=self.process_prompts(lowerCAmelCase_  )
								lowerCamelCase__	:	Union[str, Any]				=self.process_prompts(lowerCAmelCase_  )
								if save_final and save_path is None:
											lowerCamelCase__	:	Union[str, Any]				=os.path.join('''./outputs/''',						'''_'''.join(pos_prompts['''prompts''']  )  )
								if not os.path.exists(lowerCAmelCase_  ):
											os.makedirs(lowerCAmelCase_  )
								else:
											lowerCamelCase__	:	Optional[Any]				=save_path + '''_''' + get_timestamp()
											os.makedirs(lowerCAmelCase_  )
								lowerCamelCase__	:	Optional[int]				=save_path

								lowerCamelCase__	:	Tuple				=self.vqgan.decode(self.latent  )[0]
								if show_intermediate:
											print('''Original Image'''  )
											show_pil(custom_to_pil(lowerCAmelCase_  )  )

								lowerCamelCase__	:	Any				=loop_post_process(lowerCAmelCase_  )
								for iter, transformed_img in enumerate(self._optimize_CLIP(lowerCAmelCase_,						lowerCAmelCase_,						lowerCAmelCase_  )  ):
											if show_intermediate:
														show_pil(lowerCAmelCase_  )
											if save_intermediate:
														transformed_img.save(os.path.join(self.save_path,						F'''iter_{iter:03d}.png'''  )  )
											if self.log:
														wandb.log({'''Image''': wandb.Image(lowerCAmelCase_  )}  )
								if show_final:
											show_pil(lowerCAmelCase_  )
								if save_final:
											transformed_img.save(os.path.join(self.save_path,						F'''iter_{iter:03d}_final.png'''  )  )









"""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,
								    },						)




