



'''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


snake_case_			=     get_tests_dir("""fixtures/test_sentencepiece.model""")


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

snake_case_			=     250004
snake_case_			=     250020


@require_sentencepiece
@require_tokenizers
class 	a__     (				_UpperCAmelCase,			unittest.TestCase	):
	__magic_name__		:    int	    =     MBartTokenizer
	__magic_name__		:    Tuple	    =     MBartTokenizerFast
	__magic_name__		:    str	    =     True
	__magic_name__		:    Tuple	    =     True


	def 				lowercase__  (self : Optional[Any]      )     ->			Dict:

					"""simple docstring"""

					super().setUp()

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


	def 				lowercase__  (self : Dict      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= MBartTokenizer(lowercase__,					keep_accents=lowercase__      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= tokenizer.tokenize('''This is a test'''      )
					self.assertListEqual(lowercase__,					['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']      )

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

					SCREAMING_SNAKE_CASE  :						List[Any]      					= tokenizer.tokenize('''I was born in 92000, and this is falsé.'''      )
					self.assertListEqual(
					    lowercase__,					[
					        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  :						Optional[Any]      					= tokenizer.convert_tokens_to_ids(lowercase__      )
					self.assertListEqual(
					    lowercase__,					[
					        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 ^
					    ],					)

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= tokenizer.convert_ids_to_tokens(lowercase__      )
					self.assertListEqual(
					    lowercase__,					[
					        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 				lowercase__  (self : List[Any]      )     ->			Tuple:

					"""simple docstring"""

					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

					SCREAMING_SNAKE_CASE  :						Tuple      					= (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})'''      ):
													SCREAMING_SNAKE_CASE  :						Dict      					= self.rust_tokenizer_class.from_pretrained(lowercase__,					**lowercase__      )
													SCREAMING_SNAKE_CASE  :						str      					= self.tokenizer_class.from_pretrained(lowercase__,					**lowercase__      )

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

													SCREAMING_SNAKE_CASE  :						Dict      					= tokenizer_r.save_pretrained(lowercase__      )
													SCREAMING_SNAKE_CASE  :						List[Any]      					= tokenizer_p.save_pretrained(lowercase__      )

													# 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  :						Union[str, Any]      					= tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f      )
													self.assertSequenceEqual(lowercase__,					lowercase__      )

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

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

													shutil.rmtree(lowercase__      )

													# Save tokenizer rust, legacy_format=True
													SCREAMING_SNAKE_CASE  :						str      					= tempfile.mkdtemp()

													SCREAMING_SNAKE_CASE  :						Tuple      					= tokenizer_r.save_pretrained(lowercase__,					legacy_format=lowercase__      )
													SCREAMING_SNAKE_CASE  :						Any      					= tokenizer_p.save_pretrained(lowercase__      )

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

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

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

													shutil.rmtree(lowercase__      )

													# Save tokenizer rust, legacy_format=False
													SCREAMING_SNAKE_CASE  :						List[str]      					= tempfile.mkdtemp()

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

													# 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  :						Any      					= tokenizer_r.from_pretrained(lowercase__      )
													SCREAMING_SNAKE_CASE  :						str      					= tokenizer_p.from_pretrained(lowercase__      )

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

													shutil.rmtree(lowercase__      )



@require_torch
@require_sentencepiece
@require_tokenizers
class 	a__     (				unittest.TestCase	):
	__magic_name__		:    int	    =     "facebook/mbart-large-en-ro"
	__magic_name__		:    List[Any]	    =     [
	    " 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.",
	]
	__magic_name__		:    int	    =     [
	    "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.",
	]
	__magic_name__		:    int	    =     [82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]


	@classmethod
	def 				lowercase__  (cls : Dict      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						MBartTokenizer      					= MBartTokenizer.from_pretrained(
					    cls.checkpoint_name,					src_lang='''en_XX''',					tgt_lang='''ro_RO'''      )
					SCREAMING_SNAKE_CASE  :						Any      					= 1
					return cls


	def 				lowercase__  (self : List[str]      )     ->			Dict:

					"""simple docstring"""

					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR'''],					250001      )
					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN'''],					250004      )
					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO'''],					250020      )


	def 				lowercase__  (self : Tuple      )     ->			Tuple:

					"""simple docstring"""

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


	def 				lowercase__  (self : Tuple      )     ->			List[str]:

					"""simple docstring"""

					self.assertIn(lowercase__,					self.tokenizer.all_special_ids      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
					SCREAMING_SNAKE_CASE  :						Dict      					= self.tokenizer.decode(lowercase__,					skip_special_tokens=lowercase__      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= self.tokenizer.decode(generated_ids[1:],					skip_special_tokens=lowercase__      )
					self.assertEqual(lowercase__,					lowercase__      )
					self.assertNotIn(self.tokenizer.eos_token,					lowercase__      )


	def 				lowercase__  (self : List[Any]      )     ->			List[str]:

					"""simple docstring"""

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


	def 				lowercase__  (self : int      )     ->			List[str]:

					"""simple docstring"""

					self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']      ),					[250026, 250001]      )


	def 				lowercase__  (self : int      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= tempfile.mkdtemp()
					SCREAMING_SNAKE_CASE  :						Tuple      					= self.tokenizer.fairseq_tokens_to_ids
					self.tokenizer.save_pretrained(lowercase__      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= MBartTokenizer.from_pretrained(lowercase__      )
					self.assertDictEqual(new_tok.fairseq_tokens_to_ids,					lowercase__      )


	@require_torch
	def 				lowercase__  (self : Tuple      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.tokenizer(self.src_text,					text_target=self.tgt_text,					padding=lowercase__,					return_tensors='''pt'''      )
					SCREAMING_SNAKE_CASE  :						int      					= 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 				lowercase__  (self : Dict      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= self.tokenizer(
					    self.src_text,					text_target=self.tgt_text,					padding=lowercase__,					truncation=lowercase__,					max_length=len(self.expected_src_tokens      ),					return_tensors='''pt''',					)

					SCREAMING_SNAKE_CASE  :						str      					= shift_tokens_right(batch['''labels'''],					self.tokenizer.pad_token_id      )

					self.assertIsInstance(lowercase__,					lowercase__      )

					self.assertEqual((2, 14),					batch.input_ids.shape      )
					self.assertEqual((2, 14),					batch.attention_mask.shape      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= batch.input_ids.tolist()[0]
					self.assertListEqual(self.expected_src_tokens,					lowercase__      )
					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 				lowercase__  (self : List[Any]      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.tokenizer(self.src_text,					padding=lowercase__,					truncation=lowercase__,					max_length=3,					return_tensors='''pt'''      )
					SCREAMING_SNAKE_CASE  :						Dict      					= self.tokenizer(
					    text_target=self.tgt_text,					padding=lowercase__,					truncation=lowercase__,					max_length=10,					return_tensors='''pt'''      )
					SCREAMING_SNAKE_CASE  :						Any      					= targets["input_ids"]
					SCREAMING_SNAKE_CASE  :						str      					= shift_tokens_right(lowercase__,					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 				lowercase__  (self : Tuple      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.tokenizer._build_translation_inputs(
					    '''A test''',					return_tensors='''pt''',					src_lang='''en_XX''',					tgt_lang='''ar_AR'''      )

					self.assertEqual(
					    nested_simplify(lowercase__      ),					{
					        # A, test, EOS, en_XX
					        '''input_ids''': [[62, 3034, 2, 250004]],
					        '''attention_mask''': [[1, 1, 1, 1]],
					        # ar_AR
					        '''forced_bos_token_id''': 250001,
					    },					)









'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 	a__     (				_lowercase	):
	__magic_name__		:    UNetaDModel
	__magic_name__		:    ScoreSdeVeScheduler


	def __init__(self : List[str],					__UpperCAmelCase : UNetaDModel,					__UpperCAmelCase : ScoreSdeVeScheduler      )     ->			Tuple:

					"""simple docstring"""

					super().__init__()
					self.register_modules(unet=__UpperCAmelCase,					scheduler=__UpperCAmelCase      )






	@torch.no_grad()
	def __call__(self : int,					__UpperCAmelCase : int = 1,					__UpperCAmelCase : int = 2000,					__UpperCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None,					__UpperCAmelCase : Optional[str] = "pil",					__UpperCAmelCase : bool = True,					**__UpperCAmelCase : List[str],					)     ->			Union[ImagePipelineOutput, Tuple]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.unet.config.sample_size
					SCREAMING_SNAKE_CASE  :						Dict      					= (batch_size, 3, img_size, img_size)

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.unet

					SCREAMING_SNAKE_CASE  :						str      					= randn_tensor(__UpperCAmelCase,					generator=__UpperCAmelCase      ) * self.scheduler.init_noise_sigma
					SCREAMING_SNAKE_CASE  :						Any      					= sample.to(self.device      )

					self.scheduler.set_timesteps(__UpperCAmelCase      )
					self.scheduler.set_sigmas(__UpperCAmelCase      )

					for i, t in enumerate(self.progress_bar(self.scheduler.timesteps      )      ):
									SCREAMING_SNAKE_CASE  :						int      					= self.scheduler.sigmas[i] * torch.ones(shape[0],					device=self.device      )

									# correction step
									for _ in range(self.scheduler.config.correct_steps      ):
													SCREAMING_SNAKE_CASE  :						Any      					= self.unet(__UpperCAmelCase,					__UpperCAmelCase      ).sample
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.scheduler.step_correct(__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      ).prev_sample

									# prediction step
									SCREAMING_SNAKE_CASE  :						str      					= model(__UpperCAmelCase,					__UpperCAmelCase      ).sample
									SCREAMING_SNAKE_CASE  :						Dict      					= self.scheduler.step_pred(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[int]      					= output.prev_sample, output.prev_sample_mean

					SCREAMING_SNAKE_CASE  :						Dict      					= sample_mean.clamp(0,					1      )
					SCREAMING_SNAKE_CASE  :						str      					= sample.cpu().permute(0,					2,					3,					1      ).numpy()
					if output_type == "pil":
									SCREAMING_SNAKE_CASE  :						int      					= self.numpy_to_pil(__UpperCAmelCase      )

					if not return_dict:
									return (sample,)

					return ImagePipelineOutput(images=__UpperCAmelCase      )




