





import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

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      :       Any									=  2_5_0_0_0_4
lowercase      :       Optional[int]									=  2_5_0_0_2_0





@require_sentencepiece
@require_tokenizers
class  A__    (							__UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Dict         =  MBartaaTokenizer
   __A   :				Any         =  MBartaaTokenizerFast
   __A   :				Any         =  True
   __A   :				Tuple         =  True

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







          '''simple docstring'''

          super().setUp()

          # We have a SentencePiece fixture for testing
          a__						:	str =				MBartaaTokenizer(lowercase					,  src_lang='en_XX'					,  tgt_lang='ro_RO'					,  keep_accents=lowercase)
          tokenizer.save_pretrained(self.tmpdirname)

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Dict =				'<s>'
          a__						:	Union[str, Any] =				0

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase)					,  lowercase)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase)					,  lowercase)

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Tuple =				list(self.get_tokenizer().get_vocab().keys())

          self.assertEqual(vocab_keys[0]					,  '<s>')
          self.assertEqual(vocab_keys[1]					,  '<pad>')
          self.assertEqual(vocab_keys[-1]					,  '<mask>')
          self.assertEqual(len(lowercase)					,  1054)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          self.assertEqual(self.get_tokenizer().vocab_size					,  1054)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Any =				MBartaaTokenizer(lowercase					,  src_lang='en_XX'					,  tgt_lang='ro_RO'					,  keep_accents=lowercase)

          a__						:	Dict =				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]]					,  )

          a__						:	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', '.']					,  )
          a__						:	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]
              ]					,  )

          a__						:	Optional[Any] =				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>', '.']					,  )

   @slow
   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	List[str] =				{'input_ids': [[25_0004, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [25_0004, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_0004, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
          # fmt: on

          self.tokenizer_integration_test_util(
              expected_encoding=lowercase					,  model_name='facebook/mbart-large-50'					,  revision='d3913889c59cd5c9e456b269c376325eabad57e2'					,  )

   def 							__lowercase		(					self)			->						Any:







          '''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

          a__						:	Dict =				(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart50', {})
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
                        a__						:	Tuple =				self.rust_tokenizer_class.from_pretrained(lowercase					,  **lowercase)
                        a__						:	List[str] =				self.tokenizer_class.from_pretrained(lowercase					,  **lowercase)

                        a__						:	int =				tempfile.mkdtemp()

                        a__						:	Dict =				tokenizer_r.save_pretrained(lowercase)
                        a__						:	Union[str, 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))
                        a__						:	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
                        a__						:	Any =				tokenizer_r.from_pretrained(lowercase)
                        a__						:	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))
                               # 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
                        a__						:	Any =				tempfile.mkdtemp()

                        a__						:	str =				tokenizer_r.save_pretrained(lowercase					,  legacy_format=lowercase)
                        a__						:	Dict =				tokenizer_p.save_pretrained(lowercase)

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

                        # Checks everything loads correctly in the same way
                        a__						:	Union[str, Any] =				tokenizer_r.from_pretrained(lowercase)
                        a__						:	Optional[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
                        a__						:	Any =				tempfile.mkdtemp()

                        a__						:	Optional[int] =				tokenizer_r.save_pretrained(lowercase					,  legacy_format=lowercase)
                        a__						:	str =				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
                        a__						:	List[str] =				tokenizer_r.from_pretrained(lowercase)
                        a__						:	Dict =				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	):




   """simple docstring"""



   __A   :				str         =  '''facebook/mbart-large-50-one-to-many-mmt'''
   __A   :				Union[str, 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.''',
   ]
   __A   :				Dict         =  [
       '''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   :				List[Any]         =  [EN_CODE, 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]

   @classmethod
   def 							__lowercase		(					cls)			->						Any:







          '''simple docstring'''

          a__						:	MBartaaTokenizer =				MBartaaTokenizer.from_pretrained(
              cls.checkpoint_name					,  src_lang='en_XX'					,  tgt_lang='ro_RO')
          a__						:	Dict =				1
          return cls

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          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)
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']					,  25_0038)

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
          self.assertListEqual(self.expected_src_tokens					,  lowercase)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          self.assertIn(lowercase					,  self.tokenizer.all_special_ids)
          a__						:	Any =				[RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
          a__						:	Any =				self.tokenizer.decode(lowercase					,  skip_special_tokens=lowercase)
          a__						:	List[str] =				self.tokenizer.decode(generated_ids[1:]					,  skip_special_tokens=lowercase)
          self.assertEqual(lowercase					,  lowercase)
          self.assertNotIn(self.tokenizer.eos_token					,  lowercase)

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

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

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'])					,  [25_0053, 25_0001])

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	List[Any] =				tempfile.mkdtemp()
          a__						:	Dict =				self.tokenizer.fairseq_tokens_to_ids
          self.tokenizer.save_pretrained(lowercase)
          a__						:	Optional[int] =				MBartaaTokenizer.from_pretrained(lowercase)
          self.assertDictEqual(new_tok.fairseq_tokens_to_ids					,  lowercase)

   @require_torch
   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	Union[str, Any] =				self.tokenizer(self.src_text					,  text_target=self.tgt_text					,  padding=lowercase					,  return_tensors='pt')
          a__						:	Dict =				shift_tokens_right(batch['labels']					,  self.tokenizer.pad_token_id)

          # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
          assert batch.input_ids[1][0] == EN_CODE
          assert batch.input_ids[1][-1] == 2
          assert batch.labels[1][0] == RO_CODE
          assert batch.labels[1][-1] == 2
          assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]

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







          '''simple docstring'''

          a__						:	List[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'					,  )
          a__						:	Optional[int] =				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)
          a__						:	Union[str, Any] =				batch.input_ids.tolist()[0]
          self.assertListEqual(self.expected_src_tokens					,  lowercase)
          self.assertEqual(2					,  batch.decoder_input_ids[0, 0])  # decoder_start_token_id
          # Test that special tokens are reset
          self.assertEqual(self.tokenizer.prefix_tokens					,  [EN_CODE])
          self.assertEqual(self.tokenizer.suffix_tokens					,  [self.tokenizer.eos_token_id])

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Any =				self.tokenizer(self.src_text					,  padding=lowercase					,  truncation=lowercase					,  max_length=3					,  return_tensors='pt')
          a__						:	int =				self.tokenizer(
              text_target=self.tgt_text					,  padding=lowercase					,  truncation=lowercase					,  max_length=10					,  return_tensors='pt')
          a__						:	Optional[int] =				targets['input_ids']
          a__						:	Optional[Any] =				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)			->						int:







          '''simple docstring'''

          a__						:	Tuple =				self.tokenizer._build_translation_inputs(
              'A test'					,  return_tensors='pt'					,  src_lang='en_XX'					,  tgt_lang='ar_AR')

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






'''simple docstring'''



import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
	import onnxruntime as ort





@nightly
@require_onnxruntime
@require_torch_gpu
class 							__UpperCamelCase				(		unittest.TestCase					):




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  ort.SessionOptions()
						__a			:       Dict					    =  False
						return options





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo.png'							)
						__a			:       int					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'							)
						__a			:       Dict					    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'							)

						# using the PNDM scheduler by default
						__a			:       str					    =  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
						    'CompVis/stable-diffusion-v1-4'						,				revision='onnx'						,				safety_checker=__a						,				feature_extractor=__a						,				provider=self.gpu_provider						,				sess_options=self.gpu_options						,				)
						pipe.set_progress_bar_config(disable=__a							)

						__a			:       Tuple					    =  'A red cat sitting on a park bench'

						__a			:       int					    =  np.random.RandomState(0							)
						__a			:       Tuple					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				strength=0.75						,				guidance_scale=7.5						,				num_inference_steps=15						,				generator=__a						,				output_type='np'						,				)

						__a			:       Tuple					    =  output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 1E-2



