






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


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


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

__A					=	25_00_04
__A					=	25_00_20



@require_sentencepiece
@require_tokenizers
class 				_A			(     UpperCamelCase					,					unittest.TestCase				):
    """simple docstring"""

    lowerCamelCase	:							Tuple				= MBartTokenizer
    lowerCamelCase	:							List[str]				= MBartTokenizerFast
    lowerCamelCase	:							int				= True
    lowerCamelCase	:							List[str]				= True



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

           # We have a SentencePiece fixture for testing
           __UpperCAmelCase     				=MBartTokenizer(__SCREAMING_SNAKE_CASE ,     keep_accents=__SCREAMING_SNAKE_CASE      )
           tokenizer.save_pretrained(self.tmpdirname      )



    def _a							(				self      :     Tuple      )      ->							Optional[int]:
           __UpperCAmelCase     				=MBartTokenizer(__SCREAMING_SNAKE_CASE ,     keep_accents=__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=tokenizer.tokenize("""This is a test"""      )
           self.assertListEqual(__SCREAMING_SNAKE_CASE ,     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]      )

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

           __UpperCAmelCase     				=tokenizer.tokenize("""I was born in 92000, and this is falsé."""      )
           self.assertListEqual(
               __SCREAMING_SNAKE_CASE ,     [
                   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""",
                   """.""",
               ] ,     )
           __UpperCAmelCase     				=tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE      )
           self.assertListEqual(
               __SCREAMING_SNAKE_CASE ,     [
                   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 ^
               ] ,     )

           __UpperCAmelCase     				=tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE      )
           self.assertListEqual(
               __SCREAMING_SNAKE_CASE ,     [
                   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 _a							(				self      :     Optional[Any]      )      ->							Optional[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

           __UpperCAmelCase     				=(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})'''      ):
                         __UpperCAmelCase     				=self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )
                         __UpperCAmelCase     				=self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )

                         __UpperCAmelCase     				=tempfile.mkdtemp()

                         __UpperCAmelCase     				=tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE      )
                         __UpperCAmelCase     				=tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE      )

                         # 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      )      )
                         __UpperCAmelCase     				=tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f      )
                         self.assertSequenceEqual(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )

                         # Checks everything loads correctly in the same way
                         __UpperCAmelCase     				=tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE      )
                         __UpperCAmelCase     				=tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE      )

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

                         shutil.rmtree(__SCREAMING_SNAKE_CASE      )

                         # Save tokenizer rust, legacy_format=True
                         __UpperCAmelCase     				=tempfile.mkdtemp()

                         __UpperCAmelCase     				=tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE ,     legacy_format=__SCREAMING_SNAKE_CASE      )
                         __UpperCAmelCase     				=tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE      )

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

                         # Checks everything loads correctly in the same way
                         __UpperCAmelCase     				=tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE      )
                         __UpperCAmelCase     				=tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE      )

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

                         shutil.rmtree(__SCREAMING_SNAKE_CASE      )

                         # Save tokenizer rust, legacy_format=False
                         __UpperCAmelCase     				=tempfile.mkdtemp()

                         __UpperCAmelCase     				=tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE ,     legacy_format=__SCREAMING_SNAKE_CASE      )
                         __UpperCAmelCase     				=tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE      )

                         # 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
                         __UpperCAmelCase     				=tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE      )
                         __UpperCAmelCase     				=tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE      )

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

                         shutil.rmtree(__SCREAMING_SNAKE_CASE      )




@require_torch
@require_sentencepiece
@require_tokenizers
class 				_A			(     unittest.TestCase				):
    """simple docstring"""

    lowerCamelCase	:							List[str]				= 'facebook/mbart-large-en-ro'
    lowerCamelCase	:							Tuple				= [
        ' 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.',
    ]
    lowerCamelCase	:							List[str]				= [
        '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.',
    ]
    lowerCamelCase	:							str				= [8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2, EN_CODE]



    @classmethod
    def _a							(				cls      :     int      )      ->							Union[str, Any]:
           __UpperCAmelCase     				=MBartTokenizer.from_pretrained(
               cls.checkpoint_name ,     src_lang="""en_XX""" ,     tgt_lang="""ro_RO"""      )
           __UpperCAmelCase     				=1
           return cls



    def _a							(				self      :     Union[str, Any]      )      ->							int:
           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 _a							(				self      :     Dict      )      ->							str:
           __UpperCAmelCase     				=self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
           self.assertListEqual(self.expected_src_tokens ,     __SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Optional[Any]      )      ->							Any:
           self.assertIn(__SCREAMING_SNAKE_CASE ,     self.tokenizer.all_special_ids      )
           __UpperCAmelCase     				=[RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
           __UpperCAmelCase     				=self.tokenizer.decode(__SCREAMING_SNAKE_CASE ,     skip_special_tokens=__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=self.tokenizer.decode(generated_ids[1:] ,     skip_special_tokens=__SCREAMING_SNAKE_CASE      )
           self.assertEqual(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )
           self.assertNotIn(self.tokenizer.eos_token ,     __SCREAMING_SNAKE_CASE      )



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



    def _a							(				self      :     int      )      ->							int:
           self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]      ) ,     [250026, 250001]      )



    def _a							(				self      :     Optional[Any]      )      ->							Optional[int]:
           __UpperCAmelCase     				=tempfile.mkdtemp()
           __UpperCAmelCase     				=self.tokenizer.fairseq_tokens_to_ids
           self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=MBartTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE      )
           self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,     __SCREAMING_SNAKE_CASE      )



    @require_torch
    def _a							(				self      :     Optional[int]      )      ->							Any:
           __UpperCAmelCase     				=self.tokenizer(self.src_text ,     text_target=self.tgt_text ,     padding=__SCREAMING_SNAKE_CASE ,     return_tensors="""pt"""      )
           __UpperCAmelCase     				=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 _a							(				self      :     Optional[Any]      )      ->							Optional[Any]:
           __UpperCAmelCase     				=self.tokenizer(
               self.src_text ,     text_target=self.tgt_text ,     padding=__SCREAMING_SNAKE_CASE ,     truncation=__SCREAMING_SNAKE_CASE ,     max_length=len(self.expected_src_tokens      ) ,     return_tensors="""pt""" ,     )

           __UpperCAmelCase     				=shift_tokens_right(batch["""labels"""] ,     self.tokenizer.pad_token_id      )

           self.assertIsInstance(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )

           self.assertEqual((2, 14) ,     batch.input_ids.shape      )
           self.assertEqual((2, 14) ,     batch.attention_mask.shape      )
           __UpperCAmelCase     				=batch.input_ids.tolist()[0]
           self.assertListEqual(self.expected_src_tokens ,     __SCREAMING_SNAKE_CASE      )
           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 _a							(				self      :     Optional[Any]      )      ->							Dict:
           __UpperCAmelCase     				=self.tokenizer(self.src_text ,     padding=__SCREAMING_SNAKE_CASE ,     truncation=__SCREAMING_SNAKE_CASE ,     max_length=3 ,     return_tensors="""pt"""      )
           __UpperCAmelCase     				=self.tokenizer(
               text_target=self.tgt_text ,     padding=__SCREAMING_SNAKE_CASE ,     truncation=__SCREAMING_SNAKE_CASE ,     max_length=10 ,     return_tensors="""pt"""      )
           __UpperCAmelCase     				=targets["""input_ids"""]
           __UpperCAmelCase     				=shift_tokens_right(__SCREAMING_SNAKE_CASE ,     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 _a							(				self      :     int      )      ->							Any:
           __UpperCAmelCase     				=self.tokenizer._build_translation_inputs(
               """A test""" ,     return_tensors="""pt""" ,     src_lang="""en_XX""" ,     tgt_lang="""ar_AR"""      )

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










import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch





@require_torch
class 			lowerCamelCase_				:


 '''simple docstring'''







 @property
 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       return self.get_dummy_input()



 @property
 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       if self.block_type == "down":
             return (4, 32, 16, 16)
       elif self.block_type == "mid":
             return (4, 32, 32, 32)
       elif self.block_type == "up":
             return (4, 32, 64, 64)

       raise ValueError(F"'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."		)



 def  lowerCAmelCase_    (				self    :						List[str]							,							_lowerCAmelCase    :						Optional[Any]=True							,							_lowerCAmelCase    :						str=False							,							_lowerCAmelCase    :						Optional[int]=False							,							_lowerCAmelCase    :						Dict=False							,							):
       SCREAMING_SNAKE_CASE_      		=     4
       SCREAMING_SNAKE_CASE_      		=     32
       SCREAMING_SNAKE_CASE_      		=     (32, 32)

       SCREAMING_SNAKE_CASE_      		=     torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     torch.device(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     (batch_size, num_channels) + sizes
       SCREAMING_SNAKE_CASE_      		=     randn_tensor(_lowerCAmelCase							,							generator=_lowerCAmelCase							,							device=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     {'hidden_states': hidden_states}

       if include_temb:
             SCREAMING_SNAKE_CASE_      		=     128
             SCREAMING_SNAKE_CASE_      		=     randn_tensor((batch_size, temb_channels)							,							generator=_lowerCAmelCase							,							device=_lowerCAmelCase		)

       if include_res_hidden_states_tuple:
             SCREAMING_SNAKE_CASE_      		=     torch.manual_seed(1		)
             SCREAMING_SNAKE_CASE_      		=     (randn_tensor(_lowerCAmelCase							,							generator=_lowerCAmelCase							,							device=_lowerCAmelCase		),)

       if include_encoder_hidden_states:
             SCREAMING_SNAKE_CASE_      		=     floats_tensor((batch_size, 32, 32)		).to(_lowerCAmelCase		)

       if include_skip_sample:
             SCREAMING_SNAKE_CASE_      		=     randn_tensor(((batch_size, 3) + sizes)							,							generator=_lowerCAmelCase							,							device=_lowerCAmelCase		)

       return dummy_input



 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     {
           'in_channels': 32,
           'out_channels': 32,
           'temb_channels': 128,
       }
       if self.block_type == "up":
             SCREAMING_SNAKE_CASE_      		=     32

       if self.block_type == "mid":
             init_dict.pop('out_channels'		)

       SCREAMING_SNAKE_CASE_      		=     self.dummy_input
       return init_dict, inputs_dict



 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.prepare_init_args_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_      		=     self.block_class(**_lowerCAmelCase		)
       unet_block.to(_lowerCAmelCase		)
       unet_block.eval()

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     unet_block(**_lowerCAmelCase		)

       if isinstance(_lowerCAmelCase							,							_lowerCAmelCase		):
             SCREAMING_SNAKE_CASE_      		=     output[0]

       self.assertEqual(output.shape							,							self.output_shape		)

       SCREAMING_SNAKE_CASE_      		=     output[0, -1, -3:, -3:]
       SCREAMING_SNAKE_CASE_      		=     torch.tensor(_lowerCAmelCase		).to(_lowerCAmelCase		)
       assert torch_all_close(output_slice.flatten()							,							_lowerCAmelCase							,							atol=5E-3		)




 @unittest.skipIf(torch_device == 'mps'							,							'Training is not supported in mps'		)
 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.prepare_init_args_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_      		=     self.block_class(**_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.train()
       SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		)

       if isinstance(_lowerCAmelCase							,							_lowerCAmelCase		):
             SCREAMING_SNAKE_CASE_      		=     output[0]

       SCREAMING_SNAKE_CASE_      		=     torch.device(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     randn_tensor(output.shape							,							device=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.functional.mse_loss(_lowerCAmelCase							,							_lowerCAmelCase		)
       loss.backward()