

"""simple docstring"""
import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_A							=  False

class       lowerCamelCase	(       unittest.TestCase   ):


  '''simple docstring'''







  def  _a      (self							,       _lowerCamelCase=32	):


        """simple docstring"""

        set_seed(0	)
        UpperCAmelCase__			:							List[str]							=    UNetaDModel(sample_size=_lowerCamelCase							,       in_channels=3							,       out_channels=3	)
        UpperCAmelCase__			:							int							=    torch.optim.SGD(model.parameters()							,       lr=0.0_001	)
        return model, optimizer




  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    """cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
        UpperCAmelCase__			:							Union[str, Any]							=    DDPMScheduler(
            num_train_timesteps=1000							,       beta_start=0.0_001							,       beta_end=0.02							,       beta_schedule="""linear"""							,       clip_sample=_lowerCamelCase							,       )
        UpperCAmelCase__			:							Optional[Any]							=    DDIMScheduler(
            num_train_timesteps=1000							,       beta_start=0.0_001							,       beta_end=0.02							,       beta_schedule="""linear"""							,       clip_sample=_lowerCamelCase							,       )

        assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

        # shared batches for DDPM and DDIM
        set_seed(0	)
        UpperCAmelCase__			:							Tuple							=    [torch.randn((4, 3, 32, 32)	).clip(-1							,       1	).to(_lowerCamelCase	) for _ in range(4	)]
        UpperCAmelCase__			:							str							=    [torch.randn((4, 3, 32, 32)	).to(_lowerCamelCase	) for _ in range(4	)]
        UpperCAmelCase__			:							Tuple							=    [torch.randint(0							,       1000							,       (4,)	).long().to(_lowerCamelCase	) for _ in range(4	)]

        # train with a DDPM scheduler
        UpperCAmelCase__					,		UpperCAmelCase__			:							Optional[Any]							=    self.get_model_optimizer(resolution=32	)
        model.train().to(_lowerCamelCase	)
        for i in range(4	):
              optimizer.zero_grad()
              UpperCAmelCase__			:							Tuple							=    ddpm_scheduler.add_noise(clean_images[i]							,       noise[i]							,       timesteps[i]	)
              UpperCAmelCase__			:							Any							=    model(_lowerCamelCase							,       timesteps[i]	).sample
              UpperCAmelCase__			:							int							=    torch.nn.functional.mse_loss(_lowerCamelCase							,       noise[i]	)
              loss.backward()
              optimizer.step()
        del model, optimizer

        # recreate the model and optimizer, and retry with DDIM
        UpperCAmelCase__					,		UpperCAmelCase__			:							List[Any]							=    self.get_model_optimizer(resolution=32	)
        model.train().to(_lowerCamelCase	)
        for i in range(4	):
              optimizer.zero_grad()
              UpperCAmelCase__			:							Any							=    ddim_scheduler.add_noise(clean_images[i]							,       noise[i]							,       timesteps[i]	)
              UpperCAmelCase__			:							str							=    model(_lowerCamelCase							,       timesteps[i]	).sample
              UpperCAmelCase__			:							List[Any]							=    torch.nn.functional.mse_loss(_lowerCamelCase							,       noise[i]	)
              loss.backward()
              optimizer.step()
        del model, optimizer

        self.assertTrue(torch.allclose(_lowerCamelCase							,       _lowerCamelCase							,       atol=1e-5	)	)
        self.assertTrue(torch.allclose(_lowerCamelCase							,       _lowerCamelCase							,       atol=1e-5	)	)




"""simple docstring"""
import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_A							=  """▁"""

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

@require_sentencepiece
@require_tokenizers
class       lowerCamelCase	(       lowerCAmelCase__					,					unittest.TestCase   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  BigBirdTokenizer
  SCREAMING_SNAKE_CASE					      =  BigBirdTokenizerFast
  SCREAMING_SNAKE_CASE					      =  True
  SCREAMING_SNAKE_CASE					      =  True



  def  _a      (self	):


        """simple docstring"""

        super().setUp()

        UpperCAmelCase__			:							Any							=    self.tokenizer_class(_lowerCamelCase							,       keep_accents=_lowerCamelCase	)
        tokenizer.save_pretrained(self.tmpdirname	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    """<s>"""
        UpperCAmelCase__			:							Optional[int]							=    1

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



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[Any]							=    list(self.get_tokenizer().get_vocab().keys()	)

        self.assertEqual(vocab_keys[0]							,       """<unk>"""	)
        self.assertEqual(vocab_keys[1]							,       """<s>"""	)
        self.assertEqual(vocab_keys[-1]							,       """[MASK]"""	)
        self.assertEqual(len(_lowerCamelCase	)							,       1004	)



  def  _a      (self	):


        """simple docstring"""

        self.assertEqual(self.get_tokenizer().vocab_size							,       1000	)



  def  _a      (self	):


        """simple docstring"""

        if not self.test_rust_tokenizer:
              return

        UpperCAmelCase__			:							List[Any]							=    self.get_tokenizer()
        UpperCAmelCase__			:							Optional[Any]							=    self.get_rust_tokenizer()

        UpperCAmelCase__			:							Dict							=    """I was born in 92000, and this is falsé."""

        UpperCAmelCase__			:							Dict							=    tokenizer.tokenize(_lowerCamelCase	)
        UpperCAmelCase__			:							int							=    rust_tokenizer.tokenize(_lowerCamelCase	)
        self.assertListEqual(_lowerCamelCase							,       _lowerCamelCase	)

        UpperCAmelCase__			:							int							=    tokenizer.encode(_lowerCamelCase							,       add_special_tokens=_lowerCamelCase	)
        UpperCAmelCase__			:							str							=    rust_tokenizer.encode(_lowerCamelCase							,       add_special_tokens=_lowerCamelCase	)
        self.assertListEqual(_lowerCamelCase							,       _lowerCamelCase	)

        UpperCAmelCase__			:							Any							=    self.get_rust_tokenizer()
        UpperCAmelCase__			:							List[Any]							=    tokenizer.encode(_lowerCamelCase	)
        UpperCAmelCase__			:							Union[str, Any]							=    rust_tokenizer.encode(_lowerCamelCase	)
        self.assertListEqual(_lowerCamelCase							,       _lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							str							=    BigBirdTokenizer(_lowerCamelCase							,       keep_accents=_lowerCamelCase	)

        UpperCAmelCase__			:							Union[str, Any]							=    tokenizer.tokenize("""This is a test"""	)
        self.assertListEqual(_lowerCamelCase							,       ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]	)

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(_lowerCamelCase	)							,       [285, 46, 10, 170, 382]							,       )

        UpperCAmelCase__			:							Dict							=    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""",
                """.""",
            ]							,       )
        UpperCAmelCase__			:							Tuple							=    tokenizer.convert_tokens_to_ids(_lowerCamelCase	)
        self.assertListEqual(
            _lowerCamelCase							,       [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]							,       )

        UpperCAmelCase__			:							Tuple							=    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>""",
                """.""",
            ]							,       )



  @cached_property
  def  _a      (self	):


        """simple docstring"""

        return BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""	)



  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    """Hello World!"""
        UpperCAmelCase__			:							Union[str, Any]							=    [65, 18536, 2260, 101, 66]

        self.assertListEqual(_lowerCamelCase							,       self.big_tokenizer.encode(_lowerCamelCase	)	)



  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    (
            """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
            """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
        )
        # fmt: off
        UpperCAmelCase__			:							Dict							=    [65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66]  # noqa: E231
        # fmt: on
        self.assertListEqual(_lowerCamelCase							,       self.big_tokenizer.encode(_lowerCamelCase	)	)



  @require_torch
  @slow
  def  _a      (self	):


        """simple docstring"""

        import torch

        from transformers import BigBirdConfig, BigBirdModel

        # Build sequence
        UpperCAmelCase__			:							int							=    list(self.big_tokenizer.get_vocab().keys()	)[:10]
        UpperCAmelCase__			:							str							=    """ """.join(_lowerCamelCase	)
        UpperCAmelCase__			:							int							=    self.big_tokenizer.encode_plus(_lowerCamelCase							,       return_tensors="""pt"""							,       return_token_type_ids=_lowerCamelCase	)
        UpperCAmelCase__			:							str							=    self.big_tokenizer.batch_encode_plus(
            [sequence + """ """ + sequence]							,       return_tensors="""pt"""							,       return_token_type_ids=_lowerCamelCase	)

        UpperCAmelCase__			:							Optional[int]							=    BigBirdConfig(attention_type="""original_full"""	)
        UpperCAmelCase__			:							Optional[int]							=    BigBirdModel(_lowerCamelCase	)

        assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

        with torch.no_grad():
              model(**_lowerCamelCase	)
              model(**_lowerCamelCase	)



  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""	)
        UpperCAmelCase__			:							int							=    tokenizer.decode(tokenizer("""Paris is the [MASK]."""	).input_ids	)

        self.assertTrue(decoded_text == """[CLS] Paris is the[MASK].[SEP]"""	)




  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    {"""input_ids""": [[65, 39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114, 66], [65, 448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 66, 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], [65, 484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 66, 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]], """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, 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, 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=_lowerCamelCase							,       model_name="""google/bigbird-roberta-base"""							,       revision="""215c99f1600e06f83acce68422f2035b2b5c3510"""							,       )

