






"""simple docstring"""




import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
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__   :					Any							=   get_tests_dir('fixtures/test_sentencepiece_no_bos.model')


@require_sentencepiece
@require_tokenizers
class 			__magic_name__       (       SCREAMING_SNAKE_CASE_   ,					unittest.TestCase ):
       UpperCamelCase_   =		PegasusTokenizer
       UpperCamelCase_   =		PegasusTokenizerFast
       UpperCamelCase_   =		True
       UpperCamelCase_   =		True
       def 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         super().setUp()

         # We have a SentencePiece fixture for testing
         _lowercase:			List[str]       =     PegasusTokenizer(UpperCamelCase__       )
         tokenizer.save_pretrained(self.tmpdirname       )
       @cached_property
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         return PegasusTokenizer.from_pretrained('''google/pegasus-large'''       )
       def 							lowercase_							(     self ,				**A_       )     ->	Any:





         """simple docstring"""




         return PegasusTokenizer.from_pretrained(self.tmpdirname ,				**UpperCamelCase__       )
       def 							lowercase_							(     self ,				A_       )     ->	Dict:





         """simple docstring"""




         return ("This is a test", "This is a test")
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         _lowercase:			List[Any]       =     '''</s>'''
         _lowercase:			List[Any]       =     1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__       ) ,				UpperCamelCase__       )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__       ) ,				UpperCamelCase__       )
       def 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			Optional[int]       =     list(self.get_tokenizer().get_vocab().keys()       )

         self.assertEqual(vocab_keys[0] ,				'''<pad>'''       )
         self.assertEqual(vocab_keys[1] ,				'''</s>'''       )
         self.assertEqual(vocab_keys[-1] ,				'''v'''       )
         self.assertEqual(len(UpperCamelCase__       ) ,				1103       )
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         self.assertEqual(self.get_tokenizer().vocab_size ,				1103       )
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         _lowercase:			Tuple       =     self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
         _lowercase:			List[str]       =     self.tokenizer_class.from_pretrained(self.tmpdirname       )
         _lowercase:			List[Any]       =     (
             '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
             ''' </s> <pad> <pad> <pad>'''
         )
         _lowercase:			str       =     rust_tokenizer([raw_input_str] ,				return_tensors=UpperCamelCase__ ,				add_special_tokens=UpperCamelCase__       ).input_ids[0]
         _lowercase:			List[str]       =     py_tokenizer([raw_input_str] ,				return_tensors=UpperCamelCase__ ,				add_special_tokens=UpperCamelCase__       ).input_ids[0]
         self.assertListEqual(UpperCamelCase__ ,				UpperCamelCase__       )
       def 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			List[str]       =     self._large_tokenizer
         # <mask_1> masks whole sentence while <mask_2> masks single word
         _lowercase:			List[str]       =     '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
         _lowercase:			List[Any]       =     [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
         _lowercase:			Union[str, Any]       =     tokenizer([raw_input_str] ,				return_tensors=UpperCamelCase__       ).input_ids[0]
         self.assertListEqual(UpperCamelCase__ ,				UpperCamelCase__       )
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         _lowercase:			Any       =     self._large_tokenizer
         # The tracebacks for the following asserts are **better** without messages or self.assertEqual
         assert tokenizer.vocab_size == 9_6103
         assert tokenizer.pad_token_id == 0
         assert tokenizer.eos_token_id == 1
         assert tokenizer.offset == 103
         assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
         assert tokenizer.unk_token == "<unk>"
         assert tokenizer.model_max_length == 1024
         _lowercase:			List[Any]       =     '''To ensure a smooth flow of bank resolutions.'''
         _lowercase:			Union[str, Any]       =     [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
         _lowercase:			List[str]       =     tokenizer([raw_input_str] ,				return_tensors=UpperCamelCase__       ).input_ids[0]
         self.assertListEqual(UpperCamelCase__ ,				UpperCamelCase__       )
         assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]       ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
       @require_torch
       def 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     ['''This is going to be way too long.''' * 150, '''short example''']
         _lowercase:			Tuple       =     ['''not super long but more than 5 tokens''', '''tiny''']
         _lowercase:			Tuple       =     self._large_tokenizer(UpperCamelCase__ ,				padding=UpperCamelCase__ ,				truncation=UpperCamelCase__ ,				return_tensors='''pt'''       )
         _lowercase:			Any       =     self._large_tokenizer(
             text_target=UpperCamelCase__ ,				max_length=5 ,				padding=UpperCamelCase__ ,				truncation=UpperCamelCase__ ,				return_tensors='''pt'''       )

         assert batch.input_ids.shape == (2, 1024)
         assert batch.attention_mask.shape == (2, 1024)
         assert targets["input_ids"].shape == (2, 5)
         assert len(UpperCamelCase__       ) == 2  # input_ids, attention_mask.



       @slow
       def 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     {'''input_ids''': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 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]], '''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=UpperCamelCase__ ,				model_name='''google/bigbird-pegasus-large-arxiv''' ,				revision='''ba85d0851d708441f91440d509690f1ab6353415''' ,				)


@require_sentencepiece
@require_tokenizers
class 			__magic_name__       (       SCREAMING_SNAKE_CASE_   ,					unittest.TestCase ):
       UpperCamelCase_   =		PegasusTokenizer
       UpperCamelCase_   =		PegasusTokenizerFast
       UpperCamelCase_   =		True
       UpperCamelCase_   =		True
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         super().setUp()

         # We have a SentencePiece fixture for testing
         _lowercase:			Optional[int]       =     PegasusTokenizer(UpperCamelCase__ ,				offset=0 ,				mask_token_sent=UpperCamelCase__ ,				mask_token='''[MASK]'''       )
         tokenizer.save_pretrained(self.tmpdirname       )
       @cached_property
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''       )
       def 							lowercase_							(     self ,				**A_       )     ->	Optional[Any]:





         """simple docstring"""




         return PegasusTokenizer.from_pretrained(self.tmpdirname ,				**UpperCamelCase__       )
       def 							lowercase_							(     self ,				A_       )     ->	Any:





         """simple docstring"""




         return ("This is a test", "This is a test")
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         _lowercase:			Any       =     self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
         _lowercase:			Tuple       =     self.tokenizer_class.from_pretrained(self.tmpdirname       )
         _lowercase:			Tuple       =     (
             '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
             ''' <pad> <pad> <pad>'''
         )
         _lowercase:			List[str]       =     rust_tokenizer([raw_input_str] ,				return_tensors=UpperCamelCase__ ,				add_special_tokens=UpperCamelCase__       ).input_ids[0]
         _lowercase:			str       =     py_tokenizer([raw_input_str] ,				return_tensors=UpperCamelCase__ ,				add_special_tokens=UpperCamelCase__       ).input_ids[0]
         self.assertListEqual(UpperCamelCase__ ,				UpperCamelCase__       )
       @require_torch
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Dict       =     ['''This is going to be way too long.''' * 1000, '''short example''']
         _lowercase:			str       =     ['''not super long but more than 5 tokens''', '''tiny''']
         _lowercase:			List[Any]       =     self._large_tokenizer(UpperCamelCase__ ,				padding=UpperCamelCase__ ,				truncation=UpperCamelCase__ ,				return_tensors='''pt'''       )
         _lowercase:			Dict       =     self._large_tokenizer(
             text_target=UpperCamelCase__ ,				max_length=5 ,				padding=UpperCamelCase__ ,				truncation=UpperCamelCase__ ,				return_tensors='''pt'''       )

         assert batch.input_ids.shape == (2, 4096)
         assert batch.attention_mask.shape == (2, 4096)
         assert targets["input_ids"].shape == (2, 5)
         assert len(UpperCamelCase__       ) == 2  # input_ids, attention_mask.



       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			Tuple       =     (
             '''This is an example string that is used to test the original TF implementation against the HF'''
             ''' implementation'''
         )

         _lowercase:			List[str]       =     self._large_tokenizer(UpperCamelCase__       ).input_ids

         self.assertListEqual(
             UpperCamelCase__ ,				[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1] ,				)


"""simple docstring"""




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		(KDPMaDiscreteScheduler,)
       UpperCamelCase_   =		10
       def 							lowercase_							(     self ,				**A_       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Tuple       =     {
             '''num_train_timesteps''': 1100,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
         }

         config.update(**A_       )
         return config
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=A_       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01] ,				[0.00_02, 0.0_02, 0.02]       ):
           self.check_over_configs(beta_start=A_ ,				beta_end=A_       )
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=A_       )
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=A_       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[int]       =     self.scheduler_classes[0]
         _lowercase:			List[str]       =     self.get_scheduler_config(prediction_type='''v_prediction'''       )
         _lowercase:			List[Any]       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps       )

         _lowercase:			List[str]       =     self.dummy_model()
         _lowercase:			Dict       =     self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:			List[Any]       =     sample.to(A_       )

         for i, t in enumerate(scheduler.timesteps       ):
           _lowercase:			Optional[int]       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			Optional[Any]       =     model(A_ ,				A_       )

           _lowercase:			List[str]       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			str       =     output.prev_sample

         _lowercase:			Dict       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Optional[int]       =     torch.mean(torch.abs(A_       )       )

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 4.69_34E-07       ) < 1E-2
           assert abs(result_mean.item() - 6.11_12E-10       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07       ) < 1E-2
           assert abs(result_mean.item() - 0.00_02       ) < 1E-3
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         if torch_device == "mps":
           return
         _lowercase:			List[str]       =     self.scheduler_classes[0]
         _lowercase:			Optional[int]       =     self.get_scheduler_config()
         _lowercase:			int       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps       )

         _lowercase:			Tuple       =     self.dummy_model()
         _lowercase:			Tuple       =     self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:			Any       =     sample.to(A_       )

         for i, t in enumerate(scheduler.timesteps       ):
           _lowercase:			Any       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			List[Any]       =     model(A_ ,				A_       )

           _lowercase:			Any       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			List[Any]       =     output.prev_sample

         _lowercase:			Optional[Any]       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Tuple       =     torch.mean(torch.abs(A_       )       )

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3



       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         if torch_device == "mps":
           return
         _lowercase:			int       =     self.scheduler_classes[0]
         _lowercase:			Union[str, Any]       =     self.get_scheduler_config()
         _lowercase:			Optional[Any]       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps ,				device=A_       )

         _lowercase:			List[str]       =     self.dummy_model()
         _lowercase:			Union[str, Any]       =     self.dummy_sample_deter.to(A_       ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
           _lowercase:			Optional[Any]       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			int       =     model(A_ ,				A_       )

           _lowercase:			Tuple       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			int       =     output.prev_sample

         _lowercase:			Union[str, Any]       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Dict       =     torch.mean(torch.abs(A_       )       )

         if str(A_       ).startswith('''cpu'''       ):
           # The following sum varies between 148 and 156 on mps. Why?
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
