




import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case										=       "▁"

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







@require_sentencepiece
class 						lowercase							(   UpperCamelCase__,unittest.TestCase		):
     _a							 =						BertGenerationTokenizer
     _a							 =						False
     _a							 =						True

     def 					a__	(  self				)						->							str:
           super().setUp()

           _A							:     Tuple			=						BertGenerationTokenizer(_a		,     keep_accents=_a				)
           tokenizer.save_pretrained(self.tmpdirname				)

     def 					a__	(  self				)						->							Union[str, Any]:
           _A							:     List[Any]			=						"""<s>"""
           _A							:     Optional[int]			=						1

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

     def 					a__	(  self				)						->							List[Any]:
           _A							:     List[str]			=						list(self.get_tokenizer().get_vocab().keys()				)

           self.assertEqual(vocab_keys[0]		,     """<unk>"""				)
           self.assertEqual(vocab_keys[1]		,     """<s>"""				)
           self.assertEqual(vocab_keys[-1]		,     """<pad>"""				)
           self.assertEqual(len(_a				)		,     1002				)

     def 					a__	(  self				)						->							Union[str, Any]:
           self.assertEqual(self.get_tokenizer().vocab_size		,     1000				)

     def 					a__	(  self				)						->							Union[str, Any]:
           _A							:     str			=						BertGenerationTokenizer(_a		,     keep_accents=_a				)

           _A							:     Tuple			=						tokenizer.tokenize("""This is a test"""				)
           self.assertListEqual(_a		,     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]				)

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

           _A							:     Union[str, Any]			=						tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
           self.assertListEqual(
               _a		,     [
                   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							:     List[str]			=						tokenizer.convert_tokens_to_ids(_a				)
           self.assertListEqual(
               _a		,     [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]		,     )

           _A							:     Union[str, Any]			=						tokenizer.convert_ids_to_tokens(_a				)
           self.assertListEqual(
               _a		,     [
                   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				)						->							Tuple:
           return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""				)

     @slow
     def 					a__	(  self				)						->							List[Any]:
           _A							:     List[Any]			=						"""Hello World!"""
           _A							:     List[str]			=						[1_8536, 2260, 101]

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

     @slow
     def 					a__	(  self				)						->							List[str]:
           _A							:     str			=						(
               """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"""
           )
           _A							:     List[Any]			=						[
               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,
               3_4324,
               497,
               391,
               408,
               1_1342,
               1244,
               385,
               100,
               938,
               985,
               456,
               574,
               362,
               1_2597,
               3200,
               3129,
               1172,
           ]

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

     @require_torch
     @slow
     def 					a__	(  self				)						->							Optional[Any]:
           import torch

           from transformers import BertGenerationConfig, BertGenerationEncoder

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

           _A							:     Optional[int]			=						BertGenerationConfig()
           _A							:     int			=						BertGenerationEncoder(_a				)

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

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



     @slow
     def 					a__	(  self				)						->							Optional[Any]:
           # fmt: off
           _A							:     Any			=						{"""input_ids""": [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=_a		,     model_name="""google/bert_for_seq_generation_L-24_bbc_encoder"""		,     revision="""c817d1fd1be2ffa69431227a1fe320544943d4db"""		,     )







import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest







@require_torchsde
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						(DPMSolverSDEScheduler,)
     _a							 =						1_0

     def 					a__	(  self		,     **_a				)						->							Optional[Any]:
           _A							:     str			=						{
               """num_train_timesteps""": 1100,
               """beta_start""": 0.0001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
               """noise_sampler_seed""": 0,
           }

           config.update(**_a				)
           return config

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

     def 					a__	(  self				)						->							Optional[int]:
           for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,     [0.0002, 0.002, 0.02]				):
                 self.check_over_configs(beta_start=_a		,     beta_end=_a				)

     def 					a__	(  self				)						->							Any:
           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=_a				)

     def 					a__	(  self				)						->							Optional[int]:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=_a				)

     def 					a__	(  self				)						->							Optional[int]:
           _A							:     Any			=						self.scheduler_classes[0]
           _A							:     List[str]			=						self.get_scheduler_config()
           _A							:     Optional[Any]			=						scheduler_class(**_a				)

           scheduler.set_timesteps(self.num_inference_steps				)

           _A							:     Dict			=						self.dummy_model()
           _A							:     Any			=						self.dummy_sample_deter * scheduler.init_noise_sigma
           _A							:     Dict			=						sample.to(_a				)

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

                 _A							:     str			=						model(_a		,     _a				)

                 _A							:     List[Any]			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     Optional[int]			=						output.prev_sample

           _A							:     Dict			=						torch.sum(torch.abs(_a				)				)
           _A							:     Dict			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 167.47821044921875				) < 1e-2
                 assert abs(result_mean.item() - 0.2178705964565277				) < 1e-3
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 171.59352111816406				) < 1e-2
                 assert abs(result_mean.item() - 0.22342906892299652				) < 1e-3
           else:
                 assert abs(result_sum.item() - 162.52383422851562				) < 1e-2
                 assert abs(result_mean.item() - 0.211619570851326				) < 1e-3

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Dict			=						self.scheduler_classes[0]
           _A							:     Optional[int]			=						self.get_scheduler_config(prediction_type="""v_prediction"""				)
           _A							:     Optional[Any]			=						scheduler_class(**_a				)

           scheduler.set_timesteps(self.num_inference_steps				)

           _A							:     Tuple			=						self.dummy_model()
           _A							:     int			=						self.dummy_sample_deter * scheduler.init_noise_sigma
           _A							:     Tuple			=						sample.to(_a				)

           for i, t in enumerate(scheduler.timesteps				):
                 _A							:     int			=						scheduler.scale_model_input(_a		,     _a				)

                 _A							:     Tuple			=						model(_a		,     _a				)

                 _A							:     Dict			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     Optional[int]			=						output.prev_sample

           _A							:     Optional[Any]			=						torch.sum(torch.abs(_a				)				)
           _A							:     List[Any]			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 124.77149200439453				) < 1e-2
                 assert abs(result_mean.item() - 0.16226289014816284				) < 1e-3
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 128.1663360595703				) < 1e-2
                 assert abs(result_mean.item() - 0.16688326001167297				) < 1e-3
           else:
                 assert abs(result_sum.item() - 119.8487548828125				) < 1e-2
                 assert abs(result_mean.item() - 0.1560530662536621				) < 1e-3

     def 					a__	(  self				)						->							List[str]:
           _A							:     Union[str, Any]			=						self.scheduler_classes[0]
           _A							:     List[Any]			=						self.get_scheduler_config()
           _A							:     List[str]			=						scheduler_class(**_a				)

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

           _A							:     Union[str, Any]			=						self.dummy_model()
           _A							:     Optional[Any]			=						self.dummy_sample_deter.to(_a				) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                 _A							:     int			=						scheduler.scale_model_input(_a		,     _a				)

                 _A							:     List[Any]			=						model(_a		,     _a				)

                 _A							:     Dict			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     Dict			=						output.prev_sample

           _A							:     str			=						torch.sum(torch.abs(_a				)				)
           _A							:     str			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 167.46957397460938				) < 1e-2
                 assert abs(result_mean.item() - 0.21805934607982635				) < 1e-3
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 171.59353637695312				) < 1e-2
                 assert abs(result_mean.item() - 0.22342908382415771				) < 1e-3
           else:
                 assert abs(result_sum.item() - 162.52383422851562				) < 1e-2
                 assert abs(result_mean.item() - 0.211619570851326				) < 1e-3



     def 					a__	(  self				)						->							Union[str, Any]:
           _A							:     List[Any]			=						self.scheduler_classes[0]
           _A							:     Optional[Any]			=						self.get_scheduler_config()
           _A							:     int			=						scheduler_class(**_a		,     use_karras_sigmas=_a				)

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

           _A							:     Optional[Any]			=						self.dummy_model()
           _A							:     Dict			=						self.dummy_sample_deter.to(_a				) * scheduler.init_noise_sigma
           _A							:     str			=						sample.to(_a				)

           for t in scheduler.timesteps:
                 _A							:     Optional[int]			=						scheduler.scale_model_input(_a		,     _a				)

                 _A							:     List[Any]			=						model(_a		,     _a				)

                 _A							:     Dict			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     List[str]			=						output.prev_sample

           _A							:     str			=						torch.sum(torch.abs(_a				)				)
           _A							:     List[str]			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 176.66974135742188				) < 1e-2
                 assert abs(result_mean.item() - 0.23003872730981811				) < 1e-2
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 177.63653564453125				) < 1e-2
                 assert abs(result_mean.item() - 0.23003872730981811				) < 1e-2
           else:
                 assert abs(result_sum.item() - 170.3135223388672				) < 1e-2
                 assert abs(result_mean.item() - 0.23003872730981811				) < 1e-2

