"""simple docstring"""




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 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			str							=		(DPMSolverSDEScheduler,)
     _UpperCamelCase							:			Optional[int]							=		10






     def     __A  (		self				,     **a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     {
           """num_train_timesteps""": 1100,
           """beta_start""": 0.0_0_0_1,
           """beta_end""": 0.0_2,
           """beta_schedule""": """linear""",
           """noise_sampler_seed""": 0,
       }

       config.update(**a__  )
       return config






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






     def     __A  (		self  ):
       for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]				,     [0.0_0_0_2, 0.0_0_2, 0.0_2]  ):
         self.check_over_configs(beta_start=a__				,     beta_end=a__  )






     def     __A  (		self  ):
       for schedule in ["linear", "scaled_linear"]:
         self.check_over_configs(beta_schedule=a__  )






     def     __A  (		self  ):
       for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     self.scheduler_classes[0]
       _lowerCAmelCase						:       Any  			=     self.get_scheduler_config()
       _lowerCAmelCase						:       Tuple  			=     scheduler_class(**a__  )

       scheduler.set_timesteps(self.num_inference_steps  )

       _lowerCAmelCase						:       int  			=     self.dummy_model()
       _lowerCAmelCase						:       Union[str, Any]  			=     self.dummy_sample_deter * scheduler.init_noise_sigma
       _lowerCAmelCase						:       int  			=     sample.to(a__  )

       for i, t in enumerate(scheduler.timesteps  ):
         _lowerCAmelCase						:       int  			=     scheduler.scale_model_input(a__				,     a__  )

         _lowerCAmelCase						:       Optional[int]  			=     model(a__				,     a__  )

         _lowerCAmelCase						:       List[Any]  			=     scheduler.step(a__				,     a__				,     a__  )
         _lowerCAmelCase						:       Dict  			=     output.prev_sample

       _lowerCAmelCase						:       Dict  			=     torch.sum(torch.abs(a__  )  )
       _lowerCAmelCase						:       Optional[Any]  			=     torch.mean(torch.abs(a__  )  )

       if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5  ) < 1e-2
         assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7  ) < 1e-3
       elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6  ) < 1e-2
         assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2  ) < 1e-3
       else:
         assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2  ) < 1e-2
         assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6  ) < 1e-3






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     self.scheduler_classes[0]
       _lowerCAmelCase						:       str  			=     self.get_scheduler_config(prediction_type="""v_prediction"""  )
       _lowerCAmelCase						:       Union[str, Any]  			=     scheduler_class(**a__  )

       scheduler.set_timesteps(self.num_inference_steps  )

       _lowerCAmelCase						:       Any  			=     self.dummy_model()
       _lowerCAmelCase						:       List[str]  			=     self.dummy_sample_deter * scheduler.init_noise_sigma
       _lowerCAmelCase						:       List[Any]  			=     sample.to(a__  )

       for i, t in enumerate(scheduler.timesteps  ):
         _lowerCAmelCase						:       int  			=     scheduler.scale_model_input(a__				,     a__  )

         _lowerCAmelCase						:       int  			=     model(a__				,     a__  )

         _lowerCAmelCase						:       List[str]  			=     scheduler.step(a__				,     a__				,     a__  )
         _lowerCAmelCase						:       Optional[int]  			=     output.prev_sample

       _lowerCAmelCase						:       Any  			=     torch.sum(torch.abs(a__  )  )
       _lowerCAmelCase						:       str  			=     torch.mean(torch.abs(a__  )  )

       if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3  ) < 1e-2
         assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4  ) < 1e-3
       elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3  ) < 1e-2
         assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7  ) < 1e-3
       else:
         assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5  ) < 1e-2
         assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1  ) < 1e-3






     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     self.scheduler_classes[0]
       _lowerCAmelCase						:       Optional[Any]  			=     self.get_scheduler_config()
       _lowerCAmelCase						:       str  			=     scheduler_class(**a__  )

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

       _lowerCAmelCase						:       Tuple  			=     self.dummy_model()
       _lowerCAmelCase						:       Optional[Any]  			=     self.dummy_sample_deter.to(a__  ) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
         _lowerCAmelCase						:       Union[str, Any]  			=     scheduler.scale_model_input(a__				,     a__  )

         _lowerCAmelCase						:       Tuple  			=     model(a__				,     a__  )

         _lowerCAmelCase						:       Optional[int]  			=     scheduler.step(a__				,     a__				,     a__  )
         _lowerCAmelCase						:       List[str]  			=     output.prev_sample

       _lowerCAmelCase						:       int  			=     torch.sum(torch.abs(a__  )  )
       _lowerCAmelCase						:       List[str]  			=     torch.mean(torch.abs(a__  )  )

       if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8  ) < 1e-2
         assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5  ) < 1e-3
       elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2  ) < 1e-2
         assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1  ) < 1e-3
       else:
         assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2  ) < 1e-2
         assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6  ) < 1e-3






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     self.scheduler_classes[0]
       _lowerCAmelCase						:       Dict  			=     self.get_scheduler_config()
       _lowerCAmelCase						:       Optional[int]  			=     scheduler_class(**a__				,     use_karras_sigmas=a__  )

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

       _lowerCAmelCase						:       Any  			=     self.dummy_model()
       _lowerCAmelCase						:       int  			=     self.dummy_sample_deter.to(a__  ) * scheduler.init_noise_sigma
       _lowerCAmelCase						:       int  			=     sample.to(a__  )

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

         _lowerCAmelCase						:       Optional[int]  			=     model(a__				,     a__  )

         _lowerCAmelCase						:       Union[str, Any]  			=     scheduler.step(a__				,     a__				,     a__  )
         _lowerCAmelCase						:       Tuple  			=     output.prev_sample

       _lowerCAmelCase						:       List[Any]  			=     torch.sum(torch.abs(a__  )  )
       _lowerCAmelCase						:       Dict  			=     torch.mean(torch.abs(a__  )  )

       if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8  ) < 1e-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1  ) < 1e-2
       elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5  ) < 1e-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1  ) < 1e-2
       else:
         assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2  ) < 1e-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1  ) < 1e-2





"""simple docstring"""




import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_a      :		Optional[int]           =   get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class 						__A							( SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			List[Any]							=		XGLMTokenizer
     _UpperCamelCase							:			List[Any]							=		XGLMTokenizerFast
     _UpperCamelCase							:			Dict							=		True
     _UpperCamelCase							:			Tuple							=		True






     def     __A  (		self  ):
       super().setUp()

       # We have a SentencePiece fixture for testing
       _lowerCAmelCase						:       List[Any]  			=     XGLMTokenizer(a__				,     keep_accents=a__  )
       tokenizer.save_pretrained(self.tmpdirname  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     """<pad>"""
       _lowerCAmelCase						:       List[Any]  			=     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  ):
       _lowerCAmelCase						:       Optional[int]  			=     list(self.get_tokenizer().get_vocab().keys()  )

       self.assertEqual(vocab_keys[0]				,     """<s>"""  )
       self.assertEqual(vocab_keys[1]				,     """<pad>"""  )
       self.assertEqual(len(a__  )				,     1008  )






     def     __A  (		self  ):
       self.assertEqual(self.get_tokenizer().vocab_size				,     1008  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     XGLMTokenizer(a__				,     keep_accents=a__  )

       _lowerCAmelCase						:       Dict  			=     tokenizer.tokenize("""This is a test"""  )
       self.assertListEqual(a__				,     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]  )

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

       _lowerCAmelCase						:       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""",
               """.""",
           ]				,     )
       _lowerCAmelCase						:       List[str]  			=     tokenizer.convert_tokens_to_ids(a__  )
       self.assertListEqual(
           a__				,     [
               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]
           ]				,     )

       _lowerCAmelCase						:       Optional[int]  			=     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  ):
       return XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""  )






     def     __A  (		self  ):
       with tempfile.NamedTemporaryFile() as f:
         shutil.copyfile(a__				,     f.name  )
         _lowerCAmelCase						:       Union[str, Any]  			=     XGLMTokenizer(f.name				,     keep_accents=a__  )
         _lowerCAmelCase						:       List[str]  			=     pickle.dumps(a__  )
       pickle.loads(a__  )






     def     __A  (		self  ):
       if not self.test_rust_tokenizer:
         return

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

       _lowerCAmelCase						:       Tuple  			=     """I was born in 92000, and this is falsé."""

       _lowerCAmelCase						:       List[Any]  			=     tokenizer.tokenize(a__  )
       _lowerCAmelCase						:       Tuple  			=     rust_tokenizer.tokenize(a__  )
       self.assertListEqual(a__				,     a__  )

       _lowerCAmelCase						:       Union[str, Any]  			=     tokenizer.encode(a__				,     add_special_tokens=a__  )
       _lowerCAmelCase						:       str  			=     rust_tokenizer.encode(a__				,     add_special_tokens=a__  )
       self.assertListEqual(a__				,     a__  )

       _lowerCAmelCase						:       int  			=     self.get_rust_tokenizer()
       _lowerCAmelCase						:       Dict  			=     tokenizer.encode(a__  )
       _lowerCAmelCase						:       List[Any]  			=     rust_tokenizer.encode(a__  )
       self.assertListEqual(a__				,     a__  )






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     """Hello World!"""
       _lowerCAmelCase						:       Optional[int]  			=     [2, 31227, 4447, 35]

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






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       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
       _lowerCAmelCase						:       List[str]  			=     [2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 71630, 28085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 13675, 377, 652, 7580, 10341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 202277, 17892, 33, 60, 87, 4, 3234, 157, 61, 2667, 52376, 19, 88, 23, 735]
       # fmt: on

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






     @slow
     def     __A  (		self  ):
       # fmt: off
       _lowerCAmelCase						:       List[str]  			=     {
           """input_ids""": [[2, 108825, 1163, 15, 88010, 473, 15898, 157, 13672, 1857, 312, 8, 238021, 1163, 53, 13672, 1857, 312, 8, 53283, 182396, 8, 18566, 16, 36733, 4101, 8, 230, 244017, 122553, 7, 15, 132597, 4, 293, 12511, 7610, 4, 3414, 132597, 9, 4, 32361, 362, 4, 734, 28512, 32569, 18, 4, 32361, 26096, 14982, 73, 18715, 21433, 235261, 15, 492, 12427, 16, 53, 18715, 21433, 65454, 15, 23659, 563, 16, 278, 597, 2843, 595, 7931, 182396, 64186, 22, 886, 595, 132981, 53, 25540, 3449, 43982, 39901, 5951, 878, 330, 4, 27694, 80269, 312, 53, 6517, 11780, 611, 20408, 5], [2, 6, 132597, 67, 42897, 33, 592, 8, 163729, 25540, 361, 136997, 109514, 173230, 7, 501, 60, 102913, 196, 5631, 235, 63243, 473, 6, 231757, 74, 5277, 7905, 53, 3095, 37317, 22, 454, 183874, 5], [2, 268, 31298, 46530, 6, 132935, 43831, 7, 597, 32, 24, 3688, 9865, 5]],
           """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, 1, 1, 1]]
       }  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=a__				,     model_name="""facebook/xglm-564M"""				,     padding=a__				,     )




