
"""simple docstring"""





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class __lowerCAmelCase				(					UpperCamelCase_      ):




   """simple docstring"""







   snake_case	      =				(EulerDiscreteScheduler,)
   snake_case	      =				10




   def      lowerCamelCase__     (				self						:					Any       ,   **_snake_case						:					List[str]	)      -> Any:







        """simple docstring"""
        A_				 =						{
            'num_train_timesteps': 1_100,
            'beta_start': 0.0_0_0_1,
            'beta_end': 0.0_2,
            'beta_schedule': 'linear',
        }

        config.update(**__a	)
        return config




   def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Optional[Any]:







        """simple docstring"""






        for timesteps in [10, 50, 100, 1_000]:
             self.check_over_configs(num_train_timesteps=__a	)




   def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Union[str, Any]:







        """simple docstring"""






        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      lowerCamelCase__     (				self						:					Optional[int]	)      -> Union[str, Any]:







        """simple docstring"""






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




   def      lowerCamelCase__     (				self						:					str	)      -> List[str]:







        """simple docstring"""






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




   def      lowerCamelCase__     (				self						:					List[str]	)      -> Dict:







        """simple docstring"""
        A_				 =						self.scheduler_classes[0]
        A_				 =						self.get_scheduler_config()
        A_				 =						scheduler_class(**__a	)

        scheduler.set_timesteps(self.num_inference_steps	)

        A_				 =						torch.manual_seed(0	)

        A_				 =						self.dummy_model()
        A_				 =						self.dummy_sample_deter * scheduler.init_noise_sigma
        A_				 =						sample.to(__a	)

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

             A_				 =						model(__a       ,   __a	)

             A_				 =						scheduler.step(__a       ,   __a       ,   __a       ,   generator=__a	)
             A_				 =						output.prev_sample

        A_				 =						torch.sum(torch.abs(__a	)	)
        A_				 =						torch.mean(torch.abs(__a	)	)

        assert abs(result_sum.item() - 1_0.0_8_0_7	) < 1e-2
        assert abs(result_mean.item() - 0.0_1_3_1	) < 1e-3




   def      lowerCamelCase__     (				self						:					List[str]	)      -> int:







        """simple docstring"""
        A_				 =						self.scheduler_classes[0]
        A_				 =						self.get_scheduler_config(prediction_type="v_prediction"	)
        A_				 =						scheduler_class(**__a	)

        scheduler.set_timesteps(self.num_inference_steps	)

        A_				 =						torch.manual_seed(0	)

        A_				 =						self.dummy_model()
        A_				 =						self.dummy_sample_deter * scheduler.init_noise_sigma
        A_				 =						sample.to(__a	)

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

             A_				 =						model(__a       ,   __a	)

             A_				 =						scheduler.step(__a       ,   __a       ,   __a       ,   generator=__a	)
             A_				 =						output.prev_sample

        A_				 =						torch.sum(torch.abs(__a	)	)
        A_				 =						torch.mean(torch.abs(__a	)	)

        assert abs(result_sum.item() - 0.0_0_0_2	) < 1e-2
        assert abs(result_mean.item() - 2.2676e-06	) < 1e-3




   def      lowerCamelCase__     (				self						:					Dict	)      -> Any:







        """simple docstring"""
        A_				 =						self.scheduler_classes[0]
        A_				 =						self.get_scheduler_config()
        A_				 =						scheduler_class(**__a	)

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

        A_				 =						torch.manual_seed(0	)

        A_				 =						self.dummy_model()
        A_				 =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        A_				 =						sample.to(__a	)

        for t in scheduler.timesteps:
             A_				 =						scheduler.scale_model_input(__a       ,   __a	)

             A_				 =						model(__a       ,   __a	)

             A_				 =						scheduler.step(__a       ,   __a       ,   __a       ,   generator=__a	)
             A_				 =						output.prev_sample

        A_				 =						torch.sum(torch.abs(__a	)	)
        A_				 =						torch.mean(torch.abs(__a	)	)

        assert abs(result_sum.item() - 1_0.0_8_0_7	) < 1e-2
        assert abs(result_mean.item() - 0.0_1_3_1	) < 1e-3






   def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> List[str]:







        """simple docstring"""
        A_				 =						self.scheduler_classes[0]
        A_				 =						self.get_scheduler_config()
        A_				 =						scheduler_class(**__a       ,   use_karras_sigmas=__a	)

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

        A_				 =						torch.manual_seed(0	)

        A_				 =						self.dummy_model()
        A_				 =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        A_				 =						sample.to(__a	)

        for t in scheduler.timesteps:
             A_				 =						scheduler.scale_model_input(__a       ,   __a	)

             A_				 =						model(__a       ,   __a	)

             A_				 =						scheduler.step(__a       ,   __a       ,   __a       ,   generator=__a	)
             A_				 =						output.prev_sample

        A_				 =						torch.sum(torch.abs(__a	)	)
        A_				 =						torch.mean(torch.abs(__a	)	)

        assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9	) < 1e-2
        assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3	) < 1e-3






"""simple docstring"""





import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class __lowerCAmelCase				(					_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				TransfoXLTokenizer
			snake_case	      =				False
			snake_case	      =				False




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Optional[int]:







								"""simple docstring"""
								super().setUp()

								A_				 =						[
								    "<unk>",
								    "[CLS]",
								    "[SEP]",
								    "want",
								    "unwanted",
								    "wa",
								    "un",
								    "running",
								    ",",
								    "low",
								    "l",
								]
								A_				 =						os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES["vocab_file"]	)
								with open(self.vocab_file       ,   "w"       ,   encoding="utf-8"	) as vocab_writer:
													vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)




			def      lowerCamelCase__     (				self						:					str       ,   **_snake_case						:					Any	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						True
								return TransfoXLTokenizer.from_pretrained(self.tmpdirname       ,   **_snake_case	)




			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					Optional[Any]	)      -> Any:







								"""simple docstring"""
								A_				 =						"<unk> UNwanted , running"
								A_				 =						"<unk> unwanted, running"
								return input_text, output_text




			def      lowerCamelCase__     (				self						:					Dict	)      -> int:







								"""simple docstring"""
								A_				 =						TransfoXLTokenizer(vocab_file=self.vocab_file       ,   lower_case=_snake_case	)

								A_				 =						tokenizer.tokenize("<unk> UNwanted , running"	)
								self.assertListEqual(_snake_case       ,   ["<unk>", "unwanted", ",", "running"]	)

								self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case	)       ,   [0, 4, 8, 7]	)




			def      lowerCamelCase__     (				self						:					List[str]	)      -> int:







								"""simple docstring"""
								A_				 =						TransfoXLTokenizer(lower_case=_snake_case	)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHeLLo ! how  \n Are yoU ?  "	)       ,   ["hello", "!", "how", "are", "you", "?"]	)




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> str:







								"""simple docstring"""
								A_				 =						TransfoXLTokenizer(lower_case=_snake_case	)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHeLLo ! how  \n Are yoU ?  "	)       ,   ["HeLLo", "!", "how", "Are", "yoU", "?"]	)




			def      lowerCamelCase__     (				self						:					Dict	)      -> Tuple:







								"""simple docstring"""
								A_				 =						TransfoXLTokenizer(lower_case=_snake_case	)
								A_				 =						"Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"
								A_				 =						[
								    "Hello",
								    "(",
								    "bracket",
								    ")",
								    "and",
								    "side",
								    "@-@",
								    "scrolled",
								    "[",
								    "and",
								    "]",
								    "Henry",
								    "'s",
								    "$",
								    "5",
								    "@,@",
								    "000",
								    "with",
								    "3",
								    "@.@",
								    "34",
								    "m",
								    ".",
								    "What",
								    "'s",
								    "up",
								    "!",
								    "?",
								]

								self.assertListEqual(tokenizer.tokenize(_snake_case	)       ,   _snake_case	)

								self.assertEqual(tokenizer.convert_tokens_to_string(_snake_case	)       ,   _snake_case	)






			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.get_tokenizer()
								A_				 =						len(_snake_case	)

								tokenizer.add_tokens(["new1", "new2"]	)
								tokenizer.move_added_token("new1"       ,   1	)

								# Check that moved token is not copied (duplicate)
								self.assertEqual(len(_snake_case	)       ,   original_len + 2	)
								# Check that token is moved to specified id
								self.assertEqual(tokenizer.encode("new1"	)       ,   [1]	)
								self.assertEqual(tokenizer.decode([1]	)       ,   "new1"	)




