





'''simple docstring'''







import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 				__SCREAMING_SNAKE_CASE      (							lowerCAmelCase__				, unittest.TestCase   ):
      '''simple docstring'''



      lowerCAmelCase__							:   List[str]					=		PhobertTokenizer
      lowerCAmelCase__							:   int					=		False

      def 	_lowerCamelCase		(				self     :			Optional[Any]	)		->	Union[str, Any]:
           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           _lowercase				:   List[Any]	    =				['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
           _lowercase				:   Union[str, Any]	    =				dict(zip(__a ,range(len(__a	)	)	)	)
           _lowercase				:   Tuple	    =				['#version: 0.2', 'l à</w>']
           _lowercase				:   Union[str, Any]	    =				{'unk_token': '<unk>'}

           _lowercase				:   Tuple	    =				os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES['vocab_file']	)
           _lowercase				:   Dict	    =				os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES['merges_file']	)

           with open(self.vocab_file ,'w' ,encoding='utf-8'	) as fp:
                for token in vocab_tokens:
                     fp.write(F'''{token} {vocab_tokens[token]}\n'''	)
           with open(self.merges_file ,'w' ,encoding='utf-8'	) as fp:
                fp.write('\n'.join(__a	)	)

      def 	_lowerCamelCase		(				self     :			str ,**UpperCamelCase     :			str	)		->	str:
           kwargs.update(self.special_tokens_map	)
           return PhobertTokenizer.from_pretrained(self.tmpdirname ,**__a	)

      def 	_lowerCamelCase		(				self     :			Dict ,UpperCamelCase     :			Optional[Any]	)		->	str:
           _lowercase				:   List[Any]	    =				'Tôi là VinAI Research'
           _lowercase				:   Dict	    =				'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
           return input_text, output_text



      def 	_lowerCamelCase		(				self     :			str	)		->	Optional[int]:
           _lowercase				:   str	    =				PhobertTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map	)
           _lowercase				:   Tuple	    =				'Tôi là VinAI Research'
           _lowercase				:   str	    =				'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
           _lowercase				:   str	    =				tokenizer.tokenize(__a	)
           print(__a	)
           self.assertListEqual(__a ,__a	)

           _lowercase				:   str	    =				tokens + [tokenizer.unk_token]

           _lowercase				:   List[str]	    =				[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(__a	) ,__a	)



"""simple docstring"""


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Tuple							 =(KDPMaDiscreteScheduler,)
							__UpperCAmelCase		:       Optional[Any]							 =1_0

							def      snake_case							(	self       ,  **__a      ):
										__lowerCAmelCase							       =     {
										    "num_train_timesteps": 11_00,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										}

										config.update(**__a      )
										return config

							def      snake_case							(	self      ):
										for timesteps in [10, 50, 1_00, 10_00]:
													self.check_over_configs(num_train_timesteps=__a      )

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

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

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.scheduler_classes[0]
										__lowerCAmelCase							       =     self.get_scheduler_config(prediction_type="v_prediction"      )
										__lowerCAmelCase							       =     scheduler_class(**__a      )

										scheduler.set_timesteps(self.num_inference_steps      )

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

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

													__lowerCAmelCase							       =     model(__a       ,  __a      )

													__lowerCAmelCase							       =     scheduler.step(__a       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     output.prev_sample

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

										if torch_device in ["cpu", "mps"]:
													assert abs(result_sum.item() - 4.6_9_3_4e-0_7      ) < 1e-2
													assert abs(result_mean.item() - 6.1_1_1_2e-1_0      ) < 1e-3
										else:
													# CUDA
													assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7      ) < 1e-2
													assert abs(result_mean.item() - 0.0_0_0_2      ) < 1e-3

							def      snake_case							(	self      ):
										if torch_device == "mps":
													return
										__lowerCAmelCase							       =     self.scheduler_classes[0]
										__lowerCAmelCase							       =     self.get_scheduler_config()
										__lowerCAmelCase							       =     scheduler_class(**__a      )

										scheduler.set_timesteps(self.num_inference_steps      )

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

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

													__lowerCAmelCase							       =     model(__a       ,  __a      )

													__lowerCAmelCase							       =     scheduler.step(__a       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     output.prev_sample

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

										if torch_device in ["cpu", "mps"]:
													assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
													assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
										else:
													# CUDA
													assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
													assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3



							def      snake_case							(	self      ):
										if torch_device == "mps":
													return
										__lowerCAmelCase							       =     self.scheduler_classes[0]
										__lowerCAmelCase							       =     self.get_scheduler_config()
										__lowerCAmelCase							       =     scheduler_class(**__a      )

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

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

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

													__lowerCAmelCase							       =     model(__a       ,  __a      )

													__lowerCAmelCase							       =     scheduler.step(__a       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     output.prev_sample

										__lowerCAmelCase							       =     torch.sum(torch.abs(__a      )      )
										__lowerCAmelCase							       =     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() - 2_0.4_1_2_5      ) < 1e-2
													assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
										else:
													# CUDA
													assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
													assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3




