






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		(DPMSolverSinglestepScheduler,)
					a_     =		(("num_inference_steps", 2_5),)



					def 	_lowercase					( self					:  Dict							,		**__A					:  int	):
						snake_case__			:				int       =  {
						    "num_train_timesteps": 1_0_0_0,
						    "beta_start": 0.0_0_0_1,
						    "beta_end": 0.0_2,
						    "beta_schedule": "linear",
						    "solver_order": 2,
						    "prediction_type": "epsilon",
						    "thresholding": False,
						    "sample_max_value": 1.0,
						    "algorithm_type": "dpmsolver++",
						    "solver_type": "midpoint",
						    "lambda_min_clipped": -float("inf"	),
						    "variance_type": None,
						}

						config.update(**__A	)
						return config



					def 	_lowercase					( self					:  str							,		__A					:  Optional[int]=0							,		**__A					:  Optional[Any]	):
						snake_case__			:				Dict       =  dict(self.forward_default_kwargs	)
						snake_case__			:				Dict       =  kwargs.pop("num_inference_steps"							,		__A	)
						snake_case__			:				Optional[Any]       =  self.dummy_sample
						snake_case__			:				str       =  0.1 * sample
						snake_case__			:				Dict       =  [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

						for scheduler_class in self.scheduler_classes:
							snake_case__			:				Tuple       =  self.get_scheduler_config(**__A	)
							snake_case__			:				Optional[int]       =  scheduler_class(**__A	)
							scheduler.set_timesteps(__A	)
							# copy over dummy past residuals
							snake_case__			:				Optional[Any]       =  dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(__A	)
								snake_case__			:				int       =  scheduler_class.from_pretrained(__A	)
								new_scheduler.set_timesteps(__A	)
								# copy over dummy past residuals
								snake_case__			:				Dict       =  dummy_past_residuals[: new_scheduler.config.solver_order]

							snake_case__,						snake_case__			:				Any       =  sample, sample
							for t in range(__A							,		time_step + scheduler.config.solver_order + 1	):
								snake_case__			:				str       =  scheduler.step(__A							,		__A							,		__A							,		**__A	).prev_sample
								snake_case__			:				List[Any]       =  new_scheduler.step(__A							,		__A							,		__A							,		**__A	).prev_sample

								assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"



					def 	_lowercase					( self					:  List[str]	):
						pass



					def 	_lowercase					( self					:  str							,		__A					:  Union[str, Any]=0							,		**__A					:  Union[str, Any]	):
						snake_case__			:				str       =  dict(self.forward_default_kwargs	)
						snake_case__			:				int       =  kwargs.pop("num_inference_steps"							,		__A	)
						snake_case__			:				List[str]       =  self.dummy_sample
						snake_case__			:				Any       =  0.1 * sample
						snake_case__			:				int       =  [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

						for scheduler_class in self.scheduler_classes:
							snake_case__			:				List[str]       =  self.get_scheduler_config()
							snake_case__			:				Any       =  scheduler_class(**__A	)
							scheduler.set_timesteps(__A	)

							# copy over dummy past residuals (must be after setting timesteps)
							snake_case__			:				Dict       =  dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(__A	)
								snake_case__			:				str       =  scheduler_class.from_pretrained(__A	)
								# copy over dummy past residuals
								new_scheduler.set_timesteps(__A	)

								# copy over dummy past residual (must be after setting timesteps)
								snake_case__			:				Any       =  dummy_past_residuals[: new_scheduler.config.solver_order]

							snake_case__			:				List[Any]       =  scheduler.step(__A							,		__A							,		__A							,		**__A	).prev_sample
							snake_case__			:				List[Any]       =  new_scheduler.step(__A							,		__A							,		__A							,		**__A	).prev_sample

							assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"



					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  List[Any]=None							,		**__A					:  Union[str, Any]	):
						if scheduler is None:
							snake_case__			:				str       =  self.scheduler_classes[0]
							snake_case__			:				Union[str, Any]       =  self.get_scheduler_config(**__A	)
							snake_case__			:				str       =  scheduler_class(**__A	)

						snake_case__			:				Any       =  self.scheduler_classes[0]
						snake_case__			:				Any       =  self.get_scheduler_config(**__A	)
						snake_case__			:				str       =  scheduler_class(**__A	)

						snake_case__			:				int       =  1_0
						snake_case__			:				Union[str, Any]       =  self.dummy_model()
						snake_case__			:				int       =  self.dummy_sample_deter
						scheduler.set_timesteps(__A	)

						for i, t in enumerate(scheduler.timesteps	):
							snake_case__			:				int       =  model(__A							,		__A	)
							snake_case__			:				int       =  scheduler.step(__A							,		__A							,		__A	).prev_sample

						return sample



					def 	_lowercase					( self					:  List[str]	):
						snake_case__			:				Optional[int]       =  DPMSolverSinglestepScheduler(**self.get_scheduler_config()	)
						snake_case__			:				Tuple       =  5_0
						snake_case__			:				Optional[int]       =  self.dummy_model()
						snake_case__			:				Optional[Any]       =  self.dummy_sample_deter
						scheduler.set_timesteps(__A	)

						# make sure that the first t is uneven
						for i, t in enumerate(scheduler.timesteps[3:]	):
							snake_case__			:				int       =  model(__A							,		__A	)
							snake_case__			:				Any       =  scheduler.step(__A							,		__A							,		__A	).prev_sample

						snake_case__			:				str       =  torch.mean(torch.abs(__A	)	)

						assert abs(result_mean.item() - 0.2_5_7_4	) < 1e-3



					def 	_lowercase					( self					:  str	):
						for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=__A	)



					def 	_lowercase					( self					:  Tuple	):
						# make sure that iterating over schedulers with same config names gives same results
						# for defaults
						snake_case__			:				List[Any]       =  DPMSolverSinglestepScheduler(**self.get_scheduler_config()	)
						snake_case__			:				Any       =  self.full_loop(scheduler=__A	)
						snake_case__			:				Any       =  torch.mean(torch.abs(__A	)	)

						assert abs(result_mean.item() - 0.2_7_9_1	) < 1e-3

						snake_case__			:				Any       =  DEISMultistepScheduler.from_config(scheduler.config	)
						snake_case__			:				Any       =  DPMSolverMultistepScheduler.from_config(scheduler.config	)
						snake_case__			:				Tuple       =  UniPCMultistepScheduler.from_config(scheduler.config	)
						snake_case__			:				Dict       =  DPMSolverSinglestepScheduler.from_config(scheduler.config	)

						snake_case__			:				Tuple       =  self.full_loop(scheduler=__A	)
						snake_case__			:				Union[str, Any]       =  torch.mean(torch.abs(__A	)	)

						assert abs(result_mean.item() - 0.2_7_9_1	) < 1e-3



					def 	_lowercase					( self					:  List[str]	):
						self.check_over_configs(thresholding=__A	)
						for order in [1, 2, 3]:
							for solver_type in ["midpoint", "heun"]:
								for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "sample"]:
										self.check_over_configs(
										    thresholding=__A							,		prediction_type=__A							,		sample_max_value=__A							,		algorithm_type="dpmsolver++"							,		solver_order=__A							,		solver_type=__A							,		)



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



					def 	_lowercase					( self					:  List[str]	):
						for algorithm_type in ["dpmsolver", "dpmsolver++"]:
							for solver_type in ["midpoint", "heun"]:
								for order in [1, 2, 3]:
									for prediction_type in ["epsilon", "sample"]:
										self.check_over_configs(
										    solver_order=__A							,		solver_type=__A							,		prediction_type=__A							,		algorithm_type=__A							,		)
										snake_case__			:				Dict       =  self.full_loop(
										    solver_order=__A							,		solver_type=__A							,		prediction_type=__A							,		algorithm_type=__A							,		)
										assert not torch.isnan(__A	).any(), "Samples have nan numbers"



					def 	_lowercase					( self					:  int	):
						self.check_over_configs(lower_order_final=__A	)
						self.check_over_configs(lower_order_final=__A	)



					def 	_lowercase					( self					:  str	):
						self.check_over_configs(lambda_min_clipped=-float("inf"	)	)
						self.check_over_configs(lambda_min_clipped=-5.1	)



					def 	_lowercase					( self					:  Optional[Any]	):
						self.check_over_configs(variance_type=__A	)
						self.check_over_configs(variance_type="learned_range"	)



					def 	_lowercase					( self					:  Optional[Any]	):
						for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
							self.check_over_forward(num_inference_steps=__A							,		time_step=0	)



					def 	_lowercase					( self					:  Any	):
						snake_case__			:				int       =  self.full_loop()
						snake_case__			:				Optional[Any]       =  torch.mean(torch.abs(__A	)	)

						assert abs(result_mean.item() - 0.2_7_9_1	) < 1e-3



					def 	_lowercase					( self					:  str	):
						snake_case__			:				int       =  self.full_loop(use_karras_sigmas=__A	)
						snake_case__			:				List[str]       =  torch.mean(torch.abs(__A	)	)

						assert abs(result_mean.item() - 0.2_2_4_8	) < 1e-3



					def 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				Union[str, Any]       =  self.full_loop(prediction_type="v_prediction"	)
						snake_case__			:				int       =  torch.mean(torch.abs(__A	)	)

						assert abs(result_mean.item() - 0.1_4_5_3	) < 1e-3



					def 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				Dict       =  self.full_loop(prediction_type="v_prediction"							,		use_karras_sigmas=__A	)
						snake_case__			:				Dict       =  torch.mean(torch.abs(__A	)	)

						assert abs(result_mean.item() - 0.0_6_4_9	) < 1e-3






					def 	_lowercase					( self					:  Tuple	):
						snake_case__			:				Optional[int]       =  self.scheduler_classes[0]
						snake_case__			:				Union[str, Any]       =  self.get_scheduler_config(thresholding=__A							,		dynamic_thresholding_ratio=0	)
						snake_case__			:				int       =  scheduler_class(**__A	)

						snake_case__			:				str       =  1_0
						snake_case__			:				Optional[int]       =  self.dummy_model()
						snake_case__			:				str       =  self.dummy_sample_deter.half()
						scheduler.set_timesteps(__A	)

						for i, t in enumerate(scheduler.timesteps	):
							snake_case__			:				List[Any]       =  model(__A							,		__A	)
							snake_case__			:				List[Any]       =  scheduler.step(__A							,		__A							,		__A	).prev_sample

						assert sample.dtype == torch.floataa











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__						(    UpperCamelCase_			,       unittest.TestCase						):





					"""simple docstring"""






					a_     =		PhobertTokenizer
					a_     =		False



					def 	_lowercase					( self					:  List[str]	):
						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						snake_case__			:				Optional[int]       =  ["T@@", "i", "I", "R@@", "r", "e@@"]
						snake_case__			:				int       =  dict(zip(__A							,		range(len(__A	)	)	)	)
						snake_case__			:				Dict       =  ["#version: 0.2", "l à</w>"]
						snake_case__			:				Optional[Any]       =  {"unk_token": "<unk>"}

						snake_case__			:				Tuple       =  os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["vocab_file"]	)
						snake_case__			:				Union[str, Any]       =  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 	_lowercase					( self					:  List[str]							,		**__A					:  Any	):
						kwargs.update(self.special_tokens_map	)
						return PhobertTokenizer.from_pretrained(self.tmpdirname							,		**__A	)



					def 	_lowercase					( self					:  Tuple							,		__A					:  List[Any]	):
						snake_case__			:				str       =  "Tôi là VinAI Research"
						snake_case__			:				int       =  "T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
						return input_text, output_text






					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				int       =  PhobertTokenizer(self.vocab_file							,		self.merges_file							,		**self.special_tokens_map	)
						snake_case__			:				Tuple       =  "Tôi là VinAI Research"
						snake_case__			:				List[Any]       =  "T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
						snake_case__			:				int       =  tokenizer.tokenize(__A	)
						print(__A	)
						self.assertListEqual(__A							,		__A	)

						snake_case__			:				Any       =  tokens + [tokenizer.unk_token]

						snake_case__			:				Any       =  [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	)



