

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				List[str]     =    (DPMSolverSinglestepScheduler,)
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    (('''num_inference_steps''', 25),)




				def 					a							(      self   ,     **snake_case ):
											snake_case_           =					{
											    'num_train_timesteps': 1000,
											    'beta_start': 0.00_01,
											    'beta_end': 0.02,
											    '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(**snake_case )
											return config




				def 					a							(      self   ,     snake_case=0   ,     **snake_case ):
											snake_case_           =					dict(self.forward_default_kwargs )
											snake_case_           =					kwargs.pop('num_inference_steps'   ,     snake_case )
											snake_case_           =					self.dummy_sample
											snake_case_           =					0.1 * sample
											snake_case_           =					[residual + 0.2, residual + 0.15, residual + 0.10]

											for scheduler_class in self.scheduler_classes:
																		snake_case_           =					self.get_scheduler_config(**snake_case )
																		snake_case_           =					scheduler_class(**snake_case )
																		scheduler.set_timesteps(snake_case )
																		# copy over dummy past residuals
																		snake_case_           =					dummy_past_residuals[: scheduler.config.solver_order]

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

																		snake_case_       ,					snake_case_           =					sample, sample
																		for t in range(snake_case   ,     time_step + scheduler.config.solver_order + 1 ):
																									snake_case_           =					scheduler.step(snake_case   ,     snake_case   ,     snake_case   ,     **snake_case ).prev_sample
																									snake_case_           =					new_scheduler.step(snake_case   ,     snake_case   ,     snake_case   ,     **snake_case ).prev_sample

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




				def 					a							(      self ):
											pass




				def 					a							(      self   ,     snake_case=0   ,     **snake_case ):
											snake_case_           =					dict(self.forward_default_kwargs )
											snake_case_           =					kwargs.pop('num_inference_steps'   ,     snake_case )
											snake_case_           =					self.dummy_sample
											snake_case_           =					0.1 * sample
											snake_case_           =					[residual + 0.2, residual + 0.15, residual + 0.10]

											for scheduler_class in self.scheduler_classes:
																		snake_case_           =					self.get_scheduler_config()
																		snake_case_           =					scheduler_class(**snake_case )
																		scheduler.set_timesteps(snake_case )

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

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

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

																		snake_case_           =					scheduler.step(snake_case   ,     snake_case   ,     snake_case   ,     **snake_case ).prev_sample
																		snake_case_           =					new_scheduler.step(snake_case   ,     snake_case   ,     snake_case   ,     **snake_case ).prev_sample

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




				def 					a							(      self   ,     snake_case=None   ,     **snake_case ):
											if scheduler is None:
																		snake_case_           =					self.scheduler_classes[0]
																		snake_case_           =					self.get_scheduler_config(**snake_case )
																		snake_case_           =					scheduler_class(**snake_case )

											snake_case_           =					self.scheduler_classes[0]
											snake_case_           =					self.get_scheduler_config(**snake_case )
											snake_case_           =					scheduler_class(**snake_case )

											snake_case_           =					10
											snake_case_           =					self.dummy_model()
											snake_case_           =					self.dummy_sample_deter
											scheduler.set_timesteps(snake_case )

											for i, t in enumerate(scheduler.timesteps ):
																		snake_case_           =					model(snake_case   ,     snake_case )
																		snake_case_           =					scheduler.step(snake_case   ,     snake_case   ,     snake_case ).prev_sample

											return sample




				def 					a							(      self ):
											snake_case_           =					DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
											snake_case_           =					50
											snake_case_           =					self.dummy_model()
											snake_case_           =					self.dummy_sample_deter
											scheduler.set_timesteps(snake_case )

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

											snake_case_           =					torch.mean(torch.abs(snake_case ) )

											assert abs(result_mean.item() - 0.25_74 ) < 1e-3




				def 					a							(      self ):
											for timesteps in [25, 50, 100, 999, 1000]:
																		self.check_over_configs(num_train_timesteps=snake_case )




				def 					a							(      self ):
											# make sure that iterating over schedulers with same config names gives same results
											# for defaults
											snake_case_           =					DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
											snake_case_           =					self.full_loop(scheduler=snake_case )
											snake_case_           =					torch.mean(torch.abs(snake_case ) )

											assert abs(result_mean.item() - 0.27_91 ) < 1e-3

											snake_case_           =					DEISMultistepScheduler.from_config(scheduler.config )
											snake_case_           =					DPMSolverMultistepScheduler.from_config(scheduler.config )
											snake_case_           =					UniPCMultistepScheduler.from_config(scheduler.config )
											snake_case_           =					DPMSolverSinglestepScheduler.from_config(scheduler.config )

											snake_case_           =					self.full_loop(scheduler=snake_case )
											snake_case_           =					torch.mean(torch.abs(snake_case ) )

											assert abs(result_mean.item() - 0.27_91 ) < 1e-3




				def 					a							(      self ):
											self.check_over_configs(thresholding=snake_case )
											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=snake_case   ,     prediction_type=snake_case   ,     sample_max_value=snake_case   ,     algorithm_type='dpmsolver++'   ,     solver_order=snake_case   ,     solver_type=snake_case   ,     )




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




				def 					a							(      self ):
											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=snake_case   ,     solver_type=snake_case   ,     prediction_type=snake_case   ,     algorithm_type=snake_case   ,     )
																																							snake_case_           =					self.full_loop(
																																							    solver_order=snake_case   ,     solver_type=snake_case   ,     prediction_type=snake_case   ,     algorithm_type=snake_case   ,     )
																																							assert not torch.isnan(snake_case ).any(), "Samples have nan numbers"




				def 					a							(      self ):
											self.check_over_configs(lower_order_final=snake_case )
											self.check_over_configs(lower_order_final=snake_case )




				def 					a							(      self ):
											self.check_over_configs(lambda_min_clipped=-float('inf' ) )
											self.check_over_configs(lambda_min_clipped=-5.1 )




				def 					a							(      self ):
											self.check_over_configs(variance_type=snake_case )
											self.check_over_configs(variance_type='learned_range' )




				def 					a							(      self ):
											for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
																		self.check_over_forward(num_inference_steps=snake_case   ,     time_step=0 )




				def 					a							(      self ):
											snake_case_           =					self.full_loop()
											snake_case_           =					torch.mean(torch.abs(snake_case ) )

											assert abs(result_mean.item() - 0.27_91 ) < 1e-3




				def 					a							(      self ):
											snake_case_           =					self.full_loop(use_karras_sigmas=snake_case )
											snake_case_           =					torch.mean(torch.abs(snake_case ) )

											assert abs(result_mean.item() - 0.22_48 ) < 1e-3




				def 					a							(      self ):
											snake_case_           =					self.full_loop(prediction_type='v_prediction' )
											snake_case_           =					torch.mean(torch.abs(snake_case ) )

											assert abs(result_mean.item() - 0.14_53 ) < 1e-3




				def 					a							(      self ):
											snake_case_           =					self.full_loop(prediction_type='v_prediction'   ,     use_karras_sigmas=snake_case )
											snake_case_           =					torch.mean(torch.abs(snake_case ) )

											assert abs(result_mean.item() - 0.06_49 ) < 1e-3




				def 					a							(      self ):
											snake_case_           =					self.scheduler_classes[0]
											snake_case_           =					self.get_scheduler_config(thresholding=snake_case   ,     dynamic_thresholding_ratio=0 )
											snake_case_           =					scheduler_class(**snake_case )

											snake_case_           =					10
											snake_case_           =					self.dummy_model()
											snake_case_           =					self.dummy_sample_deter.half()
											scheduler.set_timesteps(snake_case )

											for i, t in enumerate(scheduler.timesteps ):
																		snake_case_           =					model(snake_case   ,     snake_case )
																		snake_case_           =					scheduler.step(snake_case   ,     snake_case   ,     snake_case ).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 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				int     =    PhobertTokenizer
				__SCREAMING_SNAKE_CASE      :				int     =    False




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

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

											snake_case_           =					os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES['vocab_file'] )
											snake_case_           =					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(snake_case ) )




				def 					a							(      self   ,     **snake_case ):
											kwargs.update(self.special_tokens_map )
											return PhobertTokenizer.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self   ,     snake_case ):
											snake_case_           =					'Tôi là VinAI Research'
											snake_case_           =					'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
											return input_text, output_text




				def 					a							(      self ):
											snake_case_           =					PhobertTokenizer(self.vocab_file   ,     self.merges_file   ,     **self.special_tokens_map )
											snake_case_           =					'Tôi là VinAI Research'
											snake_case_           =					'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
											snake_case_           =					tokenizer.tokenize(snake_case )
											print(snake_case )
											self.assertListEqual(snake_case   ,     snake_case )

											snake_case_           =					tokens + [tokenizer.unk_token]

											snake_case_           =					[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(snake_case )   ,     snake_case )




