



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			(DPMSolverSinglestepScheduler,)
     _A       =			(('num_inference_steps', 25),)







     def 						_a      (self				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		{
               "num_train_timesteps": 10_00,
               "beta_start": 0.0001,
               "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(**__a    )
           return config







     def 						_a      (self				,			__a=0				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		dict(self.forward_default_kwargs    )
           lowerCamelCase								=		kwargs.pop("num_inference_steps"				,			__a    )
           lowerCamelCase								=		self.dummy_sample
           lowerCamelCase								=		0.1 * sample
           lowerCamelCase								=		[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

                 lowerCamelCase			,							lowerCamelCase								=		sample, sample
                 for t in range(__a				,			time_step + scheduler.config.solver_order + 1    ):
                       lowerCamelCase								=		scheduler.step(__a				,			__a				,			__a				,			**__a    ).prev_sample
                       lowerCamelCase								=		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 						_a      (self    ):






           '''simple docstring'''



           pass







     def 						_a      (self				,			__a=0				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		dict(self.forward_default_kwargs    )
           lowerCamelCase								=		kwargs.pop("num_inference_steps"				,			__a    )
           lowerCamelCase								=		self.dummy_sample
           lowerCamelCase								=		0.1 * sample
           lowerCamelCase								=		[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase								=		self.get_scheduler_config()
                 lowerCamelCase								=		scheduler_class(**__a    )
                 scheduler.set_timesteps(__a    )

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

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__a    )
                       lowerCamelCase								=		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)
                       lowerCamelCase								=		dummy_past_residuals[: new_scheduler.config.solver_order]

                 lowerCamelCase								=		scheduler.step(__a				,			__a				,			__a				,			**__a    ).prev_sample
                 lowerCamelCase								=		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 						_a      (self				,			__a=None				,			**__a    ):






           '''simple docstring'''



           if scheduler is None:
                 lowerCamelCase								=		self.scheduler_classes[0]
                 lowerCamelCase								=		self.get_scheduler_config(**__a    )
                 lowerCamelCase								=		scheduler_class(**__a    )

           lowerCamelCase								=		self.scheduler_classes[0]
           lowerCamelCase								=		self.get_scheduler_config(**__a    )
           lowerCamelCase								=		scheduler_class(**__a    )

           lowerCamelCase								=		10
           lowerCamelCase								=		self.dummy_model()
           lowerCamelCase								=		self.dummy_sample_deter
           scheduler.set_timesteps(__a    )

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

           return sample







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		DPMSolverSinglestepScheduler(**self.get_scheduler_config()    )
           lowerCamelCase								=		50
           lowerCamelCase								=		self.dummy_model()
           lowerCamelCase								=		self.dummy_sample_deter
           scheduler.set_timesteps(__a    )

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

           lowerCamelCase								=		torch.mean(torch.abs(__a    )    )

           assert abs(result_mean.item() - 0.2574    ) < 1E-3







     def 						_a      (self    ):






           '''simple docstring'''



           for timesteps in [25, 50, 1_00, 9_99, 10_00]:
                 self.check_over_configs(num_train_timesteps=__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		DPMSolverSinglestepScheduler(**self.get_scheduler_config()    )
           lowerCamelCase								=		self.full_loop(scheduler=__a    )
           lowerCamelCase								=		torch.mean(torch.abs(__a    )    )

           assert abs(result_mean.item() - 0.2791    ) < 1E-3

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

           lowerCamelCase								=		self.full_loop(scheduler=__a    )
           lowerCamelCase								=		torch.mean(torch.abs(__a    )    )

           assert abs(result_mean.item() - 0.2791    ) < 1E-3







     def 						_a      (self    ):






           '''simple docstring'''



           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 						_a      (self    ):






           '''simple docstring'''



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







     def 						_a      (self    ):






           '''simple docstring'''



           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				,			)
                                   lowerCamelCase								=		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 						_a      (self    ):






           '''simple docstring'''



           self.check_over_configs(lower_order_final=__a    )
           self.check_over_configs(lower_order_final=__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           self.check_over_configs(lambda_min_clipped=-float("inf"    )    )
           self.check_over_configs(lambda_min_clipped=-5.1    )







     def 						_a      (self    ):






           '''simple docstring'''



           self.check_over_configs(variance_type=__a    )
           self.check_over_configs(variance_type="learned_range"    )







     def 						_a      (self    ):






           '''simple docstring'''



           for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
                 self.check_over_forward(num_inference_steps=__a				,			time_step=0    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.full_loop()
           lowerCamelCase								=		torch.mean(torch.abs(__a    )    )

           assert abs(result_mean.item() - 0.2791    ) < 1E-3







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.full_loop(use_karras_sigmas=__a    )
           lowerCamelCase								=		torch.mean(torch.abs(__a    )    )

           assert abs(result_mean.item() - 0.2248    ) < 1E-3







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.full_loop(prediction_type="v_prediction"    )
           lowerCamelCase								=		torch.mean(torch.abs(__a    )    )

           assert abs(result_mean.item() - 0.1453    ) < 1E-3







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.full_loop(prediction_type="v_prediction"				,			use_karras_sigmas=__a    )
           lowerCamelCase								=		torch.mean(torch.abs(__a    )    )

           assert abs(result_mean.item() - 0.0649    ) < 1E-3







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.scheduler_classes[0]
           lowerCamelCase								=		self.get_scheduler_config(thresholding=__a				,			dynamic_thresholding_ratio=0    )
           lowerCamelCase								=		scheduler_class(**__a    )

           lowerCamelCase								=		10
           lowerCamelCase								=		self.dummy_model()
           lowerCamelCase								=		self.dummy_sample_deter.half()
           scheduler.set_timesteps(__a    )

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

           assert sample.dtype == torch.floataa




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a_						:     Optional[int]	     =		logging.get_logger(__name__)

a_						:     Any	     =		{'vocab_file': 'spiece.model'}

a_						:     List[Any]	     =		{
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

a_						:     List[Any]	     =		{'bert_for_seq_generation': 5_1_2}





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			VOCAB_FILES_NAMES
     _A       =			PRETRAINED_VOCAB_FILES_MAP
     _A       =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A       =			[]
     _A       =			['input_ids', 'attention_mask']







     def __init__(self				,			__a				,			__a="<s>"				,			__a="</s>"				,			__a="<unk>"				,			__a="<pad>"				,			__a="<::::>"				,			__a = None				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		{} if sp_model_kwargs is None else sp_model_kwargs

           # Add extra_ids to the special token list
           super().__init__(
               bos_token=__a				,			eos_token=__a				,			unk_token=__a				,			pad_token=__a				,			sep_token=__a				,			sp_model_kwargs=self.sp_model_kwargs				,			**__a				,			)

           lowerCamelCase								=		vocab_file

           lowerCamelCase								=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(__a    )







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           return self.sp_model.get_piece_size()







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		{self.convert_ids_to_tokens(__a    ): i for i in range(self.vocab_size    )}
           vocab.update(self.added_tokens_encoder    )
           return vocab







     def __getstate__(self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.__dict__.copy()
           lowerCamelCase								=		None
           return state







     def __setstate__(self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		d

           # for backward compatibility
           if not hasattr(self				,			"sp_model_kwargs"    ):
                 lowerCamelCase								=		{}

           lowerCamelCase								=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(self.vocab_file    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return self.sp_model.encode(__a				,			out_type=__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return self.sp_model.piece_to_id(__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		self.sp_model.IdToPiece(__a    )
           return token







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		[]
           lowerCamelCase								=		""
           for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                       out_string += self.sp_model.decode(__a    ) + token
                       lowerCamelCase								=		[]
                 else:
                       current_sub_tokens.append(__a    )
           out_string += self.sp_model.decode(__a    )
           return out_string.strip()







     def 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



           if not os.path.isdir(__a    ):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
                 return
           lowerCamelCase								=		os.path.join(
               __a				,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

           if os.path.abspath(self.vocab_file    ) != os.path.abspath(__a    ) and os.path.isfile(self.vocab_file    ):
                 copyfile(self.vocab_file				,			__a    )
           elif not os.path.isfile(self.vocab_file    ):
                 with open(__a				,			"wb"    ) as fi:
                       lowerCamelCase								=		self.sp_model.serialized_model_proto()
                       fi.write(__a    )

           return (out_vocab_file,)