


'''simple docstring'''


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Optional[int]				      =   (DEISMultistepScheduler,)
			lowerCAmelCase       :		str				      =   (("num_inference_steps", 25),)




			def 							lowerCAmelCase__			(						self     :   Optional[Any]		,     **lowerCamelCase__     :   List[Any]       )      ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, Any]	    =					{
					    "num_train_timesteps": 10_00,
					    "beta_start": 0.0_0_0_1,
					    "beta_end": 0.0_2,
					    "beta_schedule": "linear",
					    "solver_order": 2,
					}

					config.update(**lowerCamelCase__       )
					return config




			def 							lowerCAmelCase__			(						self     :   Tuple		,     lowerCamelCase__     :   Optional[int]=0		,     **lowerCamelCase__     :   Any       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					dict(self.forward_default_kwargs       )
					_UpperCAmelCase						:				Optional[int]	    =					kwargs.pop("num_inference_steps"		,     lowerCamelCase__       )
					_UpperCAmelCase						:				str	    =					self.dummy_sample
					_UpperCAmelCase						:				Union[str, Any]	    =					0.1 * sample
					_UpperCAmelCase						:				List[Any]	    =					[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

							with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(lowerCamelCase__       )
									_UpperCAmelCase						:				Union[str, Any]	    =					scheduler_class.from_pretrained(lowerCamelCase__       )
									new_scheduler.set_timesteps(lowerCamelCase__       )
									# copy over dummy past residuals
									_UpperCAmelCase						:				Optional[int]	    =					dummy_past_residuals[: new_scheduler.config.solver_order]

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

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




			def 							lowerCAmelCase__			(						self     :   str       )      ->List[Any]:



					'''simple docstring'''







					pass




			def 							lowerCAmelCase__			(						self     :   Optional[Any]		,     lowerCamelCase__     :   str=0		,     **lowerCamelCase__     :   Tuple       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					dict(self.forward_default_kwargs       )
					_UpperCAmelCase						:				List[Any]	    =					kwargs.pop("num_inference_steps"		,     lowerCamelCase__       )
					_UpperCAmelCase						:				Any	    =					self.dummy_sample
					_UpperCAmelCase						:				str	    =					0.1 * sample
					_UpperCAmelCase						:				Optional[Any]	    =					[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

					for scheduler_class in self.scheduler_classes:
							_UpperCAmelCase						:				Optional[Any]	    =					self.get_scheduler_config()
							_UpperCAmelCase						:				int	    =					scheduler_class(**lowerCamelCase__       )
							scheduler.set_timesteps(lowerCamelCase__       )

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

							with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(lowerCamelCase__       )
									_UpperCAmelCase						:				Optional[Any]	    =					scheduler_class.from_pretrained(lowerCamelCase__       )
									# copy over dummy past residuals
									new_scheduler.set_timesteps(lowerCamelCase__       )

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

							_UpperCAmelCase						:				Optional[Any]	    =					scheduler.step(lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__		,     **lowerCamelCase__       ).prev_sample
							_UpperCAmelCase						:				Any	    =					new_scheduler.step(lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__		,     **lowerCamelCase__       ).prev_sample

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




			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   Dict=None		,     **lowerCamelCase__     :   List[str]       )      ->Any:



					'''simple docstring'''







					if scheduler is None:
							_UpperCAmelCase						:				Tuple	    =					self.scheduler_classes[0]
							_UpperCAmelCase						:				Optional[Any]	    =					self.get_scheduler_config(**lowerCamelCase__       )
							_UpperCAmelCase						:				Tuple	    =					scheduler_class(**lowerCamelCase__       )

					_UpperCAmelCase						:				Optional[Any]	    =					self.scheduler_classes[0]
					_UpperCAmelCase						:				Any	    =					self.get_scheduler_config(**lowerCamelCase__       )
					_UpperCAmelCase						:				List[Any]	    =					scheduler_class(**lowerCamelCase__       )

					_UpperCAmelCase						:				List[Any]	    =					10
					_UpperCAmelCase						:				int	    =					self.dummy_model()
					_UpperCAmelCase						:				Dict	    =					self.dummy_sample_deter
					scheduler.set_timesteps(lowerCamelCase__       )

					for i, t in enumerate(scheduler.timesteps       ):
							_UpperCAmelCase						:				Dict	    =					model(lowerCamelCase__		,     lowerCamelCase__       )
							_UpperCAmelCase						:				int	    =					scheduler.step(lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__       ).prev_sample

					return sample




			def 							lowerCAmelCase__			(						self     :   str       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					dict(self.forward_default_kwargs       )

					_UpperCAmelCase						:				int	    =					kwargs.pop("num_inference_steps"		,     lowerCamelCase__       )

					for scheduler_class in self.scheduler_classes:
							_UpperCAmelCase						:				Optional[int]	    =					self.get_scheduler_config()
							_UpperCAmelCase						:				Any	    =					scheduler_class(**lowerCamelCase__       )

							_UpperCAmelCase						:				str	    =					self.dummy_sample
							_UpperCAmelCase						:				str	    =					0.1 * sample

							if num_inference_steps is not None and hasattr(lowerCamelCase__		,     "set_timesteps"       ):
									scheduler.set_timesteps(lowerCamelCase__       )
							elif num_inference_steps is not None and not hasattr(lowerCamelCase__		,     "set_timesteps"       ):
									_UpperCAmelCase						:				Optional[Any]	    =					num_inference_steps

							# copy over dummy past residuals (must be done after set_timesteps)
							_UpperCAmelCase						:				Optional[int]	    =					[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
							_UpperCAmelCase						:				str	    =					dummy_past_residuals[: scheduler.config.solver_order]

							_UpperCAmelCase						:				Optional[Any]	    =					scheduler.timesteps[5]
							_UpperCAmelCase						:				str	    =					scheduler.timesteps[6]

							_UpperCAmelCase						:				str	    =					scheduler.step(lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__		,     **lowerCamelCase__       ).prev_sample
							_UpperCAmelCase						:				str	    =					scheduler.step(lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__		,     **lowerCamelCase__       ).prev_sample

							self.assertEqual(output_a.shape		,     sample.shape       )
							self.assertEqual(output_a.shape		,     output_a.shape       )




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



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					DEISMultistepScheduler(**self.get_scheduler_config()       )
					_UpperCAmelCase						:				Optional[Any]	    =					self.full_loop(scheduler=lowerCamelCase__       )
					_UpperCAmelCase						:				Dict	    =					torch.mean(torch.abs(lowerCamelCase__       )       )

					assert abs(result_mean.item() - 0.2_3_9_1_6       ) < 1E-3

					_UpperCAmelCase						:				Union[str, Any]	    =					DPMSolverSinglestepScheduler.from_config(scheduler.config       )
					_UpperCAmelCase						:				Optional[int]	    =					DPMSolverMultistepScheduler.from_config(scheduler.config       )
					_UpperCAmelCase						:				List[str]	    =					UniPCMultistepScheduler.from_config(scheduler.config       )
					_UpperCAmelCase						:				Optional[int]	    =					DEISMultistepScheduler.from_config(scheduler.config       )

					_UpperCAmelCase						:				Optional[int]	    =					self.full_loop(scheduler=lowerCamelCase__       )
					_UpperCAmelCase						:				Dict	    =					torch.mean(torch.abs(lowerCamelCase__       )       )

					assert abs(result_mean.item() - 0.2_3_9_1_6       ) < 1E-3




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



					'''simple docstring'''







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




			def 							lowerCAmelCase__			(						self     :   int       )      ->Optional[Any]:



					'''simple docstring'''







					self.check_over_configs(thresholding=lowerCamelCase__       )
					for order in [1, 2, 3]:
							for solver_type in ["logrho"]:
									for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample"]:
													self.check_over_configs(
													    thresholding=lowerCamelCase__		,     prediction_type=lowerCamelCase__		,     sample_max_value=lowerCamelCase__		,     algorithm_type="deis"		,     solver_order=lowerCamelCase__		,     solver_type=lowerCamelCase__		,     )




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



					'''simple docstring'''







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




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



					'''simple docstring'''







					for algorithm_type in ["deis"]:
							for solver_type in ["logrho"]:
									for order in [1, 2, 3]:
											for prediction_type in ["epsilon", "sample"]:
													self.check_over_configs(
													    solver_order=lowerCamelCase__		,     solver_type=lowerCamelCase__		,     prediction_type=lowerCamelCase__		,     algorithm_type=lowerCamelCase__		,     )
													_UpperCAmelCase						:				List[str]	    =					self.full_loop(
													    solver_order=lowerCamelCase__		,     solver_type=lowerCamelCase__		,     prediction_type=lowerCamelCase__		,     algorithm_type=lowerCamelCase__		,     )
													assert not torch.isnan(lowerCamelCase__       ).any(), "Samples have nan numbers"




			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->Any:



					'''simple docstring'''







					self.check_over_configs(lower_order_final=lowerCamelCase__       )
					self.check_over_configs(lower_order_final=lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   List[str]       )      ->Tuple:



					'''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=lowerCamelCase__		,     time_step=0       )




			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					self.full_loop()
					_UpperCAmelCase						:				Union[str, Any]	    =					torch.mean(torch.abs(lowerCamelCase__       )       )

					assert abs(result_mean.item() - 0.2_3_9_1_6       ) < 1E-3




			def 							lowerCAmelCase__			(						self     :   Tuple       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				int	    =					self.full_loop(prediction_type="v_prediction"       )
					_UpperCAmelCase						:				Optional[int]	    =					torch.mean(torch.abs(lowerCamelCase__       )       )

					assert abs(result_mean.item() - 0.0_9_1       ) < 1E-3






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



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					self.scheduler_classes[0]
					_UpperCAmelCase						:				List[Any]	    =					self.get_scheduler_config(thresholding=lowerCamelCase__		,     dynamic_thresholding_ratio=0       )
					_UpperCAmelCase						:				int	    =					scheduler_class(**lowerCamelCase__       )

					_UpperCAmelCase						:				Optional[int]	    =					10
					_UpperCAmelCase						:				List[str]	    =					self.dummy_model()
					_UpperCAmelCase						:				Optional[int]	    =					self.dummy_sample_deter.half()
					scheduler.set_timesteps(lowerCamelCase__       )

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

					assert sample.dtype == torch.floataa







'''simple docstring'''


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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_fnet import FNetTokenizer
else:
						lowerCamelCase__					=   None

lowerCamelCase__					=   logging.get_logger(__name__)
lowerCamelCase__					=   {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase__					=   {
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

lowerCamelCase__					=   {
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

lowerCamelCase__					=   '▁'






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Dict				      =   VOCAB_FILES_NAMES
			lowerCAmelCase       :		Optional[Any]				      =   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase       :		List[str]				      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase       :		Optional[int]				      =   ["input_ids", "token_type_ids"]
			lowerCAmelCase       :		Optional[Any]				      =   FNetTokenizer




			def __init__(						self     :   Dict		,     lowerCamelCase__     :   int=None		,     lowerCamelCase__     :   List[Any]=None		,     lowerCamelCase__     :   Any=False		,     lowerCamelCase__     :   List[str]=True		,     lowerCamelCase__     :   Dict=True		,     lowerCamelCase__     :   str="<unk>"		,     lowerCamelCase__     :   List[str]="[SEP]"		,     lowerCamelCase__     :   Union[str, Any]="<pad>"		,     lowerCamelCase__     :   Optional[Any]="[CLS]"		,     lowerCamelCase__     :   Any="[MASK]"		,     **lowerCamelCase__     :   Any		,     )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					(
					    AddedToken(lowerCamelCase__		,     lstrip=lowerCamelCase__		,     rstrip=lowerCamelCase__		,     normalized=lowerCamelCase__       )
					    if isinstance(lowerCamelCase__		,     lowerCamelCase__       )
					    else mask_token
					)

					super().__init__(
					    lowerCamelCase__		,     tokenizer_file=lowerCamelCase__		,     do_lower_case=lowerCamelCase__		,     remove_space=lowerCamelCase__		,     keep_accents=lowerCamelCase__		,     unk_token=lowerCamelCase__		,     sep_token=lowerCamelCase__		,     pad_token=lowerCamelCase__		,     cls_token=lowerCamelCase__		,     mask_token=lowerCamelCase__		,     **lowerCamelCase__		,     )

					_UpperCAmelCase						:				Optional[Any]	    =					do_lower_case
					_UpperCAmelCase						:				Tuple	    =					remove_space
					_UpperCAmelCase						:				List[Any]	    =					keep_accents
					_UpperCAmelCase						:				Tuple	    =					vocab_file
					_UpperCAmelCase						:				str	    =					False if not self.vocab_file else True




			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   List[int]		,     lowerCamelCase__     :   Optional[List[int]] = None       )      ->List[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					[self.sep_token_id]
					_UpperCAmelCase						:				Tuple	    =					[self.cls_token_id]
					if token_ids_a is None:
							return cls + token_ids_a + sep
					return cls + token_ids_a + sep + token_ids_a + sep




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   List[int]		,     lowerCamelCase__     :   Optional[List[int]] = None       )      ->List[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					[self.sep_token_id]
					_UpperCAmelCase						:				Optional[Any]	    =					[self.cls_token_id]

					if token_ids_a is None:
							return len(cls + token_ids_a + sep       ) * [0]
					return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]






			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Optional[str] = None       )      ->Tuple[str]:



					'''simple docstring'''







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

					if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowerCamelCase__       ):
							copyfile(self.vocab_file		,     lowerCamelCase__       )

					return (out_vocab_file,)



