



"""simple docstring"""


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	(UnCLIPScheduler,)



			def  lowercase__						(		self     :    Optional[int]				,   **__UpperCamelCase     :    int   )->int:
							_UpperCAmelCase					=    {
							    '''num_train_timesteps''': 1_0_0_0,
							    '''variance_type''': '''fixed_small_log''',
							    '''clip_sample''': True,
							    '''clip_sample_range''': 1.0,
							    '''prediction_type''': '''epsilon''',
							}

							config.update(**__UpperCamelCase   )
							return config



			def  lowercase__						(		self     :    Dict   )->Optional[Any]:




							for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
											self.check_over_configs(num_train_timesteps=__UpperCamelCase   )



			def  lowercase__						(		self     :    Dict   )->str:




							for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=__UpperCamelCase   )



			def  lowercase__						(		self     :    str   )->Any:




							for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=__UpperCamelCase   )



			def  lowercase__						(		self     :    List[str]   )->str:




							for clip_sample_range in [1, 5, 1_0, 2_0]:
											self.check_over_configs(clip_sample_range=__UpperCamelCase   )



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




							for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(prediction_type=__UpperCamelCase   )



			def  lowercase__						(		self     :    List[Any]   )->Dict:




							for time_step in [0, 5_0_0, 9_9_9]:
											for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
															if prev_timestep is not None and prev_timestep >= time_step:
																			continue

															self.check_over_forward(time_step=__UpperCamelCase				,   prev_timestep=__UpperCamelCase   )



			def  lowercase__						(		self     :    List[Any]   )->Dict:
							_UpperCAmelCase					=    self.scheduler_classes[0]
							_UpperCAmelCase					=    self.get_scheduler_config(variance_type='''fixed_small_log'''   )
							_UpperCAmelCase					=    scheduler_class(**__UpperCamelCase   )

							assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.0_000e-10   )   ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_8_7   ) - 0.0_5_4_9_6_2_5   )   ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_9_9   ) - 0.9_9_9_4_9_8_7   )   ) < 1e-5



			def  lowercase__						(		self     :    Any   )->List[Any]:
							_UpperCAmelCase					=    self.scheduler_classes[0]
							_UpperCAmelCase					=    self.get_scheduler_config(variance_type='''learned_range'''   )
							_UpperCAmelCase					=    scheduler_class(**__UpperCamelCase   )

							_UpperCAmelCase					=    0.5

							assert scheduler._get_variance(1				,   predicted_variance=__UpperCamelCase   ) - -1_0.1_7_1_2_7_9_0 < 1e-5
							assert scheduler._get_variance(4_8_7				,   predicted_variance=__UpperCamelCase   ) - -5.7_9_9_8_0_5_2 < 1e-5
							assert scheduler._get_variance(9_9_9				,   predicted_variance=__UpperCamelCase   ) - -0.0_0_1_0_0_1_1 < 1e-5



			def  lowercase__						(		self     :    Union[str, Any]   )->List[Any]:
							_UpperCAmelCase					=    self.scheduler_classes[0]
							_UpperCAmelCase					=    self.get_scheduler_config()
							_UpperCAmelCase					=    scheduler_class(**__UpperCamelCase   )

							_UpperCAmelCase					=    scheduler.timesteps

							_UpperCAmelCase					=    self.dummy_model()
							_UpperCAmelCase					=    self.dummy_sample_deter
							_UpperCAmelCase					=    torch.manual_seed(0   )

							for i, t in enumerate(__UpperCamelCase   ):
											# 1. predict noise residual
											_UpperCAmelCase					=    model(__UpperCamelCase				,   __UpperCamelCase   )

											# 2. predict previous mean of sample x_t-1
											_UpperCAmelCase					=    scheduler.step(__UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase				,   generator=__UpperCamelCase   ).prev_sample

											_UpperCAmelCase					=    pred_prev_sample

							_UpperCAmelCase					=    torch.sum(torch.abs(__UpperCamelCase   )   )
							_UpperCAmelCase					=    torch.mean(torch.abs(__UpperCamelCase   )   )

							assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5   ) < 1e-2
							assert abs(result_mean.item() - 0.3_2_8_4_7_4_3   ) < 1e-3



			def  lowercase__						(		self     :    List[Any]   )->List[str]:
							_UpperCAmelCase					=    self.scheduler_classes[0]
							_UpperCAmelCase					=    self.get_scheduler_config()
							_UpperCAmelCase					=    scheduler_class(**__UpperCamelCase   )

							scheduler.set_timesteps(2_5   )

							_UpperCAmelCase					=    scheduler.timesteps

							_UpperCAmelCase					=    self.dummy_model()
							_UpperCAmelCase					=    self.dummy_sample_deter
							_UpperCAmelCase					=    torch.manual_seed(0   )

							for i, t in enumerate(__UpperCamelCase   ):
											# 1. predict noise residual
											_UpperCAmelCase					=    model(__UpperCamelCase				,   __UpperCamelCase   )

											if i + 1 == timesteps.shape[0]:
															_UpperCAmelCase					=    None
											else:
															_UpperCAmelCase					=    timesteps[i + 1]

											# 2. predict previous mean of sample x_t-1
											_UpperCAmelCase					=    scheduler.step(
											    __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase				,   prev_timestep=__UpperCamelCase				,   generator=__UpperCamelCase   ).prev_sample

											_UpperCAmelCase					=    pred_prev_sample

							_UpperCAmelCase					=    torch.sum(torch.abs(__UpperCamelCase   )   )
							_UpperCAmelCase					=    torch.mean(torch.abs(__UpperCamelCase   )   )

							assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3   ) < 1e-2
							assert abs(result_mean.item() - 0.3_3_6_2_0_3_8   ) < 1e-3



			def  lowercase__						(		self     :    Dict   )->Union[str, Any]:
							pass



			def  lowercase__						(		self     :    List[str]   )->Optional[int]:
							pass








"""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:
					__A   :							Tuple										=   None

__A   :							Optional[Any]										=   logging.get_logger(__name__)
__A   :							Union[str, Any]										=   {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__A   :							int										=   {
    "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",
    },
}

__A   :							Optional[int]										=   {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

__A   :							List[Any]										=   "▁"
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	VOCAB_FILES_NAMES
			UpperCamelCase__			      =	PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__			      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__			      =	["""input_ids""", """token_type_ids"""]
			UpperCamelCase__			      =	FNetTokenizer



			def __init__(		self     :    Optional[Any]				,   __UpperCamelCase     :    Tuple=None				,   __UpperCamelCase     :    Dict=None				,   __UpperCamelCase     :    List[str]=False				,   __UpperCamelCase     :    Union[str, Any]=True				,   __UpperCamelCase     :    List[str]=True				,   __UpperCamelCase     :    List[Any]="<unk>"				,   __UpperCamelCase     :    Any="[SEP]"				,   __UpperCamelCase     :    Dict="<pad>"				,   __UpperCamelCase     :    Optional[int]="[CLS]"				,   __UpperCamelCase     :    List[Any]="[MASK]"				,   **__UpperCamelCase     :    Optional[Any]				,   )->Dict:
							# Mask token behave like a normal word, i.e. include the space before it and
							# is included in the raw text, there should be a match in a non-normalized sentence.
							_UpperCAmelCase					=    (
							    AddedToken(__UpperCamelCase				,   lstrip=__UpperCamelCase				,   rstrip=__UpperCamelCase				,   normalized=__UpperCamelCase   )
							    if isinstance(__UpperCamelCase				,   __UpperCamelCase   )
							    else mask_token
							)

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

							_UpperCAmelCase					=    do_lower_case
							_UpperCAmelCase					=    remove_space
							_UpperCAmelCase					=    keep_accents
							_UpperCAmelCase					=    vocab_file
							_UpperCAmelCase					=    False if not self.vocab_file else True



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    Optional[List[int]] = None   )->List[int]:
							_UpperCAmelCase					=    [self.sep_token_id]
							_UpperCAmelCase					=    [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  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    Optional[List[int]] = None   )->List[int]:
							_UpperCAmelCase					=    [self.sep_token_id]
							_UpperCAmelCase					=    [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  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    Optional[str] = None   )->Tuple[str]:
							if not os.path.isdir(__UpperCamelCase   ):
											logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
											return
							_UpperCAmelCase					=    os.path.join(
							    __UpperCamelCase				,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

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

							return (out_vocab_file,)



