



"""simple docstring"""


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""
						__UpperCamelCase						=							(DDIMParallelScheduler,)
						__UpperCamelCase						=							(("""eta""", 0.0), ("""num_inference_steps""", 50))






						def 	UpperCAmelCase__     (					self :str   ,  **lowercase_ :Tuple					)				->			Optional[int]:
												UpperCAmelCase             = {
												    'num_train_timesteps': 10_00,
												    'beta_start': 0.0001,
												    'beta_end': 0.02,
												    'beta_schedule': 'linear',
												    'clip_sample': True,
												}

												config.update(**lowercase_					)
												return config






						def 	UpperCAmelCase__     (					self :Dict   ,  **lowercase_ :Optional[int]					)				->			str:
												UpperCAmelCase             = self.scheduler_classes[0]
												UpperCAmelCase             = self.get_scheduler_config(**lowercase_					)
												UpperCAmelCase             = scheduler_class(**lowercase_					)

												UpperCAmelCase      ,    UpperCAmelCase             = 10, 0.0

												UpperCAmelCase             = self.dummy_model()
												UpperCAmelCase             = self.dummy_sample_deter

												scheduler.set_timesteps(lowercase_					)

												for t in scheduler.timesteps:
																		UpperCAmelCase             = model(lowercase_   ,  lowercase_					)
																		UpperCAmelCase             = scheduler.step(lowercase_   ,  lowercase_   ,  lowercase_   ,  lowercase_					).prev_sample

												return sample






						def 	UpperCAmelCase__     (					self :str					)				->			List[str]:
												for timesteps in [1_00, 5_00, 10_00]:
																		self.check_over_configs(num_train_timesteps=lowercase_					)






						def 	UpperCAmelCase__     (					self :str					)				->			Union[str, Any]:
												for steps_offset in [0, 1]:
																		self.check_over_configs(steps_offset=lowercase_					)

												UpperCAmelCase             = self.scheduler_classes[0]
												UpperCAmelCase             = self.get_scheduler_config(steps_offset=1					)
												UpperCAmelCase             = scheduler_class(**lowercase_					)
												scheduler.set_timesteps(5					)
												assert torch.equal(scheduler.timesteps   ,  torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]					)					)






						def 	UpperCAmelCase__     (					self :str					)				->			str:
												for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,  [0.002, 0.02, 0.2, 2]					):
																		self.check_over_configs(beta_start=lowercase_   ,  beta_end=lowercase_					)






						def 	UpperCAmelCase__     (					self :Tuple					)				->			Union[str, Any]:
												for schedule in ["linear", "squaredcos_cap_v2"]:
																		self.check_over_configs(beta_schedule=lowercase_					)






						def 	UpperCAmelCase__     (					self :str					)				->			Dict:
												for prediction_type in ["epsilon", "v_prediction"]:
																		self.check_over_configs(prediction_type=lowercase_					)






						def 	UpperCAmelCase__     (					self :int					)				->			Optional[int]:
												for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=lowercase_					)






						def 	UpperCAmelCase__     (					self :Dict					)				->			List[str]:
												for timestep_spacing in ["trailing", "leading"]:
																		self.check_over_configs(timestep_spacing=lowercase_					)






						def 	UpperCAmelCase__     (					self :Tuple					)				->			Union[str, Any]:
												for rescale_betas_zero_snr in [True, False]:
																		self.check_over_configs(rescale_betas_zero_snr=lowercase_					)






						def 	UpperCAmelCase__     (					self :int					)				->			str:
												self.check_over_configs(thresholding=lowercase_					)
												for threshold in [0.5, 1.0, 2.0]:
																		for prediction_type in ["epsilon", "v_prediction"]:
																								self.check_over_configs(
																								    thresholding=lowercase_   ,  prediction_type=lowercase_   ,  sample_max_value=lowercase_   ,  )






						def 	UpperCAmelCase__     (					self :List[Any]					)				->			Union[str, Any]:
												for t in [1, 10, 49]:
																		self.check_over_forward(time_step=lowercase_					)






						def 	UpperCAmelCase__     (					self :str					)				->			Union[str, Any]:
												for t, num_inference_steps in zip([1, 10, 50]   ,  [10, 50, 5_00]					):
																		self.check_over_forward(time_step=lowercase_   ,  num_inference_steps=lowercase_					)






						def 	UpperCAmelCase__     (					self :Tuple					)				->			Union[str, Any]:
												for t, eta in zip([1, 10, 49]   ,  [0.0, 0.5, 1.0]					):
																		self.check_over_forward(time_step=lowercase_   ,  eta=lowercase_					)






						def 	UpperCAmelCase__     (					self :List[str]					)				->			Union[str, Any]:
												UpperCAmelCase             = self.scheduler_classes[0]
												UpperCAmelCase             = self.get_scheduler_config()
												UpperCAmelCase             = scheduler_class(**lowercase_					)

												assert torch.sum(torch.abs(scheduler._get_variance(0   ,  0					) - 0.0					)					) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(4_20   ,  4_00					) - 0.1_4771					)					) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_80   ,  9_60					) - 0.3_2460					)					) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(0   ,  0					) - 0.0					)					) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(4_87   ,  4_86					) - 0.0_0979					)					) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_99   ,  9_98					) - 0.02					)					) < 1E-5






						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			List[str]:
												UpperCAmelCase             = self.scheduler_classes[0]
												UpperCAmelCase             = self.get_scheduler_config()
												UpperCAmelCase             = scheduler_class(**lowercase_					)

												UpperCAmelCase      ,    UpperCAmelCase             = 10, 0.0
												scheduler.set_timesteps(lowercase_					)

												UpperCAmelCase             = self.dummy_model()
												UpperCAmelCase             = self.dummy_sample_deter
												UpperCAmelCase             = self.dummy_sample_deter + 0.1
												UpperCAmelCase             = self.dummy_sample_deter - 0.1

												UpperCAmelCase             = samplea.shape[0]
												UpperCAmelCase             = torch.stack([samplea, samplea, samplea]   ,  dim=0					)
												UpperCAmelCase             = torch.arange(lowercase_					)[0:3, None].repeat(1   ,  lowercase_					)

												UpperCAmelCase             = model(samples.flatten(0   ,  1					)   ,  timesteps.flatten(0   ,  1					)					)
												UpperCAmelCase             = scheduler.batch_step_no_noise(lowercase_   ,  timesteps.flatten(0   ,  1					)   ,  samples.flatten(0   ,  1					)   ,  lowercase_					)

												UpperCAmelCase             = torch.sum(torch.abs(lowercase_					)					)
												UpperCAmelCase             = torch.mean(torch.abs(lowercase_					)					)

												assert abs(result_sum.item() - 1147.7904					) < 1E-2
												assert abs(result_mean.item() - 0.4982					) < 1E-3






						def 	UpperCAmelCase__     (					self :str					)				->			List[str]:
												UpperCAmelCase             = self.full_loop()

												UpperCAmelCase             = torch.sum(torch.abs(lowercase_					)					)
												UpperCAmelCase             = torch.mean(torch.abs(lowercase_					)					)

												assert abs(result_sum.item() - 172.0067					) < 1E-2
												assert abs(result_mean.item() - 0.22_3967					) < 1E-3






						def 	UpperCAmelCase__     (					self :str					)				->			List[Any]:
												UpperCAmelCase             = self.full_loop(prediction_type='v_prediction'					)

												UpperCAmelCase             = torch.sum(torch.abs(lowercase_					)					)
												UpperCAmelCase             = torch.mean(torch.abs(lowercase_					)					)

												assert abs(result_sum.item() - 52.5302					) < 1E-2
												assert abs(result_mean.item() - 0.0684					) < 1E-3






						def 	UpperCAmelCase__     (					self :Optional[Any]					)				->			Any:
												# We specify different beta, so that the first alpha is 0.99
												UpperCAmelCase             = self.full_loop(set_alpha_to_one=lowercase_   ,  beta_start=0.01					)
												UpperCAmelCase             = torch.sum(torch.abs(lowercase_					)					)
												UpperCAmelCase             = torch.mean(torch.abs(lowercase_					)					)

												assert abs(result_sum.item() - 149.8295					) < 1E-2
												assert abs(result_mean.item() - 0.1951					) < 1E-3






						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			Any:
												# We specify different beta, so that the first alpha is 0.99
												UpperCAmelCase             = self.full_loop(set_alpha_to_one=lowercase_   ,  beta_start=0.01					)
												UpperCAmelCase             = torch.sum(torch.abs(lowercase_					)					)
												UpperCAmelCase             = torch.mean(torch.abs(lowercase_					)					)

												assert abs(result_sum.item() - 149.0784					) < 1E-2
												assert abs(result_mean.item() - 0.1941					) < 1E-3








from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase		:   Any										=       logging.get_logger(__name__)


_UpperCAmelCase		:   Dict										=       {
    """nielsr/canine-s""": 2048,
}

# Unicode defines 1,114,112 total “codepoints”
_UpperCAmelCase		:   Tuple										=       111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
_UpperCAmelCase		:   List[str]										=       0

_UpperCAmelCase		:   Any										=       0xE000
_UpperCAmelCase		:   Dict										=       0xE001
_UpperCAmelCase		:   Optional[int]										=       0xE002
_UpperCAmelCase		:   Tuple										=       0xE003
_UpperCAmelCase		:   Tuple										=       0xE004

# Maps special codepoints to human-readable names.
_UpperCAmelCase		:   Dict[int, str]										=       {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
_UpperCAmelCase		:   Dict[str, int]										=       {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: str       			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




				def __init__(					self      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=False      ,   snake_case=2048      ,   **snake_case      ,   ):
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else bos_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else eos_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else sep_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else cls_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else pad_token

											# Mask token behave like a normal word, i.e. include the space before it
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else mask_token

											super().__init__(
											    bos_token=snake_case      ,   eos_token=snake_case      ,   sep_token=snake_case      ,   cls_token=snake_case      ,   pad_token=snake_case      ,   mask_token=snake_case      ,   add_prefix_space=snake_case      ,   model_max_length=snake_case      ,   **snake_case      ,   )

											# Creates a mapping for looking up the IDs of special symbols.
											snake_case_							=    {}
											for codepoint, name in SPECIAL_CODEPOINTS.items():
																		snake_case_							=    codepoint

											# Creates a mapping for looking up the string forms of special symbol IDs.
											snake_case_							=    {
											    codepoint: name for name, codepoint in self._special_codepoints.items()
											}

											snake_case_							=    UNICODE_VOCAB_SIZE
											snake_case_							=    len(self._special_codepoints       )




				@property
				def     a							(					self       ):
											return self._unicode_vocab_size




				def     a							(					self      ,   snake_case       ):
											return list(snake_case       )




				def     a							(					self      ,   snake_case       ):
											try:
																		return ord(snake_case       )
											except TypeError:
																		raise ValueError(F'''invalid token: \'{token}\''''       )




				def     a							(					self      ,   snake_case       ):
											try:
																		if index in SPECIAL_CODEPOINTS:
																									return SPECIAL_CODEPOINTS[index]
																		return chr(snake_case       )
											except TypeError:
																		raise ValueError(F'''invalid id: {index}'''       )




				def     a							(					self      ,   snake_case       ):
											return "".join(snake_case       )




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											snake_case_							=    [self.sep_token_id]
											snake_case_							=    [self.cls_token_id]

											snake_case_							=    cls + token_ids_a + sep
											if token_ids_a is not None:
																		result += token_ids_a + sep
											return result




				def     a							(					self      ,   snake_case      ,   snake_case = None      ,   snake_case = False       ):
											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=snake_case      ,   token_ids_a=snake_case      ,   already_has_special_tokens=snake_case       )

											snake_case_							=    [1] + ([0] * len(snake_case       )) + [1]
											if token_ids_a is not None:
																		result += ([0] * len(snake_case       )) + [1]
											return result




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											snake_case_							=    [self.sep_token_id]
											snake_case_							=    [self.cls_token_id]

											snake_case_							=    len(cls + token_ids_a + sep       ) * [0]
											if token_ids_a is not None:
																		result += len(token_ids_a + sep       ) * [1]
											return result




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											return ()




