


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  int					=							(EulerDiscreteScheduler,)
						SCREAMING_SNAKE_CASE__      :  Optional[Any]					=							10




						def 						lowercase_ (				self   ,						**__lowercase							)					->    Tuple:
										lowerCAmelCase_ :      Optional[Any]    				=				{
										    '''num_train_timesteps''': 1_1_0_0,
										    '''beta_start''': 0.00_01,
										    '''beta_end''': 0.02,
										    '''beta_schedule''': '''linear''',
										}

										config.update(**__lowercase							)
										return config




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
														self.check_over_configs(num_train_timesteps=__lowercase							)




						def 						lowercase_ (				self							)					->    str:
										for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]   ,						[0.00_02, 0.0_02, 0.02]							):
														self.check_over_configs(beta_start=__lowercase   ,						beta_end=__lowercase							)




						def 						lowercase_ (				self							)					->    List[str]:
										for schedule in ["linear", "scaled_linear"]:
														self.check_over_configs(beta_schedule=__lowercase							)




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




						def 						lowercase_ (				self							)					->    Optional[Any]:
										lowerCAmelCase_ :      str    				=				self.scheduler_classes[0]
										lowerCAmelCase_ :      int    				=				self.get_scheduler_config()
										lowerCAmelCase_ :      int    				=				scheduler_class(**__lowercase							)

										scheduler.set_timesteps(self.num_inference_steps							)

										lowerCAmelCase_ :      List[Any]    				=				torch.manual_seed(0							)

										lowerCAmelCase_ :      List[str]    				=				self.dummy_model()
										lowerCAmelCase_ :      Tuple    				=				self.dummy_sample_deter * scheduler.init_noise_sigma
										lowerCAmelCase_ :      List[Any]    				=				sample.to(__lowercase							)

										for i, t in enumerate(scheduler.timesteps							):
														lowerCAmelCase_ :      Dict    				=				scheduler.scale_model_input(__lowercase   ,						__lowercase							)

														lowerCAmelCase_ :      str    				=				model(__lowercase   ,						__lowercase							)

														lowerCAmelCase_ :      str    				=				scheduler.step(__lowercase   ,						__lowercase   ,						__lowercase   ,						generator=__lowercase							)
														lowerCAmelCase_ :      List[str]    				=				output.prev_sample

										lowerCAmelCase_ :      Any    				=				torch.sum(torch.abs(__lowercase							)							)
										lowerCAmelCase_ :      Optional[int]    				=				torch.mean(torch.abs(__lowercase							)							)

										assert abs(result_sum.item() - 10.08_07							) < 1e-2
										assert abs(result_mean.item() - 0.01_31							) < 1e-3




						def 						lowercase_ (				self							)					->    Dict:
										lowerCAmelCase_ :      str    				=				self.scheduler_classes[0]
										lowerCAmelCase_ :      Union[str, Any]    				=				self.get_scheduler_config(prediction_type='''v_prediction'''							)
										lowerCAmelCase_ :      Tuple    				=				scheduler_class(**__lowercase							)

										scheduler.set_timesteps(self.num_inference_steps							)

										lowerCAmelCase_ :      Tuple    				=				torch.manual_seed(0							)

										lowerCAmelCase_ :      int    				=				self.dummy_model()
										lowerCAmelCase_ :      Optional[int]    				=				self.dummy_sample_deter * scheduler.init_noise_sigma
										lowerCAmelCase_ :      List[str]    				=				sample.to(__lowercase							)

										for i, t in enumerate(scheduler.timesteps							):
														lowerCAmelCase_ :      List[Any]    				=				scheduler.scale_model_input(__lowercase   ,						__lowercase							)

														lowerCAmelCase_ :      int    				=				model(__lowercase   ,						__lowercase							)

														lowerCAmelCase_ :      str    				=				scheduler.step(__lowercase   ,						__lowercase   ,						__lowercase   ,						generator=__lowercase							)
														lowerCAmelCase_ :      str    				=				output.prev_sample

										lowerCAmelCase_ :      int    				=				torch.sum(torch.abs(__lowercase							)							)
										lowerCAmelCase_ :      Any    				=				torch.mean(torch.abs(__lowercase							)							)

										assert abs(result_sum.item() - 0.00_02							) < 1e-2
										assert abs(result_mean.item() - 2.2_676e-06							) < 1e-3




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										lowerCAmelCase_ :      Dict    				=				self.scheduler_classes[0]
										lowerCAmelCase_ :      Union[str, Any]    				=				self.get_scheduler_config()
										lowerCAmelCase_ :      Tuple    				=				scheduler_class(**__lowercase							)

										scheduler.set_timesteps(self.num_inference_steps   ,						device=__lowercase							)

										lowerCAmelCase_ :      Tuple    				=				torch.manual_seed(0							)

										lowerCAmelCase_ :      Tuple    				=				self.dummy_model()
										lowerCAmelCase_ :      int    				=				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										lowerCAmelCase_ :      Optional[Any]    				=				sample.to(__lowercase							)

										for t in scheduler.timesteps:
														lowerCAmelCase_ :      Union[str, Any]    				=				scheduler.scale_model_input(__lowercase   ,						__lowercase							)

														lowerCAmelCase_ :      Any    				=				model(__lowercase   ,						__lowercase							)

														lowerCAmelCase_ :      Optional[int]    				=				scheduler.step(__lowercase   ,						__lowercase   ,						__lowercase   ,						generator=__lowercase							)
														lowerCAmelCase_ :      Optional[Any]    				=				output.prev_sample

										lowerCAmelCase_ :      Optional[int]    				=				torch.sum(torch.abs(__lowercase							)							)
										lowerCAmelCase_ :      str    				=				torch.mean(torch.abs(__lowercase							)							)

										assert abs(result_sum.item() - 10.08_07							) < 1e-2
										assert abs(result_mean.item() - 0.01_31							) < 1e-3




						def 						lowercase_ (				self							)					->    Tuple:
										lowerCAmelCase_ :      Optional[int]    				=				self.scheduler_classes[0]
										lowerCAmelCase_ :      Dict    				=				self.get_scheduler_config()
										lowerCAmelCase_ :      int    				=				scheduler_class(**__lowercase   ,						use_karras_sigmas=__lowercase							)

										scheduler.set_timesteps(self.num_inference_steps   ,						device=__lowercase							)

										lowerCAmelCase_ :      Dict    				=				torch.manual_seed(0							)

										lowerCAmelCase_ :      int    				=				self.dummy_model()
										lowerCAmelCase_ :      List[Any]    				=				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										lowerCAmelCase_ :      Union[str, Any]    				=				sample.to(__lowercase							)

										for t in scheduler.timesteps:
														lowerCAmelCase_ :      List[str]    				=				scheduler.scale_model_input(__lowercase   ,						__lowercase							)

														lowerCAmelCase_ :      List[Any]    				=				model(__lowercase   ,						__lowercase							)

														lowerCAmelCase_ :      Optional[int]    				=				scheduler.step(__lowercase   ,						__lowercase   ,						__lowercase   ,						generator=__lowercase							)
														lowerCAmelCase_ :      Dict    				=				output.prev_sample

										lowerCAmelCase_ :      Optional[Any]    				=				torch.sum(torch.abs(__lowercase							)							)
										lowerCAmelCase_ :      Union[str, Any]    				=				torch.mean(torch.abs(__lowercase							)							)

										assert abs(result_sum.item() - 1_24.52_29_94_99_51_17_19							) < 1e-2
										assert abs(result_mean.item() - 0.1_62_13_93_26_33_39_99_63							) < 1e-3



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_xlnet import XLNetTokenizer
else:
						_UpperCAmelCase			:  Dict     =None


_UpperCAmelCase			:  Tuple     =logging.get_logger(__name__)

_UpperCAmelCase			:  Any     ={"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase			:  Any     ={
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json""",
    },
}

_UpperCAmelCase			:  Dict     ={
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

_UpperCAmelCase			:  Tuple     ="""▁"""

# Segments (not really needed)
_UpperCAmelCase			:  str     =0
_UpperCAmelCase			:  List[str]     =1
_UpperCAmelCase			:  int     =2
_UpperCAmelCase			:  Any     =3
_UpperCAmelCase			:  List[Any]     =4







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  int					=							VOCAB_FILES_NAMES
						SCREAMING_SNAKE_CASE__      :  Dict					=							PRETRAINED_VOCAB_FILES_MAP
						SCREAMING_SNAKE_CASE__      :  Optional[Any]					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						SCREAMING_SNAKE_CASE__      :  Any					=							"""left"""
						SCREAMING_SNAKE_CASE__      :  List[Any]					=							XLNetTokenizer




						def __init__(				self   ,						__lowercase=None   ,						__lowercase=None   ,						__lowercase=False   ,						__lowercase=True   ,						__lowercase=False   ,						__lowercase="<s>"   ,						__lowercase="</s>"   ,						__lowercase="<unk>"   ,						__lowercase="<sep>"   ,						__lowercase="<pad>"   ,						__lowercase="<cls>"   ,						__lowercase="<mask>"   ,						__lowercase=["<eop>", "<eod>"]   ,						**__lowercase   ,						)					->    List[Any]:
										# Mask token behave like a normal word, i.e. include the space before it
										lowerCAmelCase_ :      Any    				=				AddedToken(__lowercase   ,						lstrip=__lowercase   ,						rstrip=__lowercase							) if isinstance(__lowercase   ,						__lowercase							) else mask_token

										super().__init__(
										    vocab_file=__lowercase   ,						tokenizer_file=__lowercase   ,						do_lower_case=__lowercase   ,						remove_space=__lowercase   ,						keep_accents=__lowercase   ,						bos_token=__lowercase   ,						eos_token=__lowercase   ,						unk_token=__lowercase   ,						sep_token=__lowercase   ,						pad_token=__lowercase   ,						cls_token=__lowercase   ,						mask_token=__lowercase   ,						additional_special_tokens=__lowercase   ,						**__lowercase   ,						)

										lowerCAmelCase_ :      List[Any]    				=				3
										lowerCAmelCase_ :      Dict    				=				do_lower_case
										lowerCAmelCase_ :      Dict    				=				remove_space
										lowerCAmelCase_ :      List[str]    				=				keep_accents
										lowerCAmelCase_ :      List[str]    				=				vocab_file
										lowerCAmelCase_ :      str    				=				False if not self.vocab_file else True




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase = None							)					->    List[int]:
										lowerCAmelCase_ :      Tuple    				=				[self.sep_token_id]
										lowerCAmelCase_ :      Any    				=				[self.cls_token_id]
										if token_ids_a is None:
														return token_ids_a + sep + cls
										return token_ids_a + sep + token_ids_a + sep + cls




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase = None							)					->    List[int]:
										lowerCAmelCase_ :      Optional[Any]    				=				[self.sep_token_id]
										lowerCAmelCase_ :      List[Any]    				=				[2]

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




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase = None							)					->    Tuple[str]:
										if not self.can_save_slow_tokenizer:
														raise ValueError(
														    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
														    '''tokenizer.'''							)

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

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

										return (out_vocab_file,)