


"""simple docstring"""


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[Any]       = (EulerDiscreteScheduler,)
     _SCREAMING_SNAKE_CASE						:Tuple       = 10

     def 					_a			(						self	,    **_a  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	{
               """num_train_timesteps""": 1_100,
               """beta_start""": 0.0_001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
           }

           config.update(**_a  )
           return config

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           for timesteps in [10, 50, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=_a  )

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


           """simple docstring"""


           for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]	,    [0.0_002, 0.002, 0.02]  ):
                 self.check_over_configs(beta_start=_a	,    beta_end=_a  )

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=_a  )

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


           """simple docstring"""


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

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      str      		=	scheduler_class(**_a  )

           scheduler.set_timesteps(self.num_inference_steps  )

           SCREAMING_SNAKE_CASE__   :      str      		=	torch.manual_seed(0  )

           SCREAMING_SNAKE_CASE__   :      int      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.dummy_sample_deter * scheduler.init_noise_sigma
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	sample.to(_a  )

           for i, t in enumerate(scheduler.timesteps  ):
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler.scale_model_input(_a	,    _a  )

                 SCREAMING_SNAKE_CASE__   :      int      		=	model(_a	,    _a  )

                 SCREAMING_SNAKE_CASE__   :      Dict      		=	scheduler.step(_a	,    _a	,    _a	,    generator=_a  )
                 SCREAMING_SNAKE_CASE__   :      str      		=	output.prev_sample

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	torch.sum(torch.abs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_sum.item() - 10.0_807  ) < 1E-2
           assert abs(result_mean.item() - 0.0_131  ) < 1E-3

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.get_scheduler_config(prediction_type="""v_prediction"""  )
           SCREAMING_SNAKE_CASE__   :      int      		=	scheduler_class(**_a  )

           scheduler.set_timesteps(self.num_inference_steps  )

           SCREAMING_SNAKE_CASE__   :      int      		=	torch.manual_seed(0  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.dummy_sample_deter * scheduler.init_noise_sigma
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	sample.to(_a  )

           for i, t in enumerate(scheduler.timesteps  ):
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	scheduler.scale_model_input(_a	,    _a  )

                 SCREAMING_SNAKE_CASE__   :      int      		=	model(_a	,    _a  )

                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	scheduler.step(_a	,    _a	,    _a	,    generator=_a  )
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	output.prev_sample

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	torch.sum(torch.abs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_sum.item() - 0.0_002  ) < 1E-2
           assert abs(result_mean.item() - 2.2_6_7_6E-0_6  ) < 1E-3

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	scheduler_class(**_a  )

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

           SCREAMING_SNAKE_CASE__   :      Dict      		=	torch.manual_seed(0  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      int      		=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	sample.to(_a  )

           for t in scheduler.timesteps:
                 SCREAMING_SNAKE_CASE__   :      str      		=	scheduler.scale_model_input(_a	,    _a  )

                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model(_a	,    _a  )

                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler.step(_a	,    _a	,    _a	,    generator=_a  )
                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	output.prev_sample

           SCREAMING_SNAKE_CASE__   :      Dict      		=	torch.sum(torch.abs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_sum.item() - 10.0_807  ) < 1E-2
           assert abs(result_mean.item() - 0.0_131  ) < 1E-3





     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	scheduler_class(**_a	,    use_karras_sigmas=_a  )

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

           SCREAMING_SNAKE_CASE__   :      str      		=	torch.manual_seed(0  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	sample.to(_a  )

           for t in scheduler.timesteps:
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	scheduler.scale_model_input(_a	,    _a  )

                 SCREAMING_SNAKE_CASE__   :      int      		=	model(_a	,    _a  )

                 SCREAMING_SNAKE_CASE__   :      int      		=	scheduler.step(_a	,    _a	,    _a	,    generator=_a  )
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	output.prev_sample

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	torch.sum(torch.abs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_sum.item() - 124.52_299_499_511_719  ) < 1E-2
           assert abs(result_mean.item() - 0.16_213_932_633_399_963  ) < 1E-3







"""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_xlnet import XLNetTokenizer
else:
  a					:List[Any]    = None


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

a					:Union[str, Any]    = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a					:Optional[int]    = {
    "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",
    },
}

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

a					:int    = "▁"

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

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Tuple       = VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE						:Tuple       = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE						:str       = """left"""
     _SCREAMING_SNAKE_CASE						:Optional[Any]       = XLNetTokenizer

     def __init__(						self	,    _a=None	,    _a=None	,    _a=False	,    _a=True	,    _a=False	,    _a="<s>"	,    _a="</s>"	,    _a="<unk>"	,    _a="<sep>"	,    _a="<pad>"	,    _a="<cls>"	,    _a="<mask>"	,    _a=["<eop>", "<eod>"]	,    **_a	,    )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	AddedToken(_a	,    lstrip=_a	,    rstrip=_a  ) if isinstance(_a	,    _a  ) else mask_token

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

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	3
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	do_lower_case
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	remove_space
           SCREAMING_SNAKE_CASE__   :      int      		=	keep_accents
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	vocab_file
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	False if not self.vocab_file else True

     def 					_a			(						self	,    _a	,    _a = None  )  ->      List[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	[self.sep_token_id]
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	[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 					_a			(						self	,    _a	,    _a = None  )  ->      List[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	[self.sep_token_id]
           SCREAMING_SNAKE_CASE__   :      Optional[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 					_a			(						self	,    _a	,    _a = None  )  ->      Tuple[str]:


           """simple docstring"""


           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(_a  ):
                 logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
                 return
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	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  ):
                 copyfile(self.vocab_file	,    _a  )

           return (out_vocab_file,)




