

'''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:
       UpperCamelCase_							=					None


UpperCamelCase_							=					logging.get_logger(__name__)

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

UpperCamelCase_							=					{
    """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_							=					{
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

UpperCamelCase_							=					"""▁"""

# Segments (not really needed)
UpperCamelCase_							=					0
UpperCamelCase_							=					1
UpperCamelCase_							=					2
UpperCamelCase_							=					3
UpperCamelCase_							=					4





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):
    lowerCamelCase_							=						VOCAB_FILES_NAMES
    lowerCamelCase_							=						PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase_							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase_							=						'left'
    lowerCamelCase_							=						XLNetTokenizer







    def __init__(					self    :		Dict		,				UpperCAmelCase__    :		List[str]=None		,				UpperCAmelCase__    :		Dict=None		,				UpperCAmelCase__    :		int=False		,				UpperCAmelCase__    :		Optional[int]=True		,				UpperCAmelCase__    :		List[Any]=False		,				UpperCAmelCase__    :		Any="<s>"		,				UpperCAmelCase__    :		Union[str, Any]="</s>"		,				UpperCAmelCase__    :		Dict="<unk>"		,				UpperCAmelCase__    :		Optional[Any]="<sep>"		,				UpperCAmelCase__    :		Union[str, Any]="<pad>"		,				UpperCAmelCase__    :		Optional[Any]="<cls>"		,				UpperCAmelCase__    :		Optional[int]="<mask>"		,				UpperCAmelCase__    :		Dict=["<eop>", "<eod>"]		,				**UpperCAmelCase__    :		Union[str, Any]		,				):
          '''simple docstring'''
          lowercase   :       List[Any]								=AddedToken(UpperCAmelCase__		,				lstrip=UpperCAmelCase__		,				rstrip=UpperCAmelCase__      ) if isinstance(UpperCAmelCase__		,				UpperCAmelCase__      ) else mask_token

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

          lowercase   :       List[Any]								=3
          lowercase   :       Tuple								=do_lower_case
          lowercase   :       Tuple								=remove_space
          lowercase   :       Optional[Any]								=keep_accents
          lowercase   :       Any								=vocab_file
          lowercase   :       Optional[Any]								=False if not self.vocab_file else True







    def    lowerCamelCase_  (					self    :		Any		,				UpperCAmelCase__    :		List[int]		,				UpperCAmelCase__    :		Optional[List[int]] = None      ):
          '''simple docstring'''
          lowercase   :       Optional[Any]								=[self.sep_token_id]
          lowercase   :       Union[str, 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    lowerCamelCase_  (					self    :		str		,				UpperCAmelCase__    :		List[int]		,				UpperCAmelCase__    :		Optional[List[int]] = None      ):
          '''simple docstring'''
          lowercase   :       str								=[self.sep_token_id]
          lowercase   :       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    lowerCamelCase_  (					self    :		List[Any]		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		Optional[str] = None      ):
          '''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(UpperCAmelCase__      ):
                logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
                return
          lowercase   :       List[Any]								=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,)








'''simple docstring'''





from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				def __init__(					self    :		Optional[Any]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										super().__init__()

										# make sure scheduler can always be converted to DDIM
										lowercase   :       Any								=DDIMScheduler.from_config(scheduler.config      )

										self.register_modules(unet=UpperCAmelCase__		,				scheduler=UpperCAmelCase__      )







				@torch.no_grad()
				def __call__(					self    :		List[Any]		,				UpperCAmelCase__    :		int = 1		,				UpperCAmelCase__    :		Optional[Union[torch.Generator, List[torch.Generator]]] = None		,				UpperCAmelCase__    :		float = 0.0		,				UpperCAmelCase__    :		int = 50		,				UpperCAmelCase__    :		Optional[bool] = None		,				UpperCAmelCase__    :		Optional[str] = "pil"		,				UpperCAmelCase__    :		bool = True		,				):
										'''simple docstring'''

										# Sample gaussian noise to begin loop
										if isinstance(self.unet.config.sample_size		,				UpperCAmelCase__      ):
																lowercase   :       Optional[int]								=(
																    batch_size,
																    self.unet.config.in_channels,
																    self.unet.config.sample_size,
																    self.unet.config.sample_size,
																)
										else:
																lowercase   :       Optional[int]								=(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

										if isinstance(UpperCAmelCase__		,				UpperCAmelCase__      ) and len(UpperCAmelCase__      ) != batch_size:
																raise ValueError(
																    F'''You have passed a list of generators of length {len(UpperCAmelCase__      )}, but requested an effective batch'''
																    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''      )

										lowercase   :       str								=randn_tensor(UpperCAmelCase__		,				generator=UpperCAmelCase__		,				device=self.device		,				dtype=self.unet.dtype      )

										# set step values
										self.scheduler.set_timesteps(UpperCAmelCase__      )

										for t in self.progress_bar(self.scheduler.timesteps      ):
																# 1. predict noise model_output
																lowercase   :       Dict								=self.unet(UpperCAmelCase__		,				UpperCAmelCase__      ).sample

																# 2. predict previous mean of image x_t-1 and add variance depending on eta
																# eta corresponds to η in paper and should be between [0, 1]
																# do x_t -> x_t-1
																lowercase   :       Dict								=self.scheduler.step(
																    UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				eta=UpperCAmelCase__		,				use_clipped_model_output=UpperCAmelCase__		,				generator=UpperCAmelCase__      ).prev_sample

										lowercase   :       Optional[Any]								=(image / 2 + 0.5).clamp(0		,				1      )
										lowercase   :       Optional[Any]								=image.cpu().permute(0		,				2		,				3		,				1      ).numpy()
										if output_type == "pil":
																lowercase   :       List[str]								=self.numpy_to_pil(UpperCAmelCase__      )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=UpperCAmelCase__      )





