






"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 							__SCREAMING_SNAKE_CASE  (			unittest.TestCase						):


							@property
							def   _UpperCamelCase    (     self :  str							):
													'''simple docstring'''





													torch.manual_seed(0							)
													A__  :					str											=					UNetaDModel(
													    block_out_channels=(32, 64)     ,      layers_per_block=2     ,      sample_size=32     ,      in_channels=3     ,      out_channels=3     ,      down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")     ,      up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")     ,      )
													return model


							def   _UpperCamelCase    (     self :  List[str]							):
													'''simple docstring'''





													A__  :					str											=					self.dummy_uncond_unet
													A__  :					Any											=					KarrasVeScheduler()

													A__  :					List[str]											=					KarrasVePipeline(unet=snake_case     ,      scheduler=snake_case							)
													pipe.to(snake_case							)
													pipe.set_progress_bar_config(disable=snake_case							)

													A__  :					str											=					torch.manual_seed(0							)
													A__  :					Any											=					pipe(num_inference_steps=2     ,      generator=snake_case     ,      output_type="""numpy"""							).images

													A__  :					List[str]											=					torch.manual_seed(0							)
													A__  :					List[Any]											=					pipe(num_inference_steps=2     ,      generator=snake_case     ,      output_type="""numpy"""     ,      return_dict=snake_case							)[0]

													A__  :					str											=					image[0, -3:, -3:, -1]
													A__  :					Tuple											=					image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													A__  :					Any											=					np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

													assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
													assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2






@slow
@require_torch
class 							__SCREAMING_SNAKE_CASE  (			unittest.TestCase						):


							def   _UpperCamelCase    (     self :  List[Any]							):
													'''simple docstring'''





													A__  :					int											=					"""google/ncsnpp-celebahq-256"""
													A__  :					Optional[Any]											=					UNetaDModel.from_pretrained(snake_case							)
													A__  :					Optional[int]											=					KarrasVeScheduler()

													A__  :					List[Any]											=					KarrasVePipeline(unet=snake_case     ,      scheduler=snake_case							)
													pipe.to(snake_case							)
													pipe.set_progress_bar_config(disable=snake_case							)

													A__  :					Optional[Any]											=					torch.manual_seed(0							)
													A__  :					Optional[int]											=					pipe(num_inference_steps=20     ,      generator=snake_case     ,      output_type="""numpy"""							).images

													A__  :					List[Any]											=					image[0, -3:, -3:, -1]
													assert image.shape == (1, 256, 256, 3)
													A__  :					Optional[int]											=					np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]							)

													assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2














"""simple docstring"""



import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


A_      =							logging.get_logger(__name__)

A_      =							{'''vocab_file''': '''spiece.model'''}

A_      =							{
    '''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''',
    }
}

A_      =							{
    '''xlnet-base-cased''': None,
    '''xlnet-large-cased''': None,
}

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






class 							__SCREAMING_SNAKE_CASE  (			UpperCamelCase						):
							snake_case_       =							VOCAB_FILES_NAMES
							snake_case_       =							PRETRAINED_VOCAB_FILES_MAP
							snake_case_       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case_       =							'left'


							def __init__(     self :  Dict     ,      snake_case :  int     ,      snake_case :  List[Any]=False     ,      snake_case :  List[str]=True     ,      snake_case :  Dict=False     ,      snake_case :  Optional[Any]="<s>"     ,      snake_case :  List[str]="</s>"     ,      snake_case :  Tuple="<unk>"     ,      snake_case :  Tuple="<sep>"     ,      snake_case :  Union[str, Any]="<pad>"     ,      snake_case :  Dict="<cls>"     ,      snake_case :  Optional[Any]="<mask>"     ,      snake_case :  Optional[int]=["<eop>", "<eod>"]     ,      snake_case :  Optional[Dict[str, Any]] = None     ,      **snake_case :  Dict     ,      ):
													'''simple docstring'''





													A__  :					Optional[int]											=					AddedToken(snake_case     ,      lstrip=snake_case     ,      rstrip=snake_case							) if isinstance(snake_case     ,      snake_case							) else mask_token

													A__  :					Union[str, Any]											=					{} if sp_model_kwargs is None else sp_model_kwargs

													super().__init__(
													    do_lower_case=snake_case     ,      remove_space=snake_case     ,      keep_accents=snake_case     ,      bos_token=snake_case     ,      eos_token=snake_case     ,      unk_token=snake_case     ,      sep_token=snake_case     ,      pad_token=snake_case     ,      cls_token=snake_case     ,      mask_token=snake_case     ,      additional_special_tokens=snake_case     ,      sp_model_kwargs=self.sp_model_kwargs     ,      **snake_case     ,      )

													A__  :					str											=					3

													A__  :					str											=					do_lower_case
													A__  :					Optional[Any]											=					remove_space
													A__  :					List[Any]											=					keep_accents
													A__  :					Union[str, Any]											=					vocab_file

													A__  :					Dict											=					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
													self.sp_model.Load(snake_case							)


							@property
							def   _UpperCamelCase    (     self :  Optional[int]							):
													'''simple docstring'''





													return len(self.sp_model							)


							def   _UpperCamelCase    (     self :  List[Any]							):
													'''simple docstring'''





													A__  :					int											=					{self.convert_ids_to_tokens(snake_case							): i for i in range(self.vocab_size							)}
													vocab.update(self.added_tokens_encoder							)
													return vocab


							def __getstate__(     self :  str							):
													'''simple docstring'''





													A__  :					int											=					self.__dict__.copy()
													A__  :					int											=					None
													return state


							def __setstate__(     self :  Tuple     ,      snake_case :  Union[str, Any]							):
													'''simple docstring'''





													A__  :					int											=					d

													# for backward compatibility
													if not hasattr(self     ,      """sp_model_kwargs"""							):
																			A__  :					Optional[int]											=					{}

													A__  :					Optional[int]											=					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
													self.sp_model.Load(self.vocab_file							)


							def   _UpperCamelCase    (     self :  List[str]     ,      snake_case :  Optional[Any]							):
													'''simple docstring'''





													if self.remove_space:
																			A__  :					Optional[Any]											=					""" """.join(inputs.strip().split()							)
													else:
																			A__  :					Dict											=					inputs
													A__  :					str											=					outputs.replace("""``"""     ,      """\""""							).replace("""''"""     ,      """\""""							)

													if not self.keep_accents:
																			A__  :					Any											=					unicodedata.normalize("""NFKD"""     ,      snake_case							)
																			A__  :					Optional[int]											=					"""""".join([c for c in outputs if not unicodedata.combining(snake_case							)]							)
													if self.do_lower_case:
																			A__  :					Any											=					outputs.lower()

													return outputs


							def   _UpperCamelCase    (     self :  Union[str, Any]     ,      snake_case :  str							):
													'''simple docstring'''





													A__  :					Dict											=					self.preprocess_text(snake_case							)
													A__  :					Dict											=					self.sp_model.encode(snake_case     ,      out_type=snake_case							)
													A__  :					Optional[int]											=					[]
													for piece in pieces:
																			if len(snake_case							) > 1 and piece[-1] == str(""","""							) and piece[-2].isdigit():
																									A__  :					Optional[Any]											=					self.sp_model.EncodeAsPieces(piece[:-1].replace(snake_case     ,      """"""							)							)
																									if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																															if len(cur_pieces[0]							) == 1:
																																					A__  :					int											=					cur_pieces[1:]
																															else:
																																					A__  :					Any											=					cur_pieces[0][1:]
																									cur_pieces.append(piece[-1]							)
																									new_pieces.extend(snake_case							)
																			else:
																									new_pieces.append(snake_case							)

													return new_pieces


							def   _UpperCamelCase    (     self :  List[str]     ,      snake_case :  Tuple							):
													'''simple docstring'''





													return self.sp_model.PieceToId(snake_case							)


							def   _UpperCamelCase    (     self :  List[str]     ,      snake_case :  Any							):
													'''simple docstring'''





													return self.sp_model.IdToPiece(snake_case							)


							def   _UpperCamelCase    (     self :  Optional[int]     ,      snake_case :  Any							):
													'''simple docstring'''





													A__  :					Union[str, Any]											=					"""""".join(snake_case							).replace(snake_case     ,      """ """							).strip()
													return out_string


							def   _UpperCamelCase    (     self :  int     ,      snake_case :  List[int]     ,      snake_case :  bool = False     ,      snake_case :  bool = None     ,      snake_case :  bool = True     ,      **snake_case :  Union[str, Any]     ,      ):
													'''simple docstring'''





													A__  :					List[str]											=					kwargs.pop("""use_source_tokenizer"""     ,      snake_case							)

													A__  :					Any											=					self.convert_ids_to_tokens(snake_case     ,      skip_special_tokens=snake_case							)

													# To avoid mixing byte-level and unicode for byte-level BPT
													# we need to build string separately for added tokens and byte-level tokens
													# cf. https://github.com/huggingface/transformers/issues/1133
													A__  :					Any											=					[]
													A__  :					Any											=					[]
													for token in filtered_tokens:
																			if skip_special_tokens and token in self.all_special_ids:
																									continue
																			if token in self.added_tokens_encoder:
																									if current_sub_text:
																															sub_texts.append(self.convert_tokens_to_string(snake_case							)							)
																															A__  :					str											=					[]
																									sub_texts.append(snake_case							)
																			else:
																									current_sub_text.append(snake_case							)
													if current_sub_text:
																			sub_texts.append(self.convert_tokens_to_string(snake_case							)							)

													# Mimic the behavior of the Rust tokenizer:
													# By default, there are no spaces between special tokens
													A__  :					Dict											=					"""""".join(snake_case							)

													A__  :					int											=					(
													    clean_up_tokenization_spaces
													    if clean_up_tokenization_spaces is not None
													    else self.clean_up_tokenization_spaces
													)
													if clean_up_tokenization_spaces:
																			A__  :					Tuple											=					self.clean_up_tokenization(snake_case							)
																			return clean_text
													else:
																			return text


							def   _UpperCamelCase    (     self :  str     ,      snake_case :  List[int]     ,      snake_case :  Optional[List[int]] = None							):
													'''simple docstring'''





													A__  :					Tuple											=					[self.sep_token_id]
													A__  :					Dict											=					[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   _UpperCamelCase    (     self :  Dict     ,      snake_case :  List[int]     ,      snake_case :  Optional[List[int]] = None     ,      snake_case :  bool = False							):
													'''simple docstring'''





													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							)

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


							def   _UpperCamelCase    (     self :  str     ,      snake_case :  List[int]     ,      snake_case :  Optional[List[int]] = None							):
													'''simple docstring'''





													A__  :					Any											=					[self.sep_token_id]
													A__  :					int											=					[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   _UpperCamelCase    (     self :  Optional[Any]     ,      snake_case :  str     ,      snake_case :  Optional[str] = None							):
													'''simple docstring'''





													if not os.path.isdir(snake_case							):
																			logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
																			return
													A__  :					List[Any]											=					os.path.join(
													    snake_case     ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

													if os.path.abspath(self.vocab_file							) != os.path.abspath(snake_case							) and os.path.isfile(self.vocab_file							):
																			copyfile(self.vocab_file     ,      snake_case							)
													elif not os.path.isfile(self.vocab_file							):
																			with open(snake_case     ,      """wb"""							) as fi:
																									A__  :					Optional[Any]											=					self.sp_model.serialized_model_proto()
																									fi.write(snake_case							)

													return (out_vocab_file,)






