

"""simple docstring"""




import os
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 logging


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

A_							:			Union[str, Any]											= '▁'

A_							:			int											= {'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

A_							:			Any											= {
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

A_							:			Any											= {'vinai/bartpho-syllable': 1_024}





class    lowerCamelCase						(snake_case__			):
			lowerCamelCase__   :		Dict							=							VOCAB_FILES_NAMES
			lowerCamelCase__   :		str							=							PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__   :		Optional[int]							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__   :		Tuple							=							["""input_ids""", """attention_mask"""]

			def __init__(					self				:						Dict    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						str="<s>"    ,				__UpperCAmelCase				:						str="</s>"    ,				__UpperCAmelCase				:						Dict="</s>"    ,				__UpperCAmelCase				:						int="<s>"    ,				__UpperCAmelCase				:						Tuple="<unk>"    ,				__UpperCAmelCase				:						Union[str, Any]="<pad>"    ,				__UpperCAmelCase				:						Optional[int]="<mask>"    ,				__UpperCAmelCase				:						Optional[Dict[str, Any]] = None    ,				**__UpperCAmelCase				:						List[Any]    ,				)   ->    None:
						SCREAMING_SNAKE_CASE__								=      AddedToken(UpperCAmelCase_    ,				lstrip=UpperCAmelCase_    ,				rstrip=UpperCAmelCase_       ) if isinstance(UpperCAmelCase_    ,				UpperCAmelCase_       ) else mask_token

						SCREAMING_SNAKE_CASE__								=      {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=UpperCAmelCase_    ,				eos_token=UpperCAmelCase_    ,				unk_token=UpperCAmelCase_    ,				sep_token=UpperCAmelCase_    ,				cls_token=UpperCAmelCase_    ,				pad_token=UpperCAmelCase_    ,				mask_token=UpperCAmelCase_    ,				sp_model_kwargs=self.sp_model_kwargs    ,				**UpperCAmelCase_    ,				)

						SCREAMING_SNAKE_CASE__								=      vocab_file
						SCREAMING_SNAKE_CASE__								=      monolingual_vocab_file
						SCREAMING_SNAKE_CASE__								=      spm.SentencePieceProcessor(**self.sp_model_kwargs       )
						self.sp_model.Load(str(UpperCAmelCase_       )       )

						# Load the reduced vocab

						# Keep order of special tokens for backward compatibility
						SCREAMING_SNAKE_CASE__								=      {}
						SCREAMING_SNAKE_CASE__								=      0
						for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
									if str(UpperCAmelCase_       ) not in self.fairseq_tokens_to_ids:
												SCREAMING_SNAKE_CASE__								=      cnt
												cnt += 1
						with open(UpperCAmelCase_    ,				"""r"""    ,				encoding="""utf-8"""       ) as f:
									for line in f.readlines():
												SCREAMING_SNAKE_CASE__								=      line.strip().split()[0]
												SCREAMING_SNAKE_CASE__								=      len(self.fairseq_tokens_to_ids       )
						if str(UpperCAmelCase_       ) not in self.fairseq_tokens_to_ids:
									SCREAMING_SNAKE_CASE__								=      len(self.fairseq_tokens_to_ids       )

						SCREAMING_SNAKE_CASE__								=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}

			def __getstate__(					self				:						List[Any]       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      self.__dict__.copy()
						SCREAMING_SNAKE_CASE__								=      None
						SCREAMING_SNAKE_CASE__								=      self.sp_model.serialized_model_proto()
						return state

			def __setstate__(					self				:						int    ,				__UpperCAmelCase				:						List[Any]       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      d

						# for backward compatibility
						if not hasattr(self    ,				"""sp_model_kwargs"""       ):
									SCREAMING_SNAKE_CASE__								=      {}

						SCREAMING_SNAKE_CASE__								=      spm.SentencePieceProcessor(**self.sp_model_kwargs       )
						self.sp_model.LoadFromSerializedProto(self.sp_model_proto       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None       )   ->    List[int]:



						if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						SCREAMING_SNAKE_CASE__								=      [self.cls_token_id]
						SCREAMING_SNAKE_CASE__								=      [self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None    ,				__UpperCAmelCase				:						bool = False       )   ->    List[int]:



						if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=UpperCAmelCase_    ,				token_ids_a=UpperCAmelCase_    ,				already_has_special_tokens=UpperCAmelCase_       )

						if token_ids_a is None:
									return [1] + ([0] * len(UpperCAmelCase_       )) + [1]
						return [1] + ([0] * len(UpperCAmelCase_       )) + [1, 1] + ([0] * len(UpperCAmelCase_       )) + [1]

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None       )   ->    List[int]:
						SCREAMING_SNAKE_CASE__								=      [self.sep_token_id]
						SCREAMING_SNAKE_CASE__								=      [self.cls_token_id]

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

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Tuple:
						return len(self.fairseq_ids_to_tokens       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      {self.convert_ids_to_tokens(UpperCAmelCase_       ): i for i in range(self.vocab_size       )}
						vocab.update(self.added_tokens_encoder       )
						return vocab

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						str       )   ->    List[str]:
						return self.sp_model.encode(UpperCAmelCase_    ,				out_type=UpperCAmelCase_       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						Optional[int]       )   ->    Optional[int]:



						if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
						else:
									return self.unk_token_id

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    Any:
						return self.fairseq_ids_to_tokens[index]

			def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						List[Any]       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      """""".join(UpperCAmelCase_       ).replace(UpperCAmelCase_    ,				""" """       ).strip()
						return out_string





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[str] = None       )   ->    Tuple[str]:



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

						if os.path.abspath(self.vocab_file       ) != os.path.abspath(UpperCAmelCase_       ) and os.path.isfile(self.vocab_file       ):
									copyfile(self.vocab_file    ,				UpperCAmelCase_       )
						elif not os.path.isfile(self.vocab_file       ):
									with open(UpperCAmelCase_    ,				"""wb"""       ) as fi:
												SCREAMING_SNAKE_CASE__								=      self.sp_model.serialized_model_proto()
												fi.write(UpperCAmelCase_       )

						if os.path.abspath(self.monolingual_vocab_file       ) != os.path.abspath(
						    UpperCAmelCase_       ) and os.path.isfile(self.monolingual_vocab_file       ):
									copyfile(self.monolingual_vocab_file    ,				UpperCAmelCase_       )
						elif not os.path.isfile(self.monolingual_vocab_file       ):
									with open(UpperCAmelCase_    ,				"""w"""    ,				encoding="""utf-8"""       ) as fp:
												for token in self.fairseq_tokens_to_ids:
															if token not in self.all_special_tokens:
																		fp.write(F"""{str(UpperCAmelCase_       )} \n"""       )

						return out_vocab_file, out_monolingual_vocab_file





"""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    lowerCamelCase						(unittest.TestCase			):

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Any:
						torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      UNetaDModel(
						    block_out_channels=(3_2, 6_4)    ,				layers_per_block=2    ,				sample_size=3_2    ,				in_channels=3    ,				out_channels=3    ,				down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,				up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,				)
						return model





			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      self.dummy_uncond_unet
						SCREAMING_SNAKE_CASE__								=      KarrasVeScheduler()

						SCREAMING_SNAKE_CASE__								=      KarrasVePipeline(unet=__UpperCAmelCase    ,				scheduler=__UpperCAmelCase       )
						pipe.to(__UpperCAmelCase       )
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""       ).images

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""    ,				return_dict=__UpperCAmelCase       )[0]

						SCREAMING_SNAKE_CASE__								=      image[0, -3:, -3:, -1]
						SCREAMING_SNAKE_CASE__								=      image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 3_2, 3_2, 3)
						SCREAMING_SNAKE_CASE__								=      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    lowerCamelCase						(unittest.TestCase			):

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      """google/ncsnpp-celebahq-256"""
						SCREAMING_SNAKE_CASE__								=      UNetaDModel.from_pretrained(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      KarrasVeScheduler()

						SCREAMING_SNAKE_CASE__								=      KarrasVePipeline(unet=__UpperCAmelCase    ,				scheduler=__UpperCAmelCase       )
						pipe.to(__UpperCAmelCase       )
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2_0    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""       ).images

						SCREAMING_SNAKE_CASE__								=      image[0, -3:, -3:, -1]
						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						SCREAMING_SNAKE_CASE__								=      np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]       )

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


