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


__UpperCamelCase	: List[Any]    		=  logging.get_logger(__name__)

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

__UpperCamelCase	: Tuple    		=  {'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

__UpperCamelCase	: Tuple    		=  {
    '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',
    },
}

__UpperCamelCase	: Tuple    		=  {'vinai/bartpho-syllable': 1024}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	VOCAB_FILES_NAMES
						UpperCamelCase_  =	PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_  =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_  =	["""input_ids""", """attention_mask"""]


						def __init__( self			:       Dict					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Dict="<s>"					,	UpperCamelCase__			:       str="</s>"					,	UpperCamelCase__			:       Dict="</s>"					,	UpperCamelCase__			:       Tuple="<s>"					,	UpperCamelCase__			:       str="<unk>"					,	UpperCamelCase__			:       Dict="<pad>"					,	UpperCamelCase__			:       Tuple="<mask>"					,	UpperCamelCase__			:       Optional[Dict[str, Any]] = None					,	**UpperCamelCase__			:       Tuple					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							AddedToken(UpperCamelCase__					,	lstrip=UpperCamelCase__					,	rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__					,	UpperCamelCase__		) else mask_token

												SCREAMING_SNAKE_CASE							:					Tuple           =							{} 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							:					Optional[int]           =							vocab_file
												SCREAMING_SNAKE_CASE							:					Any           =							monolingual_vocab_file
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							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							:					Dict           =							{}
												SCREAMING_SNAKE_CASE							:					List[Any]           =							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							:					str           =							cnt
																								cnt += 1
												with open(UpperCamelCase__					,	'''r'''					,	encoding='''utf-8'''		) as f:
																		for line in f.readlines():
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							line.strip().split()[0]
																								SCREAMING_SNAKE_CASE							:					int           =							len(self.fairseq_tokens_to_ids		)
												if str(UpperCamelCase__		) not in self.fairseq_tokens_to_ids:
																		SCREAMING_SNAKE_CASE							:					Any           =							len(self.fairseq_tokens_to_ids		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							{v: k for k, v in self.fairseq_tokens_to_ids.items()}


						def __getstate__( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							self.__dict__.copy()
												SCREAMING_SNAKE_CASE							:					Tuple           =							None
												SCREAMING_SNAKE_CASE							:					str           =							self.sp_model.serialized_model_proto()
												return state


						def __setstate__( self			:       Optional[Any]					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							d

												# for backward compatibility
												if not hasattr(self					,	'''sp_model_kwargs'''		):
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							{}

												SCREAMING_SNAKE_CASE							:					int           =							spm.SentencePieceProcessor(**self.sp_model_kwargs		)
												self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       List[int]					,	UpperCamelCase__			:       Optional[List[int]] = None		):





												'''simple docstring'''





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


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       List[int]					,	UpperCamelCase__			:       Optional[List[int]] = None					,	UpperCamelCase__			:       bool = False		):





												'''simple docstring'''





												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  __A ( self			:       int					,	UpperCamelCase__			:       List[int]					,	UpperCamelCase__			:       Optional[List[int]] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[self.sep_token_id]
												SCREAMING_SNAKE_CASE							:					int           =							[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  __A ( self			:       int		):





												'''simple docstring'''





												return len(self.fairseq_ids_to_tokens		)


						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							{self.convert_ids_to_tokens(UpperCamelCase__		): i for i in range(self.vocab_size		)}
												vocab.update(self.added_tokens_encoder		)
												return vocab


						def  __A ( self			:       List[str]					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												return self.sp_model.encode(UpperCamelCase__					,	out_type=UpperCamelCase__		)


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       Optional[int]		):





												'''simple docstring'''





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


						def  __A ( self			:       str					,	UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												return self.fairseq_ids_to_tokens[index]


						def  __A ( self			:       Any					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							''''''.join(UpperCamelCase__		).replace(UpperCamelCase__					,	''' '''		).strip()
												return out_string



						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       Optional[str] = None		):





												'''simple docstring'''





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






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__UpperCamelCase	: str    		=  False





class       lowercase__		(     unittest.TestCase):
						pass






@nightly
@require_torch_gpu
class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Dict           =							pipe.dual_guided(
												    prompt='''first prompt'''					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=2					,	output_type='''numpy'''					,	).images

												with tempfile.TemporaryDirectory() as tmpdirname:
																		pipe.save_pretrained(UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							VersatileDiffusionPipeline.from_pretrained(UpperCamelCase__					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							generator.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					int           =							pipe.dual_guided(
												    prompt='''first prompt'''					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=2					,	output_type='''numpy'''					,	).images

												assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							'''cyberpunk 2077'''
												SCREAMING_SNAKE_CASE							:					Tuple           =							load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							pipe.dual_guided(
												    prompt=UpperCamelCase__					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=50					,	output_type='''numpy'''					,	).images

												SCREAMING_SNAKE_CASE							:					Tuple           =							image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					int           =							np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]		)

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

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''A painting of a squirrel eating a burger '''
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Any           =							pipe.text_to_image(
												    prompt=UpperCamelCase__					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=50					,	output_type='''numpy'''		).images

												SCREAMING_SNAKE_CASE							:					List[str]           =							image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					Any           =							np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]		)

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

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							pipe.image_variation(UpperCamelCase__					,	generator=UpperCamelCase__					,	output_type='''numpy'''		).images

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]		)

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





