





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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase_		:					int      						=    logging.get_logger(__name__)


lowercase_		:					Tuple      						=    '''▁'''

lowercase_		:					Union[str, Any]      						=    {'''vocab_file''': '''spiece.model'''}

lowercase_		:					Dict      						=    {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

lowercase_		:					Union[str, Any]      						=    {
    '''google/reformer-crime-and-punishment''': 52_4288,
}


class       lowercase					(				lowerCamelCase__  ):



						"""simple docstring"""

						_UpperCamelCase	:						List[str]    		=						VOCAB_FILES_NAMES
						_UpperCamelCase	:						int    		=						PRETRAINED_VOCAB_FILES_MAP
						_UpperCamelCase	:						str    		=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_UpperCamelCase	:						Dict    		=						["input_ids", "attention_mask"]







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





										'''simple docstring'''







										_snake_case			:      int	    = {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    eos_token=lowerCamelCase_			,					unk_token=lowerCamelCase_			,					additional_special_tokens=lowerCamelCase_			,					sp_model_kwargs=self.sp_model_kwargs			,					**lowerCamelCase_			,					)

										_snake_case			:      str	    = vocab_file
										_snake_case			:      List[Any]	    = spm.SentencePieceProcessor(**self.sp_model_kwargs       )
										self.sp_model.Load(lowerCamelCase_       )







						@property
						def 		__UpperCAmelCase							(     self      :		Optional[int]       ):





										'''simple docstring'''







										return self.sp_model.get_piece_size()







						def 		__UpperCAmelCase							(     self      :		Tuple       ):





										'''simple docstring'''







										_snake_case			:      Union[str, Any]	    = {self.convert_ids_to_tokens(lowerCamelCase_       ): i for i in range(self.vocab_size       )}
										vocab.update(self.added_tokens_encoder       )
										return vocab







						def __getstate__(     self      :		int       ):





										'''simple docstring'''







										_snake_case			:      int	    = self.__dict__.copy()
										_snake_case			:      Union[str, Any]	    = None
										return state







						def __setstate__(     self      :		List[str]			,					lowerCamelCase_      :		Optional[int]       ):





										'''simple docstring'''







										_snake_case			:      List[str]	    = d

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

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







						def 		__UpperCAmelCase							(     self      :		List[str]			,					lowerCamelCase_      :		str       ):





										'''simple docstring'''







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







						def 		__UpperCAmelCase							(     self      :		List[Any]			,					lowerCamelCase_      :		str       ):





										'''simple docstring'''







										return self.sp_model.piece_to_id(lowerCamelCase_       )







						def 		__UpperCAmelCase							(     self      :		Any			,					lowerCamelCase_      :		Optional[int]       ):





										'''simple docstring'''







										if index < self.sp_model.get_piece_size():
														_snake_case			:      List[Any]	    = self.sp_model.IdToPiece(lowerCamelCase_       )
										return token







						def 		__UpperCAmelCase							(     self      :		List[Any]			,					lowerCamelCase_      :		Tuple       ):





										'''simple docstring'''







										_snake_case			:      Any	    = []
										_snake_case			:      Dict	    = ""
										for token in tokens:
														# make sure that special tokens are not decoded using sentencepiece model
														if token in self.all_special_tokens:
																		out_string += self.sp_model.decode(lowerCamelCase_       ) + token
																		_snake_case			:      Tuple	    = []
														else:
																		current_sub_tokens.append(lowerCamelCase_       )
										out_string += self.sp_model.decode(lowerCamelCase_       )
										return out_string.strip()







						def 		__UpperCAmelCase							(     self      :		str			,					lowerCamelCase_      :		str			,					lowerCamelCase_      :		Optional[str] = None       ):





										'''simple docstring'''







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

										if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowerCamelCase_       ) and os.path.isfile(self.vocab_file       ):
														copyfile(self.vocab_file			,					lowerCamelCase_       )
										elif not os.path.isfile(self.vocab_file       ):
														with open(lowerCamelCase_			,					'wb'       ) as fi:
																		_snake_case			:      Optional[int]	    = self.sp_model.serialized_model_proto()
																		fi.write(lowerCamelCase_       )

										return (out_vocab_file,)













import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					Optional[Any]        =      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



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					Any        =      VQModel(
            block_out_channels=[32, 64]      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,   latent_channels=3      ,   )
        return model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					int        =      CLIPTextConfig(
            bos_token_id=0      ,   eos_token_id=2      ,   hidden_size=32      ,   intermediate_size=37      ,   layer_norm_eps=1e-05      ,   num_attention_heads=4      ,   num_hidden_layers=5      ,   pad_token_id=1      ,   vocab_size=1_000      ,   )
        return CLIPTextModel(snake_case	)





     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        A_       :					Dict        =      self.dummy_uncond_unet
        A_       :					Any        =      DDIMScheduler()
        A_       :					List[str]        =      self.dummy_vq_model

        A_       :					Union[str, Any]        =      LDMPipeline(unet=snake_case      ,   vqvae=snake_case      ,   scheduler=snake_case	)
        ldm.to(snake_case	)
        ldm.set_progress_bar_config(disable=snake_case	)

        A_       :					Any        =      torch.manual_seed(0	)
        A_       :					Optional[Any]        =      ldm(generator=snake_case      ,   num_inference_steps=2      ,   output_type="numpy"	).images

        A_       :					Dict        =      torch.manual_seed(0	)
        A_       :					Any        =      ldm(generator=snake_case      ,   num_inference_steps=2      ,   output_type="numpy"      ,   return_dict=snake_case	)[0]

        A_       :					int        =      image[0, -3:, -3:, -1]
        A_       :					Any        =      image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        A_       :					str        =      np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]	)
        A_       :					List[str]        =      1e-2 if torch_device != "mps" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < tolerance




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





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        A_       :					int        =      LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"	)
        ldm.to(snake_case	)
        ldm.set_progress_bar_config(disable=snake_case	)

        A_       :					int        =      torch.manual_seed(0	)
        A_       :					List[str]        =      ldm(generator=snake_case      ,   num_inference_steps=5      ,   output_type="numpy"	).images

        A_       :					Tuple        =      image[0, -3:, -3:, -1]

        assert image.shape == (1, 256, 256, 3)
        A_       :					Any        =      np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]	)
        A_       :					List[Any]        =      1e-2 if torch_device != "mps" else 3e-2

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