'''simple docstring'''






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


_UpperCAmelCase    :     Optional[Any]         =       '''▁'''

_UpperCAmelCase    :     Tuple         =       {'''vocab_file''': '''spiece.model'''}

_UpperCAmelCase    :     Optional[Any]         =       {
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}

_UpperCAmelCase    :     Tuple         =       {
    '''google/pegasus-xsum''': 5_12,
}


_UpperCAmelCase    :     str         =       logging.get_logger(__name__)







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   VOCAB_FILES_NAMES

   __lowerCAmelCase         =   VOCAB_FILES_NAMES
   __lowerCAmelCase         =   PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase         =   ["input_ids", "attention_mask"]
   def __init__(      self      :			Optional[int], UpperCamelCase__      :			str, UpperCamelCase__      :			Optional[Any]="<pad>", UpperCamelCase__      :			List[Any]="</s>", UpperCamelCase__      :			Dict="<unk>", UpperCamelCase__      :			List[Any]="<mask_2>", UpperCamelCase__      :			Dict="<mask_1>", UpperCamelCase__      :			List[str]=None, UpperCamelCase__      :			List[str]=1_03, UpperCamelCase__      :			Optional[Dict[str, Any]] = None, **UpperCamelCase__      :			List[str], )  ->			None:
        _A    		=  offset
        if additional_special_tokens is not None:
             if not isinstance(UpperCamelCase__, UpperCamelCase__     ):
                  raise TypeError(
                      f'additional_special_tokens should be of type {type(UpperCamelCase__     )}, but is'
                      f' {type(UpperCamelCase__     )}'     )

             _A    		=  (
                 ([mask_token_sent] + additional_special_tokens)
                 if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                 else additional_special_tokens
             )
             # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
             additional_special_tokens_extended += [
                 f'<unk_{i}>' for i in range(len(UpperCamelCase__     ), self.offset - 1     )
             ]

             if len(set(UpperCamelCase__     )     ) != len(UpperCamelCase__     ):
                  raise ValueError(
                      'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
                      f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'     )
             _A    		=  additional_special_tokens_extended
        else:
             _A    		=  [mask_token_sent] if mask_token_sent is not None else []
             additional_special_tokens += [f'<unk_{i}>' for i in range(2, self.offset     )]

        _A    		=  {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            eos_token=UpperCamelCase__, unk_token=UpperCamelCase__, mask_token=UpperCamelCase__, pad_token=UpperCamelCase__, mask_token_sent=UpperCamelCase__, offset=UpperCamelCase__, additional_special_tokens=UpperCamelCase__, sp_model_kwargs=self.sp_model_kwargs, **UpperCamelCase__, )
        _A    		=  mask_token_sent
        _A    		=  vocab_file
        _A    		=  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
        self.sp_model.Load(UpperCamelCase__     )

        # add special tokens to encoder dict
        _A    		=  {
            0: self.pad_token,
            1: self.eos_token,
        }

        if self.mask_token_sent is not None:
             self.encoder.update(
                 {
                     2: self.mask_token_sent,
                     3: self.mask_token,
                 }     )

        if self.offset > 0:
             # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
             # mask_token_sent is already added to list -> so start at 1
             self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1, self.offset - 1     )}     )

        _A    		=  {v: k for k, v in self.encoder.items()}
   @property
   def 	__UpperCAmelCase   (      self      :			str     )  ->			int:
        return len(self.sp_model     ) + self.offset
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Dict[str, int]:
        _A    		=  {self.convert_ids_to_tokens(UpperCamelCase__     ): i for i in range(self.vocab_size     )}
        vocab.update(self.added_tokens_encoder     )
        return vocab
   def __getstate__(      self      :			int     )  ->			Tuple:
        _A    		=  self.__dict__.copy()
        _A    		=  None
        return state
   def __setstate__(      self      :			Any, UpperCamelCase__      :			str     )  ->			List[str]:
        _A    		=  d

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

        _A    		=  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
        self.sp_model.Load(self.vocab_file     )
   def 	__UpperCAmelCase   (      self      :			int, UpperCamelCase__      :			str     )  ->			List[str]:
        return self.sp_model.encode(UpperCamelCase__, out_type=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			str     )  ->			int:
        if token in self.decoder:
             return self.decoder[token]
        elif token in self.added_tokens_decoder:
             return self.added_tokens_decoder[token]
        _A    		=  self.sp_model.piece_to_id(UpperCamelCase__     )
        return sp_id + self.offset
   def 	__UpperCAmelCase   (      self      :			List[str], UpperCamelCase__      :			int     )  ->			str:
        if index in self.encoder:
             return self.encoder[index]
        elif index in self.added_tokens_encoder:
             return self.added_tokens_encoder[index]
        else:
             _A    		=  self.sp_model.IdToPiece(index - self.offset     )
        return token
   def 	__UpperCAmelCase   (      self      :			str, UpperCamelCase__      :			Dict     )  ->			Optional[int]:
        _A    		=  []
        _A    		=  ''
        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(UpperCamelCase__     ) + token
                  _A    		=  []
             else:
                  current_sub_tokens.append(UpperCamelCase__     )
        out_string += self.sp_model.decode(UpperCamelCase__     )
        return out_string.strip()
   def 	__UpperCAmelCase   (      self      :			Optional[Any], UpperCamelCase__      :			int=False     )  ->			List[Any]:
        return 1
   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			Optional[Any]     )  ->			List[str]:
        _A    		=  set(self.all_special_ids     )  # call it once instead of inside list comp
        all_special_ids.remove(self.unk_token_id     )  # <unk> is only sometimes special

        return [1 if x in all_special_ids else 0 for x in seq]
   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			List, UpperCamelCase__      :			Optional[List] = None, UpperCamelCase__      :			bool = False     )  ->			List[int]:
        if already_has_special_tokens:
             return self._special_token_mask(UpperCamelCase__     )
        elif token_ids_a is None:
             return self._special_token_mask(UpperCamelCase__     ) + [1]
        else:
             return self._special_token_mask(token_ids_a + token_ids_a     ) + [1]
   def 	__UpperCAmelCase   (      self      :			List[str], UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			List[Any]=None     )  ->			List[int]:
        if token_ids_a is None:
             return token_ids_a + [self.eos_token_id]
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return token_ids_a + token_ids_a + [self.eos_token_id]

   def 	__UpperCAmelCase   (      self      :			List[Any], 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
        _A    		=  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__     ) 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:
                  _A    		=  self.sp_model.serialized_model_proto()
                  fi.write(UpperCamelCase__     )

        return (out_vocab_file,)







'''simple docstring'''







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowerCAmelCase     :  str											=    False
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     pass
@nightly
@require_torch_gpu
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

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







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion' )
            # remove text_unet
            pipe.remove_unused_weights()
            pipe.to(snake_case__ )
            pipe.set_progress_bar_config(disable=snake_case__ )

            _lowerCAmelCase     :				Any       =							'A painting of a squirrel eating a burger '
            _lowerCAmelCase     :				List[Any]       =							torch.manual_seed(0 )
            _lowerCAmelCase     :				Any       =							pipe(
                prompt=snake_case__   ,						generator=snake_case__   ,						guidance_scale=7.5   ,						num_inference_steps=2   ,						output_type='numpy' ).images

            with tempfile.TemporaryDirectory() as tmpdirname:
                   pipe.save_pretrained(snake_case__ )
                   _lowerCAmelCase     :				Any       =							VersatileDiffusionTextToImagePipeline.from_pretrained(snake_case__ )
            pipe.to(snake_case__ )
            pipe.set_progress_bar_config(disable=snake_case__ )

            _lowerCAmelCase     :				Optional[int]       =							generator.manual_seed(0 )
            _lowerCAmelCase     :				Tuple       =							pipe(
                prompt=snake_case__   ,						generator=snake_case__   ,						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 ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							VersatileDiffusionTextToImagePipeline.from_pretrained(
                'shi-labs/versatile-diffusion'   ,						torch_dtype=torch.floataa )
            pipe.to(snake_case__ )
            pipe.set_progress_bar_config(disable=snake_case__ )

            _lowerCAmelCase     :				Dict       =							'A painting of a squirrel eating a burger '
            _lowerCAmelCase     :				Any       =							torch.manual_seed(0 )
            _lowerCAmelCase     :				int       =							pipe(
                prompt=snake_case__   ,						generator=snake_case__   ,						guidance_scale=7.5   ,						num_inference_steps=50   ,						output_type='numpy' ).images

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

            assert image.shape == (1, 512, 512, 3)
            _lowerCAmelCase     :				Dict       =							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-2

