





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

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


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

_lowerCAmelCase				:   Optional[int]						       =						'''▁'''

_lowerCAmelCase				:   Union[str, Any]						       =						{'''vocab_file''': '''prophetnet.tokenizer'''}

_lowerCAmelCase				:   Any						       =						{
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

_lowerCAmelCase				:   Union[str, Any]						       =						{
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

_lowerCAmelCase				:   str						       =						{
    '''microsoft/xprophetnet-large-wiki100-cased''': 512,
}

def   __snake_case    (       _lowerCAmelCase			:			int						)			->    str:
   A_       :					List[str]        =      collections.OrderedDict()
   with open(_lowerCAmelCase					,					"r"					,					encoding="utf-8"						) as reader:
      A_       :					Optional[Any]        =      reader.readlines()
   for index, token in enumerate(_lowerCAmelCase						):
      A_       :					List[str]        =      token.rstrip("\n"						)
      A_       :					Dict        =      index
   return vocab





class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  VOCAB_FILES_NAMES
     __UpperCamelCase            =  PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase            =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase            =  ['''input_ids''', '''attention_mask''']



     def __init__(				self							:int      ,   snake_case							:Dict      ,   snake_case							:Union[str, Any]="[SEP]"      ,   snake_case							:Any="[SEP]"      ,   snake_case							:Any="[SEP]"      ,   snake_case							:Union[str, Any]="[UNK]"      ,   snake_case							:Dict="[PAD]"      ,   snake_case							:List[Any]="[CLS]"      ,   snake_case							:Any="[MASK]"      ,   snake_case							:Optional[Dict[str, Any]] = None      ,   **snake_case							:str      ,   ):





        '''simple docstring'''




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

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

        try:
           import sentencepiece as spm
        except ImportError:
           logger.warning(
               "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
               " pip install sentencepiece"	)
           raise

        A_       :					List[str]        =      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(str(snake_case	)	)
        A_       :					str        =      vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # put special tokens and [unused] tokens into the vocab
        A_       :					Union[str, Any]        =      {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

        for i in range(10	):
           A_       :					Optional[Any]        =      f"[unused{i}]"
           A_       :					Tuple        =      5 + i

        # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
        A_       :					str        =      12
        A_       :					List[Any]        =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}
        for k in self.fairseq_tokens_to_ids.keys():
           self.unique_no_split_tokens.append(snake_case	)



     def __getstate__(				self							:List[str]	):





        '''simple docstring'''




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



     def __setstate__(				self							:Union[str, Any]      ,   snake_case							:Optional[Any]	):





        '''simple docstring'''




        A_       :					List[str]        =      d
        try:
           import sentencepiece as spm
        except ImportError:
           logger.warning(
               "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
               " pip install sentencepiece"	)
           raise

        # for backward compatibility
        if not hasattr(self      ,   "sp_model_kwargs"	):
           A_       :					Union[str, Any]        =      {}

        A_       :					int        =      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(self.vocab_file	)



     def     SCREAMING_SNAKE_CASE		(				self							:Any      ,   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 None:
           return ([0] * len(snake_case	)) + [1]
        return ([0] * len(snake_case	)) + [1] + ([0] * len(snake_case	)) + [1]



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]      ,   snake_case							:List[int]      ,   snake_case							:Optional[List[int]] = None	):





        '''simple docstring'''




        A_       :					Optional[int]        =      [self.sep_token_id]

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



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        return len(self.sp_model	) + self.fairseq_offset



     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




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



     def     SCREAMING_SNAKE_CASE		(				self							:str      ,   snake_case							:str	):





        '''simple docstring'''




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



     def     SCREAMING_SNAKE_CASE		(				self							:Dict      ,   snake_case							:int	):





        '''simple docstring'''




        if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
        A_       :					List[str]        =      self.sp_model.PieceToId(snake_case	)

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]      ,   snake_case							:Union[str, Any]	):





        '''simple docstring'''




        if index in self.fairseq_ids_to_tokens:
           return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset	)



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   snake_case							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					str        =      "".join(snake_case	).replace(snake_case      ,   " "	).strip()
        return out_string



     def     SCREAMING_SNAKE_CASE		(				self							:Dict      ,   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_       :					int        =      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_       :					str        =      self.sp_model.serialized_model_proto()
              fi.write(snake_case	)

        return (out_vocab_file,)





     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]      ,   snake_case							:List[int]      ,   snake_case							:Optional[List[int]] = None	):





        '''simple docstring'''




        if token_ids_a is None:
           return token_ids_a + [self.sep_token_id]
        A_       :					List[Any]        =      [self.sep_token_id]
        return token_ids_a + sep + token_ids_a + sep







import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class        __magic_name__	(				lowerCamelCase__     ,					lowerCamelCase__     ,					unittest.TestCase				):





     """simple docstring"""



     __UpperCamelCase            =  IFInpaintingPipeline
     __UpperCamelCase            =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
     __UpperCamelCase            =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     __UpperCamelCase            =  PipelineTesterMixin.required_optional_params - {'''latents'''}



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        return self._get_dummy_components()



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]      ,   snake_case							:str      ,   snake_case							:List[Any]=0	):





        '''simple docstring'''




        if str(snake_case	).startswith("mps"	):
           A_       :					Optional[Any]        =      torch.manual_seed(snake_case	)
        else:
           A_       :					Any        =      torch.Generator(device=snake_case	).manual_seed(snake_case	)

        A_       :					Optional[Any]        =      floats_tensor((1, 3, 32, 32)      ,   rng=random.Random(snake_case	)	).to(snake_case	)
        A_       :					Any        =      floats_tensor((1, 3, 32, 32)      ,   rng=random.Random(snake_case	)	).to(snake_case	)

        A_       :					List[Any]        =      {
            "prompt": "A painting of a squirrel eating a burger",
            "image": image,
            "mask_image": mask_image,
            "generator": generator,
            "num_inference_steps": 2,
            "output_type": "numpy",
        }

        return inputs



     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available()      ,   reason="XFormers attention is only available with CUDA and `xformers` installed"      ,   )
     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        self._test_save_load_optional_components()



     @unittest.skipIf(torch_device != "cuda"      ,   reason="float16 requires CUDA"	)
     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        super().test_save_load_floataa(expected_max_diff=1e-1	)



     def     SCREAMING_SNAKE_CASE		(				self							:Dict	):





        '''simple docstring'''




        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        self._test_save_load_local()





     def     SCREAMING_SNAKE_CASE		(				self							:Dict	):





        '''simple docstring'''




        self._test_inference_batch_single_identical(
            expected_max_diff=1e-2      ,   )
