

'''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


_snake_case         =  logging.get_logger(__name__)

_snake_case         =  '▁'

_snake_case         =  {'vocab_file': 'sentencepiece.bpe.model'}

_snake_case         =  {
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

_snake_case         =  {
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}


class        a__  (							lowerCamelCase_  ):






   """simple docstring"""




   _SCREAMING_SNAKE_CASE   :					Tuple     	=							VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE   :					Any     	=							PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE   :					Any     	=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE   :					Any     	=							['input_ids', 'attention_mask']


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase="<s>"      ,    _UpperCamelCase="</s>"      ,    _UpperCamelCase="</s>"      ,    _UpperCamelCase="<s>"      ,    _UpperCamelCase="<unk>"      ,    _UpperCamelCase="<pad>"      ,    _UpperCamelCase="<mask>"      ,    _UpperCamelCase = None      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				int      					=       AddedToken(_UpperCamelCase      ,    lstrip=_UpperCamelCase      ,    rstrip=_UpperCamelCase  ) if isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) else mask_token

      _lowercase      :				int      					=       {} 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      ,    )

      _lowercase      :				str      					=       spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(str(_UpperCamelCase  )  )
      _lowercase      :				Optional[int]      					=       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'

      # Mimic fairseq token-to-id alignment for the first 4 token
      _lowercase      :				Tuple      					=       {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

      # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
      _lowercase      :				str      					=       1

      _lowercase      :				int      					=       len(self.sp_model  ) + self.fairseq_offset
      _lowercase      :				Tuple      					=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}


   def __getstate__(			self  ):





      """simple docstring"""





      _lowercase      :				int      					=       self.__dict__.copy()
      _lowercase      :				int      					=       None
      _lowercase      :				int      					=       self.sp_model.serialized_model_proto()
      return state


   def __setstate__(			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				List[str]      					=       d

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

      _lowercase      :				Optional[int]      					=       spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase = None  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase = None      ,    _UpperCamelCase = 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 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase = None  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       [self.sep_token_id]
      _lowercase      :				List[Any]      					=       [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 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return len(self.sp_model  ) + self.fairseq_offset + 1  # Add the <mask> token


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				str      					=       {self.convert_ids_to_tokens(_UpperCamelCase  ): i for i in range(self.vocab_size  )}
      vocab.update(self.added_tokens_encoder  )
      return vocab


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
      _lowercase      :				Dict      					=       self.sp_model.PieceToId(_UpperCamelCase  )

      # 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 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """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 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Any      					=       "".join(_UpperCamelCase  ).replace(_UpperCamelCase      ,    " "  ).strip()
      return out_string






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase = None  ):





      """simple docstring"""





      if not os.path.isdir(_UpperCamelCase  ):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
         return
      _lowercase      :				Any      					=       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:
            _lowercase      :				Optional[Any]      					=       self.sp_model.serialized_model_proto()
            fi.write(_UpperCamelCase  )

      return (out_vocab_file,)









'''simple docstring'''
import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class        a__  (							lowerCamelCase_  ,				lowerCamelCase_  ,				unittest.TestCase  ):
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							IFImgaImgSuperResolutionPipeline
   _SCREAMING_SNAKE_CASE   :					int     	=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							PipelineTesterMixin.required_optional_params - {'latents'}


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return self._get_superresolution_dummy_components()


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase=0  ):





      """simple docstring"""





      if str(_UpperCamelCase  ).startswith("mps"  ):
         _lowercase      :				Tuple      					=       torch.manual_seed(_UpperCamelCase  )
      else:
         _lowercase      :				int      					=       torch.Generator(device=_UpperCamelCase  ).manual_seed(_UpperCamelCase  )

      _lowercase      :				List[Any]      					=       floats_tensor((1, 3, 32, 32)      ,    rng=random.Random(_UpperCamelCase  )  ).to(_UpperCamelCase  )
      _lowercase      :				Optional[int]      					=       floats_tensor((1, 3, 16, 16)      ,    rng=random.Random(_UpperCamelCase  )  ).to(_UpperCamelCase  )

      _lowercase      :				List[str]      					=       {
          "prompt": "A painting of a squirrel eating a burger",
          "image": image,
          "original_image": original_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 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self._test_save_load_optional_components()


   @unittest.skipIf(torch_device != "cuda"      ,    reason="float16 requires CUDA"  )
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self._test_save_load_local()






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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






