



'''simple docstring'''





import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
 import torch


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


_lowerCAmelCase										=     logging.get_logger(__name__)
_lowerCAmelCase										=     {'''vocab_file''': '''spiece.model'''}

_lowerCAmelCase										=     {
    '''vocab_file''': {
        '''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
    }
}

_lowerCAmelCase										=     {
    '''AI-Sweden/gpt-sw3-126m''': 2048,
    '''AI-Sweden/gpt-sw3-350m''': 2048,
    '''AI-Sweden/gpt-sw3-1.6b''': 2048,
    '''AI-Sweden/gpt-sw3-6.7b''': 2048,
    '''AI-Sweden/gpt-sw3-20b''': 2048,
}






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				str           =				VOCAB_FILES_NAMES
  __lowercase							:				Optional[Any]           =				PRETRAINED_VOCAB_FILES_MAP
  __lowercase							:				Tuple           =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowercase							:				Dict           =				['''input_ids''', '''attention_mask''']




  def __init__(   self  ,__UpperCAmelCase  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase = None  ,**__UpperCAmelCase  ,)   ->		None:
      lowerCAmelCase__			:  Any											= {} if sp_model_kwargs is None else sp_model_kwargs

      lowerCAmelCase__			:  int											= kwargs.get("""name_or_path"""				)
      if name_or_path is None:
          logger.warning(
              """name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
              """ you are testing the model, this can safely be ignored"""				)
          lowerCAmelCase__			:  str											= """None"""

      # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
      lowerCAmelCase__			:  Any											= """<|endoftext|>""" if eos_token is None else eos_token
      lowerCAmelCase__			:  List[str]											= """<unk>""" if unk_token is None else unk_token
      if "gpt-sw3-7b" in name_or_path:
          lowerCAmelCase__			:  Optional[Any]											= unk_token if pad_token is None else pad_token
          lowerCAmelCase__			:  str											= eos_token if bos_token is None else bos_token
      else:
          lowerCAmelCase__			:  Optional[int]											= """<pad>""" if pad_token is None else pad_token
          lowerCAmelCase__			:  Union[str, Any]											= """<s>""" if bos_token is None else bos_token

      super().__init__(
          do_lower_case=__UpperCAmelCase  ,remove_space=__UpperCAmelCase  ,keep_accents=__UpperCAmelCase  ,bos_token=__UpperCAmelCase  ,eos_token=__UpperCAmelCase  ,unk_token=__UpperCAmelCase  ,pad_token=__UpperCAmelCase  ,sp_model_kwargs=self.sp_model_kwargs  ,**__UpperCAmelCase  ,)

      lowerCAmelCase__			:  List[str]											= do_lower_case
      lowerCAmelCase__			:  Tuple											= remove_space
      lowerCAmelCase__			:  List[Any]											= keep_accents
      lowerCAmelCase__			:  Union[str, Any]											= vocab_file

      lowerCAmelCase__			:  int											= spm.SentencePieceProcessor(**self.sp_model_kwargs				)
      self.sp_model.Load(__UpperCAmelCase				)

      # Used for whitespace normalization in input texts
      # fmt : off
      lowerCAmelCase__			:  Tuple											= {""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
      # fmt : on

      # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
      lowerCAmelCase__			:  Dict											= re.compile(
          F"""[{''.join(map(__UpperCAmelCase  ,list(range(0  ,9				)				) + list(range(11  ,32				)				) + list(range(127  ,160				)				) + [160, 173, 8203]				)				)}]"""				)




  def __getstate__(   self				)   ->		List[str]:
      lowerCAmelCase__			:  List[str]											= self.__dict__.copy()
      lowerCAmelCase__			:  Optional[Any]											= None
      return state




  def __setstate__(   self  ,__UpperCAmelCase				)   ->		Dict:
      lowerCAmelCase__			:  Dict											= d

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

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




  @property
  # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
  def 							UpperCAmelCase_     (   self				)   ->		int:
      return len(self.sp_model				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		str:
      lowerCAmelCase__			:  Optional[int]											= self.non_printing_characters_re.sub(""""""  ,__UpperCAmelCase				)

      # Normalize whitespaces
      lowerCAmelCase__			:  Optional[Any]											= """""".join([char if char not in self.whitespaces else """ """ for char in text]				)

      # NFC Unicode normalization
      lowerCAmelCase__			:  Tuple											= unicodedata.normalize("""NFC"""  ,__UpperCAmelCase				)
      return text




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		List[str]:
      lowerCAmelCase__			:  List[str]											= self.preprocess_text(__UpperCAmelCase				)
      return self.sp_model.encode(__UpperCAmelCase  ,out_type=__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		int:
      return self.sp_model.PieceToId(__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		str:
      return self.sp_model.IdToPiece(__UpperCAmelCase				)




  @staticmethod
  def 							UpperCAmelCase_     (   __UpperCAmelCase				)   ->		str:
      return out_string




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		str:
      lowerCAmelCase__			:  Optional[int]											= []
      lowerCAmelCase__			:  Tuple											= """"""
      lowerCAmelCase__			:  str											= False
      for token in tokens:
          # make sure that special tokens are not decoded using sentencepiece model
          if token in self.all_special_tokens:
              # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
              if not prev_is_special:
                  out_string += " "

              out_string += self.sp_model.decode(__UpperCAmelCase				) + token
              lowerCAmelCase__			:  List[Any]											= True
              lowerCAmelCase__			:  Tuple											= []
          else:
              current_sub_tokens.append(__UpperCAmelCase				)
              lowerCAmelCase__			:  List[Any]											= False
      out_string += self.sp_model.decode(__UpperCAmelCase				)

      return out_string




  def 							UpperCAmelCase_     (   self				)   ->		Dict[str, int]:
      lowerCAmelCase__			:  int											= {self.convert_ids_to_tokens(__UpperCAmelCase				): i for i in range(self.vocab_size				)}
      vocab.update(self.added_tokens_encoder				)
      return vocab




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase = None				)   ->		Tuple[str]:
      if not os.path.isdir(__UpperCAmelCase				):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""				)
          return
      lowerCAmelCase__			:  List[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:
              lowerCAmelCase__			:  Optional[Any]											= self.sp_model.serialized_model_proto()
              fi.write(__UpperCAmelCase				)

      return (out_vocab_file,)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase = False				)   ->		Union[List[int], List[List[int]], "torch.Tensor"]:

      if isinstance(__UpperCAmelCase  ,__UpperCAmelCase				):
          lowerCAmelCase__			:  str											= self.preprocess_text(__UpperCAmelCase				)
          lowerCAmelCase__			:  str											= self.sp_model.encode(__UpperCAmelCase				)
      else:
          lowerCAmelCase__			:  Dict											= [self.preprocess_text(__UpperCAmelCase				) for t in text]
          lowerCAmelCase__			:  Optional[int]											= self.sp_model.encode(__UpperCAmelCase				)

      if return_tensors is True or return_tensors == "pt":
          lowerCAmelCase__			:  str											= torch.tensor(__UpperCAmelCase				)

      return token_ids




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		str:
      return self.sp_model.decode(__UpperCAmelCase				)






  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		List[int]:
      lowerCAmelCase__			:  Optional[int]											= [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
      lowerCAmelCase__			:  Union[str, Any]											= (
          F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(__UpperCAmelCase				) + F"""{self.bos_token}Bot:"""
      )
      return self.encode(text=__UpperCAmelCase				)







'''simple docstring'''





import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				List[str]           =				IFInpaintingSuperResolutionPipeline
  __lowercase							:				int           =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
  __lowercase							:				List[str]           =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}					)
  __lowercase							:				Optional[Any]           =				PipelineTesterMixin.required_optional_params - {'''latents'''}




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      return self._get_superresolution_dummy_components()




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase=0				)   ->		List[Any]:
      if str(__UpperCAmelCase				).startswith("""mps"""				):
          lowerCAmelCase__			:  Any											= torch.manual_seed(__UpperCAmelCase				)
      else:
          lowerCAmelCase__			:  Dict											= torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)

      lowerCAmelCase__			:  Tuple											= floats_tensor((1, 3, 16, 16)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
      lowerCAmelCase__			:  Union[str, Any]											= floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
      lowerCAmelCase__			:  Any											= floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)

      lowerCAmelCase__			:  Dict											= {
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": image,
          """original_image""": original_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 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      self._test_save_load_optional_components()




  @unittest.skipIf(torch_device != """cuda"""  ,reason="""float16 requires CUDA"""				)
  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
      super().test_save_load_floataa(expected_max_diff=1E-1				)




  def 							UpperCAmelCase_     (   self				)   ->		str:
      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      self._test_save_load_local()






  def 							UpperCAmelCase_     (   self				)   ->		int:
      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2  ,)


