





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       :     int   =     logging.get_logger(__name__)
__lowerCamelCase       :     int   =     {"""vocab_file""": """spiece.model"""}

__lowerCamelCase       :     Optional[int]   =     {
    """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       :     Optional[int]   =     {
    """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 					A__   (       __snake_case			):
      _UpperCAmelCase	:str						    =   VOCAB_FILES_NAMES
      _UpperCAmelCase	:Tuple						    =   PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase	:List[str]						    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase	:List[str]						    =   ['input_ids', 'attention_mask']





      def __init__( self       ,							A_       ,							A_=False       ,							A_=False       ,							A_=False       ,							A_=None       ,							A_=None       ,							A_=None       ,							A_=None       ,							A_ = None       ,							**A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   {} if sp_model_kwargs is None else sp_model_kwargs

       UpperCamelCase	:					str       	=   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"   )
        UpperCamelCase	:					int       	=   "None"

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

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

       UpperCamelCase	:					List[str]       	=   do_lower_case
       UpperCamelCase	:					List[str]       	=   remove_space
       UpperCamelCase	:					Any       	=   keep_accents
       UpperCamelCase	:					str       	=   vocab_file

       UpperCamelCase	:					Union[str, Any]       	=   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(A_   )

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

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





      def __getstate__( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   self.__dict__.copy()
       UpperCamelCase	:					int       	=   None
       return state





      def __setstate__( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   d

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

       UpperCamelCase	:					Any       	=   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   ):
       '''simple docstring'''




       return len(self.sp_model   )





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   self.non_printing_characters_re.sub(""       ,							A_   )

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

       # NFC Unicode normalization
       UpperCamelCase	:					List[str]       	=   unicodedata.normalize("NFC"       ,							A_   )
       return text





      def        __UpperCamelCase( self       ,							A_       ,							**A_   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   self.preprocess_text(A_   )
       return self.sp_model.encode(A_       ,							out_type=A_   )





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       return self.sp_model.PieceToId(A_   )





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       return self.sp_model.IdToPiece(A_   )





      @staticmethod
      def        __UpperCamelCase( A_   ):
       '''simple docstring'''




       return out_string





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   []
       UpperCamelCase	:					Optional[int]       	=   ""
       UpperCamelCase	:					Optional[Any]       	=   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(A_   ) + token
         UpperCamelCase	:					Optional[int]       	=   True
         UpperCamelCase	:					List[str]       	=   []
        else:
         current_sub_tokens.append(A_   )
         UpperCamelCase	:					Dict       	=   False
       out_string += self.sp_model.decode(A_   )

       return out_string





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   {self.convert_ids_to_tokens(A_   ): i for i in range(self.vocab_size   )}
       vocab.update(self.added_tokens_encoder   )
       return vocab





      def        __UpperCamelCase( self       ,							A_       ,							A_ = None   ):
       '''simple docstring'''




       if not os.path.isdir(A_   ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
        return
       UpperCamelCase	:					Tuple       	=   os.path.join(
           A_       ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

       if os.path.abspath(self.vocab_file   ) != os.path.abspath(A_   ) and os.path.isfile(self.vocab_file   ):
        copyfile(self.vocab_file       ,							A_   )
       elif not os.path.isfile(self.vocab_file   ):
        with open(A_       ,							"wb"   ) as fi:
         UpperCamelCase	:					List[str]       	=   self.sp_model.serialized_model_proto()
         fi.write(A_   )

       return (out_vocab_file,)





      def        __UpperCamelCase( self       ,							A_       ,							A_ = False   ):
       '''simple docstring'''




       if isinstance(A_       ,							A_   ):
        UpperCamelCase	:					int       	=   self.preprocess_text(A_   )
        UpperCamelCase	:					List[str]       	=   self.sp_model.encode(A_   )
       else:
        UpperCamelCase	:					Optional[int]       	=   [self.preprocess_text(A_   ) for t in text]
        UpperCamelCase	:					Union[str, Any]       	=   self.sp_model.encode(A_   )

       if return_tensors is True or return_tensors == "pt":
        UpperCamelCase	:					int       	=   torch.tensor(A_   )

       return token_ids





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       return self.sp_model.decode(A_   )





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
       UpperCamelCase	:					str       	=   (
           F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(A_   ) + F"""{self.bos_token}Bot:"""
       )
       return self.encode(text=A_   )



import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
					import jax
					import jax.numpy as jnp
					from flax.jax_utils import replicate
					from flax.training.common_utils import shard







@slow
@require_flax
class  lowercase				(       unittest.TestCase			):




				def     a							(					self       ):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()




				def     a							(					self       ):
											snake_case_							=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/sd2-inpaint/init_image.png'       )
											snake_case_							=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'       )

											snake_case_							=    'xvjiarui/stable-diffusion-2-inpainting'
											snake_case_				,    snake_case_							=    FlaxStableDiffusionInpaintPipeline.from_pretrained(snake_case      ,   safety_checker=snake_case       )

											snake_case_							=    'Face of a yellow cat, high resolution, sitting on a park bench'

											snake_case_							=    jax.random.PRNGKey(0       )
											snake_case_							=    50

											snake_case_							=    jax.device_count()
											snake_case_							=    num_samples * [prompt]
											snake_case_							=    num_samples * [init_image]
											snake_case_							=    num_samples * [mask_image]
											snake_case_				,    snake_case_				,    snake_case_							=    pipeline.prepare_inputs(snake_case      ,   snake_case      ,   snake_case       )

											# shard inputs and rng
											snake_case_							=    replicate(snake_case       )
											snake_case_							=    jax.random.split(snake_case      ,   jax.device_count()       )
											snake_case_							=    shard(snake_case       )
											snake_case_							=    shard(snake_case       )
											snake_case_							=    shard(snake_case       )

											snake_case_							=    pipeline(
											    snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   jit=snake_case       )

											snake_case_							=    output.images.reshape(snake_case      ,   512      ,   512      ,   3       )

											snake_case_							=    images[0, 253:256, 253:256, -1]

											snake_case_							=    jnp.asarray(jax.device_get(image_slice.flatten()       )       )
											snake_case_							=    jnp.array(
											    [0.3_61_13_07, 0.37_64_97_36, 0.3_75_74_08, 0.38_21_39_53, 0.39_29_51_67, 0.3_84_16_31, 0.41_55_49_78, 0.4_13_74_75, 0.4_21_70_84]       )
											print(F'''output_slice: {output_slice}'''       )
											assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2




