


import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 			_a				(     unittest.TestCase     ):

							@property
							def  lowerCamelCase_					(				self:      Tuple				)     ->  List[str]:





									"""simple docstring"""







									torch.manual_seed(0				)
									lowercase__				= UNetaDModel(
									    block_out_channels=(32, 64)       ,		layers_per_block=2       ,		sample_size=32       ,		in_channels=3       ,		out_channels=3       ,		down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')       ,		up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')       ,		)
									return model


							def  lowerCamelCase_					(				self:      Dict				)     ->  List[str]:





									"""simple docstring"""







									lowercase__				= self.dummy_uncond_unet
									lowercase__				= ScoreSdeVeScheduler()

									lowercase__				= ScoreSdeVePipeline(unet=UpperCamelCase_       ,		scheduler=UpperCamelCase_				)
									sde_ve.to(UpperCamelCase_				)
									sde_ve.set_progress_bar_config(disable=UpperCamelCase_				)

									lowercase__				= torch.manual_seed(0				)
									lowercase__				= sde_ve(num_inference_steps=2       ,		output_type='''numpy'''       ,		generator=UpperCamelCase_				).images

									lowercase__				= torch.manual_seed(0				)
									lowercase__				= sde_ve(num_inference_steps=2       ,		output_type='''numpy'''       ,		generator=UpperCamelCase_       ,		return_dict=UpperCamelCase_				)[
									    0
									]

									lowercase__				= image[0, -3:, -3:, -1]
									lowercase__				= image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									lowercase__				= np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]				)

									assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2


@slow
@require_torch
class 			_a				(     unittest.TestCase     ):

							def  lowerCamelCase_					(				self:      Any				)     ->  Tuple:





									"""simple docstring"""







									lowercase__				= '''google/ncsnpp-church-256'''
									lowercase__				= UNetaDModel.from_pretrained(UpperCamelCase_				)

									lowercase__				= ScoreSdeVeScheduler.from_pretrained(UpperCamelCase_				)

									lowercase__				= ScoreSdeVePipeline(unet=UpperCamelCase_       ,		scheduler=UpperCamelCase_				)
									sde_ve.to(UpperCamelCase_				)
									sde_ve.set_progress_bar_config(disable=UpperCamelCase_				)

									lowercase__				= torch.manual_seed(0				)
									lowercase__				= sde_ve(num_inference_steps=10       ,		output_type='''numpy'''       ,		generator=UpperCamelCase_				).images

									lowercase__				= image[0, -3:, -3:, -1]

									assert image.shape == (1, 256, 256, 3)

									lowercase__				= np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]				)

									assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2







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


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

a_								=       {
    """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""",
    }
}

a_								=       {
    """AI-Sweden/gpt-sw3-126m""": 2_048,
    """AI-Sweden/gpt-sw3-350m""": 2_048,
    """AI-Sweden/gpt-sw3-1.6b""": 2_048,
    """AI-Sweden/gpt-sw3-6.7b""": 2_048,
    """AI-Sweden/gpt-sw3-20b""": 2_048,
}



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	VOCAB_FILES_NAMES
 lowerCAmelCase__							       =	PRETRAINED_VOCAB_FILES_MAP
 lowerCAmelCase__							       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCAmelCase__							       =	["""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						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     {} if sp_model_kwargs is None else sp_model_kwargs

    __lowerCamelCase    				=     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    				=     '''None'''

    # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
    __lowerCamelCase    				=     '''<|endoftext|>''' if eos_token is None else eos_token
    __lowerCamelCase    				=     '''<unk>''' if unk_token is None else unk_token
    if "gpt-sw3-7b" in name_or_path:
       __lowerCamelCase    				=     unk_token if pad_token is None else pad_token
       __lowerCamelCase    				=     eos_token if bos_token is None else bos_token
    else:
       __lowerCamelCase    				=     '''<pad>''' if pad_token is None else pad_token
       __lowerCamelCase    				=     '''<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    				=     do_lower_case
    __lowerCamelCase    				=     remove_space
    __lowerCamelCase    				=     keep_accents
    __lowerCamelCase    				=     vocab_file

    __lowerCamelCase    				=     spm.SentencePieceProcessor(**self.sp_model_kwargs					)
    self.sp_model.Load(__UpperCAmelCase					)

    # Used for whitespace normalization in input texts
    # fmt : off
    __lowerCamelCase    				=     {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''　''', ''' ''', ''' ''', ''' ''', ''' ''', '''￼''', ''''''}
    # fmt : on

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




 def __getstate__(    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.__dict__.copy()
    __lowerCamelCase    				=     None
    return state




 def __setstate__(    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     d

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

    __lowerCamelCase    				=     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 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return len(self.sp_model					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.non_printing_characters_re.sub(''''''						,					__UpperCAmelCase					)

    # Normalize whitespaces
    __lowerCamelCase    				=     ''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]					)

    # NFC Unicode normalization
    __lowerCamelCase    				=     unicodedata.normalize('''NFC'''						,					__UpperCAmelCase					)
    return text




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					**__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.preprocess_text(__UpperCAmelCase					)
    return self.sp_model.encode(__UpperCAmelCase						,					out_type=__UpperCAmelCase					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    return self.sp_model.PieceToId(__UpperCAmelCase					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    return self.sp_model.IdToPiece(__UpperCAmelCase					)




 @staticmethod
 def 							lowerCamelCase   (    __UpperCAmelCase					):



    '''simple docstring'''
    return out_string




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     []
    __lowerCamelCase    				=     ''''''
    __lowerCamelCase    				=     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    				=     True
          __lowerCamelCase    				=     []
       else:
          current_sub_tokens.append(__UpperCAmelCase					)
          __lowerCamelCase    				=     False
    out_string += self.sp_model.decode(__UpperCAmelCase					)

    return out_string




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     {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						,					__UpperCAmelCase = None					):



    '''simple docstring'''
    if not os.path.isdir(__UpperCAmelCase					):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""					)
       return
    __lowerCamelCase    				=     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    				=     self.sp_model.serialized_model_proto()
          fi.write(__UpperCAmelCase					)

    return (out_vocab_file,)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase = False					):



    '''simple docstring'''

    if isinstance(__UpperCAmelCase						,					__UpperCAmelCase					):
       __lowerCamelCase    				=     self.preprocess_text(__UpperCAmelCase					)
       __lowerCamelCase    				=     self.sp_model.encode(__UpperCAmelCase					)
    else:
       __lowerCamelCase    				=     [self.preprocess_text(__UpperCAmelCase					) for t in text]
       __lowerCamelCase    				=     self.sp_model.encode(__UpperCAmelCase					)

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

    return token_ids




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    return self.sp_model.decode(__UpperCAmelCase					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
    __lowerCamelCase    				=     (
        F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(__UpperCAmelCase					) + F"""{self.bos_token}Bot:"""
    )
    return self.encode(text=__UpperCAmelCase					)






