"""simple docstring"""



import os
import re
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


__lowercase		:		Union[str, Any]														=							logging.get_logger(__name__)

__lowercase		:		Optional[int]														=							{"vocab_file": "spiece.model"}

__lowercase		:		int														=							{
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    }
}

__lowercase		:		List[str]														=							{
    "google/bigbird-roberta-base": 4096,
    "google/bigbird-roberta-large": 4096,
    "google/bigbird-base-trivia-itc": 4096,
}



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					List[str]							=					VOCAB_FILES_NAMES
    UpperCamelCase_	:					str							=					PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase_	:					Optional[int]							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCamelCase_	:					Any							=					['''input_ids''', '''attention_mask''']
    UpperCamelCase_	:					List[int]							=					[]
    def __init__(		self							:     Optional[Any]	,      A_							:     List[Any]	,      A_							:     int="<unk>"	,      A_							:     Dict="<s>"	,      A_							:     Tuple="</s>"	,      A_							:     str="<pad>"	,      A_							:     Optional[Any]="[SEP]"	,      A_							:     List[str]="[MASK]"	,      A_							:     Union[str, Any]="[CLS]"	,      A_							:     Optional[Dict[str, Any]] = None	,      **A_							:     Any	,      )    ->       None:
         __snake_case				=							AddedToken(A_	,      lstrip=A_	,      rstrip=A_    ) if isinstance(A_	,      A_    ) else bos_token
         __snake_case				=							AddedToken(A_	,      lstrip=A_	,      rstrip=A_    ) if isinstance(A_	,      A_    ) else eos_token
         __snake_case				=							AddedToken(A_	,      lstrip=A_	,      rstrip=A_    ) if isinstance(A_	,      A_    ) else unk_token
         __snake_case				=							AddedToken(A_	,      lstrip=A_	,      rstrip=A_    ) if isinstance(A_	,      A_    ) else pad_token
         __snake_case				=							AddedToken(A_	,      lstrip=A_	,      rstrip=A_    ) if isinstance(A_	,      A_    ) else cls_token
         __snake_case				=							AddedToken(A_	,      lstrip=A_	,      rstrip=A_    ) if isinstance(A_	,      A_    ) else sep_token

         # Mask token behave like a normal word, i.e. include the space before it
         __snake_case				=							AddedToken(A_	,      lstrip=A_	,      rstrip=A_    ) if isinstance(A_	,      A_    ) else mask_token

         __snake_case				=							{} if sp_model_kwargs is None else sp_model_kwargs

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

         __snake_case				=							vocab_file

         __snake_case				=							spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.Load(A_    )
    @property
    def  lowercase					(		self							:     List[str]    )    ->       Tuple:
         return self.sp_model.get_piece_size()
    def  lowercase					(		self							:     Union[str, Any]    )    ->       List[str]:
         __snake_case				=							{self.convert_ids_to_tokens(A_    ): i for i in range(self.vocab_size    )}
         vocab.update(self.added_tokens_encoder    )
         return vocab
    def __getstate__(		self							:     Tuple    )    ->       Union[str, Any]:
         __snake_case				=							self.__dict__.copy()
         __snake_case				=							None
         return state
    def __setstate__(		self							:     Dict	,      A_							:     Optional[int]    )    ->       Optional[int]:
         __snake_case				=							d

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

         __snake_case				=							spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.Load(self.vocab_file    )
    def  lowercase					(		self							:     Dict	,      A_							:     str    )    ->       List[str]:
         return self.sp_model.encode(A_	,      out_type=A_    )
    def  lowercase					(		self							:     Dict	,      A_							:     int    )    ->       Tuple:
         return self.sp_model.piece_to_id(A_    )
    def  lowercase					(		self							:     Optional[Any]	,      A_							:     Optional[int]    )    ->       str:
         __snake_case				=							self.sp_model.IdToPiece(A_    )
         return token
    def  lowercase					(		self							:     Any	,      A_							:     List[str]    )    ->       str:
         __snake_case				=							[]
         __snake_case				=							''''''
         __snake_case				=							False
         for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                   if not prev_is_special:
                        out_string += " "
                   out_string += self.sp_model.decode(A_    ) + token
                   __snake_case				=							True
                   __snake_case				=							[]
              else:
                   current_sub_tokens.append(A_    )
                   __snake_case				=							False
         out_string += self.sp_model.decode(A_    )
         return out_string.strip()
    def  lowercase					(		self							:     int	,      A_							:     List[int]	,      A_							:     bool = False	,      A_							:     bool = None	,      A_							:     bool = True	,      **A_							:     List[Any]	,      )    ->       str:
         __snake_case				=							kwargs.pop('''use_source_tokenizer'''	,      A_    )

         __snake_case				=							self.convert_ids_to_tokens(A_	,      skip_special_tokens=A_    )

         # To avoid mixing byte-level and unicode for byte-level BPT
         # we need to build string separately for added tokens and byte-level tokens
         # cf. https://github.com/huggingface/transformers/issues/1133
         __snake_case				=							[]
         __snake_case				=							[]
         for token in filtered_tokens:
              if skip_special_tokens and token in self.all_special_ids:
                   continue
              if token in self.added_tokens_encoder:
                   if current_sub_text:
                        sub_texts.append(self.convert_tokens_to_string(A_    )    )
                        __snake_case				=							[]
                   sub_texts.append(A_    )
              else:
                   current_sub_text.append(A_    )
         if current_sub_text:
              sub_texts.append(self.convert_tokens_to_string(A_    )    )

         # Mimic the behavior of the Rust tokenizer:
         # No space before [MASK] and [SEP]
         if spaces_between_special_tokens:
              __snake_case				=							re.sub(R''' (\[(MASK|SEP)\])'''	,      R'''\1'''	,      ''' '''.join(A_    )    )
         else:
              __snake_case				=							''''''.join(A_    )

         __snake_case				=							(
             clean_up_tokenization_spaces
             if clean_up_tokenization_spaces is not None
             else self.clean_up_tokenization_spaces
         )
         if clean_up_tokenization_spaces:
              __snake_case				=							self.clean_up_tokenization(A_    )
              return clean_text
         else:
              return text
    def  lowercase					(		self							:     Any	,      A_							:     str	,      A_							:     Optional[str] = None    )    ->       Tuple[str]:
         if not os.path.isdir(A_    ):
              logger.error(f"Vocabulary path ({save_directory}) should be a directory"    )
              return
         __snake_case				=							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:
                   __snake_case				=							self.sp_model.serialized_model_proto()
                   fi.write(A_    )

         return (out_vocab_file,)
    def  lowercase					(		self							:     Dict	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       List[int]:
         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         __snake_case				=							[self.cls_token_id]
         __snake_case				=							[self.sep_token_id]
         return cls + token_ids_a + sep + token_ids_a + sep
    def  lowercase					(		self							:     Any	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None	,      A_							:     bool = False    )    ->       List[int]:
         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=A_	,      token_ids_a=A_	,      already_has_special_tokens=A_    )

         if token_ids_a is None:
              return [1] + ([0] * len(A_    )) + [1]
         return [1] + ([0] * len(A_    )) + [1] + ([0] * len(A_    )) + [1]







    def  lowercase					(		self							:     Tuple	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       List[int]:
         __snake_case				=							[self.sep_token_id]
         __snake_case				=							[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    ) * [0] + len(token_ids_a + sep    ) * [1]
"""simple docstring"""



import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



@slow
@require_torch_gpu
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     Tuple    )    ->       str:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
    def  lowercase					(		self							:     int    )    ->       Tuple:
         __snake_case				=							StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''    )
         __snake_case				=							sd_pipe.to(A_    )
         sd_pipe.set_progress_bar_config(disable=A_    )

         sd_pipe.set_scheduler('''sample_euler'''    )

         __snake_case				=							'''A painting of a squirrel eating a burger'''
         __snake_case				=							torch.manual_seed(0    )
         __snake_case				=							sd_pipe([prompt]	,      generator=A_	,      guidance_scale=9.0	,      num_inference_steps=20	,      output_type='''np'''    )

         __snake_case				=							output.images

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

         assert image.shape == (1, 512, 512, 3)
         __snake_case				=							np.array([0.04_47, 0.04_92, 0.04_68, 0.04_08, 0.03_83, 0.04_08, 0.03_54, 0.03_80, 0.03_39]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
    def  lowercase					(		self							:     Optional[Any]    )    ->       Tuple:
         __snake_case				=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''    )
         __snake_case				=							sd_pipe.to(A_    )
         sd_pipe.set_progress_bar_config(disable=A_    )

         sd_pipe.set_scheduler('''sample_euler'''    )

         __snake_case				=							'''A painting of a squirrel eating a burger'''
         __snake_case				=							torch.manual_seed(0    )
         __snake_case				=							sd_pipe([prompt]	,      generator=A_	,      guidance_scale=9.0	,      num_inference_steps=20	,      output_type='''np'''    )

         __snake_case				=							output.images

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

         assert image.shape == (1, 512, 512, 3)
         __snake_case				=							np.array([0.12_37, 0.13_20, 0.14_38, 0.13_59, 0.13_90, 0.11_32, 0.12_77, 0.11_75, 0.11_12]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5E-1







    def  lowercase					(		self							:     List[str]    )    ->       Optional[Any]:
         __snake_case				=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''    )
         __snake_case				=							sd_pipe.to(A_    )
         sd_pipe.set_progress_bar_config(disable=A_    )

         sd_pipe.set_scheduler('''sample_dpmpp_2m'''    )

         __snake_case				=							'''A painting of a squirrel eating a burger'''
         __snake_case				=							torch.manual_seed(0    )
         __snake_case				=							sd_pipe(
             [prompt]	,      generator=A_	,      guidance_scale=7.5	,      num_inference_steps=15	,      output_type='''np'''	,      use_karras_sigmas=A_	,      )

         __snake_case				=							output.images

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

         assert image.shape == (1, 512, 512, 3)
         __snake_case				=							np.array(
             [0.11_38_16_89, 0.12_11_29_21, 0.1_38_94_57, 0.12_54_96_06, 0.1_24_49_64, 0.10_83_15_17, 0.11_56_28_66, 0.10_86_78_16, 0.10_49_90_48]    )

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