"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_fnet import FNetTokenizer
else:
       __lowercase		:		int														=							None

__lowercase		:		Optional[int]														=							logging.get_logger(__name__)
__lowercase		:		str														=							{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__lowercase		:		Tuple														=							{
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

__lowercase		:		str														=							{
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

__lowercase		:		List[Any]														=							"▁"



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Any							=					VOCAB_FILES_NAMES
    UpperCamelCase_	:					Tuple							=					PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase_	:					Dict							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCamelCase_	:					Tuple							=					['''input_ids''', '''token_type_ids''']
    UpperCamelCase_	:					Union[str, Any]							=					FNetTokenizer
    def __init__(		self							:     List[Any]	,      A_							:     Dict=None	,      A_							:     List[str]=None	,      A_							:     str=False	,      A_							:     Union[str, Any]=True	,      A_							:     Union[str, Any]=True	,      A_							:     List[str]="<unk>"	,      A_							:     Optional[int]="[SEP]"	,      A_							:     Any="<pad>"	,      A_							:     Dict="[CLS]"	,      A_							:     Any="[MASK]"	,      **A_							:     Optional[Any]	,      )    ->       Union[str, Any]:
         # Mask token behave like a normal word, i.e. include the space before it and
         # is included in the raw text, there should be a match in a non-normalized sentence.
         __snake_case				=							(
             AddedToken(A_	,      lstrip=A_	,      rstrip=A_	,      normalized=A_    )
             if isinstance(A_	,      A_    )
             else mask_token
         )

         super().__init__(
             A_	,      tokenizer_file=A_	,      do_lower_case=A_	,      remove_space=A_	,      keep_accents=A_	,      unk_token=A_	,      sep_token=A_	,      pad_token=A_	,      cls_token=A_	,      mask_token=A_	,      **A_	,      )

         __snake_case				=							do_lower_case
         __snake_case				=							remove_space
         __snake_case				=							keep_accents
         __snake_case				=							vocab_file
         __snake_case				=							False if not self.vocab_file else True
    def  lowercase					(		self							:     List[str]	,      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 cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep
    def  lowercase					(		self							:     Any	,      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]







    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_    ):
              copyfile(self.vocab_file	,      A_    )

         return (out_vocab_file,)



"""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