


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       __magic_name__	(     unittest.TestCase):


					def      UpperCAmelCase__	(      self							:     Dict			)  ->      Optional[Any]:





						'''simple docstring'''






						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()


					def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Optional[Any]:





						'''simple docstring'''






						UpperCamelCase__					:    Any          =   StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''			)
						UpperCamelCase__					:    List[str]          =   sd_pipe.to(lowerCamelCase__			)
						sd_pipe.set_progress_bar_config(disable=lowerCamelCase__			)

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

						UpperCamelCase__					:    Optional[Any]          =   '''A painting of a squirrel eating a burger'''
						UpperCamelCase__					:    str          =   torch.manual_seed(0			)
						UpperCamelCase__					:    Any          =   sd_pipe([prompt]							,	generator=lowerCamelCase__							,	guidance_scale=9.0							,	num_inference_steps=20							,	output_type='''np'''			)

						UpperCamelCase__					:    int          =   output.images

						UpperCamelCase__					:    List[Any]          =   image[0, -3:, -3:, -1]

						assert image.shape == (1, 512, 512, 3)
						UpperCamelCase__					:    Union[str, Any]          =   np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]			)

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


					def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Optional[int]:





						'''simple docstring'''






						UpperCamelCase__					:    Dict          =   StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''			)
						UpperCamelCase__					:    Optional[int]          =   sd_pipe.to(lowerCamelCase__			)
						sd_pipe.set_progress_bar_config(disable=lowerCamelCase__			)

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

						UpperCamelCase__					:    int          =   '''A painting of a squirrel eating a burger'''
						UpperCamelCase__					:    Optional[Any]          =   torch.manual_seed(0			)
						UpperCamelCase__					:    Dict          =   sd_pipe([prompt]							,	generator=lowerCamelCase__							,	guidance_scale=9.0							,	num_inference_steps=20							,	output_type='''np'''			)

						UpperCamelCase__					:    Any          =   output.images

						UpperCamelCase__					:    str          =   image[0, -3:, -3:, -1]

						assert image.shape == (1, 512, 512, 3)
						UpperCamelCase__					:    str          =   np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]			)

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


					def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      Optional[Any]:





						'''simple docstring'''






						UpperCamelCase__					:    Any          =   StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''			)
						UpperCamelCase__					:    Optional[int]          =   sd_pipe.to(lowerCamelCase__			)
						sd_pipe.set_progress_bar_config(disable=lowerCamelCase__			)

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

						UpperCamelCase__					:    Dict          =   '''A painting of a squirrel eating a burger'''
						UpperCamelCase__					:    Tuple          =   torch.manual_seed(0			)
						UpperCamelCase__					:    List[Any]          =   sd_pipe(
						    [prompt]							,	generator=lowerCamelCase__							,	guidance_scale=7.5							,	num_inference_steps=15							,	output_type='''np'''							,	use_karras_sigmas=lowerCamelCase__							,	)

						UpperCamelCase__					:    List[str]          =   output.images

						UpperCamelCase__					:    Optional[int]          =   image[0, -3:, -3:, -1]

						assert image.shape == (1, 512, 512, 3)
						UpperCamelCase__					:    Optional[int]          =   np.array(
						    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]			)

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






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_camembert import CamembertTokenizer
else:
       __UpperCamelCase       :       Tuple						       =				None


__UpperCamelCase       :       Optional[Any]						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Any						       =				{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__UpperCamelCase       :       Tuple						       =				{
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

__UpperCamelCase       :       Optional[Any]						       =				{
    "camembert-base": 512,
}

__UpperCamelCase       :       Optional[Any]						       =				"▁"

class       __magic_name__	(     __lowerCAmelCase):
     A:		int			=      VOCAB_FILES_NAMES
     A:		Union[str, Any]			=      PRETRAINED_VOCAB_FILES_MAP
     A:		Optional[int]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:		List[str]			=      ["input_ids", "attention_mask"]
     A:		Dict			=      CamembertTokenizer


     def __init__(      self							:     int							,	lowerCamelCase__							:     Any=None							,	lowerCamelCase__							:     Any=None							,	lowerCamelCase__							:     Dict="<s>"							,	lowerCamelCase__							:     List[str]="</s>"							,	lowerCamelCase__							:     int="</s>"							,	lowerCamelCase__							:     List[str]="<s>"							,	lowerCamelCase__							:     Optional[int]="<unk>"							,	lowerCamelCase__							:     Union[str, Any]="<pad>"							,	lowerCamelCase__							:     List[Any]="<mask>"							,	lowerCamelCase__							:     List[Any]=["<s>NOTUSED", "</s>NOTUSED"]							,	**lowerCamelCase__							:     Optional[int]							,	)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   AddedToken(lowerCamelCase__							,	lstrip=lowerCamelCase__							,	rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__							,	lowerCamelCase__			) else mask_token

      super().__init__(
          lowerCamelCase__							,	tokenizer_file=lowerCamelCase__							,	bos_token=lowerCamelCase__							,	eos_token=lowerCamelCase__							,	sep_token=lowerCamelCase__							,	cls_token=lowerCamelCase__							,	unk_token=lowerCamelCase__							,	pad_token=lowerCamelCase__							,	mask_token=lowerCamelCase__							,	additional_special_tokens=lowerCamelCase__							,	**lowerCamelCase__							,	)

      UpperCamelCase__					:    Tuple          =   vocab_file
      UpperCamelCase__					:    Optional[Any]          =   False if not self.vocab_file else True


     def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCamelCase__					:    Any          =   [self.cls_token_id]
      UpperCamelCase__					:    Dict          =   [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   [self.sep_token_id]
      UpperCamelCase__					:    Tuple          =   [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 + sep + token_ids_a + sep			) * [0]


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     Optional[str] = None			)  ->      Tuple[str]:





      '''simple docstring'''






      if not self.can_save_slow_tokenizer:
       raise ValueError(
           '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
           '''tokenizer.'''			)

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

      if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCamelCase__			):
       copyfile(self.vocab_file							,	lowerCamelCase__			)

      return (out_vocab_file,)


