


'''simple docstring'''






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


__UpperCAmelCase       							=				logging.get_logger(__name__)

__UpperCAmelCase       							=				{'''vocab_file''': '''vocab.txt'''}

__UpperCAmelCase       							=				{
    '''vocab_file''': {
        '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt''',
        '''YituTech/conv-bert-medium-small''': (
            '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'''
        ),
        '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt''',
    }
}

__UpperCAmelCase       							=				{
    '''YituTech/conv-bert-base''': 512,
    '''YituTech/conv-bert-medium-small''': 512,
    '''YituTech/conv-bert-small''': 512,
}


__UpperCAmelCase       							=				{
    '''YituTech/conv-bert-base''': {'''do_lower_case''': True},
    '''YituTech/conv-bert-medium-small''': {'''do_lower_case''': True},
    '''YituTech/conv-bert-small''': {'''do_lower_case''': True},
}




class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  List[Any]      		= VOCAB_FILES_NAMES
							lowercase__     :  Optional[Any]      		= PRETRAINED_VOCAB_FILES_MAP
							lowercase__     :  Optional[int]      		= PRETRAINED_INIT_CONFIGURATION
							lowercase__     :  List[str]      		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__     :  Union[str, Any]      		= ConvBertTokenizer







							def __init__(						self   ,       lowerCamelCase_=None   ,       lowerCamelCase_=None   ,       lowerCamelCase_=True   ,       lowerCamelCase_="[UNK]"   ,       lowerCamelCase_="[SEP]"   ,       lowerCamelCase_="[PAD]"   ,       lowerCamelCase_="[CLS]"   ,       lowerCamelCase_="[MASK]"   ,       lowerCamelCase_=True   ,       lowerCamelCase_=None   ,       **lowerCamelCase_   ,       )			->				Dict:
														super().__init__(
														    lowerCamelCase_   ,       tokenizer_file=lowerCamelCase_   ,       do_lower_case=lowerCamelCase_   ,       unk_token=lowerCamelCase_   ,       sep_token=lowerCamelCase_   ,       pad_token=lowerCamelCase_   ,       cls_token=lowerCamelCase_   ,       mask_token=lowerCamelCase_   ,       tokenize_chinese_chars=lowerCamelCase_   ,       strip_accents=lowerCamelCase_   ,       **lowerCamelCase_   ,       )

														lowerCAmelCase__			  =      json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
														if (
														    normalizer_state.get('''lowercase'''   ,       lowerCamelCase_  ) != do_lower_case
														    or normalizer_state.get('''strip_accents'''   ,       lowerCamelCase_  ) != strip_accents
														    or normalizer_state.get('''handle_chinese_chars'''   ,       lowerCamelCase_  ) != tokenize_chinese_chars
														):
																					lowerCAmelCase__			  =      getattr(lowerCamelCase_   ,       normalizer_state.pop('''type'''  )  )
																					lowerCAmelCase__			  =      do_lower_case
																					lowerCAmelCase__			  =      strip_accents
																					lowerCAmelCase__			  =      tokenize_chinese_chars
																					lowerCAmelCase__			  =      normalizer_class(**lowerCamelCase_  )

														lowerCAmelCase__			  =      do_lower_case







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=None  )			->				Optional[Any]:
														lowerCAmelCase__			  =      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

														if token_ids_a:
																					output += token_ids_a + [self.sep_token_id]

														return output







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = None  )			->				List[int]:
														lowerCAmelCase__			  =      [self.sep_token_id]
														lowerCAmelCase__			  =      [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     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = None  )			->				Tuple[str]:
														lowerCAmelCase__			  =      self._tokenizer.model.save(lowerCamelCase_   ,       name=lowerCamelCase_  )
														return tuple(lowerCamelCase_  )


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 _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			Dict						)				->							Tuple:

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"						)
				SCREAMING_SNAKE_CASE :   str					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				sd_pipe.set_scheduler("sample_euler"						)

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

				SCREAMING_SNAKE_CASE :   Tuple					  =							output.images

				SCREAMING_SNAKE_CASE :   Any					  =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   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[Any]						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				sd_pipe.set_scheduler("sample_euler"						)

				SCREAMING_SNAKE_CASE :   List[str]					  =							"A painting of a squirrel eating a burger"
				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							sd_pipe([prompt]   ,   generator=a   ,   guidance_scale=9.0   ,   num_inference_steps=20   ,   output_type="np"						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							output.images

				SCREAMING_SNAKE_CASE :   Tuple					  =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   int					  =							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				:			Tuple						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				sd_pipe.set_scheduler("sample_dpmpp_2m"						)

				SCREAMING_SNAKE_CASE :   str					  =							"A painting of a squirrel eating a burger"
				SCREAMING_SNAKE_CASE :   Any					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   str					  =							sd_pipe(
				    [prompt]   ,   generator=a   ,   guidance_scale=7.5   ,   num_inference_steps=15   ,   output_type="np"   ,   use_karras_sigmas=a   ,   )

				SCREAMING_SNAKE_CASE :   str					  =							output.images

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

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   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