"""simple docstring"""






from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


lowerCAmelCase__						:						int		  =			logging.get_logger(__name__)

lowerCAmelCase__						:						Optional[int]		  =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__						:						List[Any]		  =			{
    'vocab_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
    },
    'merges_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
    },
}

lowerCAmelCase__						:						Optional[Any]		  =			{'allegro/herbert-base-cased': 514}
lowerCAmelCase__						:						int		  =			{}
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				VOCAB_FILES_NAMES
					snake_case__       					=				PRETRAINED_VOCAB_FILES_MAP
					snake_case__       					=				PRETRAINED_INIT_CONFIGURATION
					snake_case__       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					snake_case__       					=				HerbertTokenizer







					def __init__(       self     :			Union[str, Any]					,lowerCamelCase__     :			List[str]=None					,lowerCamelCase__     :			Optional[Any]=None					,lowerCamelCase__     :			Tuple=None					,lowerCamelCase__     :			Optional[int]="<s>"					,lowerCamelCase__     :			Dict="<unk>"					,lowerCamelCase__     :			Optional[Any]="<pad>"					,lowerCamelCase__     :			List[Any]="<mask>"					,lowerCamelCase__     :			List[str]="</s>"					,**lowerCamelCase__     :			Tuple					,):
									super().__init__(
									    lowerCamelCase__					,lowerCamelCase__					,tokenizer_file=lowerCamelCase__					,cls_token=lowerCamelCase__					,unk_token=lowerCamelCase__					,pad_token=lowerCamelCase__					,mask_token=lowerCamelCase__					,sep_token=lowerCamelCase__					,**lowerCamelCase__					,)







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]					,lowerCamelCase__     :			List[int]					,lowerCamelCase__     :			Optional[List[int]] = None    ):
									UpperCAmelCase__    =							[self.cls_token_id]
									UpperCAmelCase__    =							[self.sep_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 	__lowerCAmelCase		(       self     :			List[str]					,lowerCamelCase__     :			List[int]					,lowerCamelCase__     :			Optional[List[int]] = None					,lowerCamelCase__     :			bool = False    ):
									if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=lowerCamelCase__					,token_ids_a=lowerCamelCase__					,already_has_special_tokens=lowerCamelCase__    )

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







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			List[int]					,lowerCamelCase__     :			Optional[List[int]] = None    ):
									UpperCAmelCase__    =							[self.sep_token_id]
									UpperCAmelCase__    =							[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 	__lowerCAmelCase		(       self     :			Any					,lowerCamelCase__     :			str					,lowerCamelCase__     :			Optional[str] = None    ):
									UpperCAmelCase__    =							self._tokenizer.model.save(lowerCamelCase__					,name=lowerCamelCase__    )
									return tuple(lowerCamelCase__    )




"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCAmelCase__						:						Union[str, Any]		  =			False
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					pass






@nightly
@require_torch_gpu
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Any    ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def 	__lowerCAmelCase		(       self     :			Any    ):
									UpperCAmelCase__    =							VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'					,torch_dtype=torch.floataa    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )

									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe.dual_guided(
									    prompt='first prompt'					,image=lowerCamelCase__					,text_to_image_strength=0.7_5					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=2					,output_type='numpy'					,).images

									with tempfile.TemporaryDirectory() as tmpdirname:
													pipe.save_pretrained(lowerCamelCase__    )
													UpperCAmelCase__    =							VersatileDiffusionPipeline.from_pretrained(lowerCamelCase__					,torch_dtype=torch.floataa    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							generator.manual_seed(0    )
									UpperCAmelCase__    =							pipe.dual_guided(
									    prompt='first prompt'					,image=lowerCamelCase__					,text_to_image_strength=0.7_5					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=2					,output_type='numpy'					,).images

									assert np.abs(image - new_image    ).sum() < 1e-5, "Models don't have the same forward pass"







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'					,torch_dtype=torch.floataa    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							'cyberpunk 2077'
									UpperCAmelCase__    =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )
									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe.dual_guided(
									    prompt=lowerCamelCase__					,image=lowerCamelCase__					,text_to_image_strength=0.7_5					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=50					,output_type='numpy'					,).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]    )

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

									UpperCAmelCase__    =							'A painting of a squirrel eating a burger '
									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe.text_to_image(
									    prompt=lowerCamelCase__					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=50					,output_type='numpy'    ).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]    )

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

									UpperCAmelCase__    =							pipe.image_variation(lowerCamelCase__					,generator=lowerCamelCase__					,output_type='numpy'    ).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]    )

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



