



"""simple docstring"""

import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	def __init__(      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       str = "▁"     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       Union[str, AddedToken] = "<unk>"     ,       _SCREAMING_SNAKE_CASE				:       Union[str, AddedToken] = "</s>"     ,       _SCREAMING_SNAKE_CASE				:       Union[str, AddedToken] = "<pad>"     ,       )						->	List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     {
					    'pad': {'id': 0, 'token': pad_token},
					    'eos': {'id': 1, 'token': eos_token},
					    'unk': {'id': 2, 'token': unk_token},
					}

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     [None] * len(self.special_tokens    )
					for token_dict in self.special_tokens.values():
									SCREAMING_SNAKE_CASE						:							Tuple							=     token_dict['token']

					SCREAMING_SNAKE_CASE						:							Tuple							=     Tokenizer(Unigram()    )

					SCREAMING_SNAKE_CASE						:							List[str]							=     normalizers.Sequence(
					    [
					        normalizers.Nmt(),
					        normalizers.NFKC(),
					        normalizers.Replace(Regex(' {2,}'    )     ,       ' '    ),
					        normalizers.Lowercase(),
					    ]    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     pre_tokenizers.Sequence(
					    [
					        pre_tokenizers.Metaspace(replacement=_SCREAMING_SNAKE_CASE     ,       add_prefix_space=_SCREAMING_SNAKE_CASE    ),
					        pre_tokenizers.Digits(individual_digits=_SCREAMING_SNAKE_CASE    ),
					        pre_tokenizers.Punctuation(),
					    ]    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     decoders.Metaspace(replacement=_SCREAMING_SNAKE_CASE     ,       add_prefix_space=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     TemplateProcessing(
					    single=f"""$A {self.special_tokens["eos"]["token"]}"""     ,       special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])]     ,       )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     {
					    'model': 'SentencePieceUnigram',
					    'replacement': replacement,
					    'add_prefix_space': add_prefix_space,
					}

					super().__init__(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       Union[str, List[str]]     ,       _SCREAMING_SNAKE_CASE				:       int = 8_000     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     trainers.UnigramTrainer(
					    vocab_size=_SCREAMING_SNAKE_CASE     ,       special_tokens=self.special_tokens_list     ,       show_progress=_SCREAMING_SNAKE_CASE     ,       )

					if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							Dict							=     [files]
					self._tokenizer.train(_SCREAMING_SNAKE_CASE     ,       trainer=_SCREAMING_SNAKE_CASE    )

					self.add_unk_id()





	def 			_lowerCAmelCase     (      self				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Iterator[str], Iterator[Iterator[str]]]     ,       _SCREAMING_SNAKE_CASE				:       int = 8_000     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       )						->	Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     trainers.UnigramTrainer(
					    vocab_size=_SCREAMING_SNAKE_CASE     ,       special_tokens=self.special_tokens_list     ,       show_progress=_SCREAMING_SNAKE_CASE     ,       )

					self._tokenizer.train_from_iterator(_SCREAMING_SNAKE_CASE     ,       trainer=_SCREAMING_SNAKE_CASE    )

					self.add_unk_id()







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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     json.loads(self._tokenizer.to_str()    )

					SCREAMING_SNAKE_CASE						:							str							=     self.special_tokens['unk']['id']

					SCREAMING_SNAKE_CASE						:							int							=     Tokenizer.from_str(json.dumps(_SCREAMING_SNAKE_CASE    )    )










"""simple docstring"""

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


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


	'''simple docstring'''







	pass




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


	'''simple docstring'''







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





					"""simple docstring"""


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





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion'    )
					# remove text_unet
					pipe.remove_unused_weights()
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     'A painting of a squirrel eating a burger '
					SCREAMING_SNAKE_CASE						:							List[str]							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							int							=     pipe(
					    prompt=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       num_inference_steps=2     ,       output_type='numpy'    ).images

					with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							Dict							=     VersatileDiffusionTextToImagePipeline.from_pretrained(_SCREAMING_SNAKE_CASE    )
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     generator.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     pipe(
					    prompt=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       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				:       Dict    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     VersatileDiffusionTextToImagePipeline.from_pretrained(
					    'shi-labs/versatile-diffusion'     ,       torch_dtype=torch.floataa    )
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     'A painting of a squirrel eating a burger '
					SCREAMING_SNAKE_CASE						:							Dict							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Any							=     pipe(
					    prompt=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       num_inference_steps=50     ,       output_type='numpy'    ).images

					SCREAMING_SNAKE_CASE						:							Tuple							=     image[0, 253:256, 253:256, -1]

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     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-2





