


'''simple docstring'''



import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowercase	:						int       =	logging.get_logger(__name__)

__lowercase	:						List[str]       =	{'vocab_file': 'vocab.json'}

__lowercase	:						List[Any]       =	{
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

__lowercase	:						Tuple       =	{'mgp-str': 27}





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    VOCAB_FILES_NAMES
	A_				=    PRETRAINED_VOCAB_FILES_MAP
	A_				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




	def __init__(   self						,				__a						,				__a="[GO]"						,				__a="[GO]"						,				__a="[s]"						,				__a="[GO]"						,				**__a							):

						'''simple docstring'''





						super().__init__(
						    unk_token=__a						,				bos_token=__a						,				eos_token=__a						,				pad_token=__a						,				**__a						,				)

						with open(__a						,				encoding='utf-8'							) as vocab_handle:
											__a			:       Dict					    =  json.load(__a							)
						__a			:       Union[str, Any]					    =  {v: k for k, v in self.vocab.items()}




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return len(self.vocab							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return dict(self.vocab						,				**self.added_tokens_encoder							)




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						__a			:       str					    =  []
						for s in text:
											char_tokens.extend(__a							)
						return char_tokens




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						return self.vocab.get(__a						,				self.vocab.get(self.unk_token							)							)




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						return self.decoder.get(__a							)





	def       __UpperCAmelCase							(   self						,				__a						,				__a = None							):

						'''simple docstring'''





						if not os.path.isdir(__a							):
											logger.error('Vocabulary path ({}) should be a directory'.format(__a							)							)
											return
						__a			:       Any					    =  os.path.join(
						    __a						,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

						with open(__a						,				'w'						,				encoding='utf-8'							) as f:
											f.write(json.dumps(self.vocab						,				indent=2						,				sort_keys=__a						,				ensure_ascii=__a							) + '\n'							)

						return (vocab_file,)





'''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


lowercase    :							Optional[Any]      =					False


class 						__UpperCAmelCase      (					unittest.TestCase						):
							pass


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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion'  )
								# remove text_unet
								pipe.remove_unused_weights()
								pipe.to(lowerCAmelCase_  )
								pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

								_snake_case   						=		'A painting of a squirrel eating a burger '
								_snake_case   						=		torch.manual_seed(0  )
								_snake_case   						=		pipe(
								    prompt=lowerCAmelCase_    ,   generator=lowerCAmelCase_    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type='numpy'  ).images

								with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(lowerCAmelCase_  )
									_snake_case   						=		VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCAmelCase_  )
								pipe.to(lowerCAmelCase_  )
								pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

								_snake_case   						=		generator.manual_seed(0  )
								_snake_case   						=		pipe(
								    prompt=lowerCAmelCase_    ,   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  ):





								"""simple docstring"""






								_snake_case   						=		VersatileDiffusionTextToImagePipeline.from_pretrained(
								    'shi-labs/versatile-diffusion'    ,   torch_dtype=torch.floataa  )
								pipe.to(lowerCAmelCase_  )
								pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

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

								_snake_case   						=		image[0, 2_53:2_56, 2_53:2_56, -1]

								assert image.shape == (1, 5_12, 5_12, 3)
								_snake_case   						=		np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]  )

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

