
import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_rjieba
@require_tokenizers
class 						_a   (						_lowerCamelCase ,      unittest.TestCase       ):

       """simple docstring"""





       snake_case				=RoFormerTokenizer
       snake_case				=RoFormerTokenizerFast
       snake_case				=True
       snake_case				=True
       def     SCREAMING_SNAKE_CASE  (					self	):
        super().setUp()
       def     SCREAMING_SNAKE_CASE  (					self       ,					**_snake_case	):
        return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"       ,					**_SCREAMING_SNAKE_CASE	)
       def     SCREAMING_SNAKE_CASE  (					self       ,					**_snake_case	):
        return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"       ,					**_SCREAMING_SNAKE_CASE	)
       def     SCREAMING_SNAKE_CASE  (					self	):
        _UpperCAmelCase 				='永和服装饰品有限公司,今天天气非常好'
        _UpperCAmelCase 				='永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
        return input_text, output_text
       def     SCREAMING_SNAKE_CASE  (					self	):
        _UpperCAmelCase 				=self.get_tokenizer()
        _UpperCAmelCase 				=self.get_chinese_input_output_texts()
        _UpperCAmelCase 				=tokenizer.tokenize(_SCREAMING_SNAKE_CASE	)

        self.assertListEqual(_SCREAMING_SNAKE_CASE       ,					output_text.split()	)

        _UpperCAmelCase 				=tokens + [tokenizer.unk_token]
        _UpperCAmelCase 				=[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE	)       ,					_SCREAMING_SNAKE_CASE	)
       def     SCREAMING_SNAKE_CASE  (					self	):
        _UpperCAmelCase 				=self.get_rust_tokenizer()
        _UpperCAmelCase 				=self.get_chinese_input_output_texts()
        _UpperCAmelCase 				=tokenizer.tokenize(_SCREAMING_SNAKE_CASE	)
        self.assertListEqual(_SCREAMING_SNAKE_CASE       ,					output_text.split()	)
        _UpperCAmelCase 				=tokens + [tokenizer.unk_token]
        _UpperCAmelCase 				=[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE	)       ,					_SCREAMING_SNAKE_CASE	)
       def     SCREAMING_SNAKE_CASE  (					self	):
        pass
       def     SCREAMING_SNAKE_CASE  (					self	):
        pass

       def     SCREAMING_SNAKE_CASE  (					self	):
        pass










"""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





