

'''simple docstring'''







import os
import unittest

from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english


@require_tokenizers
class    A__ ( _snake_case  ,  unittest.TestCase     ):
    lowercase		   =						MobileBertTokenizer
    lowercase		   =						MobileBertTokenizerFast
    lowercase		   =						True
    lowercase		   =						True
    lowercase		   =						filter_non_english
    lowercase		   =						"google/mobilebert-uncased"



    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           super().setUp()

           A_		   = [
               """[UNK]""",
               """[CLS]""",
               """[SEP]""",
               """[PAD]""",
               """[MASK]""",
               """want""",
               """##want""",
               """##ed""",
               """wa""",
               """un""",
               """runn""",
               """##ing""",
               """,""",
               """low""",
               """lowest""",
           ]
           A_		   = os.path.join(self.tmpdirname						,			VOCAB_FILES_NAMES["""vocab_file"""]    )
           with open(self.vocab_file						,			"""w"""						,			encoding="""utf-8"""    ) as vocab_writer:
                  vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]    )    )

           A_		   = [
               (tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2])  # else the 'google/' prefix is stripped
               for tokenizer_def in self.tokenizers_list
           ]



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> Dict:





           '''simple docstring'''
           A_		   = """UNwant\u00E9d,running"""
           A_		   = """unwanted, running"""
           return input_text, output_text



    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           A_		   = self.tokenizer_class(self.vocab_file    )

           A_		   = tokenizer.tokenize("""UNwant\u00E9d,running"""    )
           self.assertListEqual(UpperCamelCase__						,			["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]    )
           self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__    )						,			[9, 6, 7, 12, 10, 11]    )



    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           if not self.test_rust_tokenizer:
                  return

           A_		   = self.get_tokenizer()
           A_		   = self.get_rust_tokenizer()

           A_		   = """UNwant\u00E9d,running"""

           A_		   = tokenizer.tokenize(UpperCamelCase__    )
           A_		   = rust_tokenizer.tokenize(UpperCamelCase__    )
           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )

           A_		   = tokenizer.encode(UpperCamelCase__						,			add_special_tokens=UpperCamelCase__    )
           A_		   = rust_tokenizer.encode(UpperCamelCase__						,			add_special_tokens=UpperCamelCase__    )
           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )

           A_		   = self.get_rust_tokenizer()
           A_		   = tokenizer.encode(UpperCamelCase__    )
           A_		   = rust_tokenizer.encode(UpperCamelCase__    )
           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )

           # With lower casing
           A_		   = self.get_tokenizer(do_lower_case=UpperCamelCase__    )
           A_		   = self.get_rust_tokenizer(do_lower_case=UpperCamelCase__    )

           A_		   = """UNwant\u00E9d,running"""

           A_		   = tokenizer.tokenize(UpperCamelCase__    )
           A_		   = rust_tokenizer.tokenize(UpperCamelCase__    )
           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )

           A_		   = tokenizer.encode(UpperCamelCase__						,			add_special_tokens=UpperCamelCase__    )
           A_		   = rust_tokenizer.encode(UpperCamelCase__						,			add_special_tokens=UpperCamelCase__    )
           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )

           A_		   = self.get_rust_tokenizer()
           A_		   = tokenizer.encode(UpperCamelCase__    )
           A_		   = rust_tokenizer.encode(UpperCamelCase__    )
           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )



    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           A_		   = BasicTokenizer()

           self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""    )						,			["""ah""", """\u535A""", """\u63A8""", """zz"""]    )



    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           A_		   = BasicTokenizer(do_lower_case=UpperCamelCase__    )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """    )						,			["""hello""", """!""", """how""", """are""", """you""", """?"""]    )
           self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""    )						,			["""hello"""]    )



    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           A_		   = BasicTokenizer(do_lower_case=UpperCamelCase__						,			strip_accents=UpperCamelCase__    )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """    )						,			["""hällo""", """!""", """how""", """are""", """you""", """?"""]    )
           self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""    )						,			["""h\u00E9llo"""]    )



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = BasicTokenizer(do_lower_case=UpperCamelCase__						,			strip_accents=UpperCamelCase__    )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """    )						,			["""hallo""", """!""", """how""", """are""", """you""", """?"""]    )
           self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""    )						,			["""hello"""]    )



    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           A_		   = BasicTokenizer(do_lower_case=UpperCamelCase__    )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """    )						,			["""hallo""", """!""", """how""", """are""", """you""", """?"""]    )
           self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""    )						,			["""hello"""]    )



    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           A_		   = BasicTokenizer(do_lower_case=UpperCamelCase__    )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """    )						,			["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]    )



    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           A_		   = BasicTokenizer(do_lower_case=UpperCamelCase__						,			strip_accents=UpperCamelCase__    )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """    )						,			["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]    )



    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           A_		   = BasicTokenizer(do_lower_case=UpperCamelCase__						,			strip_accents=UpperCamelCase__    )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """    )						,			["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]    )



    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           A_		   = BasicTokenizer(do_lower_case=UpperCamelCase__						,			never_split=["""[UNK]"""]    )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""    )						,			["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]    )



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

           A_		   = {}
           for i, token in enumerate(UpperCamelCase__    ):
                  A_		   = i
           A_		   = WordpieceTokenizer(vocab=UpperCamelCase__						,			unk_token="""[UNK]"""    )

           self.assertListEqual(tokenizer.tokenize(""""""    )						,			[]    )

           self.assertListEqual(tokenizer.tokenize("""unwanted running"""    )						,			["""un""", """##want""", """##ed""", """runn""", """##ing"""]    )

           self.assertListEqual(tokenizer.tokenize("""unwantedX running"""    )						,			["""[UNK]""", """runn""", """##ing"""]    )



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           self.assertTrue(_is_whitespace(""" """    )    )
           self.assertTrue(_is_whitespace("""\t"""    )    )
           self.assertTrue(_is_whitespace("""\r"""    )    )
           self.assertTrue(_is_whitespace("""\n"""    )    )
           self.assertTrue(_is_whitespace("""\u00A0"""    )    )

           self.assertFalse(_is_whitespace("""A"""    )    )
           self.assertFalse(_is_whitespace("""-"""    )    )



    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           self.assertTrue(_is_control("""\u0005"""    )    )

           self.assertFalse(_is_control("""A"""    )    )
           self.assertFalse(_is_control(""" """    )    )
           self.assertFalse(_is_control("""\t"""    )    )
           self.assertFalse(_is_control("""\r"""    )    )



    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           self.assertTrue(_is_punctuation("""-"""    )    )
           self.assertTrue(_is_punctuation("""$"""    )    )
           self.assertTrue(_is_punctuation("""`"""    )    )
           self.assertTrue(_is_punctuation("""."""    )    )

           self.assertFalse(_is_punctuation("""A"""    )    )
           self.assertFalse(_is_punctuation(""" """    )    )



    def    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           A_		   = self.get_tokenizer()
           A_		   = self.get_rust_tokenizer()

           # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
           self.assertListEqual([tokenizer.tokenize(UpperCamelCase__    ) for t in ["""Test""", """\xad""", """test"""]]						,			[["""[UNK]"""], [], ["""[UNK]"""]]    )

           self.assertListEqual(
               [rust_tokenizer.tokenize(UpperCamelCase__    ) for t in ["""Test""", """\xad""", """test"""]]						,			[["""[UNK]"""], [], ["""[UNK]"""]]    )



    @slow
    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           A_		   = self.tokenizer_class.from_pretrained("""google/mobilebert-uncased"""    )

           A_		   = tokenizer.encode("""sequence builders"""						,			add_special_tokens=UpperCamelCase__    )
           A_		   = tokenizer.encode("""multi-sequence build"""						,			add_special_tokens=UpperCamelCase__    )

           A_		   = tokenizer.build_inputs_with_special_tokens(UpperCamelCase__    )
           A_		   = tokenizer.build_inputs_with_special_tokens(UpperCamelCase__						,			UpperCamelCase__    )

           assert encoded_sentence == [101] + text + [102]
           assert encoded_pair == [101] + text + [102] + text_a + [102]



    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
                         A_		   = self.rust_tokenizer_class.from_pretrained(UpperCamelCase__						,			**UpperCamelCase__    )

                         A_		   = f'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
                         A_		   = tokenizer_r.encode_plus(
                             UpperCamelCase__						,			return_attention_mask=UpperCamelCase__						,			return_token_type_ids=UpperCamelCase__						,			return_offsets_mapping=UpperCamelCase__						,			add_special_tokens=UpperCamelCase__						,			)

                         A_		   = tokenizer_r.do_lower_case if hasattr(UpperCamelCase__						,			"""do_lower_case"""    ) else False
                         A_		   = (
                             [
                                 ((0, 0), tokenizer_r.cls_token),
                                 ((0, 1), """A"""),
                                 ((1, 2), ""","""),
                                 ((3, 5), """na"""),
                                 ((5, 6), """##ï"""),
                                 ((6, 8), """##ve"""),
                                 ((9, 15), tokenizer_r.mask_token),
                                 ((16, 21), """Allen"""),
                                 ((21, 23), """##NL"""),
                                 ((23, 24), """##P"""),
                                 ((25, 33), """sentence"""),
                                 ((33, 34), """."""),
                                 ((0, 0), tokenizer_r.sep_token),
                             ]
                             if not do_lower_case
                             else [
                                 ((0, 0), tokenizer_r.cls_token),
                                 ((0, 1), """a"""),
                                 ((1, 2), ""","""),
                                 ((3, 8), """naive"""),
                                 ((9, 15), tokenizer_r.mask_token),
                                 ((16, 21), """allen"""),
                                 ((21, 23), """##nl"""),
                                 ((23, 24), """##p"""),
                                 ((25, 33), """sentence"""),
                                 ((33, 34), """."""),
                                 ((0, 0), tokenizer_r.sep_token),
                             ]
                         )

                         self.assertEqual(
                             [e[1] for e in expected_results]						,			tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]    )    )
                         self.assertEqual([e[0] for e in expected_results]						,			tokens["""offset_mapping"""]    )







    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = ["""的""", """人""", """有"""]
           A_		   = """""".join(UpperCamelCase__    )
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
                         A_		   = True
                         A_		   = self.tokenizer_class.from_pretrained(UpperCamelCase__						,			**UpperCamelCase__    )
                         A_		   = self.rust_tokenizer_class.from_pretrained(UpperCamelCase__						,			**UpperCamelCase__    )

                         A_		   = tokenizer_p.encode(UpperCamelCase__						,			add_special_tokens=UpperCamelCase__    )
                         A_		   = tokenizer_r.encode(UpperCamelCase__						,			add_special_tokens=UpperCamelCase__    )

                         A_		   = tokenizer_r.convert_ids_to_tokens(UpperCamelCase__    )
                         A_		   = tokenizer_p.convert_ids_to_tokens(UpperCamelCase__    )

                         # it is expected that each Chinese character is not preceded by "##"
                         self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )
                         self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )

                         A_		   = False
                         A_		   = self.rust_tokenizer_class.from_pretrained(UpperCamelCase__						,			**UpperCamelCase__    )
                         A_		   = self.tokenizer_class.from_pretrained(UpperCamelCase__						,			**UpperCamelCase__    )

                         A_		   = tokenizer_r.encode(UpperCamelCase__						,			add_special_tokens=UpperCamelCase__    )
                         A_		   = tokenizer_p.encode(UpperCamelCase__						,			add_special_tokens=UpperCamelCase__    )

                         A_		   = tokenizer_r.convert_ids_to_tokens(UpperCamelCase__    )
                         A_		   = tokenizer_p.convert_ids_to_tokens(UpperCamelCase__    )

                         # it is expected that only the first Chinese character is not preceded by "##".
                         A_		   = [
                             f'''##{token}''' if idx != 0 else token for idx, token in enumerate(UpperCamelCase__    )
                         ]
                         self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )
                         self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )



'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


class    A__ ( unittest.TestCase     ):



    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



    @property
    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           A_		   = 1
           A_		   = 3
           A_		   = (32, 32)

           A_		   = floats_tensor((batch_size, num_channels) + sizes						,			rng=random.Random(0    )    ).to(UpperCamelCase__    )
           return image



    @property
    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           torch.manual_seed(0    )
           A_		   = UNetaDConditionModel(
               block_out_channels=(32, 64)						,			layers_per_block=2						,			sample_size=32						,			in_channels=4						,			out_channels=4						,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")						,			up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")						,			cross_attention_dim=32						,			)
           return model



    @property
    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           torch.manual_seed(0    )
           A_		   = AutoencoderKL(
               block_out_channels=[32, 64]						,			in_channels=3						,			out_channels=3						,			down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,			up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,			latent_channels=4						,			)
           return model



    @property
    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           torch.manual_seed(0    )
           A_		   = RobertaSeriesConfig(
               hidden_size=32						,			project_dim=32						,			intermediate_size=37						,			layer_norm_eps=1e-0_5						,			num_attention_heads=4						,			num_hidden_layers=5						,			pad_token_id=1						,			vocab_size=5006						,			)
           return RobertaSeriesModelWithTransformation(UpperCamelCase__    )



    @property
    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           def extract(*UpperCamelCase__						,			**UpperCamelCase__    ):


                  class    A__ :



                      def __init__(   self    )  -> Dict:





                             '''simple docstring'''
                             A_		   = torch.ones([0]    )







                      def    snake_case_     (   self						,			UpperCamelCase__    )  -> str:





                             '''simple docstring'''
                             self.pixel_values.to(UpperCamelCase__    )
                             return self

                  return Out()

           return extract



    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           A_		   = """cpu"""  # ensure determinism for the device-dependent torch.Generator
           A_		   = self.dummy_cond_unet
           A_		   = PNDMScheduler(skip_prk_steps=UpperCamelCase__    )
           A_		   = self.dummy_vae
           A_		   = self.dummy_text_encoder
           A_		   = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""    )
           A_		   = 77

           A_		   = self.dummy_image.to(UpperCamelCase__    )
           A_		   = init_image / 2 + 0.5

           # make sure here that pndm scheduler skips prk
           A_		   = AltDiffusionImgaImgPipeline(
               unet=UpperCamelCase__						,			scheduler=UpperCamelCase__						,			vae=UpperCamelCase__						,			text_encoder=UpperCamelCase__						,			tokenizer=UpperCamelCase__						,			safety_checker=UpperCamelCase__						,			feature_extractor=self.dummy_extractor						,			)
           A_		   = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor						,			do_normalize=UpperCamelCase__    )
           A_		   = alt_pipe.to(UpperCamelCase__    )
           alt_pipe.set_progress_bar_config(disable=UpperCamelCase__    )

           A_		   = """A painting of a squirrel eating a burger"""
           A_		   = torch.Generator(device=UpperCamelCase__    ).manual_seed(0    )
           A_		   = alt_pipe(
               [prompt]						,			generator=UpperCamelCase__						,			guidance_scale=6.0						,			num_inference_steps=2						,			output_type="""np"""						,			image=UpperCamelCase__						,			)

           A_		   = output.images

           A_		   = torch.Generator(device=UpperCamelCase__    ).manual_seed(0    )
           A_		   = alt_pipe(
               [prompt]						,			generator=UpperCamelCase__						,			guidance_scale=6.0						,			num_inference_steps=2						,			output_type="""np"""						,			image=UpperCamelCase__						,			return_dict=UpperCamelCase__						,			)[0]

           A_		   = image[0, -3:, -3:, -1]
           A_		   = image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           A_		   = np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5e-3
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 5e-3



    @unittest.skipIf(torch_device != """cuda"""						,			"""This test requires a GPU"""    )
    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = self.dummy_cond_unet
           A_		   = PNDMScheduler(skip_prk_steps=UpperCamelCase__    )
           A_		   = self.dummy_vae
           A_		   = self.dummy_text_encoder
           A_		   = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""    )
           A_		   = 77

           A_		   = self.dummy_image.to(UpperCamelCase__    )

           # put models in fp16
           A_		   = unet.half()
           A_		   = vae.half()
           A_		   = bert.half()

           # make sure here that pndm scheduler skips prk
           A_		   = AltDiffusionImgaImgPipeline(
               unet=UpperCamelCase__						,			scheduler=UpperCamelCase__						,			vae=UpperCamelCase__						,			text_encoder=UpperCamelCase__						,			tokenizer=UpperCamelCase__						,			safety_checker=UpperCamelCase__						,			feature_extractor=self.dummy_extractor						,			)
           A_		   = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor						,			do_normalize=UpperCamelCase__    )
           A_		   = alt_pipe.to(UpperCamelCase__    )
           alt_pipe.set_progress_bar_config(disable=UpperCamelCase__    )

           A_		   = """A painting of a squirrel eating a burger"""
           A_		   = torch.manual_seed(0    )
           A_		   = alt_pipe(
               [prompt]						,			generator=UpperCamelCase__						,			num_inference_steps=2						,			output_type="""np"""						,			image=UpperCamelCase__						,			).images

           assert image.shape == (1, 32, 32, 3)







    @unittest.skipIf(torch_device != """cuda"""						,			"""This test requires a GPU"""    )
    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg"""    )
           # resize to resolution that is divisible by 8 but not 16 or 32
           A_		   = init_image.resize((760, 504)    )

           A_		   = """BAAI/AltDiffusion"""
           A_		   = AltDiffusionImgaImgPipeline.from_pretrained(
               UpperCamelCase__						,			safety_checker=UpperCamelCase__						,			)
           pipe.to(UpperCamelCase__    )
           pipe.set_progress_bar_config(disable=UpperCamelCase__    )
           pipe.enable_attention_slicing()

           A_		   = """A fantasy landscape, trending on artstation"""

           A_		   = torch.manual_seed(0    )
           A_		   = pipe(
               prompt=UpperCamelCase__						,			image=UpperCamelCase__						,			strength=0.75						,			guidance_scale=7.5						,			generator=UpperCamelCase__						,			output_type="""np"""						,			)
           A_		   = output.images[0]

           A_		   = image[255:258, 383:386, -1]

           assert image.shape == (504, 760, 3)
           A_		   = np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]    )

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


@slow
@require_torch_gpu
class    A__ ( unittest.TestCase     ):



    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           A_		   = load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg"""    )
           A_		   = init_image.resize((768, 512)    )
           A_		   = load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"""    )

           A_		   = """BAAI/AltDiffusion"""
           A_		   = AltDiffusionImgaImgPipeline.from_pretrained(
               UpperCamelCase__						,			safety_checker=UpperCamelCase__						,			)
           pipe.to(UpperCamelCase__    )
           pipe.set_progress_bar_config(disable=UpperCamelCase__    )
           pipe.enable_attention_slicing()

           A_		   = """A fantasy landscape, trending on artstation"""

           A_		   = torch.manual_seed(0    )
           A_		   = pipe(
               prompt=UpperCamelCase__						,			image=UpperCamelCase__						,			strength=0.75						,			guidance_scale=7.5						,			generator=UpperCamelCase__						,			output_type="""np"""						,			)
           A_		   = output.images[0]

           assert image.shape == (512, 768, 3)
           # img2img is flaky across GPUs even in fp32, so using MAE here
           assert np.abs(expected_image - image    ).max() < 1e-2
