


"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Union[str, Any]										=				KandinskyVaaInpaintPipeline
		__UpperCAmelCase		:			List[Any]										=				['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
		__UpperCAmelCase		:			Dict										=				[
		    'image_embeds',
		    'negative_image_embeds',
		    'image',
		    'mask_image',
		]
		__UpperCAmelCase		:			List[str]										=				[
		    'generator',
		    'height',
		    'width',
		    'latents',
		    'guidance_scale',
		    'num_inference_steps',
		    'return_dict',
		    'guidance_scale',
		    'num_images_per_prompt',
		    'output_type',
		    'return_dict',
		]
		__UpperCAmelCase		:			Dict										=				False

		@property
		def 			__UpperCAmelCase       (  self      ):
				return 32

		@property
		def 			__UpperCAmelCase       (  self      ):
				return 32

		@property
		def 			__UpperCAmelCase       (  self      ):
				return self.time_input_dim

		@property
		def 			__UpperCAmelCase       (  self      ):
				return self.time_input_dim * 4

		@property
		def 			__UpperCAmelCase       (  self      ):
				return 100

		@property
		def 			__UpperCAmelCase       (  self      ):
				torch.manual_seed(0      )

				__a           =			{
				    '''in_channels''': 9,
				    # Out channels is double in channels because predicts mean and variance
				    '''out_channels''': 8,
				    '''addition_embed_type''': '''image''',
				    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
				    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
				    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
				    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
				    '''layers_per_block''': 1,
				    '''encoder_hid_dim''': self.text_embedder_hidden_size,
				    '''encoder_hid_dim_type''': '''image_proj''',
				    '''cross_attention_dim''': self.cross_attention_dim,
				    '''attention_head_dim''': 4,
				    '''resnet_time_scale_shift''': '''scale_shift''',
				    '''class_embed_type''': None,
				}

				__a           =			UNetaDConditionModel(**_a      )
				return model

		@property
		def 			__UpperCAmelCase       (  self      ):
				return {
				    "block_out_channels": [32, 64],
				    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
				    "in_channels": 3,
				    "latent_channels": 4,
				    "layers_per_block": 1,
				    "norm_num_groups": 8,
				    "norm_type": "spatial",
				    "num_vq_embeddings": 12,
				    "out_channels": 3,
				    "up_block_types": [
				        "AttnUpDecoderBlock2D",
				        "UpDecoderBlock2D",
				    ],
				    "vq_embed_dim": 4,
				}

		@property
		def 			__UpperCAmelCase       (  self      ):
				torch.manual_seed(0      )
				__a           =			VQModel(**self.dummy_movq_kwargs      )
				return model

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.dummy_unet
				__a           =			self.dummy_movq

				__a           =			DDIMScheduler(
				    num_train_timesteps=1_000							, beta_schedule='''linear'''							, beta_start=0.0_0085							, beta_end=0.012							, clip_sample=_a							, set_alpha_to_one=_a							, steps_offset=1							, prediction_type='''epsilon'''							, thresholding=_a							, )

				__a           =			{
				    '''unet''': unet,
				    '''scheduler''': scheduler,
				    '''movq''': movq,
				}

				return components

		def 			__UpperCAmelCase       (  self							, _a							, _a=0      ):
				__a           =			floats_tensor((1, self.text_embedder_hidden_size)							, rng=random.Random(_a      )      ).to(_a      )
				__a           =			floats_tensor((1, self.text_embedder_hidden_size)							, rng=random.Random(seed + 1      )      ).to(
				    _a      )
				# create init_image
				__a           =			floats_tensor((1, 3, 64, 64)							, rng=random.Random(_a      )      ).to(_a      )
				__a           =			image.cpu().permute(0							, 2							, 3							, 1      )[0]
				__a           =			Image.fromarray(np.uinta(_a      )      ).convert('''RGB'''      ).resize((256, 256)      )
				# create mask
				__a           =			np.ones((64, 64)							, dtype=np.floataa      )
				__a           =			0

				if str(_a      ).startswith('''mps'''      ):
						__a           =			torch.manual_seed(_a      )
				else:
						__a           =			torch.Generator(device=_a      ).manual_seed(_a      )
				__a           =			{
				    '''image''': init_image,
				    '''mask_image''': mask,
				    '''image_embeds''': image_embeds,
				    '''negative_image_embeds''': negative_image_embeds,
				    '''generator''': generator,
				    '''height''': 64,
				    '''width''': 64,
				    '''num_inference_steps''': 2,
				    '''guidance_scale''': 4.0,
				    '''output_type''': '''np''',
				}
				return inputs

		def 			__UpperCAmelCase       (  self      ):
				__a           =			'''cpu'''

				__a           =			self.get_dummy_components()

				__a           =			self.pipeline_class(**_a      )
				__a           =			pipe.to(_a      )

				pipe.set_progress_bar_config(disable=_a      )

				__a           =			pipe(**self.get_dummy_inputs(_a      )      )
				__a           =			output.images

				__a           =			pipe(
				    **self.get_dummy_inputs(_a      )							, return_dict=_a							, )[0]

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

				print(f'''image.shape {image.shape}'''      )

				assert image.shape == (1, 64, 64, 3)

				__a           =			np.array(
				    [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]      )

				assert (
				    np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
				), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
				assert (
				    np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2
				), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





		def 			__UpperCAmelCase       (  self      ):
				super().test_inference_batch_single_identical(expected_max_diff=3E-3      )







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


		'''simple docstring'''



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





		def 			__UpperCAmelCase       (  self      ):
				__a           =			load_numpy(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''      )

				__a           =			load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
				__a           =			np.ones((768, 768)							, dtype=np.floataa      )
				__a           =			0

				__a           =			'''a hat'''

				__a           =			KandinskyVaaPriorPipeline.from_pretrained(
				    '''kandinsky-community/kandinsky-2-2-prior'''							, torch_dtype=torch.floataa      )
				pipe_prior.to(_a      )

				__a           =			KandinskyVaaInpaintPipeline.from_pretrained(
				    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''							, torch_dtype=torch.floataa      )
				__a           =			pipeline.to(_a      )
				pipeline.set_progress_bar_config(disable=_a      )

				__a           =			torch.Generator(device='''cpu'''      ).manual_seed(0      )
				__a      ,     __a           =			pipe_prior(
				    _a							, generator=_a							, num_inference_steps=5							, negative_prompt=''''''							, ).to_tuple()

				__a           =			pipeline(
				    image=_a							, mask_image=_a							, image_embeds=_a							, negative_image_embeds=_a							, generator=_a							, num_inference_steps=100							, height=768							, width=768							, output_type='''np'''							, )

				__a           =			output.images[0]

				assert image.shape == (768, 768, 3)

				assert_mean_pixel_difference(_a							, _a      )










import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase		       =							get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')





@require_sentencepiece
@require_tokenizers
class   __a			(    __UpperCamelCase       ,       unittest.TestCase      ):
 __lowercase :							int        = PegasusTokenizer
 __lowercase :							Any        = PegasusTokenizerFast
 __lowercase :							Optional[int]        = True
 __lowercase :							Tuple        = True
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Any:




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

        # We have a SentencePiece fixture for testing
        lowercase__:   List[str]  			=   PegasusTokenizer(lowerCAmelCase__ )
        tokenizer.save_pretrained(self.tmpdirname )
 @cached_property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




        '''simple docstring'''
        return PegasusTokenizer.from_pretrained('google/pegasus-large' )
 def 		SCREAMING_SNAKE_CASE__   (   self				, **lowerCAmelCase__ )					->				PegasusTokenizer:




        '''simple docstring'''
        return PegasusTokenizer.from_pretrained(self.tmpdirname				, **lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        return ("This is a test", "This is a test")
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   Optional[Any]  			=   '</s>'
        lowercase__:   Union[str, Any]  			=   1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__ )				, lowerCAmelCase__ )
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__ )				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[str]:




        '''simple docstring'''
        lowercase__:   Tuple  			=   list(self.get_tokenizer().get_vocab().keys() )

        self.assertEqual(vocab_keys[0]				, '<pad>' )
        self.assertEqual(vocab_keys[1]				, '</s>' )
        self.assertEqual(vocab_keys[-1]				, 'v' )
        self.assertEqual(len(lowerCAmelCase__ )				, 1_103 )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Any:




        '''simple docstring'''
        self.assertEqual(self.get_tokenizer().vocab_size				, 1_103 )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[str]:




        '''simple docstring'''
        lowercase__:   Dict  			=   self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
        lowercase__:   Optional[int]  			=   self.tokenizer_class.from_pretrained(self.tmpdirname )
        lowercase__:   Optional[Any]  			=   (
            'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
            ' </s> <pad> <pad> <pad>'
        )
        lowercase__:   Dict  			=   rust_tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__				, add_special_tokens=lowerCAmelCase__ ).input_ids[0]
        lowercase__:   Tuple  			=   py_tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__				, add_special_tokens=lowerCAmelCase__ ).input_ids[0]
        self.assertListEqual(lowerCAmelCase__				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        lowercase__:   int  			=   self._large_tokenizer
        # <mask_1> masks whole sentence while <mask_2> masks single word
        lowercase__:   Any  			=   '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
        lowercase__:   Union[str, Any]  			=   [2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
        lowercase__:   int  			=   tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__ ).input_ids[0]
        self.assertListEqual(lowerCAmelCase__				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Dict:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   self._large_tokenizer
        # The tracebacks for the following asserts are **better** without messages or self.assertEqual
        assert tokenizer.vocab_size == 96_103
        assert tokenizer.pad_token_id == 0
        assert tokenizer.eos_token_id == 1
        assert tokenizer.offset == 103
        assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
        assert tokenizer.unk_token == "<unk>"
        assert tokenizer.model_max_length == 1_024
        lowercase__:   int  			=   'To ensure a smooth flow of bank resolutions.'
        lowercase__:   Any  			=   [413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
        lowercase__:   str  			=   tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__ ).input_ids[0]
        self.assertListEqual(lowerCAmelCase__				, lowerCAmelCase__ )
        assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
 @require_torch
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Dict:




        '''simple docstring'''
        lowercase__:   Any  			=   ['This is going to be way too long.' * 150, 'short example']
        lowercase__:   Tuple  			=   ['not super long but more than 5 tokens', 'tiny']
        lowercase__:   Dict  			=   self._large_tokenizer(lowerCAmelCase__				, padding=lowerCAmelCase__				, truncation=lowerCAmelCase__				, return_tensors='pt' )
        lowercase__:   Any  			=   self._large_tokenizer(
            text_target=lowerCAmelCase__				, max_length=5				, padding=lowerCAmelCase__				, truncation=lowerCAmelCase__				, return_tensors='pt' )

        assert batch.input_ids.shape == (2, 1_024)
        assert batch.attention_mask.shape == (2, 1_024)
        assert targets["input_ids"].shape == (2, 5)
        assert len(lowerCAmelCase__ ) == 2  # input_ids, attention_mask.
 @slow
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        # fmt: off
        lowercase__:   List[str]  			=   {'input_ids': [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=lowerCAmelCase__				, model_name='google/bigbird-pegasus-large-arxiv'				, revision='ba85d0851d708441f91440d509690f1ab6353415'				, )





@require_sentencepiece
@require_tokenizers
class   __a			(    __UpperCamelCase       ,       unittest.TestCase      ):
 __lowercase :							int        = PegasusTokenizer
 __lowercase :							Any        = PegasusTokenizerFast
 __lowercase :							Any        = True
 __lowercase :							Dict        = True
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




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

        # We have a SentencePiece fixture for testing
        lowercase__:   Union[str, Any]  			=   PegasusTokenizer(lowerCAmelCase__				, offset=0				, mask_token_sent=lowerCAmelCase__				, mask_token='[MASK]' )
        tokenizer.save_pretrained(self.tmpdirname )
 @cached_property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv' )
 def 		SCREAMING_SNAKE_CASE__   (   self				, **lowerCAmelCase__ )					->				PegasusTokenizer:




        '''simple docstring'''
        return PegasusTokenizer.from_pretrained(self.tmpdirname				, **lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Optional[int]:




        '''simple docstring'''
        return ("This is a test", "This is a test")
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[str]:




        '''simple docstring'''
        lowercase__:   str  			=   self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
        lowercase__:   str  			=   self.tokenizer_class.from_pretrained(self.tmpdirname )
        lowercase__:   Tuple  			=   (
            'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
            ' <pad> <pad> <pad>'
        )
        lowercase__:   List[Any]  			=   rust_tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__				, add_special_tokens=lowerCAmelCase__ ).input_ids[0]
        lowercase__:   Any  			=   py_tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__				, add_special_tokens=lowerCAmelCase__ ).input_ids[0]
        self.assertListEqual(lowerCAmelCase__				, lowerCAmelCase__ )
 @require_torch
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				int:




        '''simple docstring'''
        lowercase__:   List[Any]  			=   ['This is going to be way too long.' * 1_000, 'short example']
        lowercase__:   str  			=   ['not super long but more than 5 tokens', 'tiny']
        lowercase__:   Tuple  			=   self._large_tokenizer(lowerCAmelCase__				, padding=lowerCAmelCase__				, truncation=lowerCAmelCase__				, return_tensors='pt' )
        lowercase__:   Dict  			=   self._large_tokenizer(
            text_target=lowerCAmelCase__				, max_length=5				, padding=lowerCAmelCase__				, truncation=lowerCAmelCase__				, return_tensors='pt' )

        assert batch.input_ids.shape == (2, 4_096)
        assert batch.attention_mask.shape == (2, 4_096)
        assert targets["input_ids"].shape == (2, 5)
        assert len(lowerCAmelCase__ ) == 2  # input_ids, attention_mask.
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   str  			=   (
            'This is an example string that is used to test the original TF implementation against the HF'
            ' implementation'
        )

        lowercase__:   Optional[int]  			=   self._large_tokenizer(lowerCAmelCase__ ).input_ids

        self.assertListEqual(
            lowerCAmelCase__				, [182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1]				, )


