




"""simple docstring"""

import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase  :			Optional[Any]        =       get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
		from transformers.models.plbart.modeling_plbart import shift_tokens_right

_lowerCamelCase  :			int        =       50003
_lowerCamelCase  :			int        =       50002

@require_sentencepiece
@require_tokenizers
class        lowercase      (    __UpperCAmelCase     ,	unittest.TestCase):
				__lowerCAmelCase    :    Tuple         =   PLBartTokenizer
				__lowerCAmelCase    :    List[str]         =   None
				__lowerCAmelCase    :    int         =   False







				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					super().setUp()

					# We have a SentencePiece fixture for testing
					A_  :				Optional[int]	   =		PLBartTokenizer(_lowerCamelCase      ,     language_codes='''base'''      ,     keep_accents=_lowerCamelCase       )
					tokenizer.save_pretrained(self.tmpdirname       )







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		PLBartTokenizer(_lowerCamelCase      ,     language_codes='''base'''      ,     keep_accents=_lowerCamelCase       )

					A_  :				str	   =		tokenizer.tokenize('''This is a test'''       )
					self.assertListEqual(_lowerCamelCase      ,     ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

					self.assertListEqual(
					    tokenizer.convert_tokens_to_ids(_lowerCamelCase       )      ,     [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]      ,     )

					A_  :				Tuple	   =		tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
					self.assertListEqual(
					    _lowerCamelCase      ,     [
					        SPIECE_UNDERLINE + '''I''',
					        SPIECE_UNDERLINE + '''was''',
					        SPIECE_UNDERLINE + '''b''',
					        '''or''',
					        '''n''',
					        SPIECE_UNDERLINE + '''in''',
					        SPIECE_UNDERLINE + '''''',
					        '''9''',
					        '''2''',
					        '''0''',
					        '''0''',
					        '''0''',
					        ''',''',
					        SPIECE_UNDERLINE + '''and''',
					        SPIECE_UNDERLINE + '''this''',
					        SPIECE_UNDERLINE + '''is''',
					        SPIECE_UNDERLINE + '''f''',
					        '''al''',
					        '''s''',
					        '''é''',
					        '''.''',
					    ]      ,     )
					A_  :				str	   =		tokenizer.convert_tokens_to_ids(_lowerCamelCase       )
					self.assertListEqual(
					    _lowerCamelCase      ,     [
					        value + tokenizer.fairseq_offset
					        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
					    ]      ,     )

					A_  :				Optional[int]	   =		tokenizer.convert_ids_to_tokens(_lowerCamelCase       )
					self.assertListEqual(
					    _lowerCamelCase      ,     [
					        SPIECE_UNDERLINE + '''I''',
					        SPIECE_UNDERLINE + '''was''',
					        SPIECE_UNDERLINE + '''b''',
					        '''or''',
					        '''n''',
					        SPIECE_UNDERLINE + '''in''',
					        SPIECE_UNDERLINE + '''''',
					        '''<unk>''',
					        '''2''',
					        '''0''',
					        '''0''',
					        '''0''',
					        ''',''',
					        SPIECE_UNDERLINE + '''and''',
					        SPIECE_UNDERLINE + '''this''',
					        SPIECE_UNDERLINE + '''is''',
					        SPIECE_UNDERLINE + '''f''',
					        '''al''',
					        '''s''',
					        '''<unk>''',
					        '''.''',
					    ]      ,     )

					A_  :				Union[str, Any]	   =		tokenizer.vocab_size
					A_  :				Union[str, Any]	   =		[tokenizer.convert_ids_to_tokens(_lowerCamelCase       ) for x in range(end - 4      ,     _lowerCamelCase       )]

					self.assertListEqual(_lowerCamelCase      ,     ['''__java__''', '''__python__''', '''__en_XX__''', '''<mask>''']       )

					A_  :				List[Any]	   =		'''java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'''
					A_  :				int	   =		tokenizer(_lowerCamelCase       ).input_ids
					self.assertEqual(
					    tokenizer.decode(_lowerCamelCase      ,     skip_special_tokens=_lowerCamelCase      ,     clean_up_tokenization_spaces=_lowerCamelCase       )      ,     _lowerCamelCase      ,     )







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		PLBartTokenizer(_lowerCamelCase      ,     language_codes='''multi'''      ,     keep_accents=_lowerCamelCase       )

					A_  :				Union[str, Any]	   =		tokenizer.tokenize('''This is a test'''       )
					self.assertListEqual(_lowerCamelCase      ,     ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

					self.assertListEqual(
					    tokenizer.convert_tokens_to_ids(_lowerCamelCase       )      ,     [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]      ,     )

					A_  :				Tuple	   =		tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
					self.assertListEqual(
					    _lowerCamelCase      ,     [
					        SPIECE_UNDERLINE + '''I''',
					        SPIECE_UNDERLINE + '''was''',
					        SPIECE_UNDERLINE + '''b''',
					        '''or''',
					        '''n''',
					        SPIECE_UNDERLINE + '''in''',
					        SPIECE_UNDERLINE + '''''',
					        '''9''',
					        '''2''',
					        '''0''',
					        '''0''',
					        '''0''',
					        ''',''',
					        SPIECE_UNDERLINE + '''and''',
					        SPIECE_UNDERLINE + '''this''',
					        SPIECE_UNDERLINE + '''is''',
					        SPIECE_UNDERLINE + '''f''',
					        '''al''',
					        '''s''',
					        '''é''',
					        '''.''',
					    ]      ,     )
					A_  :				List[Any]	   =		tokenizer.convert_tokens_to_ids(_lowerCamelCase       )
					self.assertListEqual(
					    _lowerCamelCase      ,     [
					        value + tokenizer.fairseq_offset
					        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
					    ]      ,     )

					A_  :				List[str]	   =		tokenizer.convert_ids_to_tokens(_lowerCamelCase       )
					self.assertListEqual(
					    _lowerCamelCase      ,     [
					        SPIECE_UNDERLINE + '''I''',
					        SPIECE_UNDERLINE + '''was''',
					        SPIECE_UNDERLINE + '''b''',
					        '''or''',
					        '''n''',
					        SPIECE_UNDERLINE + '''in''',
					        SPIECE_UNDERLINE + '''''',
					        '''<unk>''',
					        '''2''',
					        '''0''',
					        '''0''',
					        '''0''',
					        ''',''',
					        SPIECE_UNDERLINE + '''and''',
					        SPIECE_UNDERLINE + '''this''',
					        SPIECE_UNDERLINE + '''is''',
					        SPIECE_UNDERLINE + '''f''',
					        '''al''',
					        '''s''',
					        '''<unk>''',
					        '''.''',
					    ]      ,     )
					A_  :				Optional[int]	   =		tokenizer.vocab_size
					A_  :				Tuple	   =		[tokenizer.convert_ids_to_tokens(_lowerCamelCase       ) for x in range(end - 7      ,     _lowerCamelCase       )]

					self.assertListEqual(
					    _lowerCamelCase      ,     ['''__java__''', '''__python__''', '''__en_XX__''', '''__javascript__''', '''__php__''', '''__ruby__''', '''__go__''']       )
					A_  :				str	   =		'''java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'''
					A_  :				List[str]	   =		tokenizer(_lowerCamelCase       ).input_ids
					self.assertEqual(
					    tokenizer.decode(_lowerCamelCase      ,     skip_special_tokens=_lowerCamelCase      ,     clean_up_tokenization_spaces=_lowerCamelCase       )      ,     _lowerCamelCase      ,     )

@require_torch
@require_sentencepiece
@require_tokenizers
class        lowercase      (    unittest.TestCase):
				__lowerCAmelCase    :    Dict         =   """uclanlp/plbart-python-en_XX"""
				__lowerCAmelCase    :    str         =   [
				    """def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])""",
				    """def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])""",
				]
				__lowerCAmelCase    :    Optional[Any]         =   [
				    """Returns the maximum value of a b c.""",
				    """Sums the values of a b c.""",
				]
				__lowerCAmelCase    :    Tuple         =   [
				    134,
				    5_452,
				    33_460,
				    33_441,
				    33_463,
				    33_465,
				    33_463,
				    33_449,
				    988,
				    20,
				    33_456,
				    19,
				    33_456,
				    771,
				    39,
				    4_258,
				    889,
				    3_318,
				    33_441,
				    33_463,
				    33_465,
				    33_463,
				    33_449,
				    2_471,
				    2,
				    PYTHON_CODE,
				]







				@classmethod
				def 					a_  (							cls   :					Tuple       ):



					"""simple docstring"""

					A_  :				PLBartTokenizer	   =		PLBartTokenizer.from_pretrained(
					    cls.checkpoint_name      ,     language_codes='''base'''      ,     src_lang='''python'''      ,     tgt_lang='''en_XX'''       )
					A_  :				Optional[Any]	   =		1
					return cls







				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__java__''']      ,     5_00_01       )
					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__python__''']      ,     5_00_02       )
					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__en_XX__''']      ,     5_00_03       )







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				List[Any]	   =		self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
					self.assertListEqual(self.expected_src_tokens      ,     _lowerCamelCase       )







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					self.assertIn(_lowerCamelCase      ,     self.tokenizer.all_special_ids       )
					A_  :				str	   =		[EN_CODE, 90_37, 3_34_42, 57, 7_52, 1_53, 14, 56, 18, 9, 2]
					A_  :				Tuple	   =		self.tokenizer.decode(_lowerCamelCase      ,     skip_special_tokens=_lowerCamelCase       )
					A_  :				Any	   =		self.tokenizer.decode(generated_ids[1:]      ,     skip_special_tokens=_lowerCamelCase       )
					self.assertEqual(_lowerCamelCase      ,     _lowerCamelCase       )
					self.assertNotIn(self.tokenizer.eos_token      ,     _lowerCamelCase       )







				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					A_  :				Any	   =		['''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''' * 20]
					self.assertIsInstance(src_text[0]      ,     _lowerCamelCase       )
					A_  :				List[Any]	   =		10
					A_  :				int	   =		self.tokenizer(_lowerCamelCase      ,     max_length=_lowerCamelCase      ,     truncation=_lowerCamelCase       ).input_ids[0]
					self.assertEqual(ids[-2]      ,     2       )
					self.assertEqual(ids[-1]      ,     _lowerCamelCase       )
					self.assertEqual(len(_lowerCamelCase       )      ,     _lowerCamelCase       )







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''__java__''']       )      ,     [5_00_04, 5_00_01]       )







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		tempfile.mkdtemp()
					A_  :				List[Any]	   =		self.tokenizer.fairseq_tokens_to_ids
					self.tokenizer.save_pretrained(_lowerCamelCase       )
					A_  :				List[Any]	   =		PLBartTokenizer.from_pretrained(_lowerCamelCase       )
					self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,     _lowerCamelCase       )







				@require_torch
				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					A_  :				List[str]	   =		self.tokenizer(self.src_text      ,     text_target=self.tgt_text      ,     padding=_lowerCamelCase      ,     return_tensors='''pt'''       )
					A_  :				Any	   =		shift_tokens_right(batch['''labels''']      ,     self.tokenizer.pad_token_id       )

					# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
					self.assertEqual(batch.input_ids[1][-2:].tolist()      ,     [2, PYTHON_CODE]       )
					self.assertEqual(batch.decoder_input_ids[1][0]      ,     _lowerCamelCase       )
					self.assertEqual(batch.decoder_input_ids[1][-1]      ,     2       )
					self.assertEqual(batch.labels[1][-2:].tolist()      ,     [2, EN_CODE]       )







				@require_torch
				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					A_  :				Any	   =		self.tokenizer(
					    self.src_text      ,     text_target=self.tgt_text      ,     padding=_lowerCamelCase      ,     truncation=_lowerCamelCase      ,     max_length=len(self.expected_src_tokens       )      ,     return_tensors='''pt'''      ,     )
					A_  :				str	   =		shift_tokens_right(batch['''labels''']      ,     self.tokenizer.pad_token_id       )

					self.assertIsInstance(_lowerCamelCase      ,     _lowerCamelCase       )

					self.assertEqual((2, 26)      ,     batch.input_ids.shape       )
					self.assertEqual((2, 26)      ,     batch.attention_mask.shape       )
					A_  :				Optional[Any]	   =		batch.input_ids.tolist()[0]
					self.assertListEqual(self.expected_src_tokens      ,     _lowerCamelCase       )
					self.assertEqual(2      ,     batch.decoder_input_ids[0, -1]       )  # EOS
					# Test that special tokens are reset
					self.assertEqual(self.tokenizer.prefix_tokens      ,     []       )
					self.assertEqual(self.tokenizer.suffix_tokens      ,     [self.tokenizer.eos_token_id, PYTHON_CODE]       )







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		self.tokenizer(self.src_text      ,     padding=_lowerCamelCase      ,     truncation=_lowerCamelCase      ,     max_length=3      ,     return_tensors='''pt'''       )
					A_  :				int	   =		self.tokenizer(
					    text_target=self.tgt_text      ,     padding=_lowerCamelCase      ,     truncation=_lowerCamelCase      ,     max_length=10      ,     return_tensors='''pt'''       )
					A_  :				Any	   =		targets['''input_ids''']
					A_  :				str	   =		shift_tokens_right(_lowerCamelCase      ,     self.tokenizer.pad_token_id       )

					self.assertEqual(batch.input_ids.shape[1]      ,     3       )
					self.assertEqual(batch.decoder_input_ids.shape[1]      ,     10       )







				@require_torch
				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					A_  :				int	   =		self.tokenizer._build_translation_inputs(
					    '''A test'''      ,     return_tensors='''pt'''      ,     src_lang='''en_XX'''      ,     tgt_lang='''java'''       )

					self.assertEqual(
					    nested_simplify(_lowerCamelCase       )      ,     {
					        # A, test, EOS, en_XX
					        '''input_ids''': [[1_50, 2_42, 2, 5_00_03]],
					        '''attention_mask''': [[1, 1, 1, 1]],
					        # java
					        '''forced_bos_token_id''': 5_00_01,
					    }      ,     )











"""simple docstring"""

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_lowerCamelCase  :			Any        =       False

class        lowercase      (    unittest.TestCase):







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

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







				@property
				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					return 12







				@property
				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					return 12







				@property
				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					return 32







				@property
				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					torch.manual_seed(0       )
					A_  :				Union[str, Any]	   =		VQModel(
					    block_out_channels=[32, 64]      ,     in_channels=3      ,     out_channels=3      ,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      ,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,     latent_channels=3      ,     num_vq_embeddings=self.num_embed      ,     vq_embed_dim=3      ,     )
					return model







				@property
				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				int	   =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )
					return tokenizer







				@property
				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					torch.manual_seed(0       )
					A_  :				Optional[int]	   =		CLIPTextConfig(
					    bos_token_id=0      ,     eos_token_id=2      ,     hidden_size=self.text_embedder_hidden_size      ,     intermediate_size=37      ,     layer_norm_eps=1E-05      ,     num_attention_heads=4      ,     num_hidden_layers=5      ,     pad_token_id=1      ,     vocab_size=10_00      ,     )
					return CLIPTextModel(_lowerCamelCase       )







				@property
				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					torch.manual_seed(0       )

					A_  :				Optional[Any]	   =		12
					A_  :				Optional[int]	   =		12

					A_  :				int	   =		{
					    '''attention_bias''': True,
					    '''cross_attention_dim''': 32,
					    '''attention_head_dim''': height * width,
					    '''num_attention_heads''': 1,
					    '''num_vector_embeds''': self.num_embed,
					    '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
					    '''norm_num_groups''': 32,
					    '''sample_size''': width,
					    '''activation_fn''': '''geglu-approximate''',
					}

					A_  :				Tuple	   =		TransformeraDModel(**_lowerCamelCase       )
					return model







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		'''cpu'''

					A_  :				Union[str, Any]	   =		self.dummy_vqvae
					A_  :				str	   =		self.dummy_text_encoder
					A_  :				List[Any]	   =		self.dummy_tokenizer
					A_  :				int	   =		self.dummy_transformer
					A_  :				Any	   =		VQDiffusionScheduler(self.num_embed       )
					A_  :				Optional[Any]	   =		LearnedClassifierFreeSamplingEmbeddings(learnable=_lowerCamelCase       )

					A_  :				Dict	   =		VQDiffusionPipeline(
					    vqvae=_lowerCamelCase      ,     text_encoder=_lowerCamelCase      ,     tokenizer=_lowerCamelCase      ,     transformer=_lowerCamelCase      ,     scheduler=_lowerCamelCase      ,     learned_classifier_free_sampling_embeddings=_lowerCamelCase      ,     )
					A_  :				List[Any]	   =		pipe.to(_lowerCamelCase       )
					pipe.set_progress_bar_config(disable=_lowerCamelCase       )

					A_  :				List[Any]	   =		'''teddy bear playing in the pool'''

					A_  :				List[Any]	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )
					A_  :				List[Any]	   =		pipe([prompt]      ,     generator=_lowerCamelCase      ,     num_inference_steps=2      ,     output_type='''np'''       )
					A_  :				Any	   =		output.images

					A_  :				List[str]	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )
					A_  :				List[Any]	   =		pipe(
					    [prompt]      ,     generator=_lowerCamelCase      ,     output_type='''np'''      ,     return_dict=_lowerCamelCase      ,     num_inference_steps=2       )[0]

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

					assert image.shape == (1, 24, 24, 3)

					A_  :				Optional[int]	   =		np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]       )

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







				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		'''cpu'''

					A_  :				int	   =		self.dummy_vqvae
					A_  :				List[str]	   =		self.dummy_text_encoder
					A_  :				Optional[Any]	   =		self.dummy_tokenizer
					A_  :				Any	   =		self.dummy_transformer
					A_  :				Any	   =		VQDiffusionScheduler(self.num_embed       )
					A_  :				Optional[int]	   =		LearnedClassifierFreeSamplingEmbeddings(
					    learnable=_lowerCamelCase      ,     hidden_size=self.text_embedder_hidden_size      ,     length=tokenizer.model_max_length       )

					A_  :				int	   =		VQDiffusionPipeline(
					    vqvae=_lowerCamelCase      ,     text_encoder=_lowerCamelCase      ,     tokenizer=_lowerCamelCase      ,     transformer=_lowerCamelCase      ,     scheduler=_lowerCamelCase      ,     learned_classifier_free_sampling_embeddings=_lowerCamelCase      ,     )
					A_  :				List[Any]	   =		pipe.to(_lowerCamelCase       )
					pipe.set_progress_bar_config(disable=_lowerCamelCase       )

					A_  :				Any	   =		'''teddy bear playing in the pool'''

					A_  :				str	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )
					A_  :				Optional[Any]	   =		pipe([prompt]      ,     generator=_lowerCamelCase      ,     num_inference_steps=2      ,     output_type='''np'''       )
					A_  :				Tuple	   =		output.images

					A_  :				Optional[int]	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )
					A_  :				List[str]	   =		pipe(
					    [prompt]      ,     generator=_lowerCamelCase      ,     output_type='''np'''      ,     return_dict=_lowerCamelCase      ,     num_inference_steps=2       )[0]

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

					assert image.shape == (1, 24, 24, 3)

					A_  :				str	   =		np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]       )

					assert np.abs(image_slice.flatten() - expected_slice       ).max() < 2.0
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2

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







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

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







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					A_  :				str	   =		load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''       )

					A_  :				int	   =		VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''       )
					A_  :				Tuple	   =		pipeline.to(_lowerCamelCase       )
					pipeline.set_progress_bar_config(disable=_lowerCamelCase       )

					# requires GPU generator for gumbel softmax
					# don't use GPU generator in tests though
					A_  :				Dict	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )
					A_  :				Union[str, Any]	   =		pipeline(
					    '''teddy bear playing in the pool'''      ,     num_images_per_prompt=1      ,     generator=_lowerCamelCase      ,     output_type='''np'''      ,     )

					A_  :				Optional[int]	   =		output.images[0]

					assert image.shape == (2_56, 2_56, 3)
					assert np.abs(expected_image - image       ).max() < 2.0





