


import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
 from transformers import SpeechaTextFeatureExtractor

_SCREAMING_SNAKE_CASE      :			Optional[Any]								=   random.Random()
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_=1.0							,UpperCamelCase_=None							,UpperCamelCase_=None    ):



     """simple docstring"""






     if rng is None:
          snake_case					=    global_rng

     snake_case					=    []
     for batch_idx in range(shape[0]    ):
          values.append([]    )
          for _ in range(shape[1]    ):
               values[-1].append(rng.random() * scale    )

     return values

@require_torch
@require_torchaudio
class   A__ (   unittest.TestCase     ):







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case=7      , __snake_case=4_0_0      , __snake_case=2_0_0_0      , __snake_case=2_4      , __snake_case=2_4      , __snake_case=0.0      , __snake_case=1_6_0_0_0      , __snake_case=True      , __snake_case=True      , ):
         snake_case					=    parent
         snake_case					=    batch_size
         snake_case					=    min_seq_length
         snake_case					=    max_seq_length
         snake_case					=    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         snake_case					=    feature_size
         snake_case					=    num_mel_bins
         snake_case					=    padding_value
         snake_case					=    sampling_rate
         snake_case					=    return_attention_mask
         snake_case					=    do_normalize
    def 		a_      (	self   ):
         return {
             "feature_size": self.feature_size,
             "num_mel_bins": self.num_mel_bins,
             "padding_value": self.padding_value,
             "sampling_rate": self.sampling_rate,
             "return_attention_mask": self.return_attention_mask,
             "do_normalize": self.do_normalize,
         }




    def 		a_      (	self      , __snake_case=False      , __snake_case=False   ):
         def _flatten(__snake_case   ):
              return list(itertools.chain(*_lowercase   )   )

         if equal_length:
              snake_case					=    [floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
         else:
              # make sure that inputs increase in size
              snake_case					=    [
                  floats_list((x, self.feature_size)   )
                  for x in range(self.min_seq_length      , self.max_seq_length      , self.seq_length_diff   )
              ]
         if numpify:
              snake_case					=    [np.asarray(_lowercase   ) for x in speech_inputs]
         return speech_inputs






@require_torch
@require_torchaudio
class   A__ (   _lowerCAmelCase	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		SpeechaTextFeatureExtractor if is_speech_available() else None
    def 		a_      (	self   ):
         snake_case					=    SpeechaTextFeatureExtractionTester(self   )
    def 		a_      (	self      , __snake_case   ):
         self.assertTrue(np.all(np.mean(_lowercase      , axis=0   ) < 1E-3   )   )
         self.assertTrue(np.all(np.abs(np.var(_lowercase      , axis=0   ) - 1   ) < 1E-3   )   )
    def 		a_      (	self   ):
         # Tests that all call wrap to encode_plus and batch_encode_plus
         snake_case					=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
         # create three inputs of length 800, 1000, and 1200
         snake_case					=    [floats_list((1, x)   )[0] for x in range(8_0_0      , 1_4_0_0      , 2_0_0   )]
         snake_case					=    [np.asarray(_lowercase   ) for speech_input in speech_inputs]

         # Test feature size
         snake_case					=    feature_extractor(_lowercase      , padding=_lowercase      , return_tensors='''np'''   ).input_features
         self.assertTrue(input_features.ndim == 3   )
         self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size   )

         # Test not batched input
         snake_case					=    feature_extractor(speech_inputs[0]      , return_tensors='''np'''   ).input_features
         snake_case					=    feature_extractor(np_speech_inputs[0]      , return_tensors='''np'''   ).input_features
         self.assertTrue(np.allclose(_lowercase      , _lowercase      , atol=1E-3   )   )

         # Test batched
         snake_case					=    feature_extractor(_lowercase      , return_tensors='''np'''   ).input_features
         snake_case					=    feature_extractor(_lowercase      , return_tensors='''np'''   ).input_features
         for enc_seq_a, enc_seq_a in zip(_lowercase      , _lowercase   ):
              self.assertTrue(np.allclose(_lowercase      , _lowercase      , atol=1E-3   )   )

         # Test 2-D numpy arrays are batched.
         snake_case					=    [floats_list((1, x)   )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
         snake_case					=    np.asarray(_lowercase   )
         snake_case					=    feature_extractor(_lowercase      , return_tensors='''np'''   ).input_features
         snake_case					=    feature_extractor(_lowercase      , return_tensors='''np'''   ).input_features
         for enc_seq_a, enc_seq_a in zip(_lowercase      , _lowercase   ):
              self.assertTrue(np.allclose(_lowercase      , _lowercase      , atol=1E-3   )   )
    def 		a_      (	self   ):
         snake_case					=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
         snake_case					=    [floats_list((1, x)   )[0] for x in range(8_0_0      , 1_4_0_0      , 2_0_0   )]

         snake_case					=    ['''longest''', '''max_length''', '''do_not_pad''']
         snake_case					=    [None, 1_6, None]
         for max_length, padding in zip(_lowercase      , _lowercase   ):
              snake_case					=    feature_extractor(
                  _lowercase      , padding=_lowercase      , max_length=_lowercase      , return_attention_mask=_lowercase   )
              snake_case					=    inputs.input_features
              snake_case					=    inputs.attention_mask
              snake_case					=    [np.sum(_lowercase   ) for x in attention_mask]

              self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]   )
              self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]   )
              self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]   )
    def 		a_      (	self   ):
         snake_case					=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
         snake_case					=    [floats_list((1, x)   )[0] for x in range(8_0_0      , 1_4_0_0      , 2_0_0   )]

         snake_case					=    ['''longest''', '''max_length''', '''do_not_pad''']
         snake_case					=    [None, 1_6, None]
         for max_length, padding in zip(_lowercase      , _lowercase   ):
              snake_case					=    feature_extractor(
                  _lowercase      , max_length=_lowercase      , padding=_lowercase      , return_tensors='''np'''      , return_attention_mask=_lowercase   )
              snake_case					=    inputs.input_features
              snake_case					=    inputs.attention_mask
              snake_case					=    [np.sum(_lowercase   ) for x in attention_mask]

              self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]   )
              self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6   )
              self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]   )
              self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6   )
              self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]   )
    def 		a_      (	self   ):
         snake_case					=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
         snake_case					=    [floats_list((1, x)   )[0] for x in range(8_0_0      , 1_4_0_0      , 2_0_0   )]
         snake_case					=    feature_extractor(
             _lowercase      , padding='''max_length'''      , max_length=4      , truncation=_lowercase      , return_tensors='''np'''      , return_attention_mask=_lowercase      , )
         snake_case					=    inputs.input_features
         snake_case					=    inputs.attention_mask
         snake_case					=    np.sum(attention_mask == 1      , axis=1   )

         self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
         self._check_zero_mean_unit_variance(input_features[1]   )
         self._check_zero_mean_unit_variance(input_features[2]   )
    def 		a_      (	self   ):
         snake_case					=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
         snake_case					=    [floats_list((1, x)   )[0] for x in range(8_0_0      , 1_4_0_0      , 2_0_0   )]
         snake_case					=    feature_extractor(
             _lowercase      , padding='''longest'''      , max_length=4      , truncation=_lowercase      , return_tensors='''np'''      , return_attention_mask=_lowercase      , )
         snake_case					=    inputs.input_features
         snake_case					=    inputs.attention_mask
         snake_case					=    np.sum(attention_mask == 1      , axis=1   )

         self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
         self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]   )
         self._check_zero_mean_unit_variance(input_features[2]   )

         # make sure that if max_length < longest -> then pad to max_length
         self.assertEqual(input_features.shape      , (3, 4, 2_4)   )

         snake_case					=    [floats_list((1, x)   )[0] for x in range(8_0_0      , 1_4_0_0      , 2_0_0   )]
         snake_case					=    feature_extractor(
             _lowercase      , padding='''longest'''      , max_length=1_6      , truncation=_lowercase      , return_tensors='''np'''      , return_attention_mask=_lowercase      , )
         snake_case					=    inputs.input_features
         snake_case					=    inputs.attention_mask
         snake_case					=    np.sum(attention_mask == 1      , axis=1   )

         self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
         self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]   )
         self._check_zero_mean_unit_variance(input_features[2]   )

         # make sure that if max_length < longest -> then pad to max_length
         self.assertEqual(input_features.shape      , (3, 6, 2_4)   )
    def 		a_      (	self   ):
         import torch

         snake_case					=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
         snake_case					=    np.random.rand(1_0_0      , 3_2   ).astype(np.floataa   )
         snake_case					=    np_speech_inputs.tolist()

         for inputs in [py_speech_inputs, np_speech_inputs]:
              snake_case					=    feature_extractor.pad([{'''input_features''': inputs}]      , return_tensors='''np'''   )
              self.assertTrue(np_processed.input_features.dtype == np.floataa   )
              snake_case					=    feature_extractor.pad([{'''input_features''': inputs}]      , return_tensors='''pt'''   )
              self.assertTrue(pt_processed.input_features.dtype == torch.floataa   )
    def 		a_      (	self      , __snake_case   ):
         from datasets import load_dataset

         snake_case					=    load_dataset('''hf-internal-testing/librispeech_asr_dummy'''      , '''clean'''      , split='''validation'''   )
         # automatic decoding with librispeech
         snake_case					=    ds.sort('''id'''   ).select(range(_lowercase   )   )[:num_samples]['''audio''']

         return [x["array"] for x in speech_samples]




    def 		a_      (	self   ):
         # fmt: off
         snake_case					=    np.array([
             -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
             -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
             -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
         ]   )
         # fmt: on

         snake_case					=    self._load_datasamples(1   )
         snake_case					=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
         snake_case					=    feature_extractor(_lowercase      , return_tensors='''pt'''   ).input_features
         self.assertEquals(input_features.shape      , (1, 5_8_4, 2_4)   )
         self.assertTrue(np.allclose(input_features[0, 0, :3_0]      , _lowercase      , atol=1E-4   )   )








import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class   A__ (   snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		UnCLIPImageVariationPipeline
    __magic_name__        =		IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
    __magic_name__        =		IMAGE_VARIATION_BATCH_PARAMS

    __magic_name__        =		[
        'generator',
        'return_dict',
        'decoder_num_inference_steps',
        'super_res_num_inference_steps',
    ]
    __magic_name__        =		False
    @property
    def 		a_      (	self   ):
         return 3_2
    @property
    def 		a_      (	self   ):
         return 3_2
    @property
    def 		a_      (	self   ):
         return self.time_input_dim
    @property
    def 		a_      (	self   ):
         return self.time_input_dim * 4
    @property
    def 		a_      (	self   ):
         return 1_0_0
    @property
    def 		a_      (	self   ):
         snake_case					=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )
         return tokenizer
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )
         snake_case					=    CLIPTextConfig(
             bos_token_id=0      , eos_token_id=2      , hidden_size=self.text_embedder_hidden_size      , projection_dim=self.text_embedder_hidden_size      , intermediate_size=3_7      , layer_norm_eps=1E-05      , num_attention_heads=4      , num_hidden_layers=5      , pad_token_id=1      , vocab_size=1_0_0_0      , )
         return CLIPTextModelWithProjection(__snake_case   )
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )
         snake_case					=    CLIPVisionConfig(
             hidden_size=self.text_embedder_hidden_size      , projection_dim=self.text_embedder_hidden_size      , num_hidden_layers=5      , num_attention_heads=4      , image_size=3_2      , intermediate_size=3_7      , patch_size=1      , )
         return CLIPVisionModelWithProjection(__snake_case   )
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )

         snake_case					=    {
             '''clip_embeddings_dim''': self.text_embedder_hidden_size,
             '''time_embed_dim''': self.time_embed_dim,
             '''cross_attention_dim''': self.cross_attention_dim,
         }

         snake_case					=    UnCLIPTextProjModel(**__snake_case   )
         return model
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )

         snake_case					=    {
             '''sample_size''': 3_2,
             # RGB in channels
             '''in_channels''': 3,
             # Out channels is double in channels because predicts mean and variance
             '''out_channels''': 6,
             '''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,
             '''cross_attention_dim''': self.cross_attention_dim,
             '''attention_head_dim''': 4,
             '''resnet_time_scale_shift''': '''scale_shift''',
             '''class_embed_type''': '''identity''',
         }

         snake_case					=    UNetaDConditionModel(**__snake_case   )
         return model
    @property
    def 		a_      (	self   ):
         return {
             "sample_size": 6_4,
             "layers_per_block": 1,
             "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
             "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
             "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
             "in_channels": 6,
             "out_channels": 3,
         }
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )

         snake_case					=    UNetaDModel(**self.dummy_super_res_kwargs   )
         return model
    @property
    def 		a_      (	self   ):
         # seeded differently to get different unet than `self.dummy_super_res_first`
         torch.manual_seed(1   )

         snake_case					=    UNetaDModel(**self.dummy_super_res_kwargs   )
         return model
    def 		a_      (	self   ):
         snake_case					=    self.dummy_decoder
         snake_case					=    self.dummy_text_proj
         snake_case					=    self.dummy_text_encoder
         snake_case					=    self.dummy_tokenizer
         snake_case					=    self.dummy_super_res_first
         snake_case					=    self.dummy_super_res_last

         snake_case					=    UnCLIPScheduler(
             variance_type='''learned_range'''      , prediction_type='''epsilon'''      , num_train_timesteps=1_0_0_0      , )

         snake_case					=    UnCLIPScheduler(
             variance_type='''fixed_small_log'''      , prediction_type='''epsilon'''      , num_train_timesteps=1_0_0_0      , )

         snake_case					=    CLIPImageProcessor(crop_size=3_2      , size=3_2   )

         snake_case					=    self.dummy_image_encoder

         return {
             "decoder": decoder,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "text_proj": text_proj,
             "feature_extractor": feature_extractor,
             "image_encoder": image_encoder,
             "super_res_first": super_res_first,
             "super_res_last": super_res_last,
             "decoder_scheduler": decoder_scheduler,
             "super_res_scheduler": super_res_scheduler,
         }
    def 		a_      (	self      , __snake_case      , __snake_case=0      , __snake_case=True   ):
         snake_case					=    floats_tensor((1, 3, 3_2, 3_2)      , rng=random.Random(__snake_case   )   ).to(__snake_case   )
         if str(__snake_case   ).startswith('''mps'''   ):
              snake_case					=    torch.manual_seed(__snake_case   )
         else:
              snake_case					=    torch.Generator(device=__snake_case   ).manual_seed(__snake_case   )

         if pil_image:
              snake_case					=    input_image * 0.5 + 0.5
              snake_case					=    input_image.clamp(0      , 1   )
              snake_case					=    input_image.cpu().permute(0      , 2      , 3      , 1   ).float().numpy()
              snake_case					=    DiffusionPipeline.numpy_to_pil(__snake_case   )[0]

         return {
             "image": input_image,
             "generator": generator,
             "decoder_num_inference_steps": 2,
             "super_res_num_inference_steps": 2,
             "output_type": "np",
         }
    def 		a_      (	self   ):
         snake_case					=    '''cpu'''

         snake_case					=    self.get_dummy_components()

         snake_case					=    self.pipeline_class(**__snake_case   )
         snake_case					=    pipe.to(__snake_case   )

         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )

         snake_case					=    pipe(**__snake_case   )
         snake_case					=    output.images

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )

         snake_case					=    pipe(
             **__snake_case      , return_dict=__snake_case      , )[0]

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

         assert image.shape == (1, 6_4, 6_4, 3)

         snake_case					=    np.array(
             [
                 0.9997,
                 0.0002,
                 0.9997,
                 0.9997,
                 0.9969,
                 0.0023,
                 0.9997,
                 0.9969,
                 0.9970,
             ]   )

         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   ):
         snake_case					=    '''cpu'''

         snake_case					=    self.get_dummy_components()

         snake_case					=    self.pipeline_class(**__snake_case   )
         snake_case					=    pipe.to(__snake_case   )

         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )

         snake_case					=    pipe(**__snake_case   )
         snake_case					=    output.images

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )

         snake_case					=    pipe(
             **__snake_case      , return_dict=__snake_case      , )[0]

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

         assert image.shape == (1, 6_4, 6_4, 3)

         snake_case					=    np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]   )

         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   ):
         snake_case					=    '''cpu'''

         snake_case					=    self.get_dummy_components()

         snake_case					=    self.pipeline_class(**__snake_case   )
         snake_case					=    pipe.to(__snake_case   )

         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )
         snake_case					=    [
             pipeline_inputs['''image'''],
             pipeline_inputs['''image'''],
         ]

         snake_case					=    pipe(**__snake_case   )
         snake_case					=    output.images

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )
         snake_case					=    [
             tuple_pipeline_inputs['''image'''],
             tuple_pipeline_inputs['''image'''],
         ]

         snake_case					=    pipe(
             **__snake_case      , return_dict=__snake_case      , )[0]

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

         assert image.shape == (2, 6_4, 6_4, 3)

         snake_case					=    np.array(
             [
                 0.9997,
                 0.9989,
                 0.0008,
                 0.0021,
                 0.9960,
                 0.0018,
                 0.0014,
                 0.0002,
                 0.9933,
             ]   )

         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   ):
         snake_case					=    torch.device('''cpu'''   )

         class   A__ :







             """simple docstring"""

             __magic_name__        =		1






         snake_case					=    self.get_dummy_components()

         snake_case					=    self.pipeline_class(**__snake_case   )
         snake_case					=    pipe.to(__snake_case   )

         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    torch.Generator(device=__snake_case   ).manual_seed(0   )
         snake_case					=    pipe.decoder.dtype
         snake_case					=    1

         snake_case					=    (
             batch_size,
             pipe.decoder.config.in_channels,
             pipe.decoder.config.sample_size,
             pipe.decoder.config.sample_size,
         )
         snake_case					=    pipe.prepare_latents(
             __snake_case      , dtype=__snake_case      , device=__snake_case      , generator=__snake_case      , latents=__snake_case      , scheduler=DummyScheduler()   )

         snake_case					=    (
             batch_size,
             pipe.super_res_first.config.in_channels // 2,
             pipe.super_res_first.config.sample_size,
             pipe.super_res_first.config.sample_size,
         )
         snake_case					=    pipe.prepare_latents(
             __snake_case      , dtype=__snake_case      , device=__snake_case      , generator=__snake_case      , latents=__snake_case      , scheduler=DummyScheduler()   )

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )

         snake_case					=    pipe(
             **__snake_case      , decoder_latents=__snake_case      , super_res_latents=__snake_case   ).images

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )
         # Don't pass image, instead pass embedding
         snake_case					=    pipeline_inputs.pop('''image'''   )
         snake_case					=    pipe.image_encoder(__snake_case   ).image_embeds

         snake_case					=    pipe(
             **__snake_case      , decoder_latents=__snake_case      , super_res_latents=__snake_case      , image_embeddings=__snake_case      , ).images

         # make sure passing text embeddings manually is identical
         assert np.abs(img_out_a - img_out_a   ).max() < 1E-4
    @skip_mps
    def 		a_      (	self   ):
         snake_case					=    torch_device == '''cpu'''

         # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
         snake_case					=    1E-2

         self._test_attention_slicing_forward_pass(
             test_max_difference=__snake_case      , expected_max_diff=__snake_case   )
    @skip_mps
    def 		a_      (	self   ):
         snake_case					=    torch_device == '''cpu'''
         snake_case					=    True
         snake_case					=    [
             '''decoder_num_inference_steps''',
             '''super_res_num_inference_steps''',
         ]

         self._test_inference_batch_single_identical(
             test_max_difference=__snake_case      , relax_max_difference=__snake_case      , additional_params_copy_to_batched_inputs=__snake_case      , )
    def 		a_      (	self   ):
         snake_case					=    [
             '''decoder_num_inference_steps''',
             '''super_res_num_inference_steps''',
         ]

         if torch_device == "mps":
              # TODO: MPS errors with larger batch sizes
              snake_case					=    [2, 3]
              self._test_inference_batch_consistent(
                  batch_sizes=__snake_case      , additional_params_copy_to_batched_inputs=__snake_case      , )
         else:
              self._test_inference_batch_consistent(
                  additional_params_copy_to_batched_inputs=__snake_case   )
    @skip_mps
    def 		a_      (	self   ):
         return super().test_dict_tuple_outputs_equivalent()
    @skip_mps
    def 		a_      (	self   ):
         return super().test_save_load_local()




    @skip_mps
    def 		a_      (	self   ):
         return super().test_save_load_optional_components()






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







    """simple docstring"""

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




    def 		a_      (	self   ):
         snake_case					=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'''   )
         snake_case					=    load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy'''   )

         snake_case					=    UnCLIPImageVariationPipeline.from_pretrained(
             '''kakaobrain/karlo-v1-alpha-image-variations'''      , torch_dtype=torch.floataa   )
         snake_case					=    pipeline.to(__snake_case   )
         pipeline.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    torch.Generator(device='''cpu'''   ).manual_seed(0   )
         snake_case					=    pipeline(
             __snake_case      , generator=__snake_case      , output_type='''np'''      , )

         snake_case					=    output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         assert_mean_pixel_difference(__snake_case      , __snake_case      , 1_5   )




