


import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class   A__ (   snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		RobertaTokenizer
    __magic_name__        =		RobertaTokenizerFast
    __magic_name__        =		True
    __magic_name__        =		{'cls_token': '<s>'}
    def 		a_      (	self   ):
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         snake_case					=    [
             '''l''',
             '''o''',
             '''w''',
             '''e''',
             '''r''',
             '''s''',
             '''t''',
             '''i''',
             '''d''',
             '''n''',
             '''\u0120''',
             '''\u0120l''',
             '''\u0120n''',
             '''\u0120lo''',
             '''\u0120low''',
             '''er''',
             '''\u0120lowest''',
             '''\u0120newer''',
             '''\u0120wider''',
             '''<unk>''',
         ]
         snake_case					=    dict(zip(__snake_case      , range(len(__snake_case   )   )   )   )
         snake_case					=    ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
         snake_case					=    {'''unk_token''': '''<unk>'''}

         snake_case					=    os.path.join(self.tmpdirname      , VOCAB_FILES_NAMES['''vocab_file''']   )
         snake_case					=    os.path.join(self.tmpdirname      , VOCAB_FILES_NAMES['''merges_file''']   )
         with open(self.vocab_file      , '''w'''      , encoding='''utf-8'''   ) as fp:
              fp.write(json.dumps(__snake_case   ) + '''\n'''   )
         with open(self.merges_file      , '''w'''      , encoding='''utf-8'''   ) as fp:
              fp.write('''\n'''.join(__snake_case   )   )
    def 		a_      (	self      , **__snake_case   ):
         kwargs.update(self.special_tokens_map   )
         return self.tokenizer_class.from_pretrained(self.tmpdirname      , **__snake_case   )
    def 		a_      (	self      , **__snake_case   ):
         kwargs.update(self.special_tokens_map   )
         return RobertaTokenizerFast.from_pretrained(self.tmpdirname      , **__snake_case   )
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    '''lower newer'''
         snake_case					=    '''lower newer'''
         return input_text, output_text
    def 		a_      (	self   ):
         snake_case					=    self.tokenizer_class(self.vocab_file      , self.merges_file      , **self.special_tokens_map   )
         snake_case					=    '''lower newer'''
         snake_case					=    ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
         snake_case					=    tokenizer.tokenize(__snake_case   )  # , add_prefix_space=True)
         self.assertListEqual(__snake_case      , __snake_case   )

         snake_case					=    tokens + [tokenizer.unk_token]
         snake_case					=    [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case   )      , __snake_case   )
    def 		a_      (	self   ):
         snake_case					=    self.get_tokenizer()

         self.assertListEqual(tokenizer.encode('''Hello world!'''      , add_special_tokens=__snake_case   )      , [0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]   )
         self.assertListEqual(
             tokenizer.encode('''Hello world! cécé herlolip 418'''      , add_special_tokens=__snake_case   )      , [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]      , )
    @slow
    def 		a_      (	self   ):
         snake_case					=    self.tokenizer_class.from_pretrained('''roberta-base'''   )

         snake_case					=    tokenizer.encode('''sequence builders'''      , add_special_tokens=__snake_case   )
         snake_case					=    tokenizer.encode('''multi-sequence build'''      , add_special_tokens=__snake_case   )

         snake_case					=    tokenizer.encode(
             '''sequence builders'''      , add_special_tokens=__snake_case      , add_prefix_space=__snake_case   )
         snake_case					=    tokenizer.encode(
             '''sequence builders'''      , '''multi-sequence build'''      , add_special_tokens=__snake_case      , add_prefix_space=__snake_case   )

         snake_case					=    tokenizer.build_inputs_with_special_tokens(__snake_case   )
         snake_case					=    tokenizer.build_inputs_with_special_tokens(__snake_case      , __snake_case   )

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode
    def 		a_      (	self   ):
         snake_case					=    self.get_tokenizer()

         snake_case					=    '''Encode this sequence.'''
         snake_case					=    tokenizer.byte_encoder[''' '''.encode('''utf-8'''   )[0]]

         # Testing encoder arguments
         snake_case					=    tokenizer.encode(__snake_case      , add_special_tokens=__snake_case      , add_prefix_space=__snake_case   )
         snake_case					=    tokenizer.convert_ids_to_tokens(encoded[0]   )[0]
         self.assertNotEqual(__snake_case      , __snake_case   )

         snake_case					=    tokenizer.encode(__snake_case      , add_special_tokens=__snake_case      , add_prefix_space=__snake_case   )
         snake_case					=    tokenizer.convert_ids_to_tokens(encoded[0]   )[0]
         self.assertEqual(__snake_case      , __snake_case   )

         tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}   )
         snake_case					=    tokenizer.encode(__snake_case      , add_special_tokens=__snake_case   )
         snake_case					=    tokenizer.convert_ids_to_tokens(encoded[1]   )[0]
         self.assertNotEqual(__snake_case      , __snake_case   )

         # Testing spaces after special tokens
         snake_case					=    '''<mask>'''
         tokenizer.add_special_tokens(
             {'''mask_token''': AddedToken(__snake_case      , lstrip=__snake_case      , rstrip=__snake_case   )}   )  # mask token has a left space
         snake_case					=    tokenizer.convert_tokens_to_ids(__snake_case   )

         snake_case					=    '''Encode <mask> sequence'''
         snake_case					=    '''Encode <mask>sequence'''

         snake_case					=    tokenizer.encode(__snake_case   )
         snake_case					=    encoded.index(__snake_case   )
         snake_case					=    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]   )[0]
         self.assertEqual(__snake_case      , __snake_case   )

         snake_case					=    tokenizer.encode(__snake_case   )
         snake_case					=    encoded.index(__snake_case   )
         snake_case					=    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]   )[0]
         self.assertNotEqual(__snake_case      , __snake_case   )
    def 		a_      (	self   ):
         pass
    def 		a_      (	self   ):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
                   snake_case					=    self.rust_tokenizer_class.from_pretrained(__snake_case      , **__snake_case   )
                   snake_case					=    self.tokenizer_class.from_pretrained(__snake_case      , **__snake_case   )
                   snake_case					=    '''A, <mask> AllenNLP sentence.'''
                   snake_case					=    tokenizer_r.encode_plus(__snake_case      , add_special_tokens=__snake_case      , return_token_type_ids=__snake_case   )
                   snake_case					=    tokenizer_p.encode_plus(__snake_case      , add_special_tokens=__snake_case      , return_token_type_ids=__snake_case   )

                   # token_type_ids should put 0 everywhere
                   self.assertEqual(sum(tokens_r['''token_type_ids''']   )      , sum(tokens_p['''token_type_ids''']   )   )

                   # attention_mask should put 1 everywhere, so sum over length should be 1
                   self.assertEqual(
                       sum(tokens_r['''attention_mask''']   ) / len(tokens_r['''attention_mask''']   )      , sum(tokens_p['''attention_mask''']   ) / len(tokens_p['''attention_mask''']   )      , )

                   snake_case					=    tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']   )
                   snake_case					=    tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']   )

                   # Rust correctly handles the space before the mask while python doesnt
                   self.assertSequenceEqual(tokens_p['''input_ids''']      , [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]   )
                   self.assertSequenceEqual(tokens_r['''input_ids''']      , [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]   )

                   self.assertSequenceEqual(
                       __snake_case      , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']   )
                   self.assertSequenceEqual(
                       __snake_case      , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']   )
    def 		a_      (	self   ):
         for trim_offsets, add_prefix_space in itertools.product([True, False]      , repeat=2   ):
              snake_case					=    self.rust_tokenizer_class.from_pretrained(
                  self.tmpdirname      , use_fast=__snake_case      , add_prefix_space=__snake_case      , trim_offsets=__snake_case   )

              snake_case					=    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()   )
              snake_case					=    json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()   )

              self.assertEqual(pre_tokenizer_state['''add_prefix_space''']      , __snake_case   )

              self.assertEqual(post_processor_state['''add_prefix_space''']      , __snake_case   )
              self.assertEqual(post_processor_state['''trim_offsets''']      , __snake_case   )




    def 		a_      (	self   ):
         # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
         # `trim_offsets`
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
                   snake_case					=    '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
                   snake_case					=    F'''{text_of_1_token} {text_of_1_token}'''

                   snake_case					=    self.rust_tokenizer_class.from_pretrained(
                       __snake_case      , use_fast=__snake_case      , add_prefix_space=__snake_case      , trim_offsets=__snake_case   )
                   snake_case					=    tokenizer_r(__snake_case      , return_offsets_mapping=__snake_case      , add_special_tokens=__snake_case   )
                   self.assertEqual(encoding.offset_mapping[0]      , (0, len(__snake_case   ))   )
                   self.assertEqual(
                       encoding.offset_mapping[1]      , (len(__snake_case   ) + 1, len(__snake_case   ) + 1 + len(__snake_case   ))      , )

                   snake_case					=    self.rust_tokenizer_class.from_pretrained(
                       __snake_case      , use_fast=__snake_case      , add_prefix_space=__snake_case      , trim_offsets=__snake_case   )
                   snake_case					=    tokenizer_r(__snake_case      , return_offsets_mapping=__snake_case      , add_special_tokens=__snake_case   )
                   self.assertEqual(encoding.offset_mapping[0]      , (0, len(__snake_case   ))   )
                   self.assertEqual(
                       encoding.offset_mapping[1]      , (len(__snake_case   ) + 1, len(__snake_case   ) + 1 + len(__snake_case   ))      , )

                   snake_case					=    self.rust_tokenizer_class.from_pretrained(
                       __snake_case      , use_fast=__snake_case      , add_prefix_space=__snake_case      , trim_offsets=__snake_case   )
                   snake_case					=    tokenizer_r(__snake_case      , return_offsets_mapping=__snake_case      , add_special_tokens=__snake_case   )
                   self.assertEqual(encoding.offset_mapping[0]      , (0, len(__snake_case   ))   )
                   self.assertEqual(
                       encoding.offset_mapping[1]      , (len(__snake_case   ), len(__snake_case   ) + 1 + len(__snake_case   ))      , )

                   snake_case					=    self.rust_tokenizer_class.from_pretrained(
                       __snake_case      , use_fast=__snake_case      , add_prefix_space=__snake_case      , trim_offsets=__snake_case   )
                   snake_case					=    tokenizer_r(__snake_case      , return_offsets_mapping=__snake_case      , add_special_tokens=__snake_case   )
                   self.assertEqual(encoding.offset_mapping[0]      , (0, len(__snake_case   ))   )
                   self.assertEqual(
                       encoding.offset_mapping[1]      , (len(__snake_case   ), len(__snake_case   ) + 1 + len(__snake_case   ))      , )

                   snake_case					=    F''' {text}'''

                   # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                   #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                   # )
                   # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                   # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                   # self.assertEqual(
                   #     encoding.offset_mapping[1],
                   #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                   # )

                   snake_case					=    self.rust_tokenizer_class.from_pretrained(
                       __snake_case      , use_fast=__snake_case      , add_prefix_space=__snake_case      , trim_offsets=__snake_case   )
                   snake_case					=    tokenizer_r(__snake_case      , return_offsets_mapping=__snake_case      , add_special_tokens=__snake_case   )
                   self.assertEqual(encoding.offset_mapping[0]      , (1, 1 + len(__snake_case   ))   )
                   self.assertEqual(
                       encoding.offset_mapping[1]      , (1 + len(__snake_case   ) + 1, 1 + len(__snake_case   ) + 1 + len(__snake_case   ))      , )

                   snake_case					=    self.rust_tokenizer_class.from_pretrained(
                       __snake_case      , use_fast=__snake_case      , add_prefix_space=__snake_case      , trim_offsets=__snake_case   )
                   snake_case					=    tokenizer_r(__snake_case      , return_offsets_mapping=__snake_case      , add_special_tokens=__snake_case   )
                   self.assertEqual(encoding.offset_mapping[0]      , (0, 1 + len(__snake_case   ))   )
                   self.assertEqual(
                       encoding.offset_mapping[1]      , (1 + len(__snake_case   ), 1 + len(__snake_case   ) + 1 + len(__snake_case   ))      , )

                   snake_case					=    self.rust_tokenizer_class.from_pretrained(
                       __snake_case      , use_fast=__snake_case      , add_prefix_space=__snake_case      , trim_offsets=__snake_case   )
                   snake_case					=    tokenizer_r(__snake_case      , return_offsets_mapping=__snake_case      , add_special_tokens=__snake_case   )
                   self.assertEqual(encoding.offset_mapping[0]      , (0, 1 + len(__snake_case   ))   )
                   self.assertEqual(
                       encoding.offset_mapping[1]      , (1 + len(__snake_case   ), 1 + len(__snake_case   ) + 1 + len(__snake_case   ))      , )








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   )




