


import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel

class 						A__							:




						def __init__(       self					:      int				,    _a					:      Dict				,    _a					:      List[Any]=99				,    _a					:      int=13				,    _a					:      Optional[Any]=7				,    _a					:      Optional[int]=9				,    _a					:      List[Any]=True				,    _a					:      Union[str, Any]=True				,    _a					:      int=False				,    _a					:      Optional[int]=32				,    _a					:      Union[str, Any]=5				,    _a					:      Tuple=4				,    _a					:      int=37				,    _a					:      Any=8				,    _a					:      List[str]=0.1				,    _a					:      Tuple=0.0_02				,    _a					:      Any=1				,    _a					:      List[Any]=0				,    _a					:      List[str]=0				,    _a					:      int=None				,    _a					:      List[str]=None				,    )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =parent
										_SCREAMING_SNAKE_CASE			  =batch_size
										_SCREAMING_SNAKE_CASE			  =encoder_seq_length
										_SCREAMING_SNAKE_CASE			  =decoder_seq_length
										# For common tests
										_SCREAMING_SNAKE_CASE			  =self.decoder_seq_length
										_SCREAMING_SNAKE_CASE			  =is_training
										_SCREAMING_SNAKE_CASE			  =use_attention_mask
										_SCREAMING_SNAKE_CASE			  =use_labels
										_SCREAMING_SNAKE_CASE			  =vocab_size
										_SCREAMING_SNAKE_CASE			  =hidden_size
										_SCREAMING_SNAKE_CASE			  =num_hidden_layers
										_SCREAMING_SNAKE_CASE			  =num_attention_heads
										_SCREAMING_SNAKE_CASE			  =d_ff
										_SCREAMING_SNAKE_CASE			  =relative_attention_num_buckets
										_SCREAMING_SNAKE_CASE			  =dropout_rate
										_SCREAMING_SNAKE_CASE			  =initializer_factor
										_SCREAMING_SNAKE_CASE			  =eos_token_id
										_SCREAMING_SNAKE_CASE			  =pad_token_id
										_SCREAMING_SNAKE_CASE			  =decoder_start_token_id
										_SCREAMING_SNAKE_CASE			  =None
										_SCREAMING_SNAKE_CASE			  =decoder_layers




						def 							__UpperCamelCase				(       self					:      int  )							->     List[Any]:


										"""simple docstring"""





										return TaConfig.from_pretrained('''google/umt5-base'''  )




						def 							__UpperCamelCase				(       self					:      Any				,    _a					:      List[Any]				,    _a					:      str				,    _a					:      Optional[Any]				,    _a					:      List[str]=None				,    _a					:      str=None				,    _a					:      Tuple=None				,    _a					:      Dict=None				,    _a					:      Optional[int]=None				,    )							->     str:


										"""simple docstring"""





										if attention_mask is None:
														_SCREAMING_SNAKE_CASE			  =input_ids.ne(config.pad_token_id  )
										if decoder_attention_mask is None:
														_SCREAMING_SNAKE_CASE			  =decoder_input_ids.ne(config.pad_token_id  )
										if head_mask is None:
														_SCREAMING_SNAKE_CASE			  =torch.ones(config.num_hidden_layers				,    config.num_attention_heads				,    device=lowerCAmelCase__  )
										if decoder_head_mask is None:
														_SCREAMING_SNAKE_CASE			  =torch.ones(config.num_decoder_layers				,    config.num_attention_heads				,    device=lowerCAmelCase__  )
										if cross_attn_head_mask is None:
														_SCREAMING_SNAKE_CASE			  =torch.ones(
														    config.num_decoder_layers				,    config.num_attention_heads				,    device=lowerCAmelCase__  )
										return {
										    "input_ids": input_ids,
										    "decoder_input_ids": decoder_input_ids,
										    "attention_mask": attention_mask,
										    "decoder_attention_mask": decoder_attention_mask,
										    "head_mask": head_mask,
										    "decoder_head_mask": decoder_head_mask,
										    "cross_attn_head_mask": cross_attn_head_mask,
										}




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =ids_tensor([self.batch_size, self.encoder_seq_length]				,    self.vocab_size  )
										_SCREAMING_SNAKE_CASE			  =ids_tensor([self.batch_size, self.decoder_seq_length]				,    self.vocab_size  )

										# we need to clamp the input ids here to avoid having pad token in between
										# this is because for NllbMoe the position_ids are prepared such that
										# all pad tokens have pos id = 2 and rest are between 2..seq_length
										# and the seq_length here is seq_length - num_pad_tokens
										# but when using past, there is no way of knowing if the past input ids had
										# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
										# position_ids being off by num_pad_tokens in past input
										_SCREAMING_SNAKE_CASE			  =input_ids.clamp(self.pad_token_id + 1  )
										_SCREAMING_SNAKE_CASE			  =decoder_input_ids.clamp(self.pad_token_id + 1  )

										_SCREAMING_SNAKE_CASE			  =self.get_config()
										_SCREAMING_SNAKE_CASE			  =config.num_attention_heads
										_SCREAMING_SNAKE_CASE			  =self.prepare_inputs_dict(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__  )
										return config, input_dict




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =self.prepare_config_and_inputs()
										return config, inputs_dict




						def 							__UpperCamelCase				(       self					:      Dict  )							->     List[str]:


										"""simple docstring"""





										return TaConfig(
										    vocab_size=166				,    d_model=self.hidden_size				,    d_ff=self.d_ff				,    d_kv=self.hidden_size // self.num_attention_heads				,    num_layers=self.num_hidden_layers				,    num_decoder_layers=self.decoder_layers				,    num_heads=self.num_attention_heads				,    relative_attention_num_buckets=self.relative_attention_num_buckets				,    dropout_rate=self.dropout_rate				,    initializer_factor=self.initializer_factor				,    eos_token_id=self.eos_token_id				,    bos_token_id=self.pad_token_id				,    pad_token_id=self.pad_token_id				,    decoder_start_token_id=self.decoder_start_token_id				,    )




						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     List[str]:


										"""simple docstring"""





										return TaConfig(
										    vocab_size=self.vocab_size				,    d_model=self.hidden_size				,    d_ff=self.d_ff				,    d_kv=self.hidden_size // self.num_attention_heads				,    num_layers=self.num_hidden_layers				,    num_decoder_layers=self.decoder_layers				,    num_heads=self.num_attention_heads				,    relative_attention_num_buckets=self.relative_attention_num_buckets				,    dropout_rate=self.dropout_rate				,    initializer_factor=self.initializer_factor				,    eos_token_id=self.eos_token_id				,    bos_token_id=self.pad_token_id				,    pad_token_id=self.pad_token_id				,    decoder_start_token_id=self.decoder_start_token_id				,    )




						def 							__UpperCamelCase				(       self					:      int				,    _a					:      List[Any]				,    _a					:      Union[str, Any]				,    _a					:      List[Any]				,    _a					:      Tuple				,    _a					:      Tuple				,    _a					:      int				,    )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =UMTaModel(config=lowerCAmelCase__  )
										model.to(lowerCAmelCase__  )
										model.eval()
										_SCREAMING_SNAKE_CASE			  =model(
										    input_ids=lowerCAmelCase__				,    decoder_input_ids=lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    decoder_attention_mask=lowerCAmelCase__				,    )
										_SCREAMING_SNAKE_CASE			  =model(input_ids=lowerCAmelCase__				,    decoder_input_ids=lowerCAmelCase__  )
										_SCREAMING_SNAKE_CASE			  =result.last_hidden_state
										_SCREAMING_SNAKE_CASE			  =result.past_key_values
										_SCREAMING_SNAKE_CASE			  =result.encoder_last_hidden_state

										self.parent.assertEqual(encoder_output.size()				,    (self.batch_size, self.encoder_seq_length, self.hidden_size)  )
										self.parent.assertEqual(decoder_output.size()				,    (self.batch_size, self.decoder_seq_length, self.hidden_size)  )
										# There should be `num_layers` key value embeddings stored in decoder_past
										self.parent.assertEqual(len(lowerCAmelCase__  )				,    config.num_layers  )
										# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
										self.parent.assertEqual(len(decoder_past[0]  )				,    4  )




						def 							__UpperCamelCase				(       self					:      str				,    _a					:      Union[str, Any]				,    _a					:      Any				,    _a					:      Optional[int]				,    _a					:      Optional[Any]				,    _a					:      List[Any]				,    _a					:      Any				,    )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =UMTaModel(config=lowerCAmelCase__  ).get_decoder().to(lowerCAmelCase__  ).eval()
										# first forward pass
										_SCREAMING_SNAKE_CASE			  =model(lowerCAmelCase__				,    use_cache=lowerCAmelCase__  )
										_SCREAMING_SNAKE_CASE			  =model(lowerCAmelCase__  )
										_SCREAMING_SNAKE_CASE			  =model(lowerCAmelCase__				,    use_cache=lowerCAmelCase__  )

										self.parent.assertTrue(len(lowerCAmelCase__  ) == len(lowerCAmelCase__  )  )
										self.parent.assertTrue(len(lowerCAmelCase__  ) == len(lowerCAmelCase__  ) + 1  )

										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										_SCREAMING_SNAKE_CASE			  =ids_tensor((self.batch_size, 1)				,    config.vocab_size  )

										# append to next input_ids and
										_SCREAMING_SNAKE_CASE			  =torch.cat([input_ids, next_tokens]				,    dim=-1  )

										_SCREAMING_SNAKE_CASE			  =model(lowerCAmelCase__  )['''last_hidden_state''']
										_SCREAMING_SNAKE_CASE			  =model(lowerCAmelCase__				,    past_key_values=lowerCAmelCase__  )['''last_hidden_state''']

										# select random slice
										_SCREAMING_SNAKE_CASE			  =ids_tensor((1,)				,    output_from_past.shape[-1]  ).item()
										_SCREAMING_SNAKE_CASE			  =output_from_no_past[:, -1, random_slice_idx].detach()
										_SCREAMING_SNAKE_CASE			  =output_from_past[:, 0, random_slice_idx].detach()

										# test that outputs are equal for slice
										self.parent.assertTrue(torch.allclose(lowerCAmelCase__				,    lowerCAmelCase__				,    atol=1E-3  )  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      str				,    _a					:      List[str]				,    )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =UMTaModel(config=lowerCAmelCase__  ).to(lowerCAmelCase__  ).half().eval()
										_SCREAMING_SNAKE_CASE			  =model(**lowerCAmelCase__  )['''last_hidden_state''']
										self.parent.assertFalse(torch.isnan(lowerCAmelCase__  ).any().item()  )

@require_torch
class 						A__							(		_snake_case   ,     _snake_case   ,     _snake_case   ,     unittest.TestCase  ):
						UpperCAmelCase             =						(
						    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
						)
						UpperCAmelCase             =						(UMTaForConditionalGeneration,) if is_torch_available() else ()
						UpperCAmelCase             =						(
						    {
						        "conversational": UMTaForConditionalGeneration,
						        "feature-extraction": UMTaModel,
						        "summarization": UMTaForConditionalGeneration,
						        "text2text-generation": UMTaForConditionalGeneration,
						        "translation": UMTaForConditionalGeneration,
						        "question-answering": UMTaForQuestionAnswering,
						    }
						    if is_torch_available()
						    else {}
						)
						UpperCAmelCase             =						True
						UpperCAmelCase             =						False
						UpperCAmelCase             =						False
						UpperCAmelCase             =						True
						UpperCAmelCase             =						True
						# The small UMT5 model needs higher percentages for CPU/MP tests
						UpperCAmelCase             =						[0.8, 0.9]




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =UMTaModelTester(self  )




						@unittest.skip('''Test has a segmentation fault on torch 1.8.0'''  )
						def 							__UpperCamelCase				(       self					:      Tuple  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs()
										_SCREAMING_SNAKE_CASE			  =UMTaModel(config_and_inputs[0]  ).to(lowerCAmelCase__  )
										with tempfile.TemporaryDirectory() as tmpdirname:
														torch.onnx.export(
														    lowerCAmelCase__				,    (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])				,    f"{tmpdirname}/t5_test.onnx"				,    export_params=lowerCAmelCase__				,    opset_version=9				,    input_names=['''input_ids''', '''decoder_input_ids''']				,    )




						@unittest.skipIf(torch_device == '''cpu'''				,    '''Cant do half precision'''  )
						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model_fpaa_forward(*lowerCAmelCase__  )




						def 							__UpperCamelCase				(       self					:      int  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
										_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs()
										_SCREAMING_SNAKE_CASE			  =config_and_inputs[0]
										_SCREAMING_SNAKE_CASE			  =UMTaForConditionalGeneration(lowerCAmelCase__  ).eval()
										model.to(lowerCAmelCase__  )

										_SCREAMING_SNAKE_CASE			  ={
										    '''head_mask''': torch.zeros(config.num_layers				,    config.num_heads				,    device=lowerCAmelCase__  ),
										    '''decoder_head_mask''': torch.zeros(config.num_decoder_layers				,    config.num_heads				,    device=lowerCAmelCase__  ),
										    '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers				,    config.num_heads				,    device=lowerCAmelCase__  ),
										}

										for attn_name, (name, mask) in zip(lowerCAmelCase__				,    head_masking.items()  ):
														_SCREAMING_SNAKE_CASE			  ={name: mask}
														# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
														if name == "head_mask":
																		_SCREAMING_SNAKE_CASE			  =torch.ones(
																		    config.num_decoder_layers				,    config.num_heads				,    device=lowerCAmelCase__  )

														_SCREAMING_SNAKE_CASE			  =model.generate(
														    config_and_inputs[1]['''input_ids''']				,    num_beams=1				,    max_length=3				,    output_attentions=lowerCAmelCase__				,    return_dict_in_generate=lowerCAmelCase__				,    **lowerCAmelCase__				,    )
														# We check the state of decoder_attentions and cross_attentions just from the last step
														_SCREAMING_SNAKE_CASE			  =out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
														self.assertEqual(sum([w.sum().item() for w in attn_weights]  )				,    0.0  )




						@unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.'''  )
						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Optional[int]:


										"""simple docstring"""





										pass

@require_torch
@require_sentencepiece
@require_tokenizers
class 						A__							(		unittest.TestCase  ):




						@slow
						@unittest.skip(
						    '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'''  )
						def 							__UpperCamelCase				(       self					:      Tuple  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''				,    return_dict=lowerCAmelCase__  ).to(lowerCAmelCase__  )
										_SCREAMING_SNAKE_CASE			  =AutoTokenizer.from_pretrained('''google/umt5-small'''				,    use_fast=lowerCAmelCase__				,    legacy=lowerCAmelCase__  )
										_SCREAMING_SNAKE_CASE			  =[
										    '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
										    '''No se como puedo <extra_id_0>.''',
										    '''This is the reason why we <extra_id_0> them.''',
										    '''The <extra_id_0> walks in <extra_id_1>, seats''',
										    '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
										]
										_SCREAMING_SNAKE_CASE			  =tokenizer(lowerCAmelCase__				,    return_tensors='''pt'''				,    padding=lowerCAmelCase__  ).input_ids
										# fmt: off
										_SCREAMING_SNAKE_CASE			  =torch.tensor(
										    [
										        [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
										        [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
										        [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
										        [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
										        [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
										    ]  )
										# fmt: on
										torch.testing.assert_allclose(lowerCAmelCase__				,    lowerCAmelCase__  )

										_SCREAMING_SNAKE_CASE			  =model.generate(input_ids.to(lowerCAmelCase__  )  )
										_SCREAMING_SNAKE_CASE			  =[
										    '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
										    '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
										    '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
										    '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
										    '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
										]
										_SCREAMING_SNAKE_CASE			  =tokenizer.batch_decode(lowerCAmelCase__  )
										self.assertEqual(lowerCAmelCase__				,    lowerCAmelCase__  )



import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						KandinskyVaaPipeline
						UpperCAmelCase             =						[
						    "image_embeds",
						    "negative_image_embeds",
						]
						UpperCAmelCase             =						["image_embeds", "negative_image_embeds"]
						UpperCAmelCase             =						[
						    "generator",
						    "height",
						    "width",
						    "latents",
						    "guidance_scale",
						    "num_inference_steps",
						    "return_dict",
						    "guidance_scale",
						    "num_images_per_prompt",
						    "output_type",
						    "return_dict",
						]
						UpperCAmelCase             =						False




						@property
						def 							__UpperCamelCase				(       self					:      Tuple  )							->     str:


										"""simple docstring"""





										return 32




						@property
						def 							__UpperCamelCase				(       self					:      Any  )							->     Optional[Any]:


										"""simple docstring"""





										return 32




						@property
						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     Tuple:


										"""simple docstring"""





										return self.time_input_dim




						@property
						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     Tuple:


										"""simple docstring"""





										return self.time_input_dim * 4




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


										"""simple docstring"""





										return 100




						@property
						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     Optional[int]:


										"""simple docstring"""





										torch.manual_seed(0  )

										_SCREAMING_SNAKE_CASE			  ={
										    '''in_channels''': 4,
										    # 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,
										}

										_SCREAMING_SNAKE_CASE			  =UNetaDConditionModel(**_a  )
										return model




						@property
						def 							__UpperCamelCase				(       self					:      Tuple  )							->     Tuple:


										"""simple docstring"""





										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					:      Any  )							->     Tuple:


										"""simple docstring"""





										torch.manual_seed(0  )
										_SCREAMING_SNAKE_CASE			  =VQModel(**self.dummy_movq_kwargs  )
										return model




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.dummy_unet
										_SCREAMING_SNAKE_CASE			  =self.dummy_movq

										_SCREAMING_SNAKE_CASE			  =DDIMScheduler(
										    num_train_timesteps=1000				,    beta_schedule='''linear'''				,    beta_start=0.0_00_85				,    beta_end=0.0_12				,    clip_sample=_a				,    set_alpha_to_one=_a				,    steps_offset=1				,    prediction_type='''epsilon'''				,    thresholding=_a				,    )

										_SCREAMING_SNAKE_CASE			  ={
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''movq''': movq,
										}
										return components




						def 							__UpperCamelCase				(       self					:      Optional[int]				,    _a					:      Tuple				,    _a					:      Tuple=0  )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =floats_tensor((1, self.text_embedder_hidden_size)				,    rng=random.Random(_a  )  ).to(_a  )
										_SCREAMING_SNAKE_CASE			  =floats_tensor((1, self.text_embedder_hidden_size)				,    rng=random.Random(seed + 1  )  ).to(
										    _a  )
										if str(_a  ).startswith('''mps'''  ):
														_SCREAMING_SNAKE_CASE			  =torch.manual_seed(_a  )
										else:
														_SCREAMING_SNAKE_CASE			  =torch.Generator(device=_a  ).manual_seed(_a  )
										_SCREAMING_SNAKE_CASE			  ={
										    '''image_embeds''': image_embeds,
										    '''negative_image_embeds''': negative_image_embeds,
										    '''generator''': generator,
										    '''height''': 64,
										    '''width''': 64,
										    '''guidance_scale''': 4.0,
										    '''num_inference_steps''': 2,
										    '''output_type''': '''np''',
										}
										return inputs




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''cpu'''

										_SCREAMING_SNAKE_CASE			  =self.get_dummy_components()

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

										pipe.set_progress_bar_config(disable=_a  )

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

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

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

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

										_SCREAMING_SNAKE_CASE			  =np.array(
										    [0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46]  )

										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()}"

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




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     Dict:


										"""simple docstring"""





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




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'''  )

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

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

										_SCREAMING_SNAKE_CASE			  ='''red cat, 4k photo'''

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

										_SCREAMING_SNAKE_CASE			  =torch.Generator(device='''cuda'''  ).manual_seed(0  )
										_SCREAMING_SNAKE_CASE			  =pipeline(
										    image_embeds=_a				,    negative_image_embeds=_a				,    generator=_a				,    num_inference_steps=100				,    output_type='''np'''				,    )

										_SCREAMING_SNAKE_CASE			  =output.images[0]

										assert image.shape == (512, 512, 3)

										assert_mean_pixel_difference(_a				,    _a  )