
'''simple docstring'''


import unittest

from transformers import ElectraConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_FOR_PRETRAINING_MAPPING,
					    ElectraForCausalLM,
					    ElectraForMaskedLM,
					    ElectraForMultipleChoice,
					    ElectraForPreTraining,
					    ElectraForQuestionAnswering,
					    ElectraForSequenceClassification,
					    ElectraForTokenClassification,
					    ElectraModel,
					)
					from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST

class 		__lowercase			:

			def __init__(self     ,				A     ,				A=1_3     ,				A=7     ,				A=True     ,				A=True     ,				A=True     ,				A=True     ,				A=9_9     ,				A=3_2     ,				A=5     ,				A=4     ,				A=3_7     ,				A="gelu"     ,				A=0.1     ,				A=0.1     ,				A=5_1_2     ,				A=1_6     ,				A=2     ,				A=0.02     ,				A=3     ,				A=4     ,				A=None     ,				):
								lowerCamelCase_   : Tuple       =						parent
								lowerCamelCase_   : Any       =						batch_size
								lowerCamelCase_   : List[str]       =						seq_length
								lowerCamelCase_   : Union[str, Any]       =						is_training
								lowerCamelCase_   : List[Any]       =						use_input_mask
								lowerCamelCase_   : List[str]       =						use_token_type_ids
								lowerCamelCase_   : Optional[int]       =						use_labels
								lowerCamelCase_   : List[Any]       =						vocab_size
								lowerCamelCase_   : Dict       =						hidden_size
								lowerCamelCase_   : str       =						num_hidden_layers
								lowerCamelCase_   : List[str]       =						num_attention_heads
								lowerCamelCase_   : Dict       =						intermediate_size
								lowerCamelCase_   : Union[str, Any]       =						hidden_act
								lowerCamelCase_   : Optional[int]       =						hidden_dropout_prob
								lowerCamelCase_   : Tuple       =						attention_probs_dropout_prob
								lowerCamelCase_   : Optional[int]       =						max_position_embeddings
								lowerCamelCase_   : Union[str, Any]       =						type_vocab_size
								lowerCamelCase_   : Optional[Any]       =						type_sequence_label_size
								lowerCamelCase_   : Dict       =						initializer_range
								lowerCamelCase_   : Dict       =						num_labels
								lowerCamelCase_   : List[Any]       =						num_choices
								lowerCamelCase_   : Union[str, Any]       =						scope

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : str       =						ids_tensor([self.batch_size, self.seq_length]     ,				self.vocab_size )

								lowerCamelCase_   : int       =						None
								if self.use_input_mask:
													lowerCamelCase_   : int       =						random_attention_mask([self.batch_size, self.seq_length] )

								lowerCamelCase_   : List[Any]       =						None
								if self.use_token_type_ids:
													lowerCamelCase_   : Optional[Any]       =						ids_tensor([self.batch_size, self.seq_length]     ,				self.type_vocab_size )

								lowerCamelCase_   : List[Any]       =						None
								lowerCamelCase_   : List[str]       =						None
								lowerCamelCase_   : Union[str, Any]       =						None
								if self.use_labels:
													lowerCamelCase_   : List[Any]       =						ids_tensor([self.batch_size]     ,				self.type_sequence_label_size )
													lowerCamelCase_   : str       =						ids_tensor([self.batch_size, self.seq_length]     ,				self.num_labels )
													lowerCamelCase_   : Dict       =						ids_tensor([self.batch_size]     ,				self.num_choices )
													lowerCamelCase_   : Dict       =						ids_tensor([self.batch_size, self.seq_length]     ,				1 )

								lowerCamelCase_   : Tuple       =						self.get_config()

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    input_mask,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								    fake_token_labels,
								)

			def     UpperCAmelCase__	(self ):
								return ElectraConfig(
								    vocab_size=self.vocab_size     ,				hidden_size=self.hidden_size     ,				num_hidden_layers=self.num_hidden_layers     ,				num_attention_heads=self.num_attention_heads     ,				intermediate_size=self.intermediate_size     ,				hidden_act=self.hidden_act     ,				hidden_dropout_prob=self.hidden_dropout_prob     ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,				max_position_embeddings=self.max_position_embeddings     ,				type_vocab_size=self.type_vocab_size     ,				is_decoder=A     ,				initializer_range=self.initializer_range     ,				)

			def     UpperCAmelCase__	(self ):
								(
								    (
								    lowerCamelCase_
								), (
								    lowerCamelCase_
								), (
								    lowerCamelCase_
								), (
								    lowerCamelCase_
								), (
								    lowerCamelCase_
								), (
								    lowerCamelCase_
								), (
								    lowerCamelCase_
								), (
								    lowerCamelCase_
								), 
								)   : Optional[Any]       =						self.prepare_config_and_inputs()

								lowerCamelCase_   : int       =						True
								lowerCamelCase_   : Dict       =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
								lowerCamelCase_   : Tuple       =						ids_tensor([self.batch_size, self.seq_length]     ,				vocab_size=2 )

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    input_mask,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)

			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				):
								lowerCamelCase_   : Optional[int]       =						ElectraModel(config=A )
								model.to(A )
								model.eval()
								lowerCamelCase_   : List[Any]       =						model(A     ,				attention_mask=A     ,				token_type_ids=A )
								lowerCamelCase_   : Dict       =						model(A     ,				token_type_ids=A )
								lowerCamelCase_   : List[str]       =						model(A )
								self.parent.assertEqual(result.last_hidden_state.shape     ,				(self.batch_size, self.seq_length, self.hidden_size) )

			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				):
								lowerCamelCase_   : Any       =						True
								lowerCamelCase_   : str       =						ElectraModel(A )
								model.to(A )
								model.eval()
								lowerCamelCase_   : Dict       =						model(
								    A     ,				attention_mask=A     ,				token_type_ids=A     ,				encoder_hidden_states=A     ,				encoder_attention_mask=A     ,				)
								lowerCamelCase_   : int       =						model(
								    A     ,				attention_mask=A     ,				token_type_ids=A     ,				encoder_hidden_states=A     ,				)
								lowerCamelCase_   : Optional[int]       =						model(A     ,				attention_mask=A     ,				token_type_ids=A )
								self.parent.assertEqual(result.last_hidden_state.shape     ,				(self.batch_size, self.seq_length, self.hidden_size) )

			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				):
								lowerCamelCase_   : List[str]       =						ElectraForMaskedLM(config=A )
								model.to(A )
								model.eval()
								lowerCamelCase_   : Any       =						model(A     ,				attention_mask=A     ,				token_type_ids=A     ,				labels=A )
								self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.seq_length, self.vocab_size) )

			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				):
								lowerCamelCase_   : Union[str, Any]       =						ElectraForCausalLM(config=A )
								model.to(A )
								model.eval()
								lowerCamelCase_   : Tuple       =						model(A     ,				attention_mask=A     ,				token_type_ids=A     ,				labels=A )
								self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.seq_length, self.vocab_size) )

			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				):
								lowerCamelCase_   : Tuple       =						self.num_labels
								lowerCamelCase_   : Optional[int]       =						ElectraForTokenClassification(config=A )
								model.to(A )
								model.eval()
								lowerCamelCase_   : Union[str, Any]       =						model(A     ,				attention_mask=A     ,				token_type_ids=A     ,				labels=A )
								self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.seq_length, self.num_labels) )

			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				):
								lowerCamelCase_   : Optional[int]       =						self.num_labels
								lowerCamelCase_   : Optional[int]       =						ElectraForPreTraining(config=A )
								model.to(A )
								model.eval()
								lowerCamelCase_   : Dict       =						model(A     ,				attention_mask=A     ,				token_type_ids=A     ,				labels=A )
								self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.seq_length) )

			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				):
								lowerCamelCase_   : Dict       =						self.num_labels
								lowerCamelCase_   : Any       =						ElectraForSequenceClassification(A )
								model.to(A )
								model.eval()
								lowerCamelCase_   : Optional[Any]       =						model(A     ,				attention_mask=A     ,				token_type_ids=A     ,				labels=A )
								self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.num_labels) )

			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				):
								lowerCamelCase_   : List[str]       =						ElectraForQuestionAnswering(config=A )
								model.to(A )
								model.eval()
								lowerCamelCase_   : Any       =						model(
								    A     ,				attention_mask=A     ,				token_type_ids=A     ,				start_positions=A     ,				end_positions=A     ,				)
								self.parent.assertEqual(result.start_logits.shape     ,				(self.batch_size, self.seq_length) )
								self.parent.assertEqual(result.end_logits.shape     ,				(self.batch_size, self.seq_length) )

			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				):
								lowerCamelCase_   : int       =						self.num_choices
								lowerCamelCase_   : Optional[Any]       =						ElectraForMultipleChoice(config=A )
								model.to(A )
								model.eval()
								lowerCamelCase_   : List[Any]       =						input_ids.unsqueeze(1 ).expand(-1     ,				self.num_choices     ,				-1 ).contiguous()
								lowerCamelCase_   : Optional[Any]       =						token_type_ids.unsqueeze(1 ).expand(-1     ,				self.num_choices     ,				-1 ).contiguous()
								lowerCamelCase_   : str       =						input_mask.unsqueeze(1 ).expand(-1     ,				self.num_choices     ,				-1 ).contiguous()
								lowerCamelCase_   : Union[str, Any]       =						model(
								    A     ,				attention_mask=A     ,				token_type_ids=A     ,				labels=A     ,				)
								self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.num_choices) )






			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[Any]       =						self.prepare_config_and_inputs()
								(
								    (
								    lowerCamelCase_
								), (
								    lowerCamelCase_
								), (
								    lowerCamelCase_
								), (
								    lowerCamelCase_
								), (
								    lowerCamelCase_
								), (
								    lowerCamelCase_
								), (
								    lowerCamelCase_
								), (
								    lowerCamelCase_
								), 
								)   : List[str]       =						config_and_inputs
								lowerCamelCase_   : Optional[Any]       =						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
								return config, inputs_dict

@require_torch
class 		__lowercase			(					_lowercase ,       _lowercase ,       unittest.TestCase      ):
			lowerCamelCase     :						str		 =     (
			    (
			        ElectraModel,
			        ElectraForPreTraining,
			        ElectraForMaskedLM,
			        ElectraForCausalLM,
			        ElectraForMultipleChoice,
			        ElectraForTokenClassification,
			        ElectraForSequenceClassification,
			        ElectraForQuestionAnswering,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase     :						Tuple		 =     (
			    {
			        "feature-extraction": ElectraModel,
			        "fill-mask": ElectraForMaskedLM,
			        "question-answering": ElectraForQuestionAnswering,
			        "text-classification": ElectraForSequenceClassification,
			        "text-generation": ElectraForCausalLM,
			        "token-classification": ElectraForTokenClassification,
			        "zero-shot": ElectraForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase     :						List[Any]		 =     True

			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A=False ):
								lowerCamelCase_   : Optional[Any]       =						super()._prepare_for_class(A     ,				A     ,				return_labels=A )

								if return_labels:
													if model_class in get_values(A ):
																		lowerCamelCase_   : int       =						torch.zeros(
																		    (self.model_tester.batch_size, self.model_tester.seq_length)     ,				dtype=torch.long     ,				device=A )
								return inputs_dict

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Any       =						ElectraModelTester(self )
								lowerCamelCase_   : Optional[int]       =						ConfigTester(self     ,				config_class=A     ,				hidden_size=3_7 )

			def     UpperCAmelCase__	(self ):
								self.config_tester.run_common_tests()

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : int       =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_electra_model(*A )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : str       =						self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_electra_model_as_decoder(*A )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : int       =						self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
													lowerCamelCase_   : List[Any]       =						type
													self.model_tester.create_and_check_electra_model(*A )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[Any]       =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_electra_for_masked_lm(*A )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Tuple       =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_electra_for_token_classification(*A )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Optional[int]       =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_electra_for_pretraining(*A )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : int       =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_electra_for_sequence_classification(*A )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : int       =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_electra_for_question_answering(*A )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Tuple       =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_electra_for_multiple_choice(*A )

			@slow
			def     UpperCAmelCase__	(self ):
								for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase_   : Union[str, Any]       =						ElectraModel.from_pretrained(A )
													self.assertIsNotNone(A )






			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Any       =						self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_electra_for_causal_lm(*A )

@require_torch
class 		__lowercase			(					unittest.TestCase      ):

			@slow
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : int       =						ElectraModel.from_pretrained('''google/electra-small-discriminator''' )
								lowerCamelCase_   : int       =						torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
								lowerCamelCase_   : Tuple       =						torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
								lowerCamelCase_   : Any       =						model(A     ,				attention_mask=A )[0]
								lowerCamelCase_   : Optional[Any]       =						torch.Size((1, 1_1, 2_5_6) )
								self.assertEqual(output.shape     ,				A )
								lowerCamelCase_   : Any       =						torch.tensor(
								    [[[0.44_71, 0.68_21, -0.32_65], [0.46_27, 0.52_55, -0.36_68], [0.45_32, 0.33_13, -0.43_44]]] )

								self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,				A     ,				atol=1E-4 ) )




'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 		__lowercase			(					_lowercase ,       unittest.TestCase      ):
			lowerCamelCase     :						List[str]		 =     KandinskyPipeline
			lowerCamelCase     :						List[str]		 =     [
			    "prompt",
			    "image_embeds",
			    "negative_image_embeds",
			]
			lowerCamelCase     :						str		 =     ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
			lowerCamelCase     :						Dict		 =     [
			    "generator",
			    "height",
			    "width",
			    "latents",
			    "guidance_scale",
			    "negative_prompt",
			    "num_inference_steps",
			    "return_dict",
			    "guidance_scale",
			    "num_images_per_prompt",
			    "output_type",
			    "return_dict",
			]
			lowerCamelCase     :						Union[str, Any]		 =     False

			@property
			def     UpperCAmelCase__	(self ):
								return 3_2

			@property
			def     UpperCAmelCase__	(self ):
								return 3_2

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

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

			@property
			def     UpperCAmelCase__	(self ):
								return 1_0_0

			@property
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : int       =						XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
								return tokenizer

			@property
			def     UpperCAmelCase__	(self ):
								torch.manual_seed(0 )
								lowerCamelCase_   : Any       =						MCLIPConfig(
								    numDims=self.cross_attention_dim     ,				transformerDimensions=self.text_embedder_hidden_size     ,				hidden_size=self.text_embedder_hidden_size     ,				intermediate_size=3_7     ,				num_attention_heads=4     ,				num_hidden_layers=5     ,				vocab_size=1_0_0_5     ,				)

								lowerCamelCase_   : Union[str, Any]       =						MultilingualCLIP(A )
								lowerCamelCase_   : Dict       =						text_encoder.eval()

								return text_encoder

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

								lowerCamelCase_   : Dict       =						{
								    '''in_channels''': 4,
								    # Out channels is double in channels because predicts mean and variance
								    '''out_channels''': 8,
								    '''addition_embed_type''': '''text_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''': '''text_image_proj''',
								    '''cross_attention_dim''': self.cross_attention_dim,
								    '''attention_head_dim''': 4,
								    '''resnet_time_scale_shift''': '''scale_shift''',
								    '''class_embed_type''': None,
								}

								lowerCamelCase_   : Union[str, Any]       =						UNetaDConditionModel(**A )
								return model

			@property
			def     UpperCAmelCase__	(self ):
								return {
								    "block_out_channels": [3_2, 6_4],
								    "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": 1_2,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}

			@property
			def     UpperCAmelCase__	(self ):
								torch.manual_seed(0 )
								lowerCamelCase_   : Dict       =						VQModel(**self.dummy_movq_kwargs )
								return model

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[str]       =						self.dummy_text_encoder
								lowerCamelCase_   : Tuple       =						self.dummy_tokenizer
								lowerCamelCase_   : int       =						self.dummy_unet
								lowerCamelCase_   : str       =						self.dummy_movq

								lowerCamelCase_   : str       =						DDIMScheduler(
								    num_train_timesteps=1_0_0_0     ,				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     ,				)

								lowerCamelCase_   : int       =						{
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''movq''': movq,
								}
								return components

			def     UpperCAmelCase__	(self     ,				A     ,				A=0 ):
								lowerCamelCase_   : Union[str, Any]       =						floats_tensor((1, self.cross_attention_dim)     ,				rng=random.Random(A ) ).to(A )
								lowerCamelCase_   : List[Any]       =						floats_tensor((1, self.cross_attention_dim)     ,				rng=random.Random(seed + 1 ) ).to(A )
								if str(A ).startswith('''mps''' ):
													lowerCamelCase_   : List[str]       =						torch.manual_seed(A )
								else:
													lowerCamelCase_   : Optional[Any]       =						torch.Generator(device=A ).manual_seed(A )
								lowerCamelCase_   : Any       =						{
								    '''prompt''': '''horse''',
								    '''image_embeds''': image_embeds,
								    '''negative_image_embeds''': negative_image_embeds,
								    '''generator''': generator,
								    '''height''': 6_4,
								    '''width''': 6_4,
								    '''guidance_scale''': 4.0,
								    '''num_inference_steps''': 2,
								    '''output_type''': '''np''',
								}
								return inputs






			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : int       =						'''cpu'''

								lowerCamelCase_   : Optional[Any]       =						self.get_dummy_components()

								lowerCamelCase_   : Tuple       =						self.pipeline_class(**A )
								lowerCamelCase_   : List[str]       =						pipe.to(A )

								pipe.set_progress_bar_config(disable=A )

								lowerCamelCase_   : Optional[Any]       =						pipe(**self.get_dummy_inputs(A ) )
								lowerCamelCase_   : int       =						output.images

								lowerCamelCase_   : List[Any]       =						pipe(
								    **self.get_dummy_inputs(A )     ,				return_dict=A     ,				)[0]

								lowerCamelCase_   : List[Any]       =						image[0, -3:, -3:, -1]
								lowerCamelCase_   : List[Any]       =						image_from_tuple[0, -3:, -3:, -1]

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

								lowerCamelCase_   : int       =						np.array(
								    [0.32_86_63, 1.0, 0.23_21_68_73, 1.0, 0.92_71_75_64, 0.4_63_90_46, 0.96_89_47_77, 0.31_71_33_78, 0.6_29_39_53] )

								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 		__lowercase			(					unittest.TestCase      ):

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






			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Tuple       =						load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinsky/kandinsky_text2img_cat_fp16.npy''' )

								lowerCamelCase_   : str       =						KandinskyPriorPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-1-prior'''     ,				torch_dtype=torch.floataa )
								pipe_prior.to(A )

								lowerCamelCase_   : List[Any]       =						KandinskyPipeline.from_pretrained('''kandinsky-community/kandinsky-2-1'''     ,				torch_dtype=torch.floataa )
								lowerCamelCase_   : Tuple       =						pipeline.to(A )
								pipeline.set_progress_bar_config(disable=A )

								lowerCamelCase_   : List[str]       =						'''red cat, 4k photo'''

								lowerCamelCase_   : Optional[Any]       =						torch.Generator(device='''cuda''' ).manual_seed(0 )
								lowerCamelCase_, lowerCamelCase_   : Any       =						pipe_prior(
								    A     ,				generator=A     ,				num_inference_steps=5     ,				negative_prompt=''''''     ,				).to_tuple()

								lowerCamelCase_   : Optional[Any]       =						torch.Generator(device='''cuda''' ).manual_seed(0 )
								lowerCamelCase_   : Optional[Any]       =						pipeline(
								    A     ,				image_embeds=A     ,				negative_image_embeds=A     ,				generator=A     ,				num_inference_steps=1_0_0     ,				output_type='''np'''     ,				)

								lowerCamelCase_   : Union[str, Any]       =						output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)

								assert_mean_pixel_difference(A     ,				A )


