



import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import (
				    TFEfficientFormerForImageClassification,
				    TFEfficientFormerForImageClassificationWithTeacher,
				    TFEfficientFormerModel,
				)
				from transformers.models.efficientformer.modeling_tf_efficientformer import (
				    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				)


if is_vision_available():
				from PIL import Image

				from transformers import EfficientFormerImageProcessor

class       snake_case__:

					"""simple docstring"""






					def __init__(							self					:	List[str]      ,   SCREAMING_SNAKE_CASE					:	Dict      ,   SCREAMING_SNAKE_CASE					:	int = 13      ,   SCREAMING_SNAKE_CASE					:	int = 64      ,   SCREAMING_SNAKE_CASE					:	int = 2      ,   SCREAMING_SNAKE_CASE					:	int = 3      ,   SCREAMING_SNAKE_CASE					:	int = 3      ,   SCREAMING_SNAKE_CASE					:	bool = True      ,   SCREAMING_SNAKE_CASE					:	bool = True      ,   SCREAMING_SNAKE_CASE					:	int = 128      ,   SCREAMING_SNAKE_CASE					:	Dict=[16, 32, 64, 128]      ,   SCREAMING_SNAKE_CASE					:	int = 7      ,   SCREAMING_SNAKE_CASE					:	int = 4      ,   SCREAMING_SNAKE_CASE					:	int = 37      ,   SCREAMING_SNAKE_CASE					:	str = "gelu"      ,   SCREAMING_SNAKE_CASE					:	float = 0.1      ,   SCREAMING_SNAKE_CASE					:	float = 0.1      ,   SCREAMING_SNAKE_CASE					:	int = 10      ,   SCREAMING_SNAKE_CASE					:	float = 0.02      ,   SCREAMING_SNAKE_CASE					:	int = 2      ,   SCREAMING_SNAKE_CASE					:	int = 1      ,   SCREAMING_SNAKE_CASE					:	int = 128      ,   SCREAMING_SNAKE_CASE					:	List[int] = [2, 2, 2, 2]      ,   SCREAMING_SNAKE_CASE					:	int = 2      ,   SCREAMING_SNAKE_CASE					:	int = 2      ,   ):
									lowercase__     :    int													=     parent
									lowercase__     :    List[str]													=     batch_size
									lowercase__     :    List[Any]													=     image_size
									lowercase__     :    Union[str, Any]													=     patch_size
									lowercase__     :    Any													=     num_channels
									lowercase__     :    Union[str, Any]													=     is_training
									lowercase__     :    Union[str, Any]													=     use_labels
									lowercase__     :    Tuple													=     hidden_size
									lowercase__     :    Tuple													=     num_hidden_layers
									lowercase__     :    int													=     num_attention_heads
									lowercase__     :    Union[str, Any]													=     intermediate_size
									lowercase__     :    int													=     hidden_act
									lowercase__     :    Tuple													=     hidden_dropout_prob
									lowercase__     :    Optional[int]													=     attention_probs_dropout_prob
									lowercase__     :    List[str]													=     type_sequence_label_size
									lowercase__     :    str													=     initializer_range
									lowercase__     :    Optional[int]													=     encoder_stride
									lowercase__     :    Optional[int]													=     num_attention_outputs
									lowercase__     :    Any													=     embed_dim
									lowercase__     :    Dict													=     embed_dim + 1
									lowercase__     :    Union[str, Any]													=     resolution
									lowercase__     :    Optional[int]													=     depths
									lowercase__     :    Union[str, Any]													=     hidden_sizes
									lowercase__     :    Dict													=     dim
									lowercase__     :    List[str]													=     mlp_expansion_ratio







					def  snake_case     (							self					:	Dict     ):
									lowercase__     :    int													=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

									lowercase__     :    str													=     None
									if self.use_labels:
													lowercase__     :    Tuple													=     ids_tensor([self.batch_size]      ,   self.type_sequence_label_size     )

									lowercase__     :    List[str]													=     self.get_config()

									return config, pixel_values, labels







					def  snake_case     (							self					:	Optional[int]     ):
									return EfficientFormerConfig(
									    image_size=self.image_size      ,   patch_size=self.patch_size      ,   num_channels=self.num_channels      ,   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      ,   is_decoder=__UpperCAmelCase      ,   initializer_range=self.initializer_range      ,   encoder_stride=self.encoder_stride      ,   resolution=self.resolution      ,   depths=self.depths      ,   hidden_sizes=self.hidden_sizes      ,   dim=self.dim      ,   mlp_expansion_ratio=self.mlp_expansion_ratio      ,   )







					def  snake_case     (							self					:	Dict      ,   SCREAMING_SNAKE_CASE					:	Optional[int]      ,   SCREAMING_SNAKE_CASE					:	int      ,   SCREAMING_SNAKE_CASE					:	Dict     ):
									lowercase__     :    Optional[Any]													=     TFEfficientFormerModel(config=__UpperCAmelCase     )
									lowercase__     :    int													=     model(__UpperCAmelCase      ,   training=__UpperCAmelCase     )
									self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size)     )







					def  snake_case     (							self					:	List[Any]      ,   SCREAMING_SNAKE_CASE					:	List[str]      ,   SCREAMING_SNAKE_CASE					:	Tuple      ,   SCREAMING_SNAKE_CASE					:	Tuple     ):
									lowercase__     :    Optional[int]													=     self.type_sequence_label_size
									lowercase__     :    Union[str, Any]													=     TFEfficientFormerForImageClassification(__UpperCAmelCase     )
									lowercase__     :    Dict													=     model(__UpperCAmelCase      ,   labels=__UpperCAmelCase      ,   training=__UpperCAmelCase     )
									self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.type_sequence_label_size)     )

									# test greyscale images
									lowercase__     :    Union[str, Any]													=     1
									lowercase__     :    Any													=     TFEfficientFormerForImageClassification(__UpperCAmelCase     )

									lowercase__     :    List[Any]													=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
									lowercase__     :    List[str]													=     model(__UpperCAmelCase      ,   labels=__UpperCAmelCase     )
									self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.type_sequence_label_size)     )







					def  snake_case     (							self					:	Tuple     ):
									lowercase__     :    Union[str, Any]													=     self.prepare_config_and_inputs()
									lowercase__     :    List[str]													=     config_and_inputs
									lowercase__     :    int													=     {"pixel_values": pixel_values}
									return config, inputs_dict






@require_tf
class       snake_case__(_a				,			_a				,			unittest.TestCase			):

					"""simple docstring"""






					lowercase_								=		(
					    (
					        TFEfficientFormerModel,
					        TFEfficientFormerForImageClassificationWithTeacher,
					        TFEfficientFormerForImageClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					lowercase_								=		(
					    {
					        """feature-extraction""": TFEfficientFormerModel,
					        """image-classification""": (
					            TFEfficientFormerForImageClassification,
					            TFEfficientFormerForImageClassificationWithTeacher,
					        ),
					    }
					    if is_tf_available()
					    else {}
					)

					lowercase_								=		False

					lowercase_								=		False
					lowercase_								=		False
					lowercase_								=		False
					lowercase_								=		False







					def  snake_case     (							self					:	List[Any]     ):
									lowercase__     :    Any													=     TFEfficientFormerModelTester(self     )
									lowercase__     :    Optional[Any]													=     ConfigTester(
									    self      ,   config_class=__UpperCAmelCase      ,   has_text_modality=__UpperCAmelCase      ,   hidden_size=37     )







					def  snake_case     (							self					:	str     ):
									self.config_tester.run_common_tests()







					@unittest.skip(reason="EfficientFormer does not use inputs_embeds"     )
					def  snake_case     (							self					:	int     ):
									pass







					@unittest.skip(reason="EfficientFormer does not support input and output embeddings"     )
					def  snake_case     (							self					:	Optional[Any]     ):
									pass







					def  snake_case     (							self					:	Optional[Any]     ):
									lowercase__     :    Dict													=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													lowercase__     :    Dict													=     model_class(__UpperCAmelCase     )
													lowercase__     :    Optional[Any]													=     inspect.signature(model.call     )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowercase__     :    List[str]													=     [*signature.parameters.keys()]

													lowercase__     :    List[Any]													=     ["pixel_values"]
													self.assertListEqual(arg_names[:1]      ,   __UpperCAmelCase     )







					def  snake_case     (							self					:	str     ):
									def check_hidden_states_output(SCREAMING_SNAKE_CASE					:	List[Any]      ,   SCREAMING_SNAKE_CASE					:	str      ,   SCREAMING_SNAKE_CASE					:	Optional[Any]     ):
													lowercase__     :    int													=     model_class(__UpperCAmelCase     )

													lowercase__     :    Optional[int]													=     model(**self._prepare_for_class(__UpperCAmelCase      ,   __UpperCAmelCase     )      ,   training=__UpperCAmelCase     )
													lowercase__     :    List[str]													=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													lowercase__     :    Optional[Any]													=     getattr(
													    self.model_tester      ,   "expected_num_hidden_layers"      ,   self.model_tester.num_hidden_layers + 1     )
													self.assertEqual(len(__UpperCAmelCase     )      ,   __UpperCAmelCase     )

													if hasattr(self.model_tester      ,   "encoder_seq_length"     ):
																	lowercase__     :    Optional[Any]													=     self.model_tester.encoder_seq_length
																	if hasattr(self.model_tester      ,   "chunk_length"     ) and self.model_tester.chunk_length > 1:
																					lowercase__     :    Any													=     seq_length * self.model_tester.chunk_length
													else:
																	lowercase__     :    str													=     self.model_tester.seq_length

													self.assertListEqual(
													    list(hidden_states[-1].shape[-2:]     )      ,   [seq_length, self.model_tester.hidden_size]      ,   )

													if config.is_encoder_decoder:
																	lowercase__     :    Union[str, Any]													=     outputs.decoder_hidden_states

																	self.asseretIsInstance(__UpperCAmelCase      ,   (list, tuple)     )
																	self.assertEqual(len(__UpperCAmelCase     )      ,   __UpperCAmelCase     )
																	lowercase__     :    int													=     getattr(self.model_tester      ,   "seq_length"      ,   __UpperCAmelCase     )
																	lowercase__     :    Any													=     getattr(self.model_tester      ,   "decoder_seq_length"      ,   __UpperCAmelCase     )

																	self.assertListEqual(
																	    list(hidden_states[-1].shape[-2:]     )      ,   [decoder_seq_length, self.model_tester.hidden_size]      ,   )

									lowercase__     :    Any													=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													lowercase__     :    int													=     True
													check_hidden_states_output(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase     )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowercase__     :    Optional[Any]													=     True

													check_hidden_states_output(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase     )







					def  snake_case     (							self					:	Optional[int]      ,   SCREAMING_SNAKE_CASE					:	Dict      ,   SCREAMING_SNAKE_CASE					:	Union[str, Any]      ,   SCREAMING_SNAKE_CASE					:	Union[str, Any]=False     ):
									lowercase__     :    str													=     super()._prepare_for_class(__UpperCAmelCase      ,   __UpperCAmelCase      ,   return_labels=__UpperCAmelCase     )

									if return_labels:
													if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
																	del inputs_dict["labels"]

									return inputs_dict







					def  snake_case     (							self					:	str     ):
									lowercase__     :    Dict													=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__UpperCAmelCase     )







					@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"     )
					def  snake_case     (							self					:	Union[str, Any]     ):
									lowercase__     :    Any													=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase     )







					def  snake_case     (							self					:	str     ):
									lowercase__     :    Any													=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase     )







					@slow
					def  snake_case     (							self					:	int     ):
									for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowercase__     :    Optional[int]													=     TFEfficientFormerModel.from_pretrained(__UpperCAmelCase     )
													self.assertIsNotNone(__UpperCAmelCase     )







					def  snake_case     (							self					:	Union[str, Any]     ):
									lowercase__     :    List[str]													=     self.model_tester.prepare_config_and_inputs_for_common()
									lowercase__     :    Dict													=     True

									lowercase__     :    Dict													=     getattr(self.model_tester      ,   "seq_length"      ,   __UpperCAmelCase     )
									lowercase__     :    List[str]													=     getattr(self.model_tester      ,   "encoder_seq_length"      ,   __UpperCAmelCase     )
									lowercase__     :    List[Any]													=     getattr(self.model_tester      ,   "key_length"      ,   __UpperCAmelCase     )
									lowercase__     :    List[str]													=     getattr(self.model_tester      ,   "chunk_length"      ,   __UpperCAmelCase     )

									if chunk_length is not None and hasattr(self.model_tester      ,   "num_hashes"     ):
													lowercase__     :    Tuple													=     encoder_seq_length * self.model_tester.num_hashes

									for model_class in self.all_model_classes:
													lowercase__     :    Optional[int]													=     True
													lowercase__     :    Dict													=     False
													lowercase__     :    Union[str, Any]													=     True
													lowercase__     :    Any													=     model_class(__UpperCAmelCase     )

													lowercase__     :    Union[str, Any]													=     model(**self._prepare_for_class(__UpperCAmelCase      ,   __UpperCAmelCase     )      ,   training=__UpperCAmelCase     )
													lowercase__     :    Union[str, Any]													=     outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
													self.assertEqual(len(__UpperCAmelCase     )      ,   self.model_tester.num_attention_outputs     )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													lowercase__     :    str													=     True
													lowercase__     :    Dict													=     model_class(__UpperCAmelCase     )
													lowercase__     :    int													=     model(**self._prepare_for_class(__UpperCAmelCase      ,   __UpperCAmelCase     )      ,   training=__UpperCAmelCase     )

													lowercase__     :    Any													=     outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
													self.assertEqual(len(__UpperCAmelCase     )      ,   self.model_tester.num_attention_outputs     )

													if chunk_length is not None:
																	self.assertListEqual(
																	    list(attentions[0].shape[-4:]     )      ,   [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]      ,   )
													else:
																	self.assertListEqual(
																	    list(attentions[0].shape[-3:]     )      ,   [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]      ,   )







					def  snake_case     (							self					:	Optional[int]     ):
									# We use a simplified version of this test for EfficientFormer because it requires training=False
									# and Keras refuses to let us force that during functional construction
									lowercase__     :    int													=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													# Prepare our model
													lowercase__     :    Any													=     model_class(__UpperCAmelCase     )
													# These are maximally general inputs for the model, with multiple None dimensions
													# Hopefully this will catch any conditionals that fail for flexible shapes
													lowercase__     :    str													=     {
													    key: tf.keras.Input(shape=val.shape[1:]      ,   dtype=val.dtype      ,   name=__UpperCAmelCase     )
													    for key, val in model.input_signature.items()
													    if key in model.dummy_inputs
													}
													lowercase__     :    Optional[int]													=     model(__UpperCAmelCase     )
													self.assertTrue(outputs_dict is not None     )






def 					__lowerCamelCase					(   ):

				"""simple docstring"""


				lowercase__     :    Tuple													=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
				return image

@require_tf
@require_vision
class       snake_case__(unittest.TestCase			):

					"""simple docstring"""






					@cached_property
					def  snake_case     (							self					:	Tuple     ):
									return (
									    EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"     )
									    if is_vision_available()
									    else None
									)







					@slow
					def  snake_case     (							self					:	Optional[int]     ):
									lowercase__     :    Any													=     TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"     )
									lowercase__     :    Optional[int]													=     self.default_image_processor
									lowercase__     :    int													=     prepare_img()
									lowercase__     :    Optional[Any]													=     image_processor(images=__UpperCAmelCase      ,   return_tensors="tf"     )
									# forward pass
									lowercase__     :    Tuple													=     model(**__UpperCAmelCase      ,   training=__UpperCAmelCase     )
									# verify the logits
									lowercase__     :    str													=     tf.TensorShape((1, 1_000)     )
									self.assertEqual(outputs.logits.shape      ,   __UpperCAmelCase     )
									lowercase__     :    Any													=     tf.constant([-0.0_555, 0.4_825, -0.0_852]     )
									self.assertTrue(np.allclose(outputs.logits[0, :3]      ,   __UpperCAmelCase      ,   atol=1E-4     )     )







					@slow
					def  snake_case     (							self					:	Optional[Any]     ):
									lowercase__     :    List[Any]													=     TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
									    "snap-research/efficientformer-l1-300"     )
									lowercase__     :    int													=     self.default_image_processor
									lowercase__     :    int													=     prepare_img()
									lowercase__     :    str													=     image_processor(images=__UpperCAmelCase      ,   return_tensors="tf"     )
									# forward pass
									lowercase__     :    Union[str, Any]													=     model(**__UpperCAmelCase      ,   training=__UpperCAmelCase     )
									# verify the logits
									lowercase__     :    str													=     tf.TensorShape((1, 1_000)     )
									self.assertEqual(outputs.logits.shape      ,   __UpperCAmelCase     )
									lowercase__     :    Any													=     tf.constant([-0.1_312, 0.4_353, -1.0_499]     )
									self.assertTrue(np.allclose(outputs.logits[0, :3]      ,   __UpperCAmelCase      ,   atol=1E-4     )     )








"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, 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  (  _a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							Dict	=       KandinskyVaaControlnetPipeline
    UpperCAmelCase       :							List[str]	=       ["""image_embeds""", """negative_image_embeds""", """hint"""]
    UpperCAmelCase       :							Optional[Any]	=       ["""image_embeds""", """negative_image_embeds""", """hint"""]
    UpperCAmelCase       :							Dict	=       [
        """generator""",
        """height""",
        """width""",
        """latents""",
        """guidance_scale""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    UpperCAmelCase       :							Optional[int]	=       False






    @property
    def 	__snake_case							(					self				:    Optional[Any]):
      return 32






    @property
    def 	__snake_case							(					self				:    Dict):
      return 32






    @property
    def 	__snake_case							(					self				:    Dict):
      return self.time_input_dim






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






    @property
    def 	__snake_case							(					self				:    str):
      return 100






    @property
    def 	__snake_case							(					self				:    str):
      torch.manual_seed(0)

      a						:		str      							=			{
          "in_channels": 8,
          # Out channels is double in channels because predicts mean and variance
          "out_channels": 8,
          "addition_embed_type": "image_hint",
          "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,
      }

      a						:		Dict      							=			UNetaDConditionModel(**__UpperCAmelCase)
      return model






    @property
    def 	__snake_case							(					self				:    str):
      return {
          "block_out_channels": [32, 32, 64, 64],
          "down_block_types": [
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
          "vq_embed_dim": 4,
      }






    @property
    def 	__snake_case							(					self				:    Union[str, Any]):
      torch.manual_seed(0)
      a						:		Dict      							=			VQModel(**self.dummy_movq_kwargs)
      return model






    def 	__snake_case							(					self				:    Optional[Any]):
      a						:		Optional[Any]      							=			self.dummy_unet
      a						:		int      							=			self.dummy_movq

      a						:		str      							=			DDIMScheduler(
          num_train_timesteps=1000				, beta_schedule="linear"				, beta_start=0.00_085				, beta_end=0.012				, clip_sample=__UpperCAmelCase				, set_alpha_to_one=__UpperCAmelCase				, steps_offset=1				, prediction_type="epsilon"				, thresholding=__UpperCAmelCase				, )

      a						:		Optional[Any]      							=			{
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }
      return components






    def 	__snake_case							(					self				:    Optional[int]				, __UpperCAmelCase				:    Optional[Any]				, __UpperCAmelCase				:    int=0):
      a						:		Optional[int]      							=			floats_tensor((1, self.text_embedder_hidden_size)				, rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)
      a						:		List[str]      							=			floats_tensor((1, self.text_embedder_hidden_size)				, rng=random.Random(seed + 1)).to(
          __UpperCAmelCase)

      # create hint
      a						:		Union[str, Any]      							=			floats_tensor((1, 3, 64, 64)				, rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)

      if str(__UpperCAmelCase).startswith("mps"):
        a						:		Union[str, Any]      							=			torch.manual_seed(__UpperCAmelCase)
      else:
        a						:		List[Any]      							=			torch.Generator(device=__UpperCAmelCase).manual_seed(__UpperCAmelCase)
      a						:		str      							=			{
          "image_embeds": image_embeds,
          "negative_image_embeds": negative_image_embeds,
          "hint": hint,
          "generator": generator,
          "height": 64,
          "width": 64,
          "guidance_scale": 4.0,
          "num_inference_steps": 2,
          "output_type": "np",
      }
      return inputs






    def 	__snake_case							(					self				:    Dict):
      a						:		str      							=			"cpu"

      a						:		Tuple      							=			self.get_dummy_components()

      a						:		Dict      							=			self.pipeline_class(**__UpperCAmelCase)
      a						:		Optional[int]      							=			pipe.to(__UpperCAmelCase)

      pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		Optional[Any]      							=			pipe(**self.get_dummy_inputs(__UpperCAmelCase))
      a						:		Any      							=			output.images

      a						:		Any      							=			pipe(
          **self.get_dummy_inputs(__UpperCAmelCase)				, return_dict=__UpperCAmelCase				, )[0]

      a						:		Union[str, Any]      							=			image[0, -3:, -3:, -1]
      a						:		int      							=			image_from_tuple[0, -3:, -3:, -1]

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

      a						:		Tuple      							=			np.array(
          [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595])

      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  ):







    """simple docstring"""




    def 	__snake_case							(					self				:    Optional[int]):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def 	__snake_case							(					self				:    List[str]):
      a						:		List[Any]      							=			load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy")

      a						:		Union[str, Any]      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/hint_image_cat.png")
      a						:		List[Any]      							=			torch.from_numpy(np.array(__UpperCAmelCase)).float() / 255.0
      a						:		str      							=			hint.permute(2				, 0				, 1).unsqueeze(0)

      a						:		Optional[int]      							=			KandinskyVaaPriorPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-prior"				, torch_dtype=torch.floataa)
      pipe_prior.to(__UpperCAmelCase)

      a						:		List[str]      							=			KandinskyVaaControlnetPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-controlnet-depth"				, torch_dtype=torch.floataa)
      a						:		int      							=			pipeline.to(__UpperCAmelCase)
      pipeline.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		Tuple      							=			"A robot, 4k photo"

      a						:		Any      							=			torch.Generator(device="cuda").manual_seed(0)
      a							,		a						:		int      							=			pipe_prior(
          __UpperCAmelCase				, generator=__UpperCAmelCase				, num_inference_steps=5				, negative_prompt=""				, ).to_tuple()

      a						:		str      							=			torch.Generator(device="cuda").manual_seed(0)
      a						:		Union[str, Any]      							=			pipeline(
          image_embeds=__UpperCAmelCase				, negative_image_embeds=__UpperCAmelCase				, hint=__UpperCAmelCase				, generator=__UpperCAmelCase				, num_inference_steps=100				, output_type="np"				, )

      a						:		str      							=			output.images[0]

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

      assert_mean_pixel_difference(__UpperCAmelCase				, __UpperCAmelCase)






