




import pickle

import numpy as np
from matplotlib import pyplot as plt
class 			a__    :






						def __init__(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A=0.2		,			_A=0.2			):




											"""simple docstring"""



											__lowerCAmelCase				   =       bp_numa
											__lowerCAmelCase				   =       bp_numa
											__lowerCAmelCase				   =       bp_numa
											__lowerCAmelCase				   =       conva_get[:2]
											__lowerCAmelCase				   =       conva_get[2]
											__lowerCAmelCase				   =       size_pa
											__lowerCAmelCase				   =       rate_w
											__lowerCAmelCase				   =       rate_t
											__lowerCAmelCase				   =       [
											    np.mat(-1 * np.random.rand(self.conva[0]		,			self.conva[0]			) + 0.5			)
											    for i in range(self.conva[1]			)
											]
											__lowerCAmelCase				   =       np.mat(-1 * np.random.rand(self.num_bpa		,			self.num_bpa			) + 0.5			)
											__lowerCAmelCase				   =       np.mat(-1 * np.random.rand(self.num_bpa		,			self.num_bpa			) + 0.5			)
											__lowerCAmelCase				   =       -2 * np.random.rand(self.conva[1]			) + 1
											__lowerCAmelCase				   =       -2 * np.random.rand(self.num_bpa			) + 1
											__lowerCAmelCase				   =       -2 * np.random.rand(self.num_bpa			) + 1






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       {
											    "num_bp1": self.num_bpa,
											    "num_bp2": self.num_bpa,
											    "num_bp3": self.num_bpa,
											    "conv1": self.conva,
											    "step_conv1": self.step_conva,
											    "size_pooling1": self.size_poolinga,
											    "rate_weight": self.rate_weight,
											    "rate_thre": self.rate_thre,
											    "w_conv1": self.w_conva,
											    "wkj": self.wkj,
											    "vji": self.vji,
											    "thre_conv1": self.thre_conva,
											    "thre_bp2": self.thre_bpa,
											    "thre_bp3": self.thre_bpa,
											}
											with open(_A		,			"wb"			) as f:
																pickle.dump(_A		,			_A			)

											print(f"""Model saved： {save_path}"""			)






						@classmethod
						def 				__SCREAMING_SNAKE_CASE(   cls		,			_A			):




											"""simple docstring"""



											with open(_A		,			"rb"			) as f:
																__lowerCAmelCase				   =       pickle.load(_A			)  # noqa: S301

											__lowerCAmelCase				   =       model_dic.get("conv1"			)
											conv_get.append(model_dic.get("step_conv1"			)			)
											__lowerCAmelCase				   =       model_dic.get("size_pooling1"			)
											__lowerCAmelCase				   =       model_dic.get("num_bp1"			)
											__lowerCAmelCase				   =       model_dic.get("num_bp2"			)
											__lowerCAmelCase				   =       model_dic.get("num_bp3"			)
											__lowerCAmelCase				   =       model_dic.get("rate_weight"			)
											__lowerCAmelCase				   =       model_dic.get("rate_thre"			)
											# create model instance
											__lowerCAmelCase				   =       CNN(_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			)
											# modify model parameter
											__lowerCAmelCase				   =       model_dic.get("w_conv1"			)
											__lowerCAmelCase				   =       model_dic.get("wkj"			)
											__lowerCAmelCase				   =       model_dic.get("vji"			)
											__lowerCAmelCase				   =       model_dic.get("thre_conv1"			)
											__lowerCAmelCase				   =       model_dic.get("thre_bp2"			)
											__lowerCAmelCase				   =       model_dic.get("thre_bp3"			)
											return conv_ins






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											return 1 / (1 + np.exp(-1 * x			))






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											return round(_A		,			3			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       convs[0]
											__lowerCAmelCase				   =       convs[1]
											__lowerCAmelCase				   =       np.shape(_A			)[0]
											# get the data slice of original image data, data_focus
											__lowerCAmelCase				   =       []
											for i_focus in range(0		,			size_data - size_conv + 1		,			_A			):
																for j_focus in range(0		,			size_data - size_conv + 1		,			_A			):
																					__lowerCAmelCase				   =       data[
																					    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
																					]
																					data_focus.append(_A			)
        # calculate the feature map of every single kernel, and saved as list of matrix
											__lowerCAmelCase				   =       []
											__lowerCAmelCase				   =       int((size_data - size_conv) / conv_step + 1			)
											for i_map in range(_A			):
																__lowerCAmelCase				   =       []
																for i_focus in range(len(_A			)			):
																					__lowerCAmelCase				   =       (
																					    np.sum(np.multiply(data_focus[i_focus]		,			w_convs[i_map]			)			)
																					    - thre_convs[i_map]
																					)
																					featuremap.append(self.sig(_A			)			)
																__lowerCAmelCase				   =       np.asmatrix(_A			).reshape(
																    _A		,			_A			)
																data_featuremap.append(_A			)

											# expanding the data slice to One dimenssion
											__lowerCAmelCase				   =       []
											for each_focus in data_focus:
																focusa_list.extend(self.Expand_Mat(_A			)			)
											__lowerCAmelCase				   =       np.asarray(_A			)
											return focus_list, data_featuremap






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A="average_pool"			):




											"""simple docstring"""



											__lowerCAmelCase				   =       len(featuremaps[0]			)
											__lowerCAmelCase				   =       int(size_map / size_pooling			)
											__lowerCAmelCase				   =       []
											for i_map in range(len(_A			)			):
																__lowerCAmelCase				   =       featuremaps[i_map]
																__lowerCAmelCase				   =       []
																for i_focus in range(0		,			_A		,			_A			):
																					for j_focus in range(0		,			_A		,			_A			):
																										__lowerCAmelCase				   =       feature_map[
																										    i_focus : i_focus + size_pooling,
																										    j_focus : j_focus + size_pooling,
																										]
																										if pooling_type == "average_pool":
																															# average pooling
																															map_pooled.append(np.average(_A			)			)
																										elif pooling_type == "max_pooling":
																															# max pooling
																															map_pooled.append(np.max(_A			)			)
																__lowerCAmelCase				   =       np.asmatrix(_A			).reshape(_A		,			_A			)
																featuremap_pooled.append(_A			)
											return featuremap_pooled






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       []
											for i in range(len(_A			)			):
																__lowerCAmelCase				   =       np.shape(data[i]			)
																__lowerCAmelCase				   =       data[i].reshape(1		,			shapes[0] * shapes[1]			)
																__lowerCAmelCase				   =       data_listed.getA().tolist()[0]
																data_expanded.extend(_A			)
											__lowerCAmelCase				   =       np.asarray(_A			)
											return data_expanded






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       np.asarray(_A			)
											__lowerCAmelCase				   =       np.shape(_A			)
											__lowerCAmelCase				   =       data_mat.reshape(1		,			shapes[0] * shapes[1]			)
											return data_expanded






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       []
											__lowerCAmelCase				   =       0
											for i_map in range(_A			):
																__lowerCAmelCase				   =       np.ones((size_map, size_map)			)
																for i in range(0		,			_A		,			_A			):
																					for j in range(0		,			_A		,			_A			):
																										__lowerCAmelCase				   =       pd_pool[
																										    i_pool
																										]
																										__lowerCAmelCase				   =       i_pool + 1
																__lowerCAmelCase				   =       np.multiply(
																    _A		,			np.multiply(out_map[i_map]		,			(1 - out_map[i_map])			)			)
																pd_all.append(_A			)
											return pd_all






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A=bool			):




											"""simple docstring"""



											print("----------------------Start Training-------------------------"			)
											print((" - - Shape: Train_Data  ", np.shape(_A			))			)
											print((" - - Shape: Teach_Data  ", np.shape(_A			))			)
											__lowerCAmelCase				   =       0
											__lowerCAmelCase				   =       []
											__lowerCAmelCase				   =       1_0_0_0_0
											while rp < n_repeat and mse >= error_accuracy:
																__lowerCAmelCase				   =       0
																print(f"""-------------Learning Time {rp}--------------"""			)
																for p in range(len(_A			)			):
																					# print('------------Learning Image: %d--------------'%p)
																					__lowerCAmelCase				   =       np.asmatrix(datas_train[p]			)
																					__lowerCAmelCase				   =       np.asarray(datas_teach[p]			)
																					__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.convolute(
																					    _A		,			self.conva		,			self.w_conva		,			self.thre_conva		,			conv_step=self.step_conva		,			)
																					__lowerCAmelCase				   =       self.pooling(_A		,			self.size_poolinga			)
																					__lowerCAmelCase				   =       np.shape(_A			)
																					__lowerCAmelCase				   =       self._expand(_A			)
																					__lowerCAmelCase				   =       data_bp_input

																					__lowerCAmelCase				   =       np.dot(_A		,			self.vji.T			) - self.thre_bpa
																					__lowerCAmelCase				   =       self.sig(_A			)
																					__lowerCAmelCase				   =       np.dot(_A		,			self.wkj.T			) - self.thre_bpa
																					__lowerCAmelCase				   =       self.sig(_A			)

																					# --------------Model Leaning ------------------------
																					# calculate error and gradient---------------
																					__lowerCAmelCase				   =       np.multiply(
																					    (data_teach - bp_outa)		,			np.multiply(_A		,			(1 - bp_outa)			)			)
																					__lowerCAmelCase				   =       np.multiply(
																					    np.dot(_A		,			self.wkj			)		,			np.multiply(_A		,			(1 - bp_outa)			)			)
																					__lowerCAmelCase				   =       np.dot(_A		,			self.vji			)

																					__lowerCAmelCase				   =       pd_i_all / (self.size_poolinga * self.size_poolinga)
																					__lowerCAmelCase				   =       pd_conva_pooled.T.getA().tolist()
																					__lowerCAmelCase				   =       self._calculate_gradient_from_pool(
																					    _A		,			_A		,			shape_featuremapa[0]		,			shape_featuremapa[1]		,			self.size_poolinga		,			)
																					# weight and threshold learning process---------
																					# convolution layer
																					for k_conv in range(self.conva[1]			):
																										__lowerCAmelCase				   =       self._expand_mat(pd_conva_all[k_conv]			)
																										__lowerCAmelCase				   =       self.rate_weight * np.dot(_A		,			_A			)

																										__lowerCAmelCase				   =       self.w_conva[k_conv] + delta_w.reshape(
																										    (self.conva[0], self.conva[0])			)

																										__lowerCAmelCase				   =       (
																										    self.thre_conva[k_conv]
																										    - np.sum(pd_conva_all[k_conv]			) * self.rate_thre
																										)
																					# all connected layer
																					__lowerCAmelCase				   =       self.wkj + pd_k_all.T * bp_outa * self.rate_weight
																					__lowerCAmelCase				   =       self.vji + pd_j_all.T * bp_outa * self.rate_weight
																					__lowerCAmelCase				   =       self.thre_bpa - pd_k_all * self.rate_thre
																					__lowerCAmelCase				   =       self.thre_bpa - pd_j_all * self.rate_thre
																					# calculate the sum error of all single image
																					__lowerCAmelCase				   =       np.sum(abs(data_teach - bp_outa			)			)
																					error_count += errors
																					# print('   ----Teach      ',data_teach)
																					# print('   ----BP_output  ',bp_out3)
																__lowerCAmelCase				   =       rp + 1
																__lowerCAmelCase				   =       error_count / patterns
																all_mse.append(_A			)

											def draw_error():
																__lowerCAmelCase				   =       [error_accuracy for i in range(int(n_repeat * 1.2			)			)]
																plt.plot(_A		,			"+-"			)
																plt.plot(_A		,			"r--"			)
																plt.xlabel("Learning Times"			)
																plt.ylabel("All_mse"			)
																plt.grid(_A		,			alpha=0.5			)
																plt.show()

											print("------------------Training Complished---------------------"			)
											print((" - - Training epoch: ", rp, f"""     - - Mse: {mse:.6f}""")			)
											if draw_e:
																draw_error()
											return mse






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       []
											print("-------------------Start Testing-------------------------"			)
											print((" - - Shape: Test_Data  ", np.shape(_A			))			)
											for p in range(len(_A			)			):
																__lowerCAmelCase				   =       np.asmatrix(datas_test[p]			)
																__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.convolute(
																    _A		,			self.conva		,			self.w_conva		,			self.thre_conva		,			conv_step=self.step_conva		,			)
																__lowerCAmelCase				   =       self.pooling(_A		,			self.size_poolinga			)
																__lowerCAmelCase				   =       self._expand(_A			)

																__lowerCAmelCase				   =       data_bp_input
																__lowerCAmelCase				   =       bp_outa * self.vji.T - self.thre_bpa
																__lowerCAmelCase				   =       self.sig(_A			)
																__lowerCAmelCase				   =       bp_outa * self.wkj.T - self.thre_bpa
																__lowerCAmelCase				   =       self.sig(_A			)
																produce_out.extend(bp_outa.getA().tolist()			)
											__lowerCAmelCase				   =       [list(map(self.do_round		,			_A			)			) for each in produce_out]
											return np.asarray(_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       np.asmatrix(_A			)
											__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.convolute(
											    _A		,			self.conva		,			self.w_conva		,			self.thre_conva		,			conv_step=self.step_conva		,			)
											__lowerCAmelCase				   =       self.pooling(_A		,			self.size_poolinga			)

											return data_conveda, data_pooleda


if __name__ == "__main__":
					pass










import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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__    (					snake_case__ ,				unittest.TestCase      ):
						_a							:    Dict					=    KandinskyImgaImgPipeline
						_a							:    List[Any]					=    ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
						_a							:    str					=    [
						    """prompt""",
						    """negative_prompt""",
						    """image_embeds""",
						    """negative_image_embeds""",
						    """image""",
						]
						_a							:    List[Any]					=    [
						    """generator""",
						    """height""",
						    """width""",
						    """strength""",
						    """guidance_scale""",
						    """negative_prompt""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						_a							:    int					=    False






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return 3_2






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return 3_2






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return self.time_input_dim






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return self.time_input_dim * 4






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return 1_0_0






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"			)
											return tokenizer






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											torch.manual_seed(0			)
											__lowerCAmelCase				   =       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				   =       MultilingualCLIP(_A			)
											__lowerCAmelCase				   =       text_encoder.eval()

											return text_encoder






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											torch.manual_seed(0			)

											__lowerCAmelCase				   =       {
											    "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				   =       UNetaDConditionModel(**_A			)
											return model






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											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 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.dummy_text_encoder
											__lowerCAmelCase				   =       self.dummy_tokenizer
											__lowerCAmelCase				   =       self.dummy_unet
											__lowerCAmelCase				   =       self.dummy_movq

											__lowerCAmelCase				   =       {
											    "num_train_timesteps": 1_0_0_0,
											    "beta_schedule": "linear",
											    "beta_start": 0.0_00_85,
											    "beta_end": 0.0_12,
											    "clip_sample": False,
											    "set_alpha_to_one": False,
											    "steps_offset": 0,
											    "prediction_type": "epsilon",
											    "thresholding": False,
											}

											__lowerCAmelCase				   =       DDIMScheduler(**_A			)

											__lowerCAmelCase				   =       {
											    "text_encoder": text_encoder,
											    "tokenizer": tokenizer,
											    "unet": unet,
											    "scheduler": scheduler,
											    "movq": movq,
											}

											return components






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A=0			):




											"""simple docstring"""



											__lowerCAmelCase				   =       floats_tensor((1, self.cross_attention_dim)		,			rng=random.Random(_A			)			).to(_A			)
											__lowerCAmelCase				   =       floats_tensor((1, self.cross_attention_dim)		,			rng=random.Random(seed + 1			)			).to(_A			)
											# create init_image
											__lowerCAmelCase				   =       floats_tensor((1, 3, 6_4, 6_4)		,			rng=random.Random(_A			)			).to(_A			)
											__lowerCAmelCase				   =       image.cpu().permute(0		,			2		,			3		,			1			)[0]
											__lowerCAmelCase				   =       Image.fromarray(np.uinta(_A			)			).convert("RGB"			).resize((2_5_6, 2_5_6)			)

											if str(_A			).startswith("mps"			):
																__lowerCAmelCase				   =       torch.manual_seed(_A			)
											else:
																__lowerCAmelCase				   =       torch.Generator(device=_A			).manual_seed(_A			)
											__lowerCAmelCase				   =       {
											    "prompt": "horse",
											    "image": init_image,
											    "image_embeds": image_embeds,
											    "negative_image_embeds": negative_image_embeds,
											    "generator": generator,
											    "height": 6_4,
											    "width": 6_4,
											    "num_inference_steps": 1_0,
											    "guidance_scale": 7.0,
											    "strength": 0.2,
											    "output_type": "np",
											}
											return inputs






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "cpu"

											__lowerCAmelCase				   =       self.get_dummy_components()

											__lowerCAmelCase				   =       self.pipeline_class(**_A			)
											__lowerCAmelCase				   =       pipe.to(_A			)

											pipe.set_progress_bar_config(disable=_A			)

											__lowerCAmelCase				   =       pipe(**self.get_dummy_inputs(_A			)			)
											__lowerCAmelCase				   =       output.images

											__lowerCAmelCase				   =       pipe(
											    **self.get_dummy_inputs(_A			)		,			return_dict=_A		,			)[0]

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

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

											__lowerCAmelCase				   =       np.array(
											    [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]			)
											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 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       load_numpy(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
											    "/kandinsky/kandinsky_img2img_frog.npy"			)

											__lowerCAmelCase				   =       load_image(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"			)
											__lowerCAmelCase				   =       "A red cartoon frog, 4k"

											__lowerCAmelCase				   =       KandinskyPriorPipeline.from_pretrained(
											    "kandinsky-community/kandinsky-2-1-prior"		,			torch_dtype=torch.floataa			)
											pipe_prior.to(_A			)

											__lowerCAmelCase				   =       KandinskyImgaImgPipeline.from_pretrained(
											    "kandinsky-community/kandinsky-2-1"		,			torch_dtype=torch.floataa			)
											__lowerCAmelCase				   =       pipeline.to(_A			)

											pipeline.set_progress_bar_config(disable=_A			)

											__lowerCAmelCase				   =       torch.Generator(device="cpu"			).manual_seed(0			)
											__lowerCAmelCase   ,							__lowerCAmelCase				   =       pipe_prior(
											    _A		,			generator=_A		,			num_inference_steps=5		,			negative_prompt=""		,			).to_tuple()

											__lowerCAmelCase				   =       pipeline(
											    _A		,			image=_A		,			image_embeds=_A		,			negative_image_embeds=_A		,			generator=_A		,			num_inference_steps=1_0_0		,			height=7_6_8		,			width=7_6_8		,			strength=0.2		,			output_type="np"		,			)

											__lowerCAmelCase				   =       output.images[0]

											assert image.shape == (7_6_8, 7_6_8, 3)

											assert_mean_pixel_difference(_A		,			_A			)




