

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       snake_case_  (						__A      ,			unittest.TestCase   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Optional[Any]		     =							KandinskyImgaImgPipeline
			SCREAMING_SNAKE_CASE	:  int		     =							["prompt", "image_embeds", "negative_image_embeds", "image"]
			SCREAMING_SNAKE_CASE	:  str		     =							[
			    "prompt",
			    "negative_prompt",
			    "image_embeds",
			    "negative_image_embeds",
			    "image",
			]
			SCREAMING_SNAKE_CASE	:  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",
			]
			SCREAMING_SNAKE_CASE	:  Optional[Any]		     =							False







			@property
			def 							snake_case__(	self					:							Union[str, Any]				) ->Tuple:
									return 3_2







			@property
			def 							snake_case__(	self					:							Any				) ->List[Any]:
									return 3_2







			@property
			def 							snake_case__(	self					:							Any				) ->Tuple:
									return self.time_input_dim







			@property
			def 							snake_case__(	self					:							Any				) ->List[Any]:
									return self.time_input_dim * 4







			@property
			def 							snake_case__(	self					:							Dict				) ->List[Any]:
									return 1_0_0







			@property
			def 							snake_case__(	self					:							Optional[Any]				) ->Optional[int]:
									snake_case_  =      XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''				)
									return tokenizer







			@property
			def 							snake_case__(	self					:							int				) ->List[Any]:
									torch.manual_seed(0				)
									snake_case_  =      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				,  )

									snake_case_  =      MultilingualCLIP(_UpperCamelCase				)
									snake_case_  =      text_encoder.eval()

									return text_encoder







			@property
			def 							snake_case__(	self					:							Union[str, Any]				) ->str:
									torch.manual_seed(0				)

									snake_case_  =      {
									    '''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,
									}

									snake_case_  =      UNetaDConditionModel(**_UpperCamelCase				)
									return model







			@property
			def 							snake_case__(	self					:							int				) ->Dict:
									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 							snake_case__(	self					:							Any				) ->Dict:
									torch.manual_seed(0				)
									snake_case_  =      VQModel(**self.dummy_movq_kwargs				)
									return model







			def 							snake_case__(	self					:							Tuple				) ->str:
									snake_case_  =      self.dummy_text_encoder
									snake_case_  =      self.dummy_tokenizer
									snake_case_  =      self.dummy_unet
									snake_case_  =      self.dummy_movq

									snake_case_  =      {
									    '''num_train_timesteps''': 1_0_0_0,
									    '''beta_schedule''': '''linear''',
									    '''beta_start''': 0.00085,
									    '''beta_end''': 0.012,
									    '''clip_sample''': False,
									    '''set_alpha_to_one''': False,
									    '''steps_offset''': 0,
									    '''prediction_type''': '''epsilon''',
									    '''thresholding''': False,
									}

									snake_case_  =      DDIMScheduler(**_UpperCamelCase				)

									snake_case_  =      {
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''movq''': movq,
									}

									return components







			def 							snake_case__(	self					:							List[str]				,  _UpperCamelCase					:							Optional[Any]				,  _UpperCamelCase					:							Tuple=0				) ->Optional[Any]:
									snake_case_  =      floats_tensor((1, self.cross_attention_dim)				,  rng=random.Random(_UpperCamelCase				)				).to(_UpperCamelCase				)
									snake_case_  =      floats_tensor((1, self.cross_attention_dim)				,  rng=random.Random(seed + 1				)				).to(_UpperCamelCase				)
									# create init_image
									snake_case_  =      floats_tensor((1, 3, 6_4, 6_4)				,  rng=random.Random(_UpperCamelCase				)				).to(_UpperCamelCase				)
									snake_case_  =      image.cpu().permute(0				,  2				,  3				,  1				)[0]
									snake_case_  =      Image.fromarray(np.uinta(_UpperCamelCase				)				).convert('''RGB'''				).resize((2_5_6, 2_5_6)				)

									if str(_UpperCamelCase				).startswith('''mps'''				):
															snake_case_  =      torch.manual_seed(_UpperCamelCase				)
									else:
															snake_case_  =      torch.Generator(device=_UpperCamelCase				).manual_seed(_UpperCamelCase				)
									snake_case_  =      {
									    '''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 							snake_case__(	self					:							str				) ->str:
									snake_case_  =      '''cpu'''

									snake_case_  =      self.get_dummy_components()

									snake_case_  =      self.pipeline_class(**_UpperCamelCase				)
									snake_case_  =      pipe.to(_UpperCamelCase				)

									pipe.set_progress_bar_config(disable=_UpperCamelCase				)

									snake_case_  =      pipe(**self.get_dummy_inputs(_UpperCamelCase				)				)
									snake_case_  =      output.images

									snake_case_  =      pipe(
									    **self.get_dummy_inputs(_UpperCamelCase				)				,  return_dict=_UpperCamelCase				,  )[0]

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

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

									snake_case_  =      np.array(
									    [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]				)
									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       snake_case_  (						unittest.TestCase   ):







			'''simple docstring'''

			def 							snake_case__(	self					:							str				) ->Any:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







			def 							snake_case__(	self					:							Dict				) ->Any:
									snake_case_  =      load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/kandinsky/kandinsky_img2img_frog.npy'''				)

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

									snake_case_  =      KandinskyPriorPipeline.from_pretrained(
									    '''kandinsky-community/kandinsky-2-1-prior'''				,  torch_dtype=torch.floataa				)
									pipe_prior.to(_UpperCamelCase				)

									snake_case_  =      KandinskyImgaImgPipeline.from_pretrained(
									    '''kandinsky-community/kandinsky-2-1'''				,  torch_dtype=torch.floataa				)
									snake_case_  =      pipeline.to(_UpperCamelCase				)

									pipeline.set_progress_bar_config(disable=_UpperCamelCase				)

									snake_case_  =      torch.Generator(device='''cpu'''				).manual_seed(0				)
									snake_case_,       snake_case_  =      pipe_prior(
									    _UpperCamelCase				,  generator=_UpperCamelCase				,  num_inference_steps=5				,  negative_prompt=''''''				,  ).to_tuple()

									snake_case_  =      pipeline(
									    _UpperCamelCase				,  image=_UpperCamelCase				,  image_embeds=_UpperCamelCase				,  negative_image_embeds=_UpperCamelCase				,  generator=_UpperCamelCase				,  num_inference_steps=1_0_0				,  height=7_6_8				,  width=7_6_8				,  strength=0.2				,  output_type='''np'''				,  )

									snake_case_  =      output.images[0]

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

									assert_mean_pixel_difference(_UpperCamelCase				,  _UpperCamelCase				)






def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:

		def count_of_possible_combinations(snake_case							) -> int:
				if target < 0:
						return 0
				if target == 0:
						return 1
				return sum(count_of_possible_combinations(target - item							) for item in array							)

		return count_of_possible_combinations(snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:

		def count_of_possible_combinations_with_dp_array(
		    snake_case						,   snake_case							) -> int:
				if target < 0:
						return 0
				if target == 0:
						return 1
				if dp_array[target] != -1:
						return dp_array[target]
				__lowercase											=     sum(
				    count_of_possible_combinations_with_dp_array(target - item						,   snake_case							)
				    for item in array							)
				__lowercase											=     answer
				return answer

		__lowercase											=     [-1] * (target + 1)
		return count_of_possible_combinations_with_dp_array(snake_case						,   snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:
		__lowercase											=     [0] * (target + 1)
		__lowercase											=     1

		for i in range(1						,   target + 1							):
				for j in range(snake_case							):
						if i - array[j] >= 0:
								dp_array[i] += dp_array[i - array[j]]

		return dp_array[target]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		SCREAMING_SNAKE_CASE_   :      Tuple			=   3
		SCREAMING_SNAKE_CASE_   :      Dict			=   5
		SCREAMING_SNAKE_CASE_   :      Any			=   [1, 2, 5]
		print(combination_sum_iv(n, array, target))




