import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, 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   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
       _UpperCAmelCase						    =     KandinskyInpaintPipeline
       _UpperCAmelCase						    =     ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
       _UpperCAmelCase						    =     [
           'prompt',
           'negative_prompt',
           'image_embeds',
           'negative_image_embeds',
           'image',
           'mask_image',
       ]
       _UpperCAmelCase						    =     [
           'generator',
           'height',
           'width',
           'latents',
           'guidance_scale',
           'negative_prompt',
           'num_inference_steps',
           'return_dict',
           'guidance_scale',
           'num_images_per_prompt',
           'output_type',
           'return_dict',
       ]
       _UpperCAmelCase						    =     False







       @property
       def 				snake_case  (				self     :    Tuple		):
          return 32







       @property
       def 				snake_case  (				self     :    str		):
          return 32







       @property
       def 				snake_case  (				self     :    List[Any]		):
          return self.time_input_dim







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







       @property
       def 				snake_case  (				self     :    Dict		):
          return 100







       @property
       def 				snake_case  (				self     :    List[Any]		):
          lowerCamelCase						:str						  =     XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''		)
          return tokenizer







       @property
       def 				snake_case  (				self     :    Union[str, Any]		):
          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=37	,					num_attention_heads=4	,					num_hidden_layers=5	,					vocab_size=1005	,					)

          lowerCamelCase						:List[str]						  =     MultilingualCLIP(__snake_case		)
          lowerCamelCase						:List[Any]						  =     text_encoder.eval()

          return text_encoder







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

          lowerCamelCase						:List[Any]						  =     {
              '''in_channels''': 9,
              # 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						:Dict						  =     UNetaDConditionModel(**__snake_case		)
          return model







       @property
       def 				snake_case  (				self     :    List[Any]		):
          return {
              "block_out_channels": [32, 64],
              "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
              "in_channels": 3,
              "latent_channels": 4,
              "layers_per_block": 1,
              "norm_num_groups": 8,
              "norm_type": "spatial",
              "num_vq_embeddings": 12,
              "out_channels": 3,
              "up_block_types": [
                  "AttnUpDecoderBlock2D",
                  "UpDecoderBlock2D",
              ],
              "vq_embed_dim": 4,
          }







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







       def 				snake_case  (				self     :    Optional[Any]		):
          lowerCamelCase						:Union[str, Any]						  =     self.dummy_text_encoder
          lowerCamelCase						:int						  =     self.dummy_tokenizer
          lowerCamelCase						:Optional[Any]						  =     self.dummy_unet
          lowerCamelCase						:str						  =     self.dummy_movq

          lowerCamelCase						:Optional[Any]						  =     DDIMScheduler(
              num_train_timesteps=1000	,					beta_schedule='''linear'''	,					beta_start=0.0_0_0_8_5	,					beta_end=0.0_1_2	,					clip_sample=__snake_case	,					set_alpha_to_one=__snake_case	,					steps_offset=1	,					prediction_type='''epsilon'''	,					thresholding=__snake_case	,					)

          lowerCamelCase						:Optional[int]						  =     {
              '''text_encoder''': text_encoder,
              '''tokenizer''': tokenizer,
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''movq''': movq,
          }

          return components







       def 				snake_case  (				self     :    List[Any]	,					__snake_case     :    List[Any]	,					__snake_case     :    str=0		):
          lowerCamelCase						:Union[str, Any]						  =     floats_tensor((1, self.cross_attention_dim)	,					rng=random.Random(__snake_case		)		).to(__snake_case		)
          lowerCamelCase						:Tuple						  =     floats_tensor((1, self.cross_attention_dim)	,					rng=random.Random(seed + 1		)		).to(__snake_case		)
          # create init_image
          lowerCamelCase						:Tuple						  =     floats_tensor((1, 3, 64, 64)	,					rng=random.Random(__snake_case		)		).to(__snake_case		)
          lowerCamelCase						:str						  =     image.cpu().permute(0	,					2	,					3	,					1		)[0]
          lowerCamelCase						:Tuple						  =     Image.fromarray(np.uinta(__snake_case		)		).convert('''RGB'''		).resize((256, 256)		)
          # create mask
          lowerCamelCase						:Tuple						  =     np.ones((64, 64)	,					dtype=np.floataa		)
          lowerCamelCase						:Tuple						  =     0

          if str(__snake_case		).startswith('''mps'''		):
             lowerCamelCase						:Tuple						  =     torch.manual_seed(__snake_case		)
          else:
             lowerCamelCase						:Dict						  =     torch.Generator(device=__snake_case		).manual_seed(__snake_case		)
          lowerCamelCase						:Optional[int]						  =     {
              '''prompt''': '''horse''',
              '''image''': init_image,
              '''mask_image''': mask,
              '''image_embeds''': image_embeds,
              '''negative_image_embeds''': negative_image_embeds,
              '''generator''': generator,
              '''height''': 64,
              '''width''': 64,
              '''num_inference_steps''': 2,
              '''guidance_scale''': 4.0,
              '''output_type''': '''np''',
          }
          return inputs







       def 				snake_case  (				self     :    str		):
          lowerCamelCase						:Dict						  =     '''cpu'''

          lowerCamelCase						:List[str]						  =     self.get_dummy_components()

          lowerCamelCase						:Tuple						  =     self.pipeline_class(**__snake_case		)
          lowerCamelCase						:str						  =     pipe.to(__snake_case		)

          pipe.set_progress_bar_config(disable=__snake_case		)

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

          lowerCamelCase						:Tuple						  =     pipe(
              **self.get_dummy_inputs(__snake_case		)	,					return_dict=__snake_case	,					)[0]

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

          print(F"image.shape {image.shape}"		)

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

          lowerCamelCase						:Optional[Any]						  =     np.array(
              [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]		)

          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()}"







       def 				snake_case  (				self     :    Dict		):
          super().test_inference_batch_single_identical(expected_max_diff=3e-3		)


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







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







       def 				snake_case  (				self     :    Optional[Any]		):
          lowerCamelCase						:Optional[int]						  =     load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''		)

          lowerCamelCase						:List[Any]						  =     load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''		)
          lowerCamelCase						:str						  =     np.ones((768, 768)	,					dtype=np.floataa		)
          lowerCamelCase						:Optional[int]						  =     0

          lowerCamelCase						:Dict						  =     '''a hat'''

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

          lowerCamelCase						:Optional[int]						  =     KandinskyInpaintPipeline.from_pretrained(
              '''kandinsky-community/kandinsky-2-1-inpaint'''	,					torch_dtype=torch.floataa		)
          lowerCamelCase						:List[str]						  =     pipeline.to(__snake_case		)
          pipeline.set_progress_bar_config(disable=__snake_case		)

          lowerCamelCase						:Any						  =     torch.Generator(device='''cpu'''		).manual_seed(0		)
          lowerCamelCase						:Dict						  =     pipe_prior(
              __snake_case	,					generator=__snake_case	,					num_inference_steps=5	,					negative_prompt=''''''	,					).to_tuple()

          lowerCamelCase						:Optional[int]						  =     pipeline(
              __snake_case	,					image=__snake_case	,					mask_image=__snake_case	,					image_embeds=__snake_case	,					negative_image_embeds=__snake_case	,					generator=__snake_case	,					num_inference_steps=100	,					height=768	,					width=768	,					output_type='''np'''	,					)

          lowerCamelCase						:List[Any]						  =     output.images[0]

          assert image.shape == (768, 768, 3)

          assert_mean_pixel_difference(__snake_case	,					__snake_case		)




import operator as op





def  _lowerCamelCase					(   a_				: Tuple):
			lowerCamelCase						:int						  =     []
			lowerCamelCase						:List[str]						  =     lambda a_						,			a_: int(x / y)  # noqa: E731 integer division operation
			lowerCamelCase						:Optional[int]						  =     {
			    '''^''': op.pow,
			    '''*''': op.mul,
			    '''/''': div,
			    '''+''': op.add,
			    '''-''': op.sub,
			}  # operators & their respective operation

			# print table header
			print('''Symbol'''.center(8)						,			'''Action'''.center(12)						,			'''Stack'''						,			sep=''' | ''')
			print('''-''' * (30 + len(a_)))

			for x in post_fix:
						if x.isdigit():  # if x in digit
									stack.append(a_)  # append x to stack
									# output in tabular format
									print(x.rjust(8)						,			('''push(''' + x + ''')''').ljust(12)						,			''','''.join(a_)						,			sep=''' | ''')
						else:
									lowerCamelCase						:Optional[Any]						  =     stack.pop()  # pop stack
									# output in tabular format
									print(''''''.rjust(8)						,			('''pop(''' + b + ''')''').ljust(12)						,			''','''.join(a_)						,			sep=''' | ''')

									lowerCamelCase						:str						  =     stack.pop()  # pop stack
									# output in tabular format
									print(''''''.rjust(8)						,			('''pop(''' + a + ''')''').ljust(12)						,			''','''.join(a_)						,			sep=''' | ''')

									stack.append(
									    str(opr[x](int(a_)						,			int(a_))))  # evaluate the 2 values popped from stack & push result to stack
									# output in tabular format
									print(
									    x.rjust(8)						,			('''push(''' + a + x + b + ''')''').ljust(12)						,			''','''.join(a_)						,			sep=''' | '''						,			)

			return int(stack[0])


if __name__ == "__main__":
			A__           =							input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
			print("""\n\tResult = """, solve(Postfix))



