'''simple docstring'''




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       UpperCAmelCase__  (       _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       int     =    KandinskyImgaImgPipeline
   A							:       str     =    ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
   A							:       Tuple     =    [
       '''prompt''',
       '''negative_prompt''',
       '''image_embeds''',
       '''negative_image_embeds''',
       '''image''',
   ]
   A							:       Optional[int]     =    [
       '''generator''',
       '''height''',
       '''width''',
       '''strength''',
       '''guidance_scale''',
       '''negative_prompt''',
       '''num_inference_steps''',
       '''return_dict''',
       '''guidance_scale''',
       '''num_images_per_prompt''',
       '''output_type''',
       '''return_dict''',
   ]
   A							:       Any     =    False
   @property
   def 		_lowerCamelCase (self				)    ->     List[str]:
        return 32
   @property
   def 		_lowerCamelCase (self				)    ->     str:
        return 32
   @property
   def 		_lowerCamelCase (self				)    ->     List[str]:
        return self.time_input_dim
   @property
   def 		_lowerCamelCase (self				)    ->     Any:
        return self.time_input_dim * 4
   @property
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        return 100
   @property
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       int     				=					XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'				)
        return tokenizer
   @property
   def 		_lowerCamelCase (self				)    ->     Any:
        torch.manual_seed(0				)
        lowercase_					:       Optional[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=1_005       ,				)

        lowercase_					:       Tuple     				=					MultilingualCLIP(_a				)
        lowercase_					:       Optional[Any]     				=					text_encoder.eval()

        return text_encoder
   @property
   def 		_lowerCamelCase (self				)    ->     Tuple:
        torch.manual_seed(0				)

        lowercase_					:       str     				=					{
            '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,
        }

        lowercase_					:       Any     				=					UNetaDConditionModel(**_a				)
        return model
   @property
   def 		_lowerCamelCase (self				)    ->     List[str]:
        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 		_lowerCamelCase (self				)    ->     List[str]:
        torch.manual_seed(0				)
        lowercase_					:       Optional[Any]     				=					VQModel(**self.dummy_movq_kwargs				)
        return model
   def 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       int     				=					self.dummy_text_encoder
        lowercase_					:       Dict     				=					self.dummy_tokenizer
        lowercase_					:       Optional[int]     				=					self.dummy_unet
        lowercase_					:       Union[str, Any]     				=					self.dummy_movq

        lowercase_					:       Any     				=					{
            'num_train_timesteps': 1_000,
            '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,
        }

        lowercase_					:       List[str]     				=					DDIMScheduler(**_a				)

        lowercase_					:       Any     				=					{
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }

        return components
   def 		_lowerCamelCase (self       ,				_a       ,				_a=0				)    ->     Optional[int]:
        lowercase_					:       int     				=					floats_tensor((1, self.cross_attention_dim)       ,				rng=random.Random(_a				)				).to(_a				)
        lowercase_					:       str     				=					floats_tensor((1, self.cross_attention_dim)       ,				rng=random.Random(seed + 1				)				).to(_a				)
        # create init_image
        lowercase_					:       str     				=					floats_tensor((1, 3, 64, 64)       ,				rng=random.Random(_a				)				).to(_a				)
        lowercase_					:       str     				=					image.cpu().permute(0       ,				2       ,				3       ,				1				)[0]
        lowercase_					:       Dict     				=					Image.fromarray(np.uinta(_a				)				).convert('RGB'				).resize((256, 256)				)

        if str(_a				).startswith('mps'				):
             lowercase_					:       int     				=					torch.manual_seed(_a				)
        else:
             lowercase_					:       Union[str, Any]     				=					torch.Generator(device=_a				).manual_seed(_a				)
        lowercase_					:       Any     				=					{
            'prompt': 'horse',
            'image': init_image,
            'image_embeds': image_embeds,
            'negative_image_embeds': negative_image_embeds,
            'generator': generator,
            'height': 64,
            'width': 64,
            'num_inference_steps': 10,
            'guidance_scale': 7.0,
            'strength': 0.2,
            'output_type': 'np',
        }
        return inputs





   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       List[str]     				=					'cpu'

        lowercase_					:       Optional[int]     				=					self.get_dummy_components()

        lowercase_					:       Union[str, Any]     				=					self.pipeline_class(**_a				)
        lowercase_					:       Union[str, Any]     				=					pipe.to(_a				)

        pipe.set_progress_bar_config(disable=_a				)

        lowercase_					:       Any     				=					pipe(**self.get_dummy_inputs(_a				)				)
        lowercase_					:       Union[str, Any]     				=					output.images

        lowercase_					:       str     				=					pipe(
            **self.get_dummy_inputs(_a				)       ,				return_dict=_a       ,				)[0]

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

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

        lowercase_					:       str     				=					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       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   def 		_lowerCamelCase (self				)    ->     List[str]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       str     				=					load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinsky/kandinsky_img2img_frog.npy'				)

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

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

        lowercase_					:       Any     				=					KandinskyImgaImgPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1'       ,				torch_dtype=torch.floataa				)
        lowercase_					:       Any     				=					pipeline.to(_a				)

        pipeline.set_progress_bar_config(disable=_a				)

        lowercase_					:       Dict     				=					torch.Generator(device='cpu'				).manual_seed(0				)
        lowercase_				,lowercase_					:       str     				=					pipe_prior(
            _a       ,				generator=_a       ,				num_inference_steps=5       ,				negative_prompt=''       ,				).to_tuple()

        lowercase_					:       Any     				=					pipeline(
            _a       ,				image=_a       ,				image_embeds=_a       ,				negative_image_embeds=_a       ,				generator=_a       ,				num_inference_steps=100       ,				height=768       ,				width=768       ,				strength=0.2       ,				output_type='np'       ,				)

        lowercase_					:       Any     				=					output.images[0]

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

        assert_mean_pixel_difference(_a       ,				_a				)





'''simple docstring'''




import os

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[Any]     				=					len(grid[0]							)
     lowercase_					:       Union[str, Any]     				=					len(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       Union[str, Any]     				=					0
     lowercase_					:       int     				=					0
     lowercase_					:       Union[str, Any]     				=					0

     # Check vertically, horizontally, diagonally at the same time (only works
     # for nxn grid)
     for i in range(SCREAMING_SNAKE_CASE_							):
          for j in range(n_rows - 3							):
               lowercase_					:       Dict     				=					grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
               lowercase_					:       Optional[Any]     				=					grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]

               # Left-to-right diagonal (\) product
               if i < n_columns - 3:
                    lowercase_					:       Tuple     				=					(
                        grid[i][j]
                        * grid[i + 1][j + 1]
                        * grid[i + 2][j + 2]
                        * grid[i + 3][j + 3]
                    )

               # Right-to-left diagonal(/) product
               if i > 2:
                    lowercase_					:       int     				=					(
                        grid[i][j]
                        * grid[i - 1][j + 1]
                        * grid[i - 2][j + 2]
                        * grid[i - 3][j + 3]
                    )

               lowercase_					:       Optional[int]     				=					max(
                   SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
               if max_product > largest:
                    lowercase_					:       List[Any]     				=					max_product

     return largest

def _UpperCamelCase   (       ):
     lowercase_					:       int     				=					[]
     with open(os.path.dirname(SCREAMING_SNAKE_CASE_							) + '/grid.txt'							) as file:
          for line in file:
               grid.append(line.strip('\n'							).split(' '							)							)

     lowercase_					:       Dict     				=					[[int(SCREAMING_SNAKE_CASE_							) for i in grid[j]] for j in range(len(SCREAMING_SNAKE_CASE_							)							)]

     return largest_product(SCREAMING_SNAKE_CASE_							)


if __name__ == "__main__":
 print(solution())




