




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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       lowercase_		(SCREAMING_SNAKE_CASE__       ,  unittest.TestCase   ):
						lowerCAmelCase__ 			=KandinskyPipeline
						lowerCAmelCase__ 			=[
						    "prompt",
						    "image_embeds",
						    "negative_image_embeds",
						]
						lowerCAmelCase__ 			=["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
						lowerCAmelCase__ 			=[
						    "generator",
						    "height",
						    "width",
						    "latents",
						    "guidance_scale",
						    "negative_prompt",
						    "num_inference_steps",
						    "return_dict",
						    "guidance_scale",
						    "num_images_per_prompt",
						    "output_type",
						    "return_dict",
						]
						lowerCAmelCase__ 			=False



						@property
						def   __a				( self  :      Any     ):

								"""simple docstring"""

								return 32



						@property
						def   __a				( self  :      Dict     ):

								"""simple docstring"""

								return 32



						@property
						def   __a				( self  :      List[str]     ):

								"""simple docstring"""

								return self.time_input_dim



						@property
						def   __a				( self  :      Any     ):

								"""simple docstring"""

								return self.time_input_dim * 4



						@property
						def   __a				( self  :      Tuple     ):

								"""simple docstring"""

								return 1_00



						@property
						def   __a				( self  :      List[str]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'     )
								return tokenizer



						@property
						def   __a				( self  :      List[Any]     ):

								"""simple docstring"""

								torch.manual_seed(0     )
								SCREAMING_SNAKE_CASE_							=       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=10_05				,       )

								SCREAMING_SNAKE_CASE_							=       MultilingualCLIP(snake_case__     )
								SCREAMING_SNAKE_CASE_							=       text_encoder.eval()

								return text_encoder



						@property
						def   __a				( self  :      Union[str, Any]     ):

								"""simple docstring"""

								torch.manual_seed(0     )

								SCREAMING_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,
								}

								SCREAMING_SNAKE_CASE_							=       UNetaDConditionModel(**snake_case__     )
								return model



						@property
						def   __a				( self  :      str     ):

								"""simple docstring"""

								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   __a				( self  :      Tuple     ):

								"""simple docstring"""

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



						def   __a				( self  :      Any     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.dummy_text_encoder
								SCREAMING_SNAKE_CASE_							=       self.dummy_tokenizer
								SCREAMING_SNAKE_CASE_							=       self.dummy_unet
								SCREAMING_SNAKE_CASE_							=       self.dummy_movq

								SCREAMING_SNAKE_CASE_							=       DDIMScheduler(
								    num_train_timesteps=10_00				,       beta_schedule='linear'				,       beta_start=0.0_00_85				,       beta_end=0.0_12				,       clip_sample=snake_case__				,       set_alpha_to_one=snake_case__				,       steps_offset=1				,       prediction_type='epsilon'				,       thresholding=snake_case__				,       )

								SCREAMING_SNAKE_CASE_							=       {
								    'text_encoder': text_encoder,
								    'tokenizer': tokenizer,
								    'unet': unet,
								    'scheduler': scheduler,
								    'movq': movq,
								}
								return components



						def   __a				( self  :      int				,       snake_case__  :      Optional[int]				,       snake_case__  :      str=0     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       floats_tensor((1, self.cross_attention_dim)				,       rng=random.Random(snake_case__     )     ).to(snake_case__     )
								SCREAMING_SNAKE_CASE_							=       floats_tensor((1, self.cross_attention_dim)				,       rng=random.Random(seed + 1     )     ).to(snake_case__     )
								if str(snake_case__     ).startswith('mps'     ):
										SCREAMING_SNAKE_CASE_							=       torch.manual_seed(snake_case__     )
								else:
										SCREAMING_SNAKE_CASE_							=       torch.Generator(device=snake_case__     ).manual_seed(snake_case__     )
								SCREAMING_SNAKE_CASE_							=       {
								    'prompt': 'horse',
								    'image_embeds': image_embeds,
								    'negative_image_embeds': negative_image_embeds,
								    'generator': generator,
								    'height': 64,
								    'width': 64,
								    'guidance_scale': 4.0,
								    'num_inference_steps': 2,
								    'output_type': 'np',
								}
								return inputs






						def   __a				( self  :      Union[str, Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       'cpu'

								SCREAMING_SNAKE_CASE_							=       self.get_dummy_components()

								SCREAMING_SNAKE_CASE_							=       self.pipeline_class(**snake_case__     )
								SCREAMING_SNAKE_CASE_							=       pipe.to(snake_case__     )

								pipe.set_progress_bar_config(disable=snake_case__     )

								SCREAMING_SNAKE_CASE_							=       pipe(**self.get_dummy_inputs(snake_case__     )     )
								SCREAMING_SNAKE_CASE_							=       output.images

								SCREAMING_SNAKE_CASE_							=       pipe(
								    **self.get_dummy_inputs(snake_case__     )				,       return_dict=snake_case__				,       )[0]

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

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

								SCREAMING_SNAKE_CASE_							=       np.array(
								    [0.32_86_63, 1.0, 0.23_21_68_73, 1.0, 0.92_71_75_64, 0.4_63_90_46, 0.96_89_47_77, 0.31_71_33_78, 0.6_29_39_53]     )

								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       lowercase_		(unittest.TestCase   ):



						def   __a				( self  :      Optional[Any]     ):

								"""simple docstring"""

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






						def   __a				( self  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/kandinsky/kandinsky_text2img_cat_fp16.npy'     )

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

								SCREAMING_SNAKE_CASE_							=       KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'				,       torch_dtype=torch.floataa     )
								SCREAMING_SNAKE_CASE_							=       pipeline.to(snake_case__     )
								pipeline.set_progress_bar_config(disable=snake_case__     )

								SCREAMING_SNAKE_CASE_							=       'red cat, 4k photo'

								SCREAMING_SNAKE_CASE_							=       torch.Generator(device='cuda'     ).manual_seed(0     )
								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       pipe_prior(
								    snake_case__				,       generator=snake_case__				,       num_inference_steps=5				,       negative_prompt=''				,       ).to_tuple()

								SCREAMING_SNAKE_CASE_							=       torch.Generator(device='cuda'     ).manual_seed(0     )
								SCREAMING_SNAKE_CASE_							=       pipeline(
								    snake_case__				,       image_embeds=snake_case__				,       negative_image_embeds=snake_case__				,       generator=snake_case__				,       num_inference_steps=1_00				,       output_type='np'				,       )

								SCREAMING_SNAKE_CASE_							=       output.images[0]

								assert image.shape == (5_12, 5_12, 3)

								assert_mean_pixel_difference(snake_case__				,       snake_case__     )





from __future__ import annotations


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  int:
		SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       0, len(lowerCAmelCase   ) - 1
		while start_ind <= end_ind:
				if array[start_ind] == search_item:
						return start_ind
				elif array[end_ind] == search_item:
						return end_ind
				else:
						start_ind += 1
						end_ind -= 1
    # returns -1 if search_item is not found in array
		return -1


if __name__ == "__main__":
						print(double_linear_search(list(range(1_0_0)), 4_0))