






import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class 						SCREAMING_SNAKE_CASE				( snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					UnCLIPImageVariationPipeline
							A_						  =					IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
							A_						  =					IMAGE_VARIATION_BATCH_PARAMS

							A_						  =					[
							    "generator",
							    "return_dict",
							    "decoder_num_inference_steps",
							    "super_res_num_inference_steps",
							]
							A_						  =					False

							@property
							def       __A (  self:   int   )    ->				int:
												return 32

							@property
							def       __A (  self:   Tuple   )    ->				List[str]:
												return 32

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

							@property
							def       __A (  self:   Dict   )    ->				Optional[Any]:
												return self.time_input_dim * 4

							@property
							def       __A (  self:   List[str]   )    ->				Any:
												return 1_00

							@property
							def       __A (  self:   Dict   )    ->				Optional[int]:
												_A								= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )
												return tokenizer

							@property
							def       __A (  self:   Union[str, Any]   )    ->				Optional[Any]:
												torch.manual_seed(0   )
												_A								= CLIPTextConfig(
												    bos_token_id=0		,     eos_token_id=2		,     hidden_size=self.text_embedder_hidden_size		,     projection_dim=self.text_embedder_hidden_size		,     intermediate_size=37		,     layer_norm_eps=1e-05		,     num_attention_heads=4		,     num_hidden_layers=5		,     pad_token_id=1		,     vocab_size=10_00		,     )
												return CLIPTextModelWithProjection(__A   )

							@property
							def       __A (  self:   List[Any]   )    ->				Optional[int]:
												torch.manual_seed(0   )
												_A								= CLIPVisionConfig(
												    hidden_size=self.text_embedder_hidden_size		,     projection_dim=self.text_embedder_hidden_size		,     num_hidden_layers=5		,     num_attention_heads=4		,     image_size=32		,     intermediate_size=37		,     patch_size=1		,     )
												return CLIPVisionModelWithProjection(__A   )

							@property
							def       __A (  self:   List[Any]   )    ->				Tuple:
												torch.manual_seed(0   )

												_A								= {
												    '''clip_embeddings_dim''': self.text_embedder_hidden_size,
												    '''time_embed_dim''': self.time_embed_dim,
												    '''cross_attention_dim''': self.cross_attention_dim,
												}

												_A								= UnCLIPTextProjModel(**__A   )
												return model

							@property
							def       __A (  self:   Tuple   )    ->				str:
												torch.manual_seed(0   )

												_A								= {
												    '''sample_size''': 32,
												    # RGB in channels
												    '''in_channels''': 3,
												    # Out channels is double in channels because predicts mean and variance
												    '''out_channels''': 6,
												    '''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,
												    '''cross_attention_dim''': self.cross_attention_dim,
												    '''attention_head_dim''': 4,
												    '''resnet_time_scale_shift''': '''scale_shift''',
												    '''class_embed_type''': '''identity''',
												}

												_A								= UNetaDConditionModel(**__A   )
												return model

							@property
							def       __A (  self:   Union[str, Any]   )    ->				str:
												return {
												    "sample_size": 64,
												    "layers_per_block": 1,
												    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
												    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
												    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
												    "in_channels": 6,
												    "out_channels": 3,
												}

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

												_A								= UNetaDModel(**self.dummy_super_res_kwargs   )
												return model

							@property
							def       __A (  self:   int   )    ->				List[Any]:
												# seeded differently to get different unet than `self.dummy_super_res_first`
												torch.manual_seed(1   )

												_A								= UNetaDModel(**self.dummy_super_res_kwargs   )
												return model

							def       __A (  self:   Tuple   )    ->				int:
												_A								= self.dummy_decoder
												_A								= self.dummy_text_proj
												_A								= self.dummy_text_encoder
												_A								= self.dummy_tokenizer
												_A								= self.dummy_super_res_first
												_A								= self.dummy_super_res_last

												_A								= UnCLIPScheduler(
												    variance_type='''learned_range'''		,     prediction_type='''epsilon'''		,     num_train_timesteps=10_00		,     )

												_A								= UnCLIPScheduler(
												    variance_type='''fixed_small_log'''		,     prediction_type='''epsilon'''		,     num_train_timesteps=10_00		,     )

												_A								= CLIPImageProcessor(crop_size=32		,     size=32   )

												_A								= self.dummy_image_encoder

												return {
												    "decoder": decoder,
												    "text_encoder": text_encoder,
												    "tokenizer": tokenizer,
												    "text_proj": text_proj,
												    "feature_extractor": feature_extractor,
												    "image_encoder": image_encoder,
												    "super_res_first": super_res_first,
												    "super_res_last": super_res_last,
												    "decoder_scheduler": decoder_scheduler,
												    "super_res_scheduler": super_res_scheduler,
												}

							def       __A (  self:   List[Any]		,     __A:   List[Any]		,     __A:   Any=0		,     __A:   List[str]=True   )    ->				Optional[Any]:
												_A								= floats_tensor((1, 3, 32, 32)		,     rng=random.Random(__A   )   ).to(__A   )
												if str(__A   ).startswith('''mps'''   ):
																	_A								= torch.manual_seed(__A   )
												else:
																	_A								= torch.Generator(device=__A   ).manual_seed(__A   )

												if pil_image:
																	_A								= input_image * 0.5 + 0.5
																	_A								= input_image.clamp(0		,     1   )
																	_A								= input_image.cpu().permute(0		,     2		,     3		,     1   ).float().numpy()
																	_A								= DiffusionPipeline.numpy_to_pil(__A   )[0]

												return {
												    "image": input_image,
												    "generator": generator,
												    "decoder_num_inference_steps": 2,
												    "super_res_num_inference_steps": 2,
												    "output_type": "np",
												}

							def       __A (  self:   Optional[Any]   )    ->				Any:
												_A								= '''cpu'''

												_A								= self.get_dummy_components()

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

												pipe.set_progress_bar_config(disable=__A   )

												_A								= self.get_dummy_inputs(__A		,     pil_image=__A   )

												_A								= pipe(**__A   )
												_A								= output.images

												_A								= self.get_dummy_inputs(__A		,     pil_image=__A   )

												_A								= pipe(
												    **__A		,     return_dict=__A		,     )[0]

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

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

												_A								= np.array(
												    [
												        0.9_997,
												        0.0_002,
												        0.9_997,
												        0.9_997,
												        0.9_969,
												        0.0_023,
												        0.9_997,
												        0.9_969,
												        0.9_970,
												    ]   )

												assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
												assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2

							def       __A (  self:   Any   )    ->				Union[str, Any]:
												_A								= '''cpu'''

												_A								= self.get_dummy_components()

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

												pipe.set_progress_bar_config(disable=__A   )

												_A								= self.get_dummy_inputs(__A		,     pil_image=__A   )

												_A								= pipe(**__A   )
												_A								= output.images

												_A								= self.get_dummy_inputs(__A		,     pil_image=__A   )

												_A								= pipe(
												    **__A		,     return_dict=__A		,     )[0]

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

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

												_A								= np.array([0.9_997, 0.0_003, 0.9_997, 0.9_997, 0.9_970, 0.0_024, 0.9_997, 0.9_971, 0.9_971]   )

												assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
												assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2

							def       __A (  self:   Union[str, Any]   )    ->				List[Any]:
												_A								= '''cpu'''

												_A								= self.get_dummy_components()

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

												pipe.set_progress_bar_config(disable=__A   )

												_A								= self.get_dummy_inputs(__A		,     pil_image=__A   )
												_A								= [
												    pipeline_inputs['''image'''],
												    pipeline_inputs['''image'''],
												]

												_A								= pipe(**__A   )
												_A								= output.images

												_A								= self.get_dummy_inputs(__A		,     pil_image=__A   )
												_A								= [
												    tuple_pipeline_inputs['''image'''],
												    tuple_pipeline_inputs['''image'''],
												]

												_A								= pipe(
												    **__A		,     return_dict=__A		,     )[0]

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

												assert image.shape == (2, 64, 64, 3)

												_A								= np.array(
												    [
												        0.9_997,
												        0.9_989,
												        0.0_008,
												        0.0_021,
												        0.9_960,
												        0.0_018,
												        0.0_014,
												        0.0_002,
												        0.9_933,
												    ]   )

												assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
												assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2

							def       __A (  self:   int   )    ->				List[Any]:
												_A								= torch.device('''cpu'''   )





												class 						SCREAMING_SNAKE_CASE				:





																			"""simple docstring"""

																			A_						  =					1



												_A								= self.get_dummy_components()

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

												pipe.set_progress_bar_config(disable=__A   )

												_A								= torch.Generator(device=__A   ).manual_seed(0   )
												_A								= pipe.decoder.dtype
												_A								= 1

												_A								= (
												    batch_size,
												    pipe.decoder.config.in_channels,
												    pipe.decoder.config.sample_size,
												    pipe.decoder.config.sample_size,
												)
												_A								= pipe.prepare_latents(
												    __A		,     dtype=__A		,     device=__A		,     generator=__A		,     latents=__A		,     scheduler=DummyScheduler()   )

												_A								= (
												    batch_size,
												    pipe.super_res_first.config.in_channels // 2,
												    pipe.super_res_first.config.sample_size,
												    pipe.super_res_first.config.sample_size,
												)
												_A								= pipe.prepare_latents(
												    __A		,     dtype=__A		,     device=__A		,     generator=__A		,     latents=__A		,     scheduler=DummyScheduler()   )

												_A								= self.get_dummy_inputs(__A		,     pil_image=__A   )

												_A								= pipe(
												    **__A		,     decoder_latents=__A		,     super_res_latents=__A   ).images

												_A								= self.get_dummy_inputs(__A		,     pil_image=__A   )
												# Don't pass image, instead pass embedding
												_A								= pipeline_inputs.pop('''image'''   )
												_A								= pipe.image_encoder(__A   ).image_embeds

												_A								= pipe(
												    **__A		,     decoder_latents=__A		,     super_res_latents=__A		,     image_embeddings=__A		,     ).images

												# make sure passing text embeddings manually is identical
												assert np.abs(img_out_a - img_out_a   ).max() < 1e-4

							@skip_mps
							def       __A (  self:   Optional[Any]   )    ->				Tuple:
												_A								= torch_device == '''cpu'''

												# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
												_A								= 1e-2

												self._test_attention_slicing_forward_pass(
												    test_max_difference=__A		,     expected_max_diff=__A   )

							@skip_mps
							def       __A (  self:   str   )    ->				str:
												_A								= torch_device == '''cpu'''
												_A								= True
												_A								= [
												    '''decoder_num_inference_steps''',
												    '''super_res_num_inference_steps''',
												]

												self._test_inference_batch_single_identical(
												    test_max_difference=__A		,     relax_max_difference=__A		,     additional_params_copy_to_batched_inputs=__A		,     )

							def       __A (  self:   Dict   )    ->				Any:
												_A								= [
												    '''decoder_num_inference_steps''',
												    '''super_res_num_inference_steps''',
												]

												if torch_device == "mps":
																	# TODO: MPS errors with larger batch sizes
																	_A								= [2, 3]
																	self._test_inference_batch_consistent(
																	    batch_sizes=__A		,     additional_params_copy_to_batched_inputs=__A		,     )
												else:
																	self._test_inference_batch_consistent(
																	    additional_params_copy_to_batched_inputs=__A   )

							@skip_mps
							def       __A (  self:   List[Any]   )    ->				List[Any]:
												return super().test_dict_tuple_outputs_equivalent()

							@skip_mps
							def       __A (  self:   List[str]   )    ->				Optional[Any]:
												return super().test_save_load_local()



							@skip_mps
							def       __A (  self:   Tuple   )    ->				List[str]:
												return super().test_save_load_optional_components()





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





							"""simple docstring"""

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



							def       __A (  self:   List[str]   )    ->				List[Any]:
												_A								= load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'''   )
												_A								= load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy'''   )

												_A								= UnCLIPImageVariationPipeline.from_pretrained(
												    '''kakaobrain/karlo-v1-alpha-image-variations'''		,     torch_dtype=torch.floataa   )
												_A								= pipeline.to(__A   )
												pipeline.set_progress_bar_config(disable=__A   )

												_A								= torch.Generator(device='''cpu'''   ).manual_seed(0   )
												_A								= pipeline(
												    __A		,     generator=__A		,     output_type='''np'''		,     )

												_A								= output.images[0]

												assert image.shape == (2_56, 2_56, 3)

												assert_mean_pixel_difference(__A		,     __A		,     15   )











import itertools
import string
from collections.abc import Generator, Iterable




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= iter(_lowercase )
					while True:
										_A								= tuple(itertools.islice(_lowercase					,			_lowercase ) )
										if not chunk:
															return
										yield chunk




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
					_A								= ''''''

					if len(_lowercase ) < 2:
										return dirty

					for i in range(len(_lowercase ) - 1 ):
										clean += dirty[i]

										if dirty[i] == dirty[i + 1]:
															clean += "X"

					clean += dirty[-1]

					if len(_lowercase ) & 1:
										clean += "X"

					return clean




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
					# we're using a list instead of a '2d' array because it makes the math
					# for setting up the table and doing the actual encoding/decoding simpler
					_A								= []

					# copy key chars into the table if they are in `alphabet` ignoring duplicates
					for char in key.upper():
										if char not in table and char in alphabet:
															table.append(_lowercase )

    # fill the rest of the table in with the remaining alphabet chars
					for char in alphabet:
										if char not in table:
															table.append(_lowercase )

					return table




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= generate_table(_lowercase )
					_A								= prepare_input(_lowercase )
					_A								= ''''''

					# https://en.wikipedia.org/wiki/Playfair_cipher#Description
					for chara, chara in chunker(_lowercase					,			2 ):
										_A							,_A								= divmod(table.index(_lowercase )					,			5 )
										_A							,_A								= divmod(table.index(_lowercase )					,			5 )

										if rowa == rowa:
															ciphertext += table[rowa * 5 + (cola + 1) % 5]
															ciphertext += table[rowa * 5 + (cola + 1) % 5]
										elif cola == cola:
															ciphertext += table[((rowa + 1) % 5) * 5 + cola]
															ciphertext += table[((rowa + 1) % 5) * 5 + cola]
										else:  # rectangle
															ciphertext += table[rowa * 5 + cola]
															ciphertext += table[rowa * 5 + cola]

					return ciphertext




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= generate_table(_lowercase )
					_A								= ''''''

					# https://en.wikipedia.org/wiki/Playfair_cipher#Description
					for chara, chara in chunker(_lowercase					,			2 ):
										_A							,_A								= divmod(table.index(_lowercase )					,			5 )
										_A							,_A								= divmod(table.index(_lowercase )					,			5 )

										if rowa == rowa:
															plaintext += table[rowa * 5 + (cola - 1) % 5]
															plaintext += table[rowa * 5 + (cola - 1) % 5]
										elif cola == cola:
															plaintext += table[((rowa - 1) % 5) * 5 + cola]
															plaintext += table[((rowa - 1) % 5) * 5 + cola]
										else:  # rectangle
															plaintext += table[rowa * 5 + cola]
															plaintext += table[rowa * 5 + cola]

					return plaintext



