






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      (      lowerCAmelCase   ,		unittest.TestCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Tuple								=						UnCLIPImageVariationPipeline
     UpperCamelCase_				:		Optional[int]								=						IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
     UpperCamelCase_				:		int								=						IMAGE_VARIATION_BATCH_PARAMS

     UpperCamelCase_				:		Optional[Any]								=						[
         '''generator''',
         '''return_dict''',
         '''decoder_num_inference_steps''',
         '''super_res_num_inference_steps''',
     ]
     UpperCamelCase_				:		Optional[int]								=						False


     @property
     def 					_A							(					self							:						Optional[int]   ):
       return 32


     @property
     def 					_A							(					self							:						Any   ):
       return 32


     @property
     def 					_A							(					self							:						Dict   ):
       return self.time_input_dim


     @property
     def 					_A							(					self							:						Tuple   ):
       return self.time_input_dim * 4


     @property
     def 					_A							(					self							:						Tuple   ):
       return 100


     @property
     def 					_A							(					self							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )
       return tokenizer


     @property
     def 					_A							(					self							:						List[Any]   ):
       torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE							:   str						    =     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=1000       ,			)
       return CLIPTextModelWithProjection(UpperCAmelCase_   )


     @property
     def 					_A							(					self							:						Any   ):
       torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     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(UpperCAmelCase_   )


     @property
     def 					_A							(					self							:						Optional[Any]   ):
       torch.manual_seed(0   )

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     {
           "clip_embeddings_dim": self.text_embedder_hidden_size,
           "time_embed_dim": self.time_embed_dim,
           "cross_attention_dim": self.cross_attention_dim,
       }

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     UnCLIPTextProjModel(**UpperCAmelCase_   )
       return model


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

       SCREAMING_SNAKE_CASE							:   Dict						    =     {
           "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",
       }

       SCREAMING_SNAKE_CASE							:   Dict						    =     UNetaDConditionModel(**UpperCAmelCase_   )
       return model


     @property
     def 					_A							(					self							:						int   ):
       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							:						Tuple   ):
       torch.manual_seed(0   )

       SCREAMING_SNAKE_CASE							:   str						    =     UNetaDModel(**self.dummy_super_res_kwargs   )
       return model


     @property
     def 					_A							(					self							:						List[str]   ):
       # seeded differently to get different unet than `self.dummy_super_res_first`
       torch.manual_seed(1   )

       SCREAMING_SNAKE_CASE							:   str						    =     UNetaDModel(**self.dummy_super_res_kwargs   )
       return model


     def 					_A							(					self							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.dummy_decoder
       SCREAMING_SNAKE_CASE							:   str						    =     self.dummy_text_proj
       SCREAMING_SNAKE_CASE							:   List[str]						    =     self.dummy_text_encoder
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     self.dummy_tokenizer
       SCREAMING_SNAKE_CASE							:   int						    =     self.dummy_super_res_first
       SCREAMING_SNAKE_CASE							:   List[str]						    =     self.dummy_super_res_last

       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     UnCLIPScheduler(
           variance_type="learned_range"       ,			prediction_type="epsilon"       ,			num_train_timesteps=1000       ,			)

       SCREAMING_SNAKE_CASE							:   int						    =     UnCLIPScheduler(
           variance_type="fixed_small_log"       ,			prediction_type="epsilon"       ,			num_train_timesteps=1000       ,			)

       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     CLIPImageProcessor(crop_size=32       ,			size=32   )

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     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							:						Any       ,			UpperCAmelCase_							:						List[str]       ,			UpperCAmelCase_							:						List[str]=0       ,			UpperCAmelCase_							:						List[str]=True   ):
       SCREAMING_SNAKE_CASE							:   Any						    =     floats_tensor((1, 3, 32, 32)       ,			rng=random.Random(UpperCAmelCase_   )   ).to(UpperCAmelCase_   )
       if str(UpperCAmelCase_   ).startswith("mps"   ):
         SCREAMING_SNAKE_CASE							:   Dict						    =     torch.manual_seed(UpperCAmelCase_   )
       else:
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )

       if pil_image:
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     input_image * 0.5 + 0.5
         SCREAMING_SNAKE_CASE							:   Tuple						    =     input_image.clamp(0       ,			1   )
         SCREAMING_SNAKE_CASE							:   str						    =     input_image.cpu().permute(0       ,			2       ,			3       ,			1   ).float().numpy()
         SCREAMING_SNAKE_CASE							:   int						    =     DiffusionPipeline.numpy_to_pil(UpperCAmelCase_   )[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[int]   ):
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     "cpu"

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.get_dummy_components()

       SCREAMING_SNAKE_CASE							:   Any						    =     self.pipeline_class(**UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     pipe.to(UpperCAmelCase_   )

       pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Dict						    =     self.get_dummy_inputs(UpperCAmelCase_       ,			pil_image=UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     pipe(**UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     output.images

       SCREAMING_SNAKE_CASE							:   Any						    =     self.get_dummy_inputs(UpperCAmelCase_       ,			pil_image=UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   str						    =     pipe(
           **UpperCAmelCase_       ,			return_dict=UpperCAmelCase_       ,			)[0]

       SCREAMING_SNAKE_CASE							:   str						    =     image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     image_from_tuple[0, -3:, -3:, -1]

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

       SCREAMING_SNAKE_CASE							:   Tuple						    =     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							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     "cpu"

       SCREAMING_SNAKE_CASE							:   int						    =     self.get_dummy_components()

       SCREAMING_SNAKE_CASE							:   List[str]						    =     self.pipeline_class(**UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     pipe.to(UpperCAmelCase_   )

       pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   List[str]						    =     self.get_dummy_inputs(UpperCAmelCase_       ,			pil_image=UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     pipe(**UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     output.images

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.get_dummy_inputs(UpperCAmelCase_       ,			pil_image=UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     pipe(
           **UpperCAmelCase_       ,			return_dict=UpperCAmelCase_       ,			)[0]

       SCREAMING_SNAKE_CASE							:   Tuple						    =     image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE							:   int						    =     image_from_tuple[0, -3:, -3:, -1]

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

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     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							:						List[Any]   ):
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     "cpu"

       SCREAMING_SNAKE_CASE							:   Any						    =     self.get_dummy_components()

       SCREAMING_SNAKE_CASE							:   str						    =     self.pipeline_class(**UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     pipe.to(UpperCAmelCase_   )

       pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   str						    =     self.get_dummy_inputs(UpperCAmelCase_       ,			pil_image=UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Any						    =     [
           pipeline_inputs["image"],
           pipeline_inputs["image"],
       ]

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     pipe(**UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   str						    =     output.images

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.get_dummy_inputs(UpperCAmelCase_       ,			pil_image=UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   List[str]						    =     [
           tuple_pipeline_inputs["image"],
           tuple_pipeline_inputs["image"],
       ]

       SCREAMING_SNAKE_CASE							:   Any						    =     pipe(
           **UpperCAmelCase_       ,			return_dict=UpperCAmelCase_       ,			)[0]

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

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

       SCREAMING_SNAKE_CASE							:   Any						    =     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							:						Optional[int]   ):
       SCREAMING_SNAKE_CASE							:   str						    =     torch.device("cpu"   )


       class 			SCREAMING_SNAKE_CASE      :
            '''simple docstring'''


            UpperCamelCase_				:		Optional[int]								=						1





       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     self.get_dummy_components()

       SCREAMING_SNAKE_CASE							:   Dict						    =     self.pipeline_class(**UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     pipe.to(UpperCAmelCase_   )

       pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Any						    =     torch.Generator(device=UpperCAmelCase_   ).manual_seed(0   )
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     pipe.decoder.dtype
       SCREAMING_SNAKE_CASE							:   int						    =     1

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     (
           batch_size,
           pipe.decoder.config.in_channels,
           pipe.decoder.config.sample_size,
           pipe.decoder.config.sample_size,
       )
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     pipe.prepare_latents(
           UpperCAmelCase_       ,			dtype=UpperCAmelCase_       ,			device=UpperCAmelCase_       ,			generator=UpperCAmelCase_       ,			latents=UpperCAmelCase_       ,			scheduler=DummyScheduler()   )

       SCREAMING_SNAKE_CASE							:   int						    =     (
           batch_size,
           pipe.super_res_first.config.in_channels // 2,
           pipe.super_res_first.config.sample_size,
           pipe.super_res_first.config.sample_size,
       )
       SCREAMING_SNAKE_CASE							:   List[str]						    =     pipe.prepare_latents(
           UpperCAmelCase_       ,			dtype=UpperCAmelCase_       ,			device=UpperCAmelCase_       ,			generator=UpperCAmelCase_       ,			latents=UpperCAmelCase_       ,			scheduler=DummyScheduler()   )

       SCREAMING_SNAKE_CASE							:   Any						    =     self.get_dummy_inputs(UpperCAmelCase_       ,			pil_image=UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Dict						    =     pipe(
           **UpperCAmelCase_       ,			decoder_latents=UpperCAmelCase_       ,			super_res_latents=UpperCAmelCase_   ).images

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.get_dummy_inputs(UpperCAmelCase_       ,			pil_image=UpperCAmelCase_   )
       # Don't pass image, instead pass embedding
       SCREAMING_SNAKE_CASE							:   Tuple						    =     pipeline_inputs.pop("image"   )
       SCREAMING_SNAKE_CASE							:   Any						    =     pipe.image_encoder(UpperCAmelCase_   ).image_embeds

       SCREAMING_SNAKE_CASE							:   int						    =     pipe(
           **UpperCAmelCase_       ,			decoder_latents=UpperCAmelCase_       ,			super_res_latents=UpperCAmelCase_       ,			image_embeddings=UpperCAmelCase_       ,			).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							:						int   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     torch_device == "cpu"

       # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
       SCREAMING_SNAKE_CASE							:   Tuple						    =     1E-2

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


     @skip_mps
     def 					_A							(					self							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     torch_device == "cpu"
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     True
       SCREAMING_SNAKE_CASE							:   str						    =     [
           "decoder_num_inference_steps",
           "super_res_num_inference_steps",
       ]

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


     def 					_A							(					self							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     [
           "decoder_num_inference_steps",
           "super_res_num_inference_steps",
       ]

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


     @skip_mps
     def 					_A							(					self							:						Optional[Any]   ):
       return super().test_dict_tuple_outputs_equivalent()


     @skip_mps
     def 					_A							(					self							:						int   ):
       return super().test_save_load_local()






     @skip_mps
     def 					_A							(					self							:						Tuple   ):
       return super().test_save_load_optional_components()





@slow
@require_torch_gpu
class 			SCREAMING_SNAKE_CASE      (      unittest.TestCase       ):
     '''simple docstring'''


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






     def 					_A							(					self							:						str   ):
       SCREAMING_SNAKE_CASE							:   Any						    =     load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"   )
       SCREAMING_SNAKE_CASE							:   int						    =     load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/unclip/karlo_v1_alpha_cat_variation_fp16.npy"   )

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     UnCLIPImageVariationPipeline.from_pretrained(
           "kakaobrain/karlo-v1-alpha-image-variations"       ,			torch_dtype=torch.floataa   )
       SCREAMING_SNAKE_CASE							:   List[str]						    =     pipeline.to(UpperCAmelCase_   )
       pipeline.set_progress_bar_config(disable=UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     torch.Generator(device="cpu"   ).manual_seed(0   )
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     pipeline(
           UpperCAmelCase_       ,			generator=UpperCAmelCase_       ,			output_type="np"       ,			)

       SCREAMING_SNAKE_CASE							:   str						    =     output.images[0]

       assert image.shape == (256, 256, 3)

       assert_mean_pixel_difference(UpperCAmelCase_       ,			UpperCAmelCase_       ,			15   )










from __future__ import annotations

_lowerCAmelCase:						str   				=       '#'




class       lowercase_					:





				def __init__(				self)  -> None:
											a__      ={}





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> None:
											a__      =self._trie
											for char in text:
																		if char not in trie:
																									a__      ={}
																		a__      =trie[char]
											a__      =True





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> tuple | list:
											a__      =self._trie
											for char in prefix:
																		if char in trie:
																									a__      =trie[char]
																		else:
																									return []
											return self._elements(lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> tuple:
											a__      =[]
											for c, v in d.items():
																		a__      =[' '] if c == END else [(c + s) for s in self._elements(lowercase_)]
																		result.extend(lowercase_)
											return tuple(lowercase_)







_lowerCAmelCase:						Optional[int]   				=       Trie()
_lowerCAmelCase:						List[str]   				=       ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
	trie.insert_word(word)






def   _lowercase(					__a				:						str     ):
							a__      =trie.find_word(__a     )
							return tuple(string + word for word in suffixes     )






def   _lowercase(					):
							print(autocomplete_using_trie('de'     )     )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

