



'''simple docstring'''




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    lowerCamelCase__				(			__lowerCAmelCase	,    unittest.TestCase						):






      """simple docstring"""
      UpperCamelCase__			 =     UnCLIPImageVariationPipeline
      UpperCamelCase__			 =     IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
      UpperCamelCase__			 =     IMAGE_VARIATION_BATCH_PARAMS

      UpperCamelCase__			 =     [
          '''generator''',
          '''return_dict''',
          '''decoder_num_inference_steps''',
          '''super_res_num_inference_steps''',
      ]
      UpperCamelCase__			 =     False




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




      @property
      def lowerCAmelCase_					(       self							:     List[str]   ):
             return 32




      @property
      def lowerCAmelCase_					(       self							:     Union[str, Any]   ):
             return self.time_input_dim




      @property
      def lowerCAmelCase_					(       self							:     List[Any]   ):
             return self.time_input_dim * 4




      @property
      def lowerCAmelCase_					(       self							:     Tuple   ):
             return 1_00




      @property
      def lowerCAmelCase_					(       self							:     Optional[Any]   ):
             a__								= CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )
             return tokenizer




      @property
      def lowerCAmelCase_					(       self							:     str   ):
             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(__SCREAMING_SNAKE_CASE   )




      @property
      def lowerCAmelCase_					(       self							:     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(__SCREAMING_SNAKE_CASE   )




      @property
      def lowerCAmelCase_					(       self							:     Union[str, Any]   ):
             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(**__SCREAMING_SNAKE_CASE   )
             return model




      @property
      def lowerCAmelCase_					(       self							:     Union[str, Any]   ):
             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(**__SCREAMING_SNAKE_CASE   )
             return model




      @property
      def lowerCAmelCase_					(       self							:     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 lowerCAmelCase_					(       self							:     Optional[int]   ):
             torch.manual_seed(0   )

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




      @property
      def lowerCAmelCase_					(       self							:     List[str]   ):
             torch.manual_seed(1   )

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




      def lowerCAmelCase_					(       self							:     Tuple   ):
             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 lowerCAmelCase_					(       self							:     Any			,a__							:     Dict			,a__							:     Optional[Any]=0			,a__							:     Optional[Any]=True   ):
             a__								= floats_tensor((1, 3, 32, 32)			,rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
             if str(__SCREAMING_SNAKE_CASE   ).startswith("mps"   ):
                    a__								= torch.manual_seed(__SCREAMING_SNAKE_CASE   )
             else:
                    a__								= torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )

             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(__SCREAMING_SNAKE_CASE   )[0]

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




      def lowerCAmelCase_					(       self							:     Tuple   ):
             a__								= "cpu"

             a__								= self.get_dummy_components()

             a__								= self.pipeline_class(**__SCREAMING_SNAKE_CASE   )
             a__								= pipe.to(__SCREAMING_SNAKE_CASE   )

             pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

             a__								= self.get_dummy_inputs(__SCREAMING_SNAKE_CASE			,pil_image=__SCREAMING_SNAKE_CASE   )

             a__								= pipe(**__SCREAMING_SNAKE_CASE   )
             a__								= output.images

             a__								= self.get_dummy_inputs(__SCREAMING_SNAKE_CASE			,pil_image=__SCREAMING_SNAKE_CASE   )

             a__								= pipe(
                 **__SCREAMING_SNAKE_CASE			,return_dict=__SCREAMING_SNAKE_CASE			,)[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(
                 [
                     0.9997,
                     0.0002,
                     0.9997,
                     0.9997,
                     0.9969,
                     0.0023,
                     0.9997,
                     0.9969,
                     0.9970,
                 ]   )

             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 lowerCAmelCase_					(       self							:     Optional[int]   ):
             a__								= "cpu"

             a__								= self.get_dummy_components()

             a__								= self.pipeline_class(**__SCREAMING_SNAKE_CASE   )
             a__								= pipe.to(__SCREAMING_SNAKE_CASE   )

             pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

             a__								= self.get_dummy_inputs(__SCREAMING_SNAKE_CASE			,pil_image=__SCREAMING_SNAKE_CASE   )

             a__								= pipe(**__SCREAMING_SNAKE_CASE   )
             a__								= output.images

             a__								= self.get_dummy_inputs(__SCREAMING_SNAKE_CASE			,pil_image=__SCREAMING_SNAKE_CASE   )

             a__								= pipe(
                 **__SCREAMING_SNAKE_CASE			,return_dict=__SCREAMING_SNAKE_CASE			,)[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([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]   )

             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 lowerCAmelCase_					(       self							:     Optional[int]   ):
             a__								= "cpu"

             a__								= self.get_dummy_components()

             a__								= self.pipeline_class(**__SCREAMING_SNAKE_CASE   )
             a__								= pipe.to(__SCREAMING_SNAKE_CASE   )

             pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

             a__								= self.get_dummy_inputs(__SCREAMING_SNAKE_CASE			,pil_image=__SCREAMING_SNAKE_CASE   )
             a__								= [
                 pipeline_inputs["image"],
                 pipeline_inputs["image"],
             ]

             a__								= pipe(**__SCREAMING_SNAKE_CASE   )
             a__								= output.images

             a__								= self.get_dummy_inputs(__SCREAMING_SNAKE_CASE			,pil_image=__SCREAMING_SNAKE_CASE   )
             a__								= [
                 tuple_pipeline_inputs["image"],
                 tuple_pipeline_inputs["image"],
             ]

             a__								= pipe(
                 **__SCREAMING_SNAKE_CASE			,return_dict=__SCREAMING_SNAKE_CASE			,)[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(
                 [
                     0.9997,
                     0.9989,
                     0.0008,
                     0.0021,
                     0.9960,
                     0.0018,
                     0.0014,
                     0.0002,
                     0.9933,
                 ]   )

             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 lowerCAmelCase_					(       self							:     Union[str, Any]   ):
             a__								= torch.device("cpu"   )



             class    lowerCamelCase__				:






                   """simple docstring"""
                   UpperCamelCase__			 =     1
             a__								= self.get_dummy_components()

             a__								= self.pipeline_class(**__SCREAMING_SNAKE_CASE   )
             a__								= pipe.to(__SCREAMING_SNAKE_CASE   )

             pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

             a__								= torch.Generator(device=__SCREAMING_SNAKE_CASE   ).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(
                 __SCREAMING_SNAKE_CASE			,dtype=__SCREAMING_SNAKE_CASE			,device=__SCREAMING_SNAKE_CASE			,generator=__SCREAMING_SNAKE_CASE			,latents=__SCREAMING_SNAKE_CASE			,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(
                 __SCREAMING_SNAKE_CASE			,dtype=__SCREAMING_SNAKE_CASE			,device=__SCREAMING_SNAKE_CASE			,generator=__SCREAMING_SNAKE_CASE			,latents=__SCREAMING_SNAKE_CASE			,scheduler=DummyScheduler()   )

             a__								= self.get_dummy_inputs(__SCREAMING_SNAKE_CASE			,pil_image=__SCREAMING_SNAKE_CASE   )

             a__								= pipe(
                 **__SCREAMING_SNAKE_CASE			,decoder_latents=__SCREAMING_SNAKE_CASE			,super_res_latents=__SCREAMING_SNAKE_CASE   ).images

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

             a__								= pipe(
                 **__SCREAMING_SNAKE_CASE			,decoder_latents=__SCREAMING_SNAKE_CASE			,super_res_latents=__SCREAMING_SNAKE_CASE			,image_embeddings=__SCREAMING_SNAKE_CASE			,).images

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




      @skip_mps
      def lowerCAmelCase_					(       self							:     Optional[int]   ):
             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=__SCREAMING_SNAKE_CASE			,expected_max_diff=__SCREAMING_SNAKE_CASE   )




      @skip_mps
      def lowerCAmelCase_					(       self							:     Tuple   ):
             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=__SCREAMING_SNAKE_CASE			,relax_max_difference=__SCREAMING_SNAKE_CASE			,additional_params_copy_to_batched_inputs=__SCREAMING_SNAKE_CASE			,)




      def lowerCAmelCase_					(       self							:     Optional[int]   ):
             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=__SCREAMING_SNAKE_CASE			,additional_params_copy_to_batched_inputs=__SCREAMING_SNAKE_CASE			,)
             else:
                    self._test_inference_batch_consistent(
                        additional_params_copy_to_batched_inputs=__SCREAMING_SNAKE_CASE   )




      @skip_mps
      def lowerCAmelCase_					(       self							:     str   ):
             return super().test_dict_tuple_outputs_equivalent()




      @skip_mps
      def lowerCAmelCase_					(       self							:     Any   ):
             return super().test_save_load_local()




      @skip_mps
      def lowerCAmelCase_					(       self							:     List[Any]   ):
             return super().test_save_load_optional_components()



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






      """simple docstring"""




      def lowerCAmelCase_					(       self							:     List[str]   ):
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()




      def lowerCAmelCase_					(       self							:     Union[str, 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(__SCREAMING_SNAKE_CASE   )
             pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

             a__								= torch.Generator(device="cpu"   ).manual_seed(0   )
             a__								= pipeline(
                 __SCREAMING_SNAKE_CASE			,generator=__SCREAMING_SNAKE_CASE			,output_type="np"			,)

             a__								= output.images[0]

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

             assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE			,__SCREAMING_SNAKE_CASE			,15   )






'''simple docstring'''



def       _UpperCamelCase			(_lowerCamelCase				:  int ,						_lowerCamelCase				:  int ,						_lowerCamelCase				:  int						)-> float:




     '''simple docstring'''





     __snake_case            =	(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
     # formula for sum of series
     return total







def       _UpperCamelCase			()-> str:




     '''simple docstring'''





     print(sum_of_series(1 ,						1 ,						10						)						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






