





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class     lowerCamelCase(	__snake_case ,   unittest.TestCase  ):
     '''simple docstring'''


     __magic_name__        = UnCLIPPipeline
     __magic_name__        = TEXT_TO_IMAGE_PARAMS - {
         'negative_prompt',
         'height',
         'width',
         'negative_prompt_embeds',
         'guidance_scale',
         'prompt_embeds',
         'cross_attention_kwargs',
     }
     __magic_name__        = TEXT_TO_IMAGE_BATCH_PARAMS
     __magic_name__        = [
         'generator',
         'return_dict',
         'prior_num_inference_steps',
         'decoder_num_inference_steps',
         'super_res_num_inference_steps',
     ]
     __magic_name__        = False




     @property
     def 					lowerCAmelCase__		(						self       ):
         return 32




     @property
     def 					lowerCAmelCase__		(						self       ):
         return 32




     @property
     def 					lowerCAmelCase__		(						self       ):
         return self.time_input_dim




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




     @property
     def 					lowerCAmelCase__		(						self       ):
         return 100




     @property
     def 					lowerCAmelCase__		(						self       ):
         _A    =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )
         return tokenizer




     @property
     def 					lowerCAmelCase__		(						self       ):
         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=1000							,    )
         return CLIPTextModelWithProjection(snake_case_       )




     @property
     def 					lowerCAmelCase__		(						self       ):
         torch.manual_seed(0       )

         _A    =       {
             'num_attention_heads': 2,
             'attention_head_dim': 12,
             'embedding_dim': self.text_embedder_hidden_size,
             'num_layers': 1,
         }

         _A    =       PriorTransformer(**snake_case_       )
         return model




     @property
     def 					lowerCAmelCase__		(						self       ):
         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(**snake_case_       )
         return model




     @property
     def 					lowerCAmelCase__		(						self       ):
         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(**snake_case_       )
         return model




     @property
     def 					lowerCAmelCase__		(						self       ):
         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       ):
         torch.manual_seed(0       )

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




     @property
     def 					lowerCAmelCase__		(						self       ):
         # 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 					lowerCAmelCase__		(						self       ):
         _A    =       self.dummy_prior
         _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='fixed_small_log'							,    prediction_type='sample'							,    num_train_timesteps=1000							,    clip_sample_range=5.0							,    )

         _A    =       UnCLIPScheduler(
             variance_type='learned_range'							,    prediction_type='epsilon'							,    num_train_timesteps=1000							,    )

         _A    =       UnCLIPScheduler(
             variance_type='fixed_small_log'							,    prediction_type='epsilon'							,    num_train_timesteps=1000							,    )

         _A    =       {
             'prior': prior,
             'decoder': decoder,
             'text_proj': text_proj,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'super_res_first': super_res_first,
             'super_res_last': super_res_last,
             'prior_scheduler': prior_scheduler,
             'decoder_scheduler': decoder_scheduler,
             'super_res_scheduler': super_res_scheduler,
         }

         return components




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_=0       ):
         if str(snake_case_       ).startswith('mps'       ):
             _A    =       torch.manual_seed(snake_case_       )
         else:
             _A    =       torch.Generator(device=snake_case_       ).manual_seed(snake_case_       )
         _A    =       {
             'prompt': 'horse',
             'generator': generator,
             'prior_num_inference_steps': 2,
             'decoder_num_inference_steps': 2,
             'super_res_num_inference_steps': 2,
             'output_type': 'numpy',
         }
         return inputs




     def 					lowerCAmelCase__		(						self       ):
         _A    =       'cpu'

         _A    =       self.get_dummy_components()

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

         pipe.set_progress_bar_config(disable=snake_case_       )

         _A    =       pipe(**self.get_dummy_inputs(snake_case_       )       )
         _A    =       output.images

         _A    =       pipe(
             **self.get_dummy_inputs(snake_case_       )							,    return_dict=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,
                 0.9988,
                 0.0028,
                 0.9997,
                 0.9984,
                 0.9965,
                 0.0029,
                 0.9986,
                 0.0025,
             ]       )

         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       ):
         _A    =       torch.device('cpu'       )







         class     lowerCamelCase:
              '''simple docstring'''


              __magic_name__        = 1




         _A    =       self.get_dummy_components()

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

         _A    =       components['prior']
         _A    =       components['decoder']
         _A    =       components['super_res_first']
         _A    =       components['tokenizer']
         _A    =       components['text_encoder']

         _A    =       torch.Generator(device=snake_case_       ).manual_seed(0       )
         _A    =       prior.dtype
         _A    =       1

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

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

         pipe.set_progress_bar_config(disable=snake_case_       )

         _A    =       'this is a prompt example'

         _A    =       torch.Generator(device=snake_case_       ).manual_seed(0       )
         _A    =       pipe(
             [prompt]							,    generator=snake_case_							,    prior_num_inference_steps=2							,    decoder_num_inference_steps=2							,    super_res_num_inference_steps=2							,    prior_latents=snake_case_							,    decoder_latents=snake_case_							,    super_res_latents=snake_case_							,    output_type='np'							,    )
         _A    =       output.images

         _A    =       tokenizer(
             snake_case_							,    padding='max_length'							,    max_length=tokenizer.model_max_length							,    return_tensors='pt'							,    )
         _A    =       text_encoder(text_inputs.input_ids       )
         _A    =       text_inputs.attention_mask

         _A    =       torch.Generator(device=snake_case_       ).manual_seed(0       )
         _A    =       pipe(
             generator=snake_case_							,    prior_num_inference_steps=2							,    decoder_num_inference_steps=2							,    super_res_num_inference_steps=2							,    prior_latents=snake_case_							,    decoder_latents=snake_case_							,    super_res_latents=snake_case_							,    text_model_output=snake_case_							,    text_attention_mask=snake_case_							,    output_type='np'							,    )[0]

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




     @skip_mps
     def 					lowerCAmelCase__		(						self       ):
         _A    =       torch_device == 'cpu'

         self._test_attention_slicing_forward_pass(test_max_difference=snake_case_							,    expected_max_diff=0.01       )




     @skip_mps
     def 					lowerCAmelCase__		(						self       ):
         _A    =       torch_device == 'cpu'
         _A    =       True
         _A    =       [
             'prior_num_inference_steps',
             'decoder_num_inference_steps',
             'super_res_num_inference_steps',
         ]

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




     def 					lowerCAmelCase__		(						self       ):
         _A    =       [
             'prior_num_inference_steps',
             '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=snake_case_							,    additional_params_copy_to_batched_inputs=snake_case_							,    )
         else:
             self._test_inference_batch_consistent(
                 additional_params_copy_to_batched_inputs=snake_case_       )




     @skip_mps
     def 					lowerCAmelCase__		(						self       ):
         return super().test_dict_tuple_outputs_equivalent()




     @skip_mps
     def 					lowerCAmelCase__		(						self       ):
         return super().test_save_load_local()




     @skip_mps
     def 					lowerCAmelCase__		(						self       ):
         return super().test_save_load_optional_components()







@nightly
class     lowerCamelCase(	unittest.TestCase  ):
     '''simple docstring'''


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




     def 					lowerCAmelCase__		(						self       ):
         _A    =       load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/unclip/karlo_v1_alpha_horse_cpu.npy'       )

         _A    =       UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'       )
         pipeline.set_progress_bar_config(disable=snake_case_       )

         _A    =       torch.manual_seed(0       )
         _A    =       pipeline(
             'horse'							,    num_images_per_prompt=1							,    generator=snake_case_							,    output_type='np'							,    )

         _A    =       output.images[0]

         assert image.shape == (256, 256, 3)
         assert np.abs(expected_image - image       ).max() < 1E-1







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


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




     def 					lowerCAmelCase__		(						self       ):
         _A    =       load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/unclip/karlo_v1_alpha_horse_fp16.npy'       )

         _A    =       UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'							,    torch_dtype=torch.floataa       )
         _A    =       pipeline.to(snake_case_       )
         pipeline.set_progress_bar_config(disable=snake_case_       )

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

         _A    =       output.images[0]

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

         assert_mean_pixel_difference(snake_case_							,    snake_case_       )




     def 					lowerCAmelCase__		(						self       ):
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         _A    =       UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'							,    torch_dtype=torch.floataa       )
         _A    =       pipe.to(snake_case_       )
         pipe.set_progress_bar_config(disable=snake_case_       )
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         _A    =       pipe(
             'horse'							,    num_images_per_prompt=1							,    prior_num_inference_steps=2							,    decoder_num_inference_steps=2							,    super_res_num_inference_steps=2							,    output_type='np'							,    )

         _A    =       torch.cuda.max_memory_allocated()
         # make sure that less than 7 GB is allocated
         assert mem_bytes < 7 * 10**9












import numpy as np





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[Any]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       abs(predict - actual							)
    _A    =       difference.mean()

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Optional[int]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)

    _A    =       square_diff.mean()
    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Tuple:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)
    _A    =       square_diff.mean()
    _A    =       np.sqrt(_SCREAMING_SNAKE_CASE							)
    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[Any]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       np.log(predict + 1							)
    _A    =       np.log(actual + 1							)

    _A    =       log_predict - log_actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)
    _A    =       square_diff.mean()

    _A    =       np.sqrt(_SCREAMING_SNAKE_CASE							)

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Optional[int]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)
    _A    =       np.sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)
    # print(numerator, denumerator)
    _A    =       float(_SCREAMING_SNAKE_CASE							) / denumerator * 100

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Dict:




    """simple docstring"""




    return np.mean(np.array(_SCREAMING_SNAKE_CASE							) == np.array(_SCREAMING_SNAKE_CASE							)							)





