

'''simple docstring'''

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       ( A				,     unittest.TestCase				):
       lowerCAmelCase_				=	UnCLIPPipeline
       lowerCAmelCase_				=	TEXT_TO_IMAGE_PARAMS - {
           "negative_prompt",
           "height",
           "width",
           "negative_prompt_embeds",
           "guidance_scale",
           "prompt_embeds",
           "cross_attention_kwargs",
       }
       lowerCAmelCase_				=	TEXT_TO_IMAGE_BATCH_PARAMS
       lowerCAmelCase_				=	[
           "generator",
           "return_dict",
           "prior_num_inference_steps",
           "decoder_num_inference_steps",
           "super_res_num_inference_steps",
       ]
       lowerCAmelCase_				=	False







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







            """simple docstring"""







            return 32







       @property
       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            return 32







       @property
       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            return self.time_input_dim







       @property
       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            return self.time_input_dim * 4







       @property
       def      snake_case				(							self     :	Any ):







            """simple docstring"""







            return 100







       @property
       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
            return tokenizer







       @property
       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            torch.manual_seed(0 )
            __lowercase     						=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(__lowercase )







       @property
       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            torch.manual_seed(0 )

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

            __lowercase     						=PriorTransformer(**__lowercase )
            return model







       @property
       def      snake_case				(							self     :	str ):







            """simple docstring"""







            torch.manual_seed(0 )

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

            __lowercase     						=UnCLIPTextProjModel(**__lowercase )
            return model







       @property
       def      snake_case				(							self     :	str ):







            """simple docstring"""







            torch.manual_seed(0 )

            __lowercase     						={
                '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',
            }

            __lowercase     						=UNetaDConditionModel(**__lowercase )
            return model







       @property
       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            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      snake_case				(							self     :	Any ):







            """simple docstring"""







            torch.manual_seed(0 )

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







       @property
       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            torch.manual_seed(1 )

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







       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=self.dummy_prior
            __lowercase     						=self.dummy_decoder
            __lowercase     						=self.dummy_text_proj
            __lowercase     						=self.dummy_text_encoder
            __lowercase     						=self.dummy_tokenizer
            __lowercase     						=self.dummy_super_res_first
            __lowercase     						=self.dummy_super_res_last

            __lowercase     						=UnCLIPScheduler(
                variance_type='fixed_small_log'  , prediction_type='sample'  , num_train_timesteps=1000  , clip_sample_range=5.0  , )

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

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

            __lowercase     						={
                '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      snake_case				(							self     :	Tuple  , __lowercase     :	int  , __lowercase     :	Any=0 ):







            """simple docstring"""







            if str(__lowercase ).startswith('mps' ):
                 __lowercase     						=torch.manual_seed(__lowercase )
            else:
                 __lowercase     						=torch.Generator(device=__lowercase ).manual_seed(__lowercase )
            __lowercase     						={
                '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      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						='cpu'

            __lowercase     						=self.get_dummy_components()

            __lowercase     						=self.pipeline_class(**__lowercase )
            __lowercase     						=pipe.to(__lowercase )

            pipe.set_progress_bar_config(disable=__lowercase )

            __lowercase     						=pipe(**self.get_dummy_inputs(__lowercase ) )
            __lowercase     						=output.images

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

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

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

            __lowercase     						=np.array(
                [
                    0.9_9_9_7,
                    0.9_9_8_8,
                    0.0_0_2_8,
                    0.9_9_9_7,
                    0.9_9_8_4,
                    0.9_9_6_5,
                    0.0_0_2_9,
                    0.9_9_8_6,
                    0.0_0_2_5,
                ] )

            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      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=torch.device('cpu' )


            class 					lowerCAmelCase       :
                   lowerCAmelCase_				=	1






            __lowercase     						=self.get_dummy_components()

            __lowercase     						=self.pipeline_class(**__lowercase )
            __lowercase     						=pipe.to(__lowercase )

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

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

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

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

            pipe.set_progress_bar_config(disable=__lowercase )

            __lowercase     						='this is a prompt example'

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

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

            __lowercase     						=torch.Generator(device=__lowercase ).manual_seed(0 )
            __lowercase     						=pipe(
                generator=__lowercase  , prior_num_inference_steps=2  , decoder_num_inference_steps=2  , super_res_num_inference_steps=2  , prior_latents=__lowercase  , decoder_latents=__lowercase  , super_res_latents=__lowercase  , text_model_output=__lowercase  , text_attention_mask=__lowercase  , 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      snake_case				(							self     :	str ):







            """simple docstring"""







            __lowercase     						=torch_device == 'cpu'

            self._test_attention_slicing_forward_pass(test_max_difference=__lowercase  , expected_max_diff=0.0_1 )







       @skip_mps
       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            __lowercase     						=torch_device == 'cpu'
            __lowercase     						=True
            __lowercase     						=[
                'prior_num_inference_steps',
                'decoder_num_inference_steps',
                'super_res_num_inference_steps',
            ]

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







       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=[
                'prior_num_inference_steps',
                'decoder_num_inference_steps',
                'super_res_num_inference_steps',
            ]

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







       @skip_mps
       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            return super().test_dict_tuple_outputs_equivalent()







       @skip_mps
       def      snake_case				(							self     :	Union[str, Any] ):







            """simple docstring"""







            return super().test_save_load_local()







       @skip_mps
       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            return super().test_save_load_optional_components()






@nightly
class 					lowerCAmelCase       ( unittest.TestCase				):







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







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







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







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

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

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

            __lowercase     						=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				):







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







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







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







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

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

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

            __lowercase     						=output.images[0]

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

            assert_mean_pixel_difference(__lowercase  , __lowercase )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            torch.cuda.empty_cache()
            torch.cuda.reset_max_memory_allocated()
            torch.cuda.reset_peak_memory_stats()

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

            __lowercase     						=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'  , )

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





'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase							=					logging.get_logger(__name__)


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	["pixel_values"]







       def __init__(							self     :	List[Any]  , __lowercase     :	bool = True  , __lowercase     :	Optional[Dict[str, int]] = None  , __lowercase     :	PILImageResampling = PILImageResampling.BILINEAR  , __lowercase     :	bool = True  , __lowercase     :	Union[int, float] = 1 / 255  , __lowercase     :	bool = True  , __lowercase     :	Optional[Union[float, List[float]]] = None  , __lowercase     :	Optional[Union[float, List[float]]] = None  , **__lowercase     :	List[Any]  , ):







            """simple docstring"""







            super().__init__(**__lowercase )
            __lowercase     						=size if size is not None else {'height': 224, 'width': 224}
            __lowercase     						=get_size_dict(__lowercase )
            __lowercase     						=do_resize
            __lowercase     						=do_rescale
            __lowercase     						=do_normalize
            __lowercase     						=size
            __lowercase     						=resample
            __lowercase     						=rescale_factor
            __lowercase     						=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            __lowercase     						=image_std if image_std is not None else IMAGENET_STANDARD_STD







       def      snake_case				(							self     :	Dict  , __lowercase     :	np.ndarray  , __lowercase     :	Dict[str, int]  , __lowercase     :	PILImageResampling = PILImageResampling.BILINEAR  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	Union[str, Any]  , ):







            """simple docstring"""







            __lowercase     						=get_size_dict(__lowercase )
            if "height" not in size or "width" not in size:
                 raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''' )
            return resize(
                __lowercase  , size=(size['height'], size['width'])  , resample=__lowercase  , data_format=__lowercase  , **__lowercase )







       def      snake_case				(							self     :	str  , __lowercase     :	np.ndarray  , __lowercase     :	float  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	Optional[Any] ):







            """simple docstring"""







            return rescale(__lowercase  , scale=__lowercase  , data_format=__lowercase  , **__lowercase )







       def      snake_case				(							self     :	Any  , __lowercase     :	np.ndarray  , __lowercase     :	Union[float, List[float]]  , __lowercase     :	Union[float, List[float]]  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	str  , ):







            """simple docstring"""







            return normalize(__lowercase  , mean=__lowercase  , std=__lowercase  , data_format=__lowercase  , **__lowercase )







       def      snake_case				(							self     :	str  , __lowercase     :	ImageInput  , __lowercase     :	Optional[bool] = None  , __lowercase     :	Dict[str, int] = None  , __lowercase     :	PILImageResampling = None  , __lowercase     :	Optional[bool] = None  , __lowercase     :	Optional[float] = None  , __lowercase     :	Optional[bool] = None  , __lowercase     :	Optional[Union[float, List[float]]] = None  , __lowercase     :	Optional[Union[float, List[float]]] = None  , __lowercase     :	Optional[Union[str, TensorType]] = None  , __lowercase     :	Union[str, ChannelDimension] = ChannelDimension.FIRST  , **__lowercase     :	Tuple  , ):







            """simple docstring"""







            __lowercase     						=do_resize if do_resize is not None else self.do_resize
            __lowercase     						=do_rescale if do_rescale is not None else self.do_rescale
            __lowercase     						=do_normalize if do_normalize is not None else self.do_normalize
            __lowercase     						=resample if resample is not None else self.resample
            __lowercase     						=rescale_factor if rescale_factor is not None else self.rescale_factor
            __lowercase     						=image_mean if image_mean is not None else self.image_mean
            __lowercase     						=image_std if image_std is not None else self.image_std

            __lowercase     						=size if size is not None else self.size
            __lowercase     						=get_size_dict(__lowercase )

            __lowercase     						=make_list_of_images(__lowercase )

            if not valid_images(__lowercase ):
                 raise ValueError(
                     'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                     'torch.Tensor, tf.Tensor or jax.ndarray.' )

            if do_resize and size is None:
                 raise ValueError('Size must be specified if do_resize is True.' )

            if do_rescale and rescale_factor is None:
                 raise ValueError('Rescale factor must be specified if do_rescale is True.' )

            # All transformations expect numpy arrays.
            __lowercase     						=[to_numpy_array(__lowercase ) for image in images]

            if do_resize:
                 __lowercase     						=[self.resize(image=__lowercase  , size=__lowercase  , resample=__lowercase ) for image in images]

            if do_rescale:
                 __lowercase     						=[self.rescale(image=__lowercase  , scale=__lowercase ) for image in images]

            if do_normalize:
                 __lowercase     						=[self.normalize(image=__lowercase  , mean=__lowercase  , std=__lowercase ) for image in images]

            __lowercase     						=[to_channel_dimension_format(__lowercase  , __lowercase ) for image in images]

            __lowercase     						={'pixel_values': images}
            return BatchFeature(data=__lowercase  , tensor_type=__lowercase )


