


'''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       a__				( __A							,						unittest.TestCase      ):







				"""simple docstring"""



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

				__UpperCamelCase    :		Union[str, Any]	=	[
				    'generator',
				    'return_dict',
				    'decoder_num_inference_steps',
				    'super_res_num_inference_steps',
				]
				__UpperCamelCase    :		Tuple	=	False




				@property
				def 					_snake_case  (self		):
										return 32




				@property
				def 					_snake_case  (self		):
										return 32




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




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




				@property
				def 					_snake_case  (self		):
										return 1_00




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




				@property
				def 					_snake_case  (self		):
										torch.manual_seed(0		)
										__lowerCAmelCase							       =				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(__lowercase		)




				@property
				def 					_snake_case  (self		):
										torch.manual_seed(0		)
										__lowerCAmelCase							       =				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(__lowercase		)




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

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

										__lowerCAmelCase							       =				UnCLIPTextProjModel(**__lowercase		)
										return model




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

										__lowerCAmelCase							       =				{
										    '''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''',
										}

										__lowerCAmelCase							       =				UNetaDConditionModel(**__lowercase		)
										return model




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

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




				@property
				def 					_snake_case  (self		):
										# seeded differently to get different unet than `self.dummy_super_res_first`
										torch.manual_seed(1		)

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




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.dummy_decoder
										__lowerCAmelCase							       =				self.dummy_text_proj
										__lowerCAmelCase							       =				self.dummy_text_encoder
										__lowerCAmelCase							       =				self.dummy_tokenizer
										__lowerCAmelCase							       =				self.dummy_super_res_first
										__lowerCAmelCase							       =				self.dummy_super_res_last

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

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

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

										__lowerCAmelCase							       =				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 					_snake_case  (self       ,			__lowercase       ,			__lowercase=0       ,			__lowercase=True		):
										__lowerCAmelCase							       =				floats_tensor((1, 3, 32, 32)       ,			rng=random.Random(__lowercase		)		).to(__lowercase		)
										if str(__lowercase		).startswith('''mps'''		):
																__lowerCAmelCase							       =				torch.manual_seed(__lowercase		)
										else:
																__lowerCAmelCase							       =				torch.Generator(device=__lowercase		).manual_seed(__lowercase		)

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

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




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				'''cpu'''

										__lowerCAmelCase							       =				self.get_dummy_components()

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

										pipe.set_progress_bar_config(disable=__lowercase		)

										__lowerCAmelCase							       =				self.get_dummy_inputs(__lowercase       ,			pil_image=__lowercase		)

										__lowerCAmelCase							       =				pipe(**__lowercase		)
										__lowerCAmelCase							       =				output.images

										__lowerCAmelCase							       =				self.get_dummy_inputs(__lowercase       ,			pil_image=__lowercase		)

										__lowerCAmelCase							       =				pipe(
										    **__lowercase       ,			return_dict=__lowercase       ,			)[0]

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

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

										__lowerCAmelCase							       =				np.array(
										    [
										        0.9_9_9_7,
										        0.0_0_0_2,
										        0.9_9_9_7,
										        0.9_9_9_7,
										        0.9_9_6_9,
										        0.0_0_2_3,
										        0.9_9_9_7,
										        0.9_9_6_9,
										        0.9_9_7_0,
										    ]		)

										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		):
										__lowerCAmelCase							       =				'''cpu'''

										__lowerCAmelCase							       =				self.get_dummy_components()

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

										pipe.set_progress_bar_config(disable=__lowercase		)

										__lowerCAmelCase							       =				self.get_dummy_inputs(__lowercase       ,			pil_image=__lowercase		)

										__lowerCAmelCase							       =				pipe(**__lowercase		)
										__lowerCAmelCase							       =				output.images

										__lowerCAmelCase							       =				self.get_dummy_inputs(__lowercase       ,			pil_image=__lowercase		)

										__lowerCAmelCase							       =				pipe(
										    **__lowercase       ,			return_dict=__lowercase       ,			)[0]

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

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

										__lowerCAmelCase							       =				np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1]		)

										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		):
										__lowerCAmelCase							       =				'''cpu'''

										__lowerCAmelCase							       =				self.get_dummy_components()

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

										pipe.set_progress_bar_config(disable=__lowercase		)

										__lowerCAmelCase							       =				self.get_dummy_inputs(__lowercase       ,			pil_image=__lowercase		)
										__lowerCAmelCase							       =				[
										    pipeline_inputs['''image'''],
										    pipeline_inputs['''image'''],
										]

										__lowerCAmelCase							       =				pipe(**__lowercase		)
										__lowerCAmelCase							       =				output.images

										__lowerCAmelCase							       =				self.get_dummy_inputs(__lowercase       ,			pil_image=__lowercase		)
										__lowerCAmelCase							       =				[
										    tuple_pipeline_inputs['''image'''],
										    tuple_pipeline_inputs['''image'''],
										]

										__lowerCAmelCase							       =				pipe(
										    **__lowercase       ,			return_dict=__lowercase       ,			)[0]

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

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

										__lowerCAmelCase							       =				np.array(
										    [
										        0.9_9_9_7,
										        0.9_9_8_9,
										        0.0_0_0_8,
										        0.0_0_2_1,
										        0.9_9_6_0,
										        0.0_0_1_8,
										        0.0_0_1_4,
										        0.0_0_0_2,
										        0.9_9_3_3,
										    ]		)

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


										class       a__				:







														"""simple docstring"""



														__UpperCamelCase    :		List[Any]	=	1

										__lowerCAmelCase							       =				self.get_dummy_components()

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

										pipe.set_progress_bar_config(disable=__lowercase		)

										__lowerCAmelCase							       =				torch.Generator(device=__lowercase		).manual_seed(0		)
										__lowerCAmelCase							       =				pipe.decoder.dtype
										__lowerCAmelCase							       =				1

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

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

										__lowerCAmelCase							       =				self.get_dummy_inputs(__lowercase       ,			pil_image=__lowercase		)

										__lowerCAmelCase							       =				pipe(
										    **__lowercase       ,			decoder_latents=__lowercase       ,			super_res_latents=__lowercase		).images

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

										__lowerCAmelCase							       =				pipe(
										    **__lowercase       ,			decoder_latents=__lowercase       ,			super_res_latents=__lowercase       ,			image_embeddings=__lowercase       ,			).images

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




				@skip_mps
				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				torch_device == '''cpu'''

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

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




				@skip_mps
				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				torch_device == '''cpu'''
										__lowerCAmelCase							       =				True
										__lowerCAmelCase							       =				[
										    '''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		):
										__lowerCAmelCase							       =				[
										    '''decoder_num_inference_steps''',
										    '''super_res_num_inference_steps''',
										]

										if torch_device == "mps":
																# TODO: MPS errors with larger batch sizes
																__lowerCAmelCase							       =				[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		):
										return super().test_dict_tuple_outputs_equivalent()




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




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


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







				"""simple docstring"""



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




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'''		)
										__lowerCAmelCase							       =				load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy'''		)

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

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

										__lowerCAmelCase							       =				output.images[0]

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

										assert_mean_pixel_difference(__lowercase       ,			__lowercase       ,			15		)





'''simple docstring'''

from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments

