





'''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 				__snake_case     (					__magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  UnCLIPImageVariationPipeline
	__lowerCAmelCase       						=  IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
	__lowerCAmelCase       						=  IMAGE_VARIATION_BATCH_PARAMS

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





	@property
	def   _snake_case   (		self		)      ->		str:
		return 32





	@property
	def   _snake_case   (		self		)      ->		int:
		return 32





	@property
	def   _snake_case   (		self		)      ->		str:
		return self.time_input_dim





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





	@property
	def   _snake_case   (		self		)      ->		List[str]:
		return 100





	@property
	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)
		return tokenizer





	@property
	def   _snake_case   (		self		)      ->		Any:
		torch.manual_seed(0		)
		snake_case__	       =   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-0_5      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=1000      ,       )
		return CLIPTextModelWithProjection(UpperCamelCase_		)





	@property
	def   _snake_case   (		self		)      ->		List[Any]:
		torch.manual_seed(0		)
		snake_case__	       =   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   _snake_case   (		self		)      ->		Dict:
		torch.manual_seed(0		)

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

		snake_case__	       =   UnCLIPTextProjModel(**UpperCamelCase_		)
		return model





	@property
	def   _snake_case   (		self		)      ->		Optional[Any]:
		torch.manual_seed(0		)

		snake_case__	       =   {
		    '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',
		}

		snake_case__	       =   UNetaDConditionModel(**UpperCamelCase_		)
		return model





	@property
	def   _snake_case   (		self		)      ->		Optional[Any]:
		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		)      ->		Tuple:
		torch.manual_seed(0		)

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





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

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





	def   _snake_case   (		self		)      ->		Dict:
		snake_case__	       =   self.dummy_decoder
		snake_case__	       =   self.dummy_text_proj
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   self.dummy_tokenizer
		snake_case__	       =   self.dummy_super_res_first
		snake_case__	       =   self.dummy_super_res_last

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

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

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

		snake_case__	       =   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      ,       UpperCamelCase_      ,       UpperCamelCase_=0      ,       UpperCamelCase_=True		)      ->		Dict:
		snake_case__	       =   floats_tensor((1, 3, 32, 32)      ,       rng=random.Random(UpperCamelCase_		)		).to(UpperCamelCase_		)
		if str(UpperCamelCase_		).startswith('mps'		):
			snake_case__	       =   torch.manual_seed(UpperCamelCase_		)
		else:
			snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(UpperCamelCase_		)

		if pil_image:
			snake_case__	       =   input_image * 0.5 + 0.5
			snake_case__	       =   input_image.clamp(0      ,       1		)
			snake_case__	       =   input_image.cpu().permute(0      ,       2      ,       3      ,       1		).float().numpy()
			snake_case__	       =   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   _snake_case   (		self		)      ->		Dict:
		snake_case__	       =   'cpu'

		snake_case__	       =   self.get_dummy_components()

		snake_case__	       =   self.pipeline_class(**UpperCamelCase_		)
		snake_case__	       =   pipe.to(UpperCamelCase_		)

		pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   self.get_dummy_inputs(UpperCamelCase_      ,       pil_image=UpperCamelCase_		)

		snake_case__	       =   pipe(**UpperCamelCase_		)
		snake_case__	       =   output.images

		snake_case__	       =   self.get_dummy_inputs(UpperCamelCase_      ,       pil_image=UpperCamelCase_		)

		snake_case__	       =   pipe(
		    **UpperCamelCase_      ,       return_dict=UpperCamelCase_      ,       )[0]

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

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

		snake_case__	       =   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		)      ->		int:
		snake_case__	       =   'cpu'

		snake_case__	       =   self.get_dummy_components()

		snake_case__	       =   self.pipeline_class(**UpperCamelCase_		)
		snake_case__	       =   pipe.to(UpperCamelCase_		)

		pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   self.get_dummy_inputs(UpperCamelCase_      ,       pil_image=UpperCamelCase_		)

		snake_case__	       =   pipe(**UpperCamelCase_		)
		snake_case__	       =   output.images

		snake_case__	       =   self.get_dummy_inputs(UpperCamelCase_      ,       pil_image=UpperCamelCase_		)

		snake_case__	       =   pipe(
		    **UpperCamelCase_      ,       return_dict=UpperCamelCase_      ,       )[0]

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

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

		snake_case__	       =   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		)      ->		List[Any]:
		snake_case__	       =   'cpu'

		snake_case__	       =   self.get_dummy_components()

		snake_case__	       =   self.pipeline_class(**UpperCamelCase_		)
		snake_case__	       =   pipe.to(UpperCamelCase_		)

		pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   self.get_dummy_inputs(UpperCamelCase_      ,       pil_image=UpperCamelCase_		)
		snake_case__	       =   [
		    pipeline_inputs['image'],
		    pipeline_inputs['image'],
		]

		snake_case__	       =   pipe(**UpperCamelCase_		)
		snake_case__	       =   output.images

		snake_case__	       =   self.get_dummy_inputs(UpperCamelCase_      ,       pil_image=UpperCamelCase_		)
		snake_case__	       =   [
		    tuple_pipeline_inputs['image'],
		    tuple_pipeline_inputs['image'],
		]

		snake_case__	       =   pipe(
		    **UpperCamelCase_      ,       return_dict=UpperCamelCase_      ,       )[0]

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

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

		snake_case__	       =   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		)      ->		Dict:
		snake_case__	       =   torch.device('cpu'		)






		class 				__snake_case     :
			__lowerCAmelCase       						=  1





		snake_case__	       =   self.get_dummy_components()

		snake_case__	       =   self.pipeline_class(**UpperCamelCase_		)
		snake_case__	       =   pipe.to(UpperCamelCase_		)

		pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   pipe.decoder.dtype
		snake_case__	       =   1

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

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

		snake_case__	       =   self.get_dummy_inputs(UpperCamelCase_      ,       pil_image=UpperCamelCase_		)

		snake_case__	       =   pipe(
		    **UpperCamelCase_      ,       decoder_latents=UpperCamelCase_      ,       super_res_latents=UpperCamelCase_		).images

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

		snake_case__	       =   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   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   torch_device == 'cpu'

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

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





	@skip_mps
	def   _snake_case   (		self		)      ->		Any:
		snake_case__	       =   torch_device == 'cpu'
		snake_case__	       =   True
		snake_case__	       =   [
		    '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   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   [
		    'decoder_num_inference_steps',
		    'super_res_num_inference_steps',
		]

		if torch_device == "mps":
			# TODO: MPS errors with larger batch sizes
			snake_case__	       =   [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   _snake_case   (		self		)      ->		Union[str, Any]:
		return super().test_dict_tuple_outputs_equivalent()





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





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






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





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





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

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

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

		snake_case__	       =   output.images[0]

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

		assert_mean_pixel_difference(UpperCamelCase_      ,       UpperCamelCase_      ,       15		)










'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__					:			Dict          =     {
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__					:			Union[str, Any]          =     [
			    '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''CanineForMultipleChoice''',
			    '''CanineForQuestionAnswering''',
			    '''CanineForSequenceClassification''',
			    '''CanineForTokenClassification''',
			    '''CanineLayer''',
			    '''CanineModel''',
			    '''CaninePreTrainedModel''',
			    '''load_tf_weights_in_canine''',
			]


if TYPE_CHECKING:
			from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
			from .tokenization_canine import CanineTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_canine import (
						    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CanineForMultipleChoice,
						    CanineForQuestionAnswering,
						    CanineForSequenceClassification,
						    CanineForTokenClassification,
						    CanineLayer,
						    CanineModel,
						    CaninePreTrainedModel,
						    load_tf_weights_in_canine,
						)


else:
			import sys

			a__					:			List[Any]          =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



