

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 						__UpperCamelCase    (					lowercase     ,     unittest.TestCase						):
	SCREAMING_SNAKE_CASE__    				=     UnCLIPImageVariationPipeline
	SCREAMING_SNAKE_CASE__    				=     IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
	SCREAMING_SNAKE_CASE__    				=     IMAGE_VARIATION_BATCH_PARAMS

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




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


		'''simple docstring'''







		return 32




	@property
	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		return 32




	@property
	def       __A					(		self	:						Any							):


		'''simple docstring'''







		return self.time_input_dim




	@property
	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		return self.time_input_dim * 4




	@property
	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		return 100




	@property
	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







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




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


		'''simple docstring'''







		torch.manual_seed(0							)
		UpperCAmelCase_       				=			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=1_000		,	)
		return CLIPTextModelWithProjection(lowerCAmelCase							)




	@property
	def       __A					(		self	:						int							):


		'''simple docstring'''







		torch.manual_seed(0							)
		UpperCAmelCase_       				=			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(lowerCAmelCase							)




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


		'''simple docstring'''







		torch.manual_seed(0							)

		UpperCAmelCase_       				=			{
		    "clip_embeddings_dim": self.text_embedder_hidden_size,
		    "time_embed_dim": self.time_embed_dim,
		    "cross_attention_dim": self.cross_attention_dim,
		}

		UpperCAmelCase_       				=			UnCLIPTextProjModel(**lowerCAmelCase							)
		return model




	@property
	def       __A					(		self	:						int							):


		'''simple docstring'''







		torch.manual_seed(0							)

		UpperCAmelCase_       				=			{
		    "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",
		}

		UpperCAmelCase_       				=			UNetaDConditionModel(**lowerCAmelCase							)
		return model




	@property
	def       __A					(		self	:						str							):


		'''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       __A					(		self	:						Any							):


		'''simple docstring'''







		torch.manual_seed(0							)

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




	@property
	def       __A					(		self	:						Any							):


		'''simple docstring'''







		torch.manual_seed(1							)

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




	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.dummy_decoder
		UpperCAmelCase_       				=			self.dummy_text_proj
		UpperCAmelCase_       				=			self.dummy_text_encoder
		UpperCAmelCase_       				=			self.dummy_tokenizer
		UpperCAmelCase_       				=			self.dummy_super_res_first
		UpperCAmelCase_       				=			self.dummy_super_res_last

		UpperCAmelCase_       				=			UnCLIPScheduler(
		    variance_type="learned_range"		,	prediction_type="epsilon"		,	num_train_timesteps=1_000		,	)

		UpperCAmelCase_       				=			UnCLIPScheduler(
		    variance_type="fixed_small_log"		,	prediction_type="epsilon"		,	num_train_timesteps=1_000		,	)

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

		UpperCAmelCase_       				=			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       __A					(		self	:						str		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						Union[str, Any]=0		,	lowerCAmelCase	:						Any=True							):


		'''simple docstring'''







		UpperCAmelCase_       				=			floats_tensor((1, 3, 32, 32)		,	rng=random.Random(lowerCAmelCase							)							).to(lowerCAmelCase							)
		if str(lowerCAmelCase							).startswith("mps"							):
			UpperCAmelCase_       				=			torch.manual_seed(lowerCAmelCase							)
		else:
			UpperCAmelCase_       				=			torch.Generator(device=lowerCAmelCase							).manual_seed(lowerCAmelCase							)

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

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




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"cpu"

		UpperCAmelCase_       				=			self.get_dummy_components()

		UpperCAmelCase_       				=			self.pipeline_class(**lowerCAmelCase							)
		UpperCAmelCase_       				=			pipe.to(lowerCAmelCase							)

		pipe.set_progress_bar_config(disable=lowerCAmelCase							)

		UpperCAmelCase_       				=			self.get_dummy_inputs(lowerCAmelCase		,	pil_image=lowerCAmelCase							)

		UpperCAmelCase_       				=			pipe(**lowerCAmelCase							)
		UpperCAmelCase_       				=			output.images

		UpperCAmelCase_       				=			self.get_dummy_inputs(lowerCAmelCase		,	pil_image=lowerCAmelCase							)

		UpperCAmelCase_       				=			pipe(
		    **lowerCAmelCase		,	return_dict=lowerCAmelCase		,	)[0]

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

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

		UpperCAmelCase_       				=			np.array(
		    [
		        0.9_997,
		        0.0_002,
		        0.9_997,
		        0.9_997,
		        0.9_969,
		        0.0_023,
		        0.9_997,
		        0.9_969,
		        0.9_970,
		    ]							)

		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       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"cpu"

		UpperCAmelCase_       				=			self.get_dummy_components()

		UpperCAmelCase_       				=			self.pipeline_class(**lowerCAmelCase							)
		UpperCAmelCase_       				=			pipe.to(lowerCAmelCase							)

		pipe.set_progress_bar_config(disable=lowerCAmelCase							)

		UpperCAmelCase_       				=			self.get_dummy_inputs(lowerCAmelCase		,	pil_image=lowerCAmelCase							)

		UpperCAmelCase_       				=			pipe(**lowerCAmelCase							)
		UpperCAmelCase_       				=			output.images

		UpperCAmelCase_       				=			self.get_dummy_inputs(lowerCAmelCase		,	pil_image=lowerCAmelCase							)

		UpperCAmelCase_       				=			pipe(
		    **lowerCAmelCase		,	return_dict=lowerCAmelCase		,	)[0]

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

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

		UpperCAmelCase_       				=			np.array([0.9_997, 0.0_003, 0.9_997, 0.9_997, 0.9_970, 0.0_024, 0.9_997, 0.9_971, 0.9_971]							)

		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       __A					(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"cpu"

		UpperCAmelCase_       				=			self.get_dummy_components()

		UpperCAmelCase_       				=			self.pipeline_class(**lowerCAmelCase							)
		UpperCAmelCase_       				=			pipe.to(lowerCAmelCase							)

		pipe.set_progress_bar_config(disable=lowerCAmelCase							)

		UpperCAmelCase_       				=			self.get_dummy_inputs(lowerCAmelCase		,	pil_image=lowerCAmelCase							)
		UpperCAmelCase_       				=			[
		    pipeline_inputs["image"],
		    pipeline_inputs["image"],
		]

		UpperCAmelCase_       				=			pipe(**lowerCAmelCase							)
		UpperCAmelCase_       				=			output.images

		UpperCAmelCase_       				=			self.get_dummy_inputs(lowerCAmelCase		,	pil_image=lowerCAmelCase							)
		UpperCAmelCase_       				=			[
		    tuple_pipeline_inputs["image"],
		    tuple_pipeline_inputs["image"],
		]

		UpperCAmelCase_       				=			pipe(
		    **lowerCAmelCase		,	return_dict=lowerCAmelCase		,	)[0]

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

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

		UpperCAmelCase_       				=			np.array(
		    [
		        0.9_997,
		        0.9_989,
		        0.0_008,
		        0.0_021,
		        0.9_960,
		        0.0_018,
		        0.0_014,
		        0.0_002,
		        0.9_933,
		    ]							)

		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       __A					(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			torch.device("cpu"							)

		class 						__UpperCamelCase    :
			SCREAMING_SNAKE_CASE__    				=     1


		UpperCAmelCase_       				=			self.get_dummy_components()

		UpperCAmelCase_       				=			self.pipeline_class(**lowerCAmelCase							)
		UpperCAmelCase_       				=			pipe.to(lowerCAmelCase							)

		pipe.set_progress_bar_config(disable=lowerCAmelCase							)

		UpperCAmelCase_       				=			torch.Generator(device=lowerCAmelCase							).manual_seed(0							)
		UpperCAmelCase_       				=			pipe.decoder.dtype
		UpperCAmelCase_       				=			1

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

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

		UpperCAmelCase_       				=			self.get_dummy_inputs(lowerCAmelCase		,	pil_image=lowerCAmelCase							)

		UpperCAmelCase_       				=			pipe(
		    **lowerCAmelCase		,	decoder_latents=lowerCAmelCase		,	super_res_latents=lowerCAmelCase							).images

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

		UpperCAmelCase_       				=			pipe(
		    **lowerCAmelCase		,	decoder_latents=lowerCAmelCase		,	super_res_latents=lowerCAmelCase		,	image_embeddings=lowerCAmelCase		,	).images

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




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


		'''simple docstring'''







		UpperCAmelCase_       				=			torch_device == "cpu"

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

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




	@skip_mps
	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			torch_device == "cpu"
		UpperCAmelCase_       				=			True
		UpperCAmelCase_       				=			[
		    "decoder_num_inference_steps",
		    "super_res_num_inference_steps",
		]

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




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			[
		    "decoder_num_inference_steps",
		    "super_res_num_inference_steps",
		]

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




	@skip_mps
	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		return super().test_dict_tuple_outputs_equivalent()




	@skip_mps
	def       __A					(		self	:						int							):


		'''simple docstring'''







		return super().test_save_load_local()




	@skip_mps
	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		return super().test_save_load_optional_components()


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




	def       __A					(		self	:						Union[str, Any]							):


		'''simple docstring'''







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




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			load_image(
		    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"							)
		UpperCAmelCase_       				=			load_numpy(
		    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
		    "/unclip/karlo_v1_alpha_cat_variation_fp16.npy"							)

		UpperCAmelCase_       				=			UnCLIPImageVariationPipeline.from_pretrained(
		    "kakaobrain/karlo-v1-alpha-image-variations"		,	torch_dtype=torch.floataa							)
		UpperCAmelCase_       				=			pipeline.to(lowerCAmelCase							)
		pipeline.set_progress_bar_config(disable=lowerCAmelCase							)

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

		UpperCAmelCase_       				=			output.images[0]

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

		assert_mean_pixel_difference(lowerCAmelCase		,	lowerCAmelCase		,	15							)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_a:    List[str] 							=			{
    """configuration_encodec""": [
        """ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EncodecConfig""",
    ],
    """feature_extraction_encodec""": ["""EncodecFeatureExtractor"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:    List[Any] 							=			[
				    """ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """EncodecModel""",
				    """EncodecPreTrainedModel""",
				]

if TYPE_CHECKING:
				from .configuration_encodec import (
				    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    EncodecConfig,
				)
				from .feature_extraction_encodec import EncodecFeatureExtractor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_encodec import (
								    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EncodecModel,
								    EncodecPreTrainedModel,
								)

else:
				import sys

				_a:    int 							=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)