






"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 					a						( __snake_case  ,				__snake_case  ,				__snake_case  ,				unittest.TestCase		):
				SCREAMING_SNAKE_CASE						:					Tuple						=			StableDiffusionImageVariationPipeline
				SCREAMING_SNAKE_CASE						:					Dict						=			IMAGE_VARIATION_PARAMS
				SCREAMING_SNAKE_CASE						:					int						=			IMAGE_VARIATION_BATCH_PARAMS
				SCREAMING_SNAKE_CASE						:					List[str]						=			frozenset([]		)
				# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				SCREAMING_SNAKE_CASE						:					int						=			frozenset([]		)






				def        UpperCamelCase	(	self					:							int )					->		int:
						torch.manual_seed(0 )
						lowerCamelCase_   					=						UNetaDConditionModel(
						    block_out_channels=(32, 64)	,						layers_per_block=2	,						sample_size=32	,						in_channels=4	,						out_channels=4	,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')	,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')	,						cross_attention_dim=32	,						)
						lowerCamelCase_   					=						PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE )
						torch.manual_seed(0 )
						lowerCamelCase_   					=						AutoencoderKL(
						    block_out_channels=[32, 64]	,						in_channels=3	,						out_channels=3	,						down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']	,						up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']	,						latent_channels=4	,						)
						torch.manual_seed(0 )
						lowerCamelCase_   					=						CLIPVisionConfig(
						    hidden_size=32	,						projection_dim=32	,						intermediate_size=37	,						layer_norm_eps=1e-0_5	,						num_attention_heads=4	,						num_hidden_layers=5	,						image_size=32	,						patch_size=4	,						)
						lowerCamelCase_   					=						CLIPVisionModelWithProjection(__SCREAMING_SNAKE_CASE )
						lowerCamelCase_   					=						CLIPImageProcessor(crop_size=32	,						size=32 )

						lowerCamelCase_   					=						{
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'image_encoder': image_encoder,
						    'feature_extractor': feature_extractor,
						    'safety_checker': None,
						}
						return components






				def        UpperCamelCase	(	self					:							Union[str, Any]	,						__SCREAMING_SNAKE_CASE					:							Any	,						__SCREAMING_SNAKE_CASE					:							Dict=0 )					->		int:
						lowerCamelCase_   					=						floats_tensor((1, 3, 32, 32)	,						rng=random.Random(__SCREAMING_SNAKE_CASE ) )
						lowerCamelCase_   					=						image.cpu().permute(0	,						2	,						3	,						1 )[0]
						lowerCamelCase_   					=						Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE ) ).convert('RGB' ).resize((32, 32) )
						if str(__SCREAMING_SNAKE_CASE ).startswith('mps' ):
								lowerCamelCase_   					=						torch.manual_seed(__SCREAMING_SNAKE_CASE )
						else:
								lowerCamelCase_   					=						torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
						lowerCamelCase_   					=						{
						    'image': image,
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						    'output_type': 'numpy',
						}
						return inputs






				def        UpperCamelCase	(	self					:							Dict )					->		Optional[Any]:
						lowerCamelCase_   					=						'cpu'  # ensure determinism for the device-dependent torch.Generator
						lowerCamelCase_   					=						self.get_dummy_components()
						lowerCamelCase_   					=						StableDiffusionImageVariationPipeline(**__SCREAMING_SNAKE_CASE )
						lowerCamelCase_   					=						sd_pipe.to(__SCREAMING_SNAKE_CASE )
						sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

						lowerCamelCase_   					=						self.get_dummy_inputs(__SCREAMING_SNAKE_CASE )
						lowerCamelCase_   					=						sd_pipe(**__SCREAMING_SNAKE_CASE ).images
						lowerCamelCase_   					=						image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						lowerCamelCase_   					=						np.array([0.5_239, 0.5_723, 0.4_796, 0.5_049, 0.5_550, 0.4_685, 0.5_329, 0.4_891, 0.4_921] )

						assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3






				def        UpperCamelCase	(	self					:							Dict )					->		Optional[int]:
						lowerCamelCase_   					=						'cpu'  # ensure determinism for the device-dependent torch.Generator
						lowerCamelCase_   					=						self.get_dummy_components()
						lowerCamelCase_   					=						StableDiffusionImageVariationPipeline(**__SCREAMING_SNAKE_CASE )
						lowerCamelCase_   					=						sd_pipe.to(__SCREAMING_SNAKE_CASE )
						sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

						lowerCamelCase_   					=						self.get_dummy_inputs(__SCREAMING_SNAKE_CASE )
						lowerCamelCase_   					=						2 * [inputs['image']]
						lowerCamelCase_   					=						sd_pipe(**__SCREAMING_SNAKE_CASE )

						lowerCamelCase_   					=						output.images

						lowerCamelCase_   					=						image[-1, -3:, -3:, -1]

						assert image.shape == (2, 64, 64, 3)
						lowerCamelCase_   					=						np.array([0.6_892, 0.5_637, 0.5_836, 0.5_771, 0.6_254, 0.6_409, 0.5_580, 0.5_569, 0.5_289] )

						assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3






				def        UpperCamelCase	(	self					:							Dict )					->		List[Any]:
						super().test_inference_batch_single_identical(expected_max_diff=3e-3 )






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






				def        UpperCamelCase	(	self					:							Union[str, Any] )					->		Any:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






				def        UpperCamelCase	(	self					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							str	,						__SCREAMING_SNAKE_CASE					:							int="cpu"	,						__SCREAMING_SNAKE_CASE					:							str=torch.floataa	,						__SCREAMING_SNAKE_CASE					:							Union[str, Any]=0 )					->		str:
						lowerCamelCase_   					=						torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
						lowerCamelCase_   					=						load_image(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
						    '/stable_diffusion_imgvar/input_image_vermeer.png' )
						lowerCamelCase_   					=						np.random.RandomState(__SCREAMING_SNAKE_CASE ).standard_normal((1, 4, 64, 64) )
						lowerCamelCase_   					=						torch.from_numpy(__SCREAMING_SNAKE_CASE ).to(device=__SCREAMING_SNAKE_CASE	,						dtype=__SCREAMING_SNAKE_CASE )
						lowerCamelCase_   					=						{
						    'image': init_image,
						    'latents': latents,
						    'generator': generator,
						    'num_inference_steps': 3,
						    'guidance_scale': 7.5,
						    'output_type': 'numpy',
						}
						return inputs






				def        UpperCamelCase	(	self					:							int )					->		str:
						lowerCamelCase_   					=						StableDiffusionImageVariationPipeline.from_pretrained(
						    'lambdalabs/sd-image-variations-diffusers'	,						safety_checker=__SCREAMING_SNAKE_CASE )
						lowerCamelCase_   					=						sd_pipe.to(__SCREAMING_SNAKE_CASE )
						sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

						lowerCamelCase_   					=						self.get_inputs(__SCREAMING_SNAKE_CASE )
						lowerCamelCase_   					=						sd_pipe(**__SCREAMING_SNAKE_CASE ).images
						lowerCamelCase_   					=						image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 512, 512, 3)
						lowerCamelCase_   					=						np.array([0.84_491, 0.90_789, 0.75_708, 0.78_734, 0.83_485, 0.70_099, 0.66_938, 0.68_727, 0.61_379] )
						assert np.abs(image_slice - expected_slice ).max() < 6e-3






				def        UpperCamelCase	(	self					:							Tuple )					->		str:
						lowerCamelCase_   					=						0

						def callback_fn(__SCREAMING_SNAKE_CASE					:							int	,						__SCREAMING_SNAKE_CASE					:							int	,						__SCREAMING_SNAKE_CASE					:							torch.FloatTensor ) -> None:
								lowerCamelCase_   					=						True
								nonlocal number_of_steps
								number_of_steps += 1
								if step == 1:
										lowerCamelCase_   					=						latents.detach().cpu().numpy()
										assert latents.shape == (1, 4, 64, 64)
										lowerCamelCase_   					=						latents[0, -3:, -3:, -1]
										lowerCamelCase_   					=						np.array(
										    [-0.1_621, 0.2_837, -0.7_979, -0.1_221, -1.3_057, 0.7_681, -2.1_191, 0.0_464, 1.6_309] )

										assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
								elif step == 2:
										lowerCamelCase_   					=						latents.detach().cpu().numpy()
										assert latents.shape == (1, 4, 64, 64)
										lowerCamelCase_   					=						latents[0, -3:, -3:, -1]
										lowerCamelCase_   					=						np.array([0.6_299, 1.7_500, 1.1_992, -2.1_582, -1.8_994, 0.7_334, -0.7_090, 1.0_137, 1.5_273] )

										assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2

						lowerCamelCase_   					=						False

						lowerCamelCase_   					=						StableDiffusionImageVariationPipeline.from_pretrained(
						    'fusing/sd-image-variations-diffusers'	,						safety_checker=__SCREAMING_SNAKE_CASE	,						torch_dtype=torch.floataa	,						)
						pipe.to(__SCREAMING_SNAKE_CASE )
						pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
						pipe.enable_attention_slicing()

						lowerCamelCase_   					=						self.get_inputs(__SCREAMING_SNAKE_CASE	,						dtype=torch.floataa )
						pipe(**__SCREAMING_SNAKE_CASE	,						callback=__SCREAMING_SNAKE_CASE	,						callback_steps=1 )
						assert callback_fn.has_been_called
						assert number_of_steps == inputs["num_inference_steps"]






				def        UpperCamelCase	(	self					:							List[Any] )					->		str:
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						lowerCamelCase_   					=						'fusing/sd-image-variations-diffusers'
						lowerCamelCase_   					=						StableDiffusionImageVariationPipeline.from_pretrained(
						    __SCREAMING_SNAKE_CASE	,						safety_checker=__SCREAMING_SNAKE_CASE	,						torch_dtype=torch.floataa )
						lowerCamelCase_   					=						pipe.to(__SCREAMING_SNAKE_CASE )
						pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
						pipe.enable_attention_slicing(1 )
						pipe.enable_sequential_cpu_offload()

						lowerCamelCase_   					=						self.get_inputs(__SCREAMING_SNAKE_CASE	,						dtype=torch.floataa )
						lowerCamelCase_   					=						pipe(**__SCREAMING_SNAKE_CASE )

						lowerCamelCase_   					=						torch.cuda.max_memory_allocated()
						# make sure that less than 2.6 GB is allocated
						assert mem_bytes < 2.6 * 10**9






@nightly
@require_torch_gpu
class 					a						( unittest.TestCase		):






				def        UpperCamelCase	(	self					:							Optional[Any] )					->		Any:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






				def        UpperCamelCase	(	self					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							Dict	,						__SCREAMING_SNAKE_CASE					:							Optional[int]="cpu"	,						__SCREAMING_SNAKE_CASE					:							Tuple=torch.floataa	,						__SCREAMING_SNAKE_CASE					:							List[str]=0 )					->		List[Any]:
						lowerCamelCase_   					=						torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
						lowerCamelCase_   					=						load_image(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
						    '/stable_diffusion_imgvar/input_image_vermeer.png' )
						lowerCamelCase_   					=						np.random.RandomState(__SCREAMING_SNAKE_CASE ).standard_normal((1, 4, 64, 64) )
						lowerCamelCase_   					=						torch.from_numpy(__SCREAMING_SNAKE_CASE ).to(device=__SCREAMING_SNAKE_CASE	,						dtype=__SCREAMING_SNAKE_CASE )
						lowerCamelCase_   					=						{
						    'image': init_image,
						    'latents': latents,
						    'generator': generator,
						    'num_inference_steps': 50,
						    'guidance_scale': 7.5,
						    'output_type': 'numpy',
						}
						return inputs






				def        UpperCamelCase	(	self					:							List[Any] )					->		str:
						lowerCamelCase_   					=						StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers' )
						sd_pipe.to(__SCREAMING_SNAKE_CASE )
						sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

						lowerCamelCase_   					=						self.get_inputs(__SCREAMING_SNAKE_CASE )
						lowerCamelCase_   					=						sd_pipe(**__SCREAMING_SNAKE_CASE ).images[0]

						lowerCamelCase_   					=						load_numpy(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
						    '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy' )
						lowerCamelCase_   					=						np.abs(expected_image - image ).max()
						assert max_diff < 1e-3






				def        UpperCamelCase	(	self					:							str )					->		Union[str, Any]:
						lowerCamelCase_   					=						StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers' )
						lowerCamelCase_   					=						DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config )
						sd_pipe.to(__SCREAMING_SNAKE_CASE )
						sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

						lowerCamelCase_   					=						self.get_inputs(__SCREAMING_SNAKE_CASE )
						lowerCamelCase_   					=						25
						lowerCamelCase_   					=						sd_pipe(**__SCREAMING_SNAKE_CASE ).images[0]

						lowerCamelCase_   					=						load_numpy(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
						    '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy' )
						lowerCamelCase_   					=						np.abs(expected_image - image ).max()
						assert max_diff < 1e-3












"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE :    Union[str, Any]    =						{
    '''configuration_decision_transformer''': [
        '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DecisionTransformerConfig''',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE :    Optional[Any]    =						[
      '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''DecisionTransformerGPT2Model''',
      '''DecisionTransformerGPT2PreTrainedModel''',
      '''DecisionTransformerModel''',
      '''DecisionTransformerPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_decision_transformer import (
      DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DecisionTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_decision_transformer import (
        DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DecisionTransformerGPTaModel,
        DecisionTransformerGPTaPreTrainedModel,
        DecisionTransformerModel,
        DecisionTransformerPreTrainedModel,
    )


else:
  import sys

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




