
import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class       _snake_case					(     lowerCamelCase__				,						lowerCamelCase__				,						unittest.TestCase				):
			UpperCamelCase__          =    StableDiffusionXLImgaImgPipeline
			UpperCamelCase__          =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
			UpperCamelCase__          =    PipelineTesterMixin.required_optional_params - {'latents'}
			UpperCamelCase__          =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			UpperCamelCase__          =    IMAGE_TO_IMAGE_IMAGE_PARAMS
			UpperCamelCase__          =    IMAGE_TO_IMAGE_IMAGE_PARAMS







			def 			SCREAMING_SNAKE_CASE    (				self		):
				torch.manual_seed(0		)
				__magic_name__		:		List[str]	   =     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")						,   attention_head_dim=(2, 4)						,   use_linear_projection=lowercase__						,   addition_embed_type="text_time"						,   addition_time_embed_dim=8						,   transformer_layers_per_block=(1, 2)						,   projection_class_embeddings_input_dim=80						,   cross_attention_dim=64						,   )
				__magic_name__		:		int	   =     EulerDiscreteScheduler(
				    beta_start=0.0_00_85						,   beta_end=0.0_12						,   steps_offset=1						,   beta_schedule="scaled_linear"						,   timestep_spacing="leading"						,   )
				torch.manual_seed(0		)
				__magic_name__		:		Any	   =     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						,   sample_size=128						,   )
				torch.manual_seed(0		)
				__magic_name__		:		Dict	   =     CLIPTextConfig(
				    bos_token_id=0						,   eos_token_id=2						,   hidden_size=32						,   intermediate_size=37						,   layer_norm_eps=1e-05						,   num_attention_heads=4						,   num_hidden_layers=5						,   pad_token_id=1						,   vocab_size=1_000						,   hidden_act="gelu"						,   projection_dim=32						,   )
				__magic_name__		:		List[Any]	   =     CLIPTextModel(lowercase__		)
				__magic_name__		:		Any	   =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						,   local_files_only=lowercase__		)

				__magic_name__		:		Optional[int]	   =     CLIPTextModelWithProjection(lowercase__		)
				__magic_name__		:		Optional[int]	   =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						,   local_files_only=lowercase__		)

				__magic_name__		:		Any	   =     {
				    "unet": unet,
				    "scheduler": scheduler,
				    "vae": vae,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "text_encoder_2": text_encoder_a,
				    "tokenizer_2": tokenizer_a,
				    # "safety_checker": None,
				    # "feature_extractor": None,
				}
				return components







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a=0		):
				__magic_name__		:		List[str]	   =     floats_tensor((1, 3, 32, 32)						,   rng=random.Random(lowercase__		)		).to(lowercase__		)
				__magic_name__		:		Union[str, Any]	   =     image / 2 + 0.5
				if str(lowercase__		).startswith("mps"		):
					__magic_name__		:		List[Any]	   =     torch.manual_seed(lowercase__		)
				else:
					__magic_name__		:		int	   =     torch.Generator(device=lowercase__		).manual_seed(lowercase__		)
				__magic_name__		:		List[Any]	   =     {
				    "prompt": "A painting of a squirrel eating a burger",
				    "image": image,
				    "generator": generator,
				    "num_inference_steps": 2,
				    "guidance_scale": 5.0,
				    "output_type": "numpy",
				    "strength": 0.75,
				}
				return inputs







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Any	   =     "cpu"  # ensure determinism for the device-dependent torch.Generator
				__magic_name__		:		int	   =     self.get_dummy_components()
				__magic_name__		:		int	   =     StableDiffusionXLImgaImgPipeline(**lowercase__		)
				__magic_name__		:		List[Any]	   =     sd_pipe.to(lowercase__		)
				sd_pipe.set_progress_bar_config(disable=lowercase__		)

				__magic_name__		:		int	   =     self.get_dummy_inputs(lowercase__		)
				__magic_name__		:		int	   =     sd_pipe(**lowercase__		).images
				__magic_name__		:		str	   =     image[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)

				__magic_name__		:		List[Any]	   =     np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2







			def 			SCREAMING_SNAKE_CASE    (				self		):
				super().test_attention_slicing_forward_pass(expected_max_diff=3e-3		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				super().test_inference_batch_single_identical(expected_max_diff=3e-3		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				pass







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[Any]	   =     self.get_dummy_components()
				__magic_name__		:		Any	   =     StableDiffusionXLImgaImgPipeline(**lowercase__		)
				__magic_name__		:		Any	   =     sd_pipe.to(lowercase__		)
				__magic_name__		:		Dict	   =     sd_pipe.to(lowercase__		)
				sd_pipe.set_progress_bar_config(disable=lowercase__		)

				# forward without prompt embeds
				__magic_name__		:		Union[str, Any]	   =     self.get_dummy_inputs(lowercase__		)
				__magic_name__		:		Union[str, Any]	   =     3 * ["this is a negative prompt"]
				__magic_name__		:		Any	   =     negative_prompt
				__magic_name__		:		Any	   =     3 * [inputs["prompt"]]

				__magic_name__		:		Any	   =     sd_pipe(**lowercase__		)
				__magic_name__		:		Dict	   =     output.images[0, -3:, -3:, -1]

				# forward with prompt embeds
				__magic_name__		:		Dict	   =     self.get_dummy_inputs(lowercase__		)
				__magic_name__		:		str	   =     3 * ["this is a negative prompt"]
				__magic_name__		:		Optional[int]	   =     3 * [inputs.pop("prompt"		)]

				(
				    (
				    __magic_name__	
				)   ,    (
				    __magic_name__	
				)   ,    (
				    __magic_name__	
				)   ,    (
				    __magic_name__	
				)   ,    	
				)		:		Tuple	   =     sd_pipe.encode_prompt(lowercase__						,   negative_prompt=lowercase__		)

				__magic_name__		:		Tuple	   =     sd_pipe(
				    **lowercase__						,   prompt_embeds=lowercase__						,   negative_prompt_embeds=lowercase__						,   pooled_prompt_embeds=lowercase__						,   negative_pooled_prompt_embeds=lowercase__						,   )
				__magic_name__		:		Dict	   =     output.images[0, -3:, -3:, -1]

				# make sure that it's equal
				assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()		).max() < 1e-4







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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a="cpu"						,   _a=torch.floataa						,   _a=0		):
				__magic_name__		:		Tuple	   =     torch.Generator(device=lowercase__		).manual_seed(lowercase__		)
				__magic_name__		:		int	   =     np.random.RandomState(lowercase__		).standard_normal((1, 4, 64, 64)		)
				__magic_name__		:		Union[str, Any]	   =     torch.from_numpy(lowercase__		).to(device=lowercase__						,   dtype=lowercase__		)
				__magic_name__		:		Union[str, Any]	   =     {
				    "prompt": "a photograph of an astronaut riding a horse",
				    "latents": latents,
				    "generator": generator,
				    "num_inference_steps": 3,
				    "guidance_scale": 7.5,
				    "output_type": "numpy",
				}
				return inputs







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[Any]	   =     DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"		)
				pipe.to(lowercase__		)
				pipe.set_progress_bar_config(disable=lowercase__		)

				__magic_name__		:		Union[str, Any]	   =     self.get_inputs(lowercase__		)
				__magic_name__		:		Dict	   =     pipe(**lowercase__		).images
				__magic_name__		:		Tuple	   =     image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 512, 512, 3)
				__magic_name__		:		Optional[int]	   =     np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]		)
				assert np.abs(image_slice - expected_slice		).max() < 7e-3



'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase__											=	logging.get_logger(__name__)

lowerCAmelCase__											=	{
    '''facebook/convnextv2-tiny-1k-224''': '''https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json''',
}






class 			lowercase_	(lowerCamelCase__				,       lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  List[str]							=			'convnextv2'






  def __init__(      self   :     Tuple					,lowercase__   :     Dict=3					,lowercase__   :     int=4					,lowercase__   :     Optional[int]=4					,lowercase__   :     Optional[Any]=None					,lowercase__   :     List[str]=None					,lowercase__   :     Tuple="gelu"					,lowercase__   :     Dict=0.0_2					,lowercase__   :     List[Any]=1e-1_2					,lowercase__   :     Optional[int]=0.0					,lowercase__   :     int=2_2_4					,lowercase__   :     int=None					,lowercase__   :     Tuple=None					,**lowercase__   :     List[str]					,):
   super().__init__(**lowercase__			)

   __lowercase							  = num_channels
   __lowercase							  = patch_size
   __lowercase							  = num_stages
   __lowercase							  = [9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
   __lowercase							  = [3, 3, 9, 3] if depths is None else depths
   __lowercase							  = hidden_act
   __lowercase							  = initializer_range
   __lowercase							  = layer_norm_eps
   __lowercase							  = drop_path_rate
   __lowercase							  = image_size
   __lowercase							  = ['''stem'''] + [F"stage{idx}" for idx in range(1					,len(self.depths			) + 1			)]
   __lowercase  ,	__lowercase							  = get_aligned_output_features_output_indices(
       out_features=lowercase__					,out_indices=lowercase__					,stage_names=self.stage_names			)

