

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class _UpperCamelCase							(							__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =KandinskyVaaInpaintPipeline
			lowerCamelCase__           =['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
			lowerCamelCase__           =[
			    'image_embeds',
			    'negative_image_embeds',
			    'image',
			    'mask_image',
			]
			lowerCamelCase__           =[
			    'generator',
			    'height',
			    'width',
			    'latents',
			    'guidance_scale',
			    'num_inference_steps',
			    'return_dict',
			    'guidance_scale',
			    'num_images_per_prompt',
			    'output_type',
			    'return_dict',
			]
			lowerCamelCase__           =False
			@property
			def 							__UpperCamelCase (			self							: int	)							->       int:

				"""simple docstring"""

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

				"""simple docstring"""

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

				"""simple docstring"""

				return self.time_input_dim
			@property
			def 							__UpperCamelCase (			self							: Any	)							->       Any:

				"""simple docstring"""

				return self.time_input_dim * 4
			@property
			def 							__UpperCamelCase (			self							: str	)							->       Optional[int]:

				"""simple docstring"""

				return 100
			@property
			def 							__UpperCamelCase (			self							: int	)							->       List[Any]:

				"""simple docstring"""

				torch.manual_seed(0	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       {
				    "in_channels": 9,
				    # Out channels is double in channels because predicts mean and variance
				    "out_channels": 8,
				    "addition_embed_type": "image",
				    "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,
				    "encoder_hid_dim": self.text_embedder_hidden_size,
				    "encoder_hid_dim_type": "image_proj",
				    "cross_attention_dim": self.cross_attention_dim,
				    "attention_head_dim": 4,
				    "resnet_time_scale_shift": "scale_shift",
				    "class_embed_type": None,
				}

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       UNetaDConditionModel(**a	)
				return model
			@property
			def 							__UpperCamelCase (			self							: str	)							->       Optional[int]:

				"""simple docstring"""

				return {
				    "block_out_channels": [32, 64],
				    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
				    "in_channels": 3,
				    "latent_channels": 4,
				    "layers_per_block": 1,
				    "norm_num_groups": 8,
				    "norm_type": "spatial",
				    "num_vq_embeddings": 12,
				    "out_channels": 3,
				    "up_block_types": [
				        "AttnUpDecoderBlock2D",
				        "UpDecoderBlock2D",
				    ],
				    "vq_embed_dim": 4,
				}
			@property
			def 							__UpperCamelCase (			self							: str	)							->       List[Any]:

				"""simple docstring"""

				torch.manual_seed(0	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       VQModel(**self.dummy_movq_kwargs	)
				return model
			def 							__UpperCamelCase (			self							: Tuple	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       self.dummy_unet
				SCREAMING_SNAKE_CASE   :      int				     =       self.dummy_movq

				SCREAMING_SNAKE_CASE   :      List[str]				     =       DDIMScheduler(
				    num_train_timesteps=1000      ,      beta_schedule="linear"      ,      beta_start=0.0_0085      ,      beta_end=0.012      ,      clip_sample=a      ,      set_alpha_to_one=a      ,      steps_offset=1      ,      prediction_type="epsilon"      ,      thresholding=a      ,      )

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       {
				    "unet": unet,
				    "scheduler": scheduler,
				    "movq": movq,
				}

				return components
			def 							__UpperCamelCase (			self							: str      ,      a							: List[str]      ,      a							: int=0	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       floats_tensor((1, self.text_embedder_hidden_size)      ,      rng=random.Random(a	)	).to(a	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       floats_tensor((1, self.text_embedder_hidden_size)      ,      rng=random.Random(seed + 1	)	).to(
				    a	)
				# create init_image
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       floats_tensor((1, 3, 64, 64)      ,      rng=random.Random(a	)	).to(a	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       image.cpu().permute(0      ,      2      ,      3      ,      1	)[0]
				SCREAMING_SNAKE_CASE   :      Dict				     =       Image.fromarray(np.uinta(a	)	).convert("RGB"	).resize((256, 256)	)
				# create mask
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.ones((64, 64)      ,      dtype=np.floataa	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       0

				if str(a	).startswith("mps"	):
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       torch.manual_seed(a	)
				else:
					SCREAMING_SNAKE_CASE   :      str				     =       torch.Generator(device=a	).manual_seed(a	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       {
				    "image": init_image,
				    "mask_image": mask,
				    "image_embeds": image_embeds,
				    "negative_image_embeds": negative_image_embeds,
				    "generator": generator,
				    "height": 64,
				    "width": 64,
				    "num_inference_steps": 2,
				    "guidance_scale": 4.0,
				    "output_type": "np",
				}
				return inputs
			def 							__UpperCamelCase (			self							: List[str]	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       "cpu"

				SCREAMING_SNAKE_CASE   :      int				     =       self.get_dummy_components()

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.pipeline_class(**a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       pipe.to(a	)

				pipe.set_progress_bar_config(disable=a	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       pipe(**self.get_dummy_inputs(a	)	)
				SCREAMING_SNAKE_CASE   :      Any				     =       output.images

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       pipe(
				    **self.get_dummy_inputs(a	)      ,      return_dict=a      ,      )[0]

				SCREAMING_SNAKE_CASE   :      Any				     =       image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       image_from_tuple[0, -3:, -3:, -1]

				print(F"image.shape {image.shape}"	)

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

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       np.array(
				    [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]	)

				assert (
				    np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
				), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
				assert (
				    np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2
				), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






			def 							__UpperCamelCase (			self							: List[Any]	)							->       Any:

				"""simple docstring"""

				super().test_inference_batch_single_identical(expected_max_diff=3e-3	)






@slow
@require_torch_gpu
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			def 							__UpperCamelCase (			self							: Dict	)							->       Union[str, Any]:

				"""simple docstring"""

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






			def 							__UpperCamelCase (			self							: List[Any]	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       np.ones((768, 768)      ,      dtype=np.floataa	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       0

				SCREAMING_SNAKE_CASE   :      Any				     =       "a hat"

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       KandinskyVaaPriorPipeline.from_pretrained(
				    "kandinsky-community/kandinsky-2-2-prior"      ,      torch_dtype=torch.floataa	)
				pipe_prior.to(a	)

				SCREAMING_SNAKE_CASE   :      str				     =       KandinskyVaaInpaintPipeline.from_pretrained(
				    "kandinsky-community/kandinsky-2-2-decoder-inpaint"      ,      torch_dtype=torch.floataa	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       pipeline.to(a	)
				pipeline.set_progress_bar_config(disable=a	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       torch.Generator(device="cpu"	).manual_seed(0	)
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Dict				     =       pipe_prior(
				    a      ,      generator=a      ,      num_inference_steps=5      ,      negative_prompt=""      ,      ).to_tuple()

				SCREAMING_SNAKE_CASE   :      Tuple				     =       pipeline(
				    image=a      ,      mask_image=a      ,      image_embeds=a      ,      negative_image_embeds=a      ,      generator=a      ,      num_inference_steps=100      ,      height=768      ,      width=768      ,      output_type="np"      ,      )

				SCREAMING_SNAKE_CASE   :      str				     =       output.images[0]

				assert image.shape == (768, 768, 3)

				assert_mean_pixel_difference(a      ,      a	)


import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)

os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)

def  lowerCamelCase__ (					_a):
	if hor == 128:
		SCREAMING_SNAKE_CASE   :      str				     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		SCREAMING_SNAKE_CASE   :      List[str]				     =       (32, 128, 256)
		SCREAMING_SNAKE_CASE   :      List[str]				     =       ("UpResnetBlock1D", "UpResnetBlock1D")

	elif hor == 32:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (32, 64, 128, 256)
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
	SCREAMING_SNAKE_CASE   :      Any				     =       torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch")
	SCREAMING_SNAKE_CASE   :      int				     =       model.state_dict()
	SCREAMING_SNAKE_CASE   :      Dict				     =       {
	    "down_block_types": down_block_types,
	    "block_out_channels": block_out_channels,
	    "up_block_types": up_block_types,
	    "layers_per_block": 1,
	    "use_timestep_embedding": True,
	    "out_block_type": "OutConv1DBlock",
	    "norm_num_groups": 8,
	    "downsample_each_block": False,
	    "in_channels": 14,
	    "out_channels": 14,
	    "extra_in_channels": 0,
	    "time_embedding_type": "positional",
	    "flip_sin_to_cos": False,
	    "freq_shift": 1,
	    "sample_size": 65536,
	    "mid_block_type": "MidResTemporalBlock1D",
	    "act_fn": "mish",
	}
	SCREAMING_SNAKE_CASE   :      str				     =       UNetaDModel(**_a)
	print(f"length of state dict: {len(state_dict.keys())}")
	print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")
	SCREAMING_SNAKE_CASE   :      List[str]				     =       dict(zip(model.state_dict().keys()							,			hf_value_function.state_dict().keys()))
	for k, v in mapping.items():
		SCREAMING_SNAKE_CASE   :      int				     =       state_dict.pop(_a)
	hf_value_function.load_state_dict(_a)

	torch.save(hf_value_function.state_dict()							,			f"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin")
	with open(f"hub/hopper-medium-v2/unet/hor{hor}/config.json"							,			"w") as f:
		json.dump(_a							,			_a)

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      int				     =       {
	    "in_channels": 14,
	    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
	    "up_block_types": (),
	    "out_block_type": "ValueFunction",
	    "mid_block_type": "ValueFunctionMidBlock1D",
	    "block_out_channels": (32, 64, 128, 256),
	    "layers_per_block": 1,
	    "downsample_each_block": True,
	    "sample_size": 65536,
	    "out_channels": 14,
	    "extra_in_channels": 0,
	    "time_embedding_type": "positional",
	    "use_timestep_embedding": True,
	    "flip_sin_to_cos": False,
	    "freq_shift": 1,
	    "norm_num_groups": 8,
	    "act_fn": "mish",
	}

	SCREAMING_SNAKE_CASE   :      Dict				     =       torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch")
	SCREAMING_SNAKE_CASE   :      Dict				     =       model
	SCREAMING_SNAKE_CASE   :      List[str]				     =       UNetaDModel(**_a)
	print(f"length of state dict: {len(state_dict.keys())}")
	print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")

	SCREAMING_SNAKE_CASE   :      int				     =       dict(zip(state_dict.keys()							,			hf_value_function.state_dict().keys()))
	for k, v in mapping.items():
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       state_dict.pop(_a)

	hf_value_function.load_state_dict(_a)

	torch.save(hf_value_function.state_dict()							,			"hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin")
	with open("hub/hopper-medium-v2/value_function/config.json"							,			"w") as f:
		json.dump(_a							,			_a)


if __name__ == "__main__":
			unet(32)
			# unet(128)
			value_function()