





'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, 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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    __UpperCamelCase					,    __UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[Any]      =   StableDiffusionInstructPixaPixPipeline
     UpperCAmelCase__       :						List[str]      =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
     UpperCAmelCase__       :						Optional[int]      =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     UpperCAmelCase__       :						Any      =   IMAGE_TO_IMAGE_IMAGE_PARAMS
     UpperCAmelCase__       :						Any      =   IMAGE_TO_IMAGE_IMAGE_PARAMS

     def 	UpperCamelCase(					self	):
       torch.manual_seed(0	)
       _snake_case     		=				UNetaDConditionModel(
           block_out_channels=(32, 64)						,   layers_per_block=2						,   sample_size=32						,   in_channels=8						,   out_channels=4						,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")						,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")						,   cross_attention_dim=32						,   )
       _snake_case     		=				PNDMScheduler(skip_prk_steps=lowerCamelCase	)
       torch.manual_seed(0	)
       _snake_case     		=				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	)
       _snake_case     		=				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						,   )
       _snake_case     		=				CLIPTextModel(lowerCamelCase	)
       _snake_case     		=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

       _snake_case     		=				{
           "unet": unet,
           "scheduler": scheduler,
           "vae": vae,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "safety_checker": None,
           "feature_extractor": None,
       }
       return components

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=0	):
       _snake_case     		=				floats_tensor((1, 3, 32, 32)						,   rng=random.Random(lowerCamelCase	)	).to(lowerCamelCase	)
       _snake_case     		=				image.cpu().permute(0						,   2						,   3						,   1	)[0]
       _snake_case     		=				Image.fromarray(np.uinta(lowerCamelCase	)	).convert("RGB"	)
       if str(lowerCamelCase	).startswith("mps"	):
         _snake_case     		=				torch.manual_seed(lowerCamelCase	)
       else:
         _snake_case     		=				torch.Generator(device=lowerCamelCase	).manual_seed(lowerCamelCase	)
       _snake_case     		=				{
           "prompt": "A painting of a squirrel eating a burger",
           "image": image,
           "generator": generator,
           "num_inference_steps": 2,
           "guidance_scale": 6.0,
           "image_guidance_scale": 1,
           "output_type": "numpy",
       }
       return inputs

     def 	UpperCamelCase(					self	):
       _snake_case     		=				"cpu"  # ensure determinism for the device-dependent torch.Generator
       _snake_case     		=				self.get_dummy_components()
       _snake_case     		=				StableDiffusionInstructPixaPixPipeline(**lowerCamelCase	)
       _snake_case     		=				sd_pipe.to(lowerCamelCase	)
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase	)

       _snake_case     		=				self.get_dummy_inputs(lowerCamelCase	)
       _snake_case     		=				sd_pipe(**lowerCamelCase	).images
       _snake_case     		=				image[0, -3:, -3:, -1]
       assert image.shape == (1, 32, 32, 3)
       _snake_case     		=				np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]	)

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				"cpu"  # ensure determinism for the device-dependent torch.Generator
       _snake_case     		=				self.get_dummy_components()
       _snake_case     		=				StableDiffusionInstructPixaPixPipeline(**lowerCamelCase	)
       _snake_case     		=				sd_pipe.to(lowerCamelCase	)
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase	)

       _snake_case     		=				self.get_dummy_inputs(lowerCamelCase	)
       _snake_case     		=				"french fries"
       _snake_case     		=				sd_pipe(**lowerCamelCase						,   negative_prompt=lowerCamelCase	)
       _snake_case     		=				output.images
       _snake_case     		=				image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       _snake_case     		=				np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]	)

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				"cpu"  # ensure determinism for the device-dependent torch.Generator
       _snake_case     		=				self.get_dummy_components()
       _snake_case     		=				StableDiffusionInstructPixaPixPipeline(**lowerCamelCase	)
       _snake_case     		=				sd_pipe.to(lowerCamelCase	)
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase	)

       _snake_case     		=				self.get_dummy_inputs(lowerCamelCase	)
       _snake_case     		=				[inputs["prompt"]] * 2

       _snake_case     		=				np.array(inputs["image"]	).astype(np.floataa	) / 255.0
       _snake_case     		=				torch.from_numpy(lowerCamelCase	).unsqueeze(0	).to(lowerCamelCase	)
       _snake_case     		=				image / 2 + 0.5
       _snake_case     		=				image.permute(0						,   3						,   1						,   2	)
       _snake_case     		=				image.repeat(2						,   1						,   1						,   1	)

       _snake_case     		=				sd_pipe(**lowerCamelCase	).images
       _snake_case     		=				image[-1, -3:, -3:, -1]

       assert image.shape == (2, 32, 32, 3)
       _snake_case     		=				np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]	)

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				"cpu"  # ensure determinism for the device-dependent torch.Generator
       _snake_case     		=				self.get_dummy_components()
       _snake_case     		=				EulerAncestralDiscreteScheduler(
           beta_start=0.00085						,   beta_end=0.012						,   beta_schedule="scaled_linear"	)
       _snake_case     		=				StableDiffusionInstructPixaPixPipeline(**lowerCamelCase	)
       _snake_case     		=				sd_pipe.to(lowerCamelCase	)
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase	)

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

       _snake_case     		=				[round(lowerCamelCase						,   4	) for x in image_slice.flatten().tolist()]
       print(",".join([str(lowerCamelCase	) for x in slice]	)	)

       assert image.shape == (1, 32, 32, 3)
       _snake_case     		=				np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]	)

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

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






     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_dummy_components()
       _snake_case     		=				StableDiffusionInstructPixaPixPipeline(**lowerCamelCase	)
       _snake_case     		=				VaeImageProcessor(do_resize=lowerCamelCase						,   do_normalize=lowerCamelCase	)
       _snake_case     		=				pipe.to(lowerCamelCase	)
       pipe.set_progress_bar_config(disable=lowerCamelCase	)

       _snake_case     		=				pipe(**self.get_dummy_inputs_by_type(lowerCamelCase						,   input_image_type="pt"	)	)[0]

       _snake_case     		=				components["vae"]
       _snake_case     		=				self.get_dummy_inputs_by_type(lowerCamelCase						,   input_image_type="pt"	)

       for image_param in self.image_latents_params:
         if image_param in inputs.keys():
           _snake_case     		=				vae.encode(inputs[image_param]	).latent_dist.mode()

       _snake_case     		=				pipe(**lowerCamelCase	)[0]

       _snake_case     		=				np.abs(out - out_latents_inputs	).max()
       self.assertLess(lowerCamelCase						,   1e-4						,   "passing latents as image input generate different result from passing image"	)







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



     '''simple docstring'''






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

     def 	UpperCamelCase(					self						,   lowerCamelCase=0	):
       _snake_case     		=				torch.manual_seed(lowerCamelCase	)
       _snake_case     		=				load_image(
           "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"	)
       _snake_case     		=				{
           "prompt": "turn him into a cyborg",
           "image": image,
           "generator": generator,
           "num_inference_steps": 3,
           "guidance_scale": 7.5,
           "image_guidance_scale": 1.0,
           "output_type": "numpy",
       }
       return inputs

     def 	UpperCamelCase(					self	):
       _snake_case     		=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
           "timbrooks/instruct-pix2pix"						,   safety_checker=lowerCamelCase	)
       pipe.to(lowerCamelCase	)
       pipe.set_progress_bar_config(disable=lowerCamelCase	)
       pipe.enable_attention_slicing()

       _snake_case     		=				self.get_inputs()
       _snake_case     		=				pipe(**lowerCamelCase	).images
       _snake_case     		=				image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       _snake_case     		=				np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]	)

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
           "timbrooks/instruct-pix2pix"						,   safety_checker=lowerCamelCase	)
       _snake_case     		=				LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
       pipe.to(lowerCamelCase	)
       pipe.set_progress_bar_config(disable=lowerCamelCase	)
       pipe.enable_attention_slicing()

       _snake_case     		=				self.get_inputs()
       _snake_case     		=				pipe(**lowerCamelCase	).images
       _snake_case     		=				image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       _snake_case     		=				np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]	)

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
           "timbrooks/instruct-pix2pix"						,   safety_checker=lowerCamelCase	)
       _snake_case     		=				DDIMScheduler.from_config(pipe.scheduler.config	)
       pipe.to(lowerCamelCase	)
       pipe.set_progress_bar_config(disable=lowerCamelCase	)
       pipe.enable_attention_slicing()

       _snake_case     		=				self.get_inputs()
       _snake_case     		=				pipe(**lowerCamelCase	).images
       _snake_case     		=				image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       _snake_case     		=				np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]	)

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				0

       def callback_fn(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	) -> None:
         _snake_case     		=				True
         nonlocal number_of_steps
         number_of_steps += 1
         if step == 1:
           _snake_case     		=				latents.detach().cpu().numpy()
           assert latents.shape == (1, 4, 64, 64)
           _snake_case     		=				latents[0, -3:, -3:, -1]
           _snake_case     		=				np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]	)

           assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2
         elif step == 2:
           _snake_case     		=				latents.detach().cpu().numpy()
           assert latents.shape == (1, 4, 64, 64)
           _snake_case     		=				latents[0, -3:, -3:, -1]
           _snake_case     		=				np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]	)

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

       _snake_case     		=				False

       _snake_case     		=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
           "timbrooks/instruct-pix2pix"						,   safety_checker=lowerCamelCase						,   torch_dtype=torch.floataa	)
       _snake_case     		=				pipe.to(lowerCamelCase	)
       pipe.set_progress_bar_config(disable=lowerCamelCase	)
       pipe.enable_attention_slicing()

       _snake_case     		=				self.get_inputs()
       pipe(**lowerCamelCase						,   callback=lowerCamelCase						,   callback_steps=1	)
       assert callback_fn.has_been_called
       assert number_of_steps == 3

     def 	UpperCamelCase(					self	):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       _snake_case     		=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
           "timbrooks/instruct-pix2pix"						,   safety_checker=lowerCamelCase						,   torch_dtype=torch.floataa	)
       _snake_case     		=				pipe.to(lowerCamelCase	)
       pipe.set_progress_bar_config(disable=lowerCamelCase	)
       pipe.enable_attention_slicing(1	)
       pipe.enable_sequential_cpu_offload()

       _snake_case     		=				self.get_inputs()
       _snake_case     		=				pipe(**lowerCamelCase	)

       _snake_case     		=				torch.cuda.max_memory_allocated()
       # make sure that less than 2.2 GB is allocated
       assert mem_bytes < 2.2 * 10**9






     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_inputs()
       # resize to resolution that is divisible by 8 but not 16 or 32
       _snake_case     		=				inputs["image"].resize((504, 504)	)

       _snake_case     		=				"timbrooks/instruct-pix2pix"
       _snake_case     		=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
           lowerCamelCase						,   safety_checker=lowerCamelCase						,   )
       pipe.to(lowerCamelCase	)
       pipe.set_progress_bar_config(disable=lowerCamelCase	)
       pipe.enable_attention_slicing()

       _snake_case     		=				pipe(**lowerCamelCase	)
       _snake_case     		=				output.images[0]

       _snake_case     		=				image[255:258, 383:386, -1]

       assert image.shape == (504, 504, 3)
       _snake_case     		=				np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]	)

       assert np.abs(image_slice.flatten() - expected_slice	).max() < 5e-3









'''simple docstring'''



from typing import TYPE_CHECKING

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


__magic_name__   :						Any    =			{
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __magic_name__   :						List[Any]    =			[
     """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """GPTNeoXJapaneseForCausalLM""",
     """GPTNeoXJapaneseLayer""",
     """GPTNeoXJapaneseModel""",
     """GPTNeoXJapanesePreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
 from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox_japanese import (
      GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXJapaneseForCausalLM,
      GPTNeoXJapaneseLayer,
      GPTNeoXJapaneseModel,
      GPTNeoXJapanesePreTrainedModel,
  )


else:
 import sys

 __magic_name__   :						Union[str, Any]    =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


