"""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  UpperCamelCase					(				lowerCAmelCase__    ,		lowerCAmelCase__    ,		lowerCAmelCase__    ,		unittest.TestCase   ):
       SCREAMING_SNAKE_CASE_			=      StableDiffusionInstructPixaPixPipeline
       SCREAMING_SNAKE_CASE_			=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
       SCREAMING_SNAKE_CASE_			=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       SCREAMING_SNAKE_CASE_			=      IMAGE_TO_IMAGE_IMAGE_PARAMS
       SCREAMING_SNAKE_CASE_			=      IMAGE_TO_IMAGE_IMAGE_PARAMS







       def 		a_						( self)      ->      int:
            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=1000,							)
            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 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__=0)      ->      Tuple:
            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 		a_						( self)      ->      Dict:
            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 		a_						( self)      ->      Dict:
            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 		a_						( self)      ->      Union[str, Any]:
            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 		a_						( self)      ->      Any:
            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 		a_						( self)      ->      int:
            super().test_inference_batch_single_identical(expected_max_diff=3e-3)







       def 		a_						( self)      ->      int:
            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  UpperCamelCase					(				unittest.TestCase   ):







       def 		a_						( self)      ->      int:
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()







       def 		a_						( self,							lowerCAmelCase__=0)      ->      List[str]:
            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 		a_						( self)      ->      List[str]:
            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 		a_						( self)      ->      Any:
            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 		a_						( self)      ->      Dict:
            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 		a_						( self)      ->      Union[str, Any]:
            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 		a_						( self)      ->      Any:
            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 		a_						( self)      ->      str:
            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



from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCamelCase__          =		logging.get_logger(__name__)

lowerCamelCase__          =		{
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}







class      A__							(     __magic_name__	):
				lowercase			    =  'gptj'
				lowercase			    =  {
				    'max_position_embeddings': 'n_positions',
				    'hidden_size': 'n_embd',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}






				def __init__(						self					:      Optional[Any]    ,     a					:      Dict=50_400    ,     a					:      Union[str, Any]=2_048    ,     a					:      List[str]=4_096    ,     a					:      Any=28    ,     a					:      Optional[Any]=16    ,     a					:      Optional[Any]=64    ,     a					:      int=None    ,     a					:      Any="gelu_new"    ,     a					:      Union[str, Any]=0.0    ,     a					:      List[Any]=0.0    ,     a					:      List[Any]=0.0    ,     a					:      Optional[Any]=1E-5    ,     a					:      Optional[int]=0.0_2    ,     a					:      int=True    ,     a					:      str=50_256    ,     a					:      str=50_256    ,     a					:      Any=False    ,     **a					:      Dict    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		vocab_size
							lowerCAmelCase__      :      List[Any]         =		n_positions
							lowerCAmelCase__      :      str         =		n_embd
							lowerCAmelCase__      :      str         =		n_layer
							lowerCAmelCase__      :      str         =		n_head
							lowerCAmelCase__      :      Dict         =		n_inner
							lowerCAmelCase__      :      Union[str, Any]         =		rotary_dim
							lowerCAmelCase__      :      Optional[int]         =		activation_function
							lowerCAmelCase__      :      Any         =		resid_pdrop
							lowerCAmelCase__      :      int         =		embd_pdrop
							lowerCAmelCase__      :      int         =		attn_pdrop
							lowerCAmelCase__      :      List[Any]         =		layer_norm_epsilon
							lowerCAmelCase__      :      str         =		initializer_range
							lowerCAmelCase__      :      Dict         =		use_cache

							lowerCAmelCase__      :      str         =		bos_token_id
							lowerCAmelCase__      :      int         =		eos_token_id

							super().__init__(
							    bos_token_id=a    ,     eos_token_id=a    ,     tie_word_embeddings=a    ,     **a   )







class      A__							(     __magic_name__	):






				def __init__(						self					:      str    ,     a					:      PretrainedConfig    ,     a					:      str = "default"    ,     a					:      List[PatchingSpec] = None    ,     a					:      bool = False    ,     ):






							'''simple docstring'''







							super().__init__(a    ,     task=a    ,     patching_specs=a    ,     use_past=a   )
							if not getattr(self._config    ,     'pad_token_id'    ,     a   ):
										# TODO: how to do that better?
										lowerCAmelCase__      :      int         =		0






				@property
				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}   )
							if self.use_past:
										self.fill_with_past_key_values_(a    ,     direction='inputs'   )
										lowerCAmelCase__      :      Optional[Any]         =		{0: 'batch', 1: 'past_sequence + sequence'}
							else:
										lowerCAmelCase__      :      Tuple         =		{0: 'batch', 1: 'sequence'}

							return common_inputs






				@property
				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							return self._config.n_layer






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






							'''simple docstring'''







							return self._config.n_head






				def 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      PreTrainedTokenizer    ,     a					:      int = -1    ,     a					:      int = -1    ,     a					:      bool = False    ,     a					:      Optional[TensorType] = None    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		super(a    ,     self   ).generate_dummy_inputs(
							    a    ,     batch_size=a    ,     seq_length=a    ,     is_pair=a    ,     framework=a   )

							# We need to order the input in the way they appears in the forward()
							lowerCAmelCase__      :      Optional[int]         =		OrderedDict({'input_ids': common_inputs['input_ids']}   )

							# Need to add the past_keys
							if self.use_past:
										if not is_torch_available():
													raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
										else:
													import torch

													lowerCAmelCase__      ,				lowerCAmelCase__      :      int         =		common_inputs['input_ids'].shape
													# Not using the same length for past_key_values
													lowerCAmelCase__      :      Optional[int]         =		seqlen + 2
													lowerCAmelCase__      :      Tuple         =		(
													    batch,
													    self.num_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // self.num_attention_heads,
													)
													lowerCAmelCase__      :      Tuple         =		[
													    (torch.zeros(a   ), torch.zeros(a   )) for _ in range(self.num_layers   )
													]

							lowerCAmelCase__      :      Any         =		common_inputs['attention_mask']
							if self.use_past:
										lowerCAmelCase__      :      List[str]         =		ordered_inputs['attention_mask'].dtype
										lowerCAmelCase__      :      Optional[Any]         =		torch.cat(
										    [ordered_inputs['attention_mask'], torch.ones(a    ,     a    ,     dtype=a   )]    ,     dim=1   )

							return ordered_inputs






				@property
				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							return 13