




import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 			a__    (					snake_case__ ,				snake_case__ ,				unittest.TestCase      ):
						_a							:    List[str]					=    StableDiffusionDiffEditPipeline
						_a							:    Optional[Any]					=    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
						_a							:    List[str]					=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
						_a							:    List[Any]					=    frozenset(
						    []      )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
						_a							:    List[Any]					=    frozenset([]      )






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											torch.manual_seed(0			)
											__lowerCAmelCase				   =       UNetaDConditionModel(
											    block_out_channels=(3_2, 6_4)		,			layers_per_block=2		,			sample_size=3_2		,			in_channels=4		,			out_channels=4		,			down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,			up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,			cross_attention_dim=3_2		,			attention_head_dim=(2, 4)		,			use_linear_projection=_A		,			)
											__lowerCAmelCase				   =       DDIMScheduler(
											    beta_start=0.0_00_85		,			beta_end=0.0_12		,			beta_schedule="scaled_linear"		,			clip_sample=_A		,			set_alpha_to_one=_A		,			)
											__lowerCAmelCase				   =       DDIMInverseScheduler(
											    beta_start=0.0_00_85		,			beta_end=0.0_12		,			beta_schedule="scaled_linear"		,			clip_sample=_A		,			set_alpha_to_zero=_A		,			)
											torch.manual_seed(0			)
											__lowerCAmelCase				   =       AutoencoderKL(
											    block_out_channels=[3_2, 6_4]		,			in_channels=3		,			out_channels=3		,			down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,			up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,			latent_channels=4		,			sample_size=1_2_8		,			)
											torch.manual_seed(0			)
											__lowerCAmelCase				   =       CLIPTextConfig(
											    bos_token_id=0		,			eos_token_id=2		,			hidden_size=3_2		,			intermediate_size=3_7		,			layer_norm_eps=1E-0_5		,			num_attention_heads=4		,			num_hidden_layers=5		,			pad_token_id=1		,			vocab_size=1_0_0_0		,			hidden_act="gelu"		,			projection_dim=5_1_2		,			)
											__lowerCAmelCase				   =       CLIPTextModel(_A			)
											__lowerCAmelCase				   =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

											__lowerCAmelCase				   =       {
											    "unet": unet,
											    "scheduler": scheduler,
											    "inverse_scheduler": inverse_scheduler,
											    "vae": vae,
											    "text_encoder": text_encoder,
											    "tokenizer": tokenizer,
											    "safety_checker": None,
											    "feature_extractor": None,
											}

											return components






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A=0			):




											"""simple docstring"""



											__lowerCAmelCase				   =       floats_tensor((1, 1_6, 1_6)		,			rng=random.Random(_A			)			).to(_A			)
											__lowerCAmelCase				   =       floats_tensor((1, 2, 4, 1_6, 1_6)		,			rng=random.Random(_A			)			).to(_A			)
											if str(_A			).startswith("mps"			):
																__lowerCAmelCase				   =       torch.manual_seed(_A			)
											else:
																__lowerCAmelCase				   =       torch.Generator(device=_A			).manual_seed(_A			)
											__lowerCAmelCase				   =       {
											    "prompt": "a dog and a newt",
											    "mask_image": mask,
											    "image_latents": latents,
											    "generator": generator,
											    "num_inference_steps": 2,
											    "inpaint_strength": 1.0,
											    "guidance_scale": 6.0,
											    "output_type": "numpy",
											}

											return inputs






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A=0			):




											"""simple docstring"""



											__lowerCAmelCase				   =       floats_tensor((1, 3, 3_2, 3_2)		,			rng=random.Random(_A			)			).to(_A			)
											__lowerCAmelCase				   =       image.cpu().permute(0		,			2		,			3		,			1			)[0]
											__lowerCAmelCase				   =       Image.fromarray(np.uinta(_A			)			).convert("RGB"			)
											if str(_A			).startswith("mps"			):
																__lowerCAmelCase				   =       torch.manual_seed(_A			)
											else:
																__lowerCAmelCase				   =       torch.Generator(device=_A			).manual_seed(_A			)
											__lowerCAmelCase				   =       {
											    "image": image,
											    "source_prompt": "a cat and a frog",
											    "target_prompt": "a dog and a newt",
											    "generator": generator,
											    "num_inference_steps": 2,
											    "num_maps_per_mask": 2,
											    "mask_encode_strength": 1.0,
											    "guidance_scale": 6.0,
											    "output_type": "numpy",
											}

											return inputs






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A=0			):




											"""simple docstring"""



											__lowerCAmelCase				   =       floats_tensor((1, 3, 3_2, 3_2)		,			rng=random.Random(_A			)			).to(_A			)
											__lowerCAmelCase				   =       image.cpu().permute(0		,			2		,			3		,			1			)[0]
											__lowerCAmelCase				   =       Image.fromarray(np.uinta(_A			)			).convert("RGB"			)
											if str(_A			).startswith("mps"			):
																__lowerCAmelCase				   =       torch.manual_seed(_A			)
											else:
																__lowerCAmelCase				   =       torch.Generator(device=_A			).manual_seed(_A			)
											__lowerCAmelCase				   =       {
											    "image": image,
											    "prompt": "a cat and a frog",
											    "generator": generator,
											    "num_inference_steps": 2,
											    "inpaint_strength": 1.0,
											    "guidance_scale": 6.0,
											    "decode_latents": True,
											    "output_type": "numpy",
											}
											return inputs






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											if not hasattr(self.pipeline_class		,			"_optional_components"			):
																return

											__lowerCAmelCase				   =       self.get_dummy_components()
											__lowerCAmelCase				   =       self.pipeline_class(**_A			)
											pipe.to(_A			)
											pipe.set_progress_bar_config(disable=_A			)

											# set all optional components to None and update pipeline config accordingly
											for optional_component in pipe._optional_components:
																setattr(_A		,			_A		,			_A			)
											pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}			)

											__lowerCAmelCase				   =       self.get_dummy_inputs(_A			)
											__lowerCAmelCase				   =       pipe(**_A			)[0]

											with tempfile.TemporaryDirectory() as tmpdir:
																pipe.save_pretrained(_A			)
																__lowerCAmelCase				   =       self.pipeline_class.from_pretrained(_A			)
																pipe_loaded.to(_A			)
																pipe_loaded.set_progress_bar_config(disable=_A			)

											for optional_component in pipe._optional_components:
																self.assertTrue(
																    getattr(_A		,			_A			) is None		,			f"""`{optional_component}` did not stay set to None after loading."""		,			)

											__lowerCAmelCase				   =       self.get_dummy_inputs(_A			)
											__lowerCAmelCase				   =       pipe_loaded(**_A			)[0]

											__lowerCAmelCase				   =       np.abs(output - output_loaded			).max()
											self.assertLess(_A		,			1E-4			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "cpu"

											__lowerCAmelCase				   =       self.get_dummy_components()
											__lowerCAmelCase				   =       self.pipeline_class(**_A			)
											pipe.to(_A			)
											pipe.set_progress_bar_config(disable=_A			)

											__lowerCAmelCase				   =       self.get_dummy_mask_inputs(_A			)
											__lowerCAmelCase				   =       pipe.generate_mask(**_A			)
											__lowerCAmelCase				   =       mask[0, -3:, -3:]

											self.assertEqual(mask.shape		,			(1, 1_6, 1_6)			)
											__lowerCAmelCase				   =       np.array([0] * 9			)
											__lowerCAmelCase				   =       np.abs(mask_slice.flatten() - expected_slice			).max()
											self.assertLessEqual(_A		,			1E-3			)
											self.assertEqual(mask[0, -3, -4]		,			0			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "cpu"

											__lowerCAmelCase				   =       self.get_dummy_components()
											__lowerCAmelCase				   =       self.pipeline_class(**_A			)
											pipe.to(_A			)
											pipe.set_progress_bar_config(disable=_A			)

											__lowerCAmelCase				   =       self.get_dummy_inversion_inputs(_A			)
											__lowerCAmelCase				   =       pipe.invert(**_A			).images
											__lowerCAmelCase				   =       image[0, -1, -3:, -3:]

											self.assertEqual(image.shape		,			(2, 3_2, 3_2, 3)			)
											__lowerCAmelCase				   =       np.array(
											    [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]		,			)
											__lowerCAmelCase				   =       np.abs(image_slice.flatten() - expected_slice			).max()
											self.assertLessEqual(_A		,			1E-3			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											super().test_inference_batch_single_identical(expected_max_diff=5E-3			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "cpu"

											__lowerCAmelCase				   =       self.get_dummy_components()

											__lowerCAmelCase				   =       {"beta_start": 0.0_00_85, "beta_end": 0.0_12, "beta_schedule": "scaled_linear"}
											__lowerCAmelCase				   =       DPMSolverMultistepScheduler(**_A			)
											__lowerCAmelCase				   =       DPMSolverMultistepInverseScheduler(**_A			)

											__lowerCAmelCase				   =       self.pipeline_class(**_A			)
											pipe.to(_A			)
											pipe.set_progress_bar_config(disable=_A			)

											__lowerCAmelCase				   =       self.get_dummy_inversion_inputs(_A			)
											__lowerCAmelCase				   =       pipe.invert(**_A			).images
											__lowerCAmelCase				   =       image[0, -1, -3:, -3:]

											self.assertEqual(image.shape		,			(2, 3_2, 3_2, 3)			)
											__lowerCAmelCase				   =       np.array(
											    [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]		,			)
											__lowerCAmelCase				   =       np.abs(image_slice.flatten() - expected_slice			).max()
											self.assertLessEqual(_A		,			1E-3			)






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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						@classmethod
						def 				__SCREAMING_SNAKE_CASE(   cls			):




											"""simple docstring"""



											__lowerCAmelCase				   =       load_image(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"			)

											__lowerCAmelCase				   =       raw_image.convert("RGB"			).resize((7_6_8, 7_6_8)			)

											__lowerCAmelCase				   =       raw_image






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       torch.manual_seed(0			)

											__lowerCAmelCase				   =       StableDiffusionDiffEditPipeline.from_pretrained(
											    "stabilityai/stable-diffusion-2-1"		,			safety_checker=_A		,			torch_dtype=torch.floataa			)
											__lowerCAmelCase				   =       DDIMScheduler.from_config(pipe.scheduler.config			)
											__lowerCAmelCase				   =       DDIMInverseScheduler.from_config(pipe.scheduler.config			)
											pipe.enable_model_cpu_offload()
											pipe.set_progress_bar_config(disable=_A			)

											__lowerCAmelCase				   =       "a bowl of fruit"
											__lowerCAmelCase				   =       "a bowl of pears"

											__lowerCAmelCase				   =       pipe.generate_mask(
											    image=self.raw_image		,			source_prompt=_A		,			target_prompt=_A		,			generator=_A		,			)

											__lowerCAmelCase				   =       pipe.invert(
											    prompt=_A		,			image=self.raw_image		,			inpaint_strength=0.7		,			generator=_A			).latents

											__lowerCAmelCase				   =       pipe(
											    prompt=_A		,			mask_image=_A		,			image_latents=_A		,			generator=_A		,			negative_prompt=_A		,			inpaint_strength=0.7		,			output_type="numpy"		,			).images[0]

											__lowerCAmelCase				   =       (
											    np.array(
											        load_image(
											            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
											            "/diffedit/pears.png"			).resize((7_6_8, 7_6_8)			)			)
											    / 2_5_5
											)
											assert np.abs((expected_image - image).max()			) < 5E-1






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       torch.manual_seed(0			)

											__lowerCAmelCase				   =       StableDiffusionDiffEditPipeline.from_pretrained(
											    "stabilityai/stable-diffusion-2-1"		,			safety_checker=_A		,			torch_dtype=torch.floataa			)
											__lowerCAmelCase				   =       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
											__lowerCAmelCase				   =       DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config			)
											pipe.enable_model_cpu_offload()
											pipe.set_progress_bar_config(disable=_A			)

											__lowerCAmelCase				   =       "a bowl of fruit"
											__lowerCAmelCase				   =       "a bowl of pears"

											__lowerCAmelCase				   =       pipe.generate_mask(
											    image=self.raw_image		,			source_prompt=_A		,			target_prompt=_A		,			generator=_A		,			)

											__lowerCAmelCase				   =       pipe.invert(
											    prompt=_A		,			image=self.raw_image		,			inpaint_strength=0.7		,			generator=_A		,			num_inference_steps=2_5		,			).latents

											__lowerCAmelCase				   =       pipe(
											    prompt=_A		,			mask_image=_A		,			image_latents=_A		,			generator=_A		,			negative_prompt=_A		,			inpaint_strength=0.7		,			num_inference_steps=2_5		,			output_type="numpy"		,			).images[0]

											__lowerCAmelCase				   =       (
											    np.array(
											        load_image(
											            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
											            "/diffedit/pears.png"			).resize((7_6_8, 7_6_8)			)			)
											    / 2_5_5
											)
											assert np.abs((expected_image - image).max()			) < 5E-1










from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__    			=			logging.get_logger(__name__)

UpperCamelCase__    			=			{
    """edbeeching/decision-transformer-gym-hopper-medium""": (
        """https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class 			a__    (					snake_case__      ):
						_a							:    Optional[int]					=    """decision_transformer"""
						_a							:    Optional[int]					=    ["""past_key_values"""]
						_a							:    Dict					=    {
						    """max_position_embeddings""": """n_positions""",
						    """num_attention_heads""": """n_head""",
						    """num_hidden_layers""": """n_layer""",
						}






						def __init__(   self		,			_A=1_7		,			_A=4		,			_A=1_2_8		,			_A=4_0_9_6		,			_A=True		,			_A=1		,			_A=1_0_2_4		,			_A=3		,			_A=1		,			_A=None		,			_A="relu"		,			_A=0.1		,			_A=0.1		,			_A=0.1		,			_A=1E-5		,			_A=0.02		,			_A=True		,			_A=True		,			_A=5_0_2_5_6		,			_A=5_0_2_5_6		,			_A=False		,			_A=False		,			**_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       state_dim
											__lowerCAmelCase				   =       act_dim
											__lowerCAmelCase				   =       hidden_size
											__lowerCAmelCase				   =       max_ep_len
											__lowerCAmelCase				   =       action_tanh
											__lowerCAmelCase				   =       vocab_size
											__lowerCAmelCase				   =       n_positions
											__lowerCAmelCase				   =       n_layer
											__lowerCAmelCase				   =       n_head
											__lowerCAmelCase				   =       n_inner
											__lowerCAmelCase				   =       activation_function
											__lowerCAmelCase				   =       resid_pdrop
											__lowerCAmelCase				   =       embd_pdrop
											__lowerCAmelCase				   =       attn_pdrop
											__lowerCAmelCase				   =       layer_norm_epsilon
											__lowerCAmelCase				   =       initializer_range
											__lowerCAmelCase				   =       scale_attn_weights
											__lowerCAmelCase				   =       use_cache
											__lowerCAmelCase				   =       scale_attn_by_inverse_layer_idx
											__lowerCAmelCase				   =       reorder_and_upcast_attn

											__lowerCAmelCase				   =       bos_token_id
											__lowerCAmelCase				   =       eos_token_id

											super().__init__(bos_token_id=_A		,			eos_token_id=_A		,			**_A			)




