



"""simple docstring"""







import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class    UpperCamelCase     (			snake_case ,					unittest.TestCase    ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_				:    Union[str, Any]		    =   RePaintPipeline
	SCREAMING_SNAKE_CASE_				:    Tuple		    =   IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
	SCREAMING_SNAKE_CASE_				:    List[Any]		    =   PipelineTesterMixin.required_optional_params - {
	    "latents",
	    "num_images_per_prompt",
	    "callback",
	    "callback_steps",
	}
	SCREAMING_SNAKE_CASE_				:    Optional[Any]		    =   IMAGE_INPAINTING_BATCH_PARAMS
	SCREAMING_SNAKE_CASE_				:    List[Any]		    =   False






	def 					lowerCamelCase__ (			self					):
			torch.manual_seed(0					)
			torch.manual_seed(0					)
			_lowercase							:      Optional[Any]			=					UNetaDModel(
			    block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=3							,out_channels=3							,down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")							,up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")							,)
			_lowercase							:      str			=					RePaintScheduler()
			_lowercase							:      str			=					{"""unet""": unet, """scheduler""": scheduler}
			return components






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_=0					):
			if str(UpperCAmelCase_					).startswith("""mps"""					):
					_lowercase							:      Tuple			=					torch.manual_seed(UpperCAmelCase_					)
			else:
					_lowercase							:      Optional[Any]			=					torch.Generator(device=UpperCAmelCase_					).manual_seed(UpperCAmelCase_					)
			_lowercase							:      str			=					np.random.RandomState(UpperCAmelCase_					).standard_normal((1, 3, 32, 32)					)
			_lowercase							:      Optional[Any]			=					torch.from_numpy(UpperCAmelCase_					).to(device=UpperCAmelCase_							,dtype=torch.floataa					)
			_lowercase							:      List[str]			=					(image > 0).to(device=UpperCAmelCase_							,dtype=torch.floataa					)
			_lowercase							:      Union[str, Any]			=					{
			    """image""": image,
			    """mask_image""": mask,
			    """generator""": generator,
			    """num_inference_steps""": 5,
			    """eta""": 0.0,
			    """jump_length""": 2,
			    """jump_n_sample""": 2,
			    """output_type""": """numpy""",
			}
			return inputs






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      int			=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
			_lowercase							:      Any			=					self.get_dummy_components()
			_lowercase							:      Dict			=					RePaintPipeline(**UpperCAmelCase_					)
			_lowercase							:      List[Any]			=					sd_pipe.to(UpperCAmelCase_					)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

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

			assert image.shape == (1, 32, 32, 3)
			_lowercase							:      Dict			=					np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]					)

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






	@skip_mps
	def 					lowerCamelCase__ (			self					):
			return super().test_save_load_local()






	@unittest.skip("""non-deterministic pipeline"""					)
	def 					lowerCamelCase__ (			self					):
			return super().test_inference_batch_single_identical()






	@skip_mps
	def 					lowerCamelCase__ (			self					):
			return super().test_dict_tuple_outputs_equivalent()






	@skip_mps
	def 					lowerCamelCase__ (			self					):
			return super().test_save_load_optional_components()






	@skip_mps
	def 					lowerCamelCase__ (			self					):
			return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class    UpperCamelCase     (			unittest.TestCase    ):




	"""simple docstring"""






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






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      str			=					load_image(
			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
			    """repaint/celeba_hq_256.png"""					)
			_lowercase							:      List[str]			=					load_image(
			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""					)
			_lowercase							:      Tuple			=					load_numpy(
			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
			    """repaint/celeba_hq_256_result.npy"""					)

			_lowercase							:      Dict			=					"""google/ddpm-ema-celebahq-256"""
			_lowercase							:      Tuple			=					UNetaDModel.from_pretrained(UpperCAmelCase_					)
			_lowercase							:      Optional[Any]			=					RePaintScheduler.from_pretrained(UpperCAmelCase_					)

			_lowercase							:      Dict			=					RePaintPipeline(unet=UpperCAmelCase_							,scheduler=UpperCAmelCase_					).to(UpperCAmelCase_					)
			repaint.set_progress_bar_config(disable=UpperCAmelCase_					)
			repaint.enable_attention_slicing()

			_lowercase							:      str			=					torch.manual_seed(0					)
			_lowercase							:      Any			=					repaint(
			    UpperCAmelCase_							,UpperCAmelCase_							,num_inference_steps=2_50							,eta=0.0							,jump_length=10							,jump_n_sample=10							,generator=UpperCAmelCase_							,output_type="""np"""							,)
			_lowercase							:      Any			=					output.images[0]

			assert image.shape == (2_56, 2_56, 3)
			assert np.abs(expected_image - image					).mean() < 1E-2




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__   = logging.get_logger(__name__)

lowerCamelCase__   = {
    '''nghuyong/ernie-1.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-base-en''': '''https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-large-en''': '''https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-medium-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-mini-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-micro-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-nano-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json''',
    '''nghuyong/ernie-gram-zh''': '''https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json''',
    '''nghuyong/ernie-health-zh''': '''https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json''',
}




class  _UpperCAmelCase       (			lowerCAmelCase   ):



 '''simple docstring'''




 __A           = '''ernie'''







 def __init__(						self							:					Dict				,      lowercase_							:					List[Any]=30522				,      lowercase_							:					Optional[int]=768				,      lowercase_							:					Tuple=12				,      lowercase_							:					Optional[int]=12				,      lowercase_							:					Any=3072				,      lowercase_							:					int="gelu"				,      lowercase_							:					List[Any]=0.1				,      lowercase_							:					Any=0.1				,      lowercase_							:					str=512				,      lowercase_							:					Any=2				,      lowercase_							:					Any=3				,      lowercase_							:					List[Any]=False				,      lowercase_							:					List[Any]=0.02				,      lowercase_							:					str=1e-1_2				,      lowercase_							:					int=0				,      lowercase_							:					str="absolute"				,      lowercase_							:					Dict=True				,      lowercase_							:					List[str]=None				,      **lowercase_							:					Dict				,      )		->     Union[str, Any]:


   """simple docstring"""







   super().__init__(pad_token_id=lowercase_				,      **lowercase_)

   _UpperCamelCase				=  vocab_size
   _UpperCamelCase				=  hidden_size
   _UpperCamelCase				=  num_hidden_layers
   _UpperCamelCase				=  num_attention_heads
   _UpperCamelCase				=  hidden_act
   _UpperCamelCase				=  intermediate_size
   _UpperCamelCase				=  hidden_dropout_prob
   _UpperCamelCase				=  attention_probs_dropout_prob
   _UpperCamelCase				=  max_position_embeddings
   _UpperCamelCase				=  type_vocab_size
   _UpperCamelCase				=  task_type_vocab_size
   _UpperCamelCase				=  use_task_id
   _UpperCamelCase				=  initializer_range
   _UpperCamelCase				=  layer_norm_eps
   _UpperCamelCase				=  position_embedding_type
   _UpperCamelCase				=  use_cache
   _UpperCamelCase				=  classifier_dropout




class  _UpperCAmelCase       (			lowerCAmelCase   ):



 '''simple docstring'''




 @property
 def 						__UpperCAmelCase							(						self							:					Optional[int])		->     Mapping[str, Mapping[int, str]]:


   """simple docstring"""







   if self.task == "multiple-choice":
     _UpperCamelCase				=  {0: "batch", 1: "choice", 2: "sequence"}
   else:
     _UpperCamelCase				=  {0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
           ("token_type_ids", dynamic_axis),
           ("task_type_ids", dynamic_axis),
       ])




