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      SCREAMING_SNAKE_CASE__					(  lowerCAmelCase_   ,							unittest.TestCase    ):
       A							:	int          =	RePaintPipeline
       A							:	Optional[Any]          =	IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
       A							:	Optional[int]          =	PipelineTesterMixin.required_optional_params - {
           '''latents''',
           '''num_images_per_prompt''',
           '''callback''',
           '''callback_steps''',
       }
       A							:	List[str]          =	IMAGE_INPAINTING_BATCH_PARAMS
       A							:	Optional[int]          =	False

       def 			snake_case__    (							self					:     List[str]			):
           torch.manual_seed(0			)
           torch.manual_seed(0			)
           __snake_case       :				str 			=    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""")			,			)
           __snake_case       :				Union[str, Any] 			=    RePaintScheduler()
           __snake_case       :				Optional[Any] 			=    {'''unet''': unet, '''scheduler''': scheduler}
           return components

       def 			snake_case__    (							self					:     Union[str, Any]			,			_lowerCAmelCase					:     Dict			,			_lowerCAmelCase					:     int=0			):
           if str(UpperCamelCase_			).startswith("""mps"""			):
               __snake_case       :				Tuple 			=    torch.manual_seed(UpperCamelCase_			)
           else:
               __snake_case       :				List[str] 			=    torch.Generator(device=UpperCamelCase_			).manual_seed(UpperCamelCase_			)
           __snake_case       :				List[Any] 			=    np.random.RandomState(UpperCamelCase_			).standard_normal((1, 3, 32, 32)			)
           __snake_case       :				int 			=    torch.from_numpy(UpperCamelCase_			).to(device=UpperCamelCase_			,			dtype=torch.floataa			)
           __snake_case       :				Tuple 			=    (image > 0).to(device=UpperCamelCase_			,			dtype=torch.floataa			)
           __snake_case       :				Tuple 			=    {
               '''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 			snake_case__    (							self					:     Optional[int]			):
           __snake_case       :				List[str] 			=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           __snake_case       :				str 			=    self.get_dummy_components()
           __snake_case       :				str 			=    RePaintPipeline(**UpperCamelCase_			)
           __snake_case       :				str 			=    sd_pipe.to(UpperCamelCase_			)
           sd_pipe.set_progress_bar_config(disable=UpperCamelCase_			)

           __snake_case       :				List[Any] 			=    self.get_dummy_inputs(UpperCamelCase_			)
           __snake_case       :				int 			=    sd_pipe(**UpperCamelCase_			).images
           __snake_case       :				List[Any] 			=    image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           __snake_case       :				Optional[int] 			=    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 			snake_case__    (							self					:     Optional[int]			):
           return super().test_save_load_local()

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

       @skip_mps
       def 			snake_case__    (							self					:     Optional[int]			):
           return super().test_dict_tuple_outputs_equivalent()

       @skip_mps
       def 			snake_case__    (							self					:     Dict			):
           return super().test_save_load_optional_components()





       @skip_mps
       def 			snake_case__    (							self					:     int			):
           return super().test_attention_slicing_forward_pass()







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

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





       def 			snake_case__    (							self					:     Union[str, Any]			):
           __snake_case       :				Any 			=    load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
               """repaint/celeba_hq_256.png"""			)
           __snake_case       :				Optional[Any] 			=    load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""			)
           __snake_case       :				Optional[Any] 			=    load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
               """repaint/celeba_hq_256_result.npy"""			)

           __snake_case       :				int 			=    '''google/ddpm-ema-celebahq-256'''
           __snake_case       :				List[Any] 			=    UNetaDModel.from_pretrained(UpperCamelCase_			)
           __snake_case       :				str 			=    RePaintScheduler.from_pretrained(UpperCamelCase_			)

           __snake_case       :				int 			=    RePaintPipeline(unet=UpperCamelCase_			,			scheduler=UpperCamelCase_			).to(UpperCamelCase_			)
           repaint.set_progress_bar_config(disable=UpperCamelCase_			)
           repaint.enable_attention_slicing()

           __snake_case       :				str 			=    torch.manual_seed(0			)
           __snake_case       :				Tuple 			=    repaint(
               UpperCamelCase_			,			UpperCamelCase_			,			num_inference_steps=2_50			,			eta=0.0			,			jump_length=10			,			jump_n_sample=10			,			generator=UpperCamelCase_			,			output_type="""np"""			,			)
           __snake_case       :				Dict 			=    output.images[0]

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





'''simple docstring'''






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


__lowercase	=    logging.get_logger(__name__)

__lowercase	=    {
    '''google/pegasus-x-base''': '''https://huggingface.co/google/pegasus-x-base/resolve/main/config.json''',
    '''google/pegasus-x-large''': '''https://huggingface.co/google/pegasus-x-large/resolve/main/config.json''',
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}

class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  Dict								=		'''pegasus_x'''
	_UpperCamelCase							:  Optional[int]								=		['''past_key_values''']
	_UpperCamelCase							:  str								=		{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




	def __init__(						self				: Any					,						UpperCamelCase_				: Tuple=96103					,						UpperCamelCase_				: List[str]=16384					,						UpperCamelCase_				: Any=16					,						UpperCamelCase_				: Dict=4096					,						UpperCamelCase_				: str=16					,						UpperCamelCase_				: Union[str, Any]=16					,						UpperCamelCase_				: str=4096					,						UpperCamelCase_				: Any=16					,						UpperCamelCase_				: str=0.0					,						UpperCamelCase_				: Dict=0.0					,						UpperCamelCase_				: Union[str, Any]=True					,						UpperCamelCase_				: Union[str, Any]=True					,						UpperCamelCase_				: List[str]="gelu"					,						UpperCamelCase_				: Dict=1024					,						UpperCamelCase_				: str=0.1					,						UpperCamelCase_				: str=0.0					,						UpperCamelCase_				: Optional[Any]=0.0					,						UpperCamelCase_				: str=0.0_2					,						UpperCamelCase_				: Optional[Any]=0					,						UpperCamelCase_				: Tuple=True					,						UpperCamelCase_				: Tuple=0					,						UpperCamelCase_				: Optional[int]=1					,						UpperCamelCase_				: List[Any]=1					,						UpperCamelCase_				: List[Any]=32					,						UpperCamelCase_				: Optional[int]=512					,						UpperCamelCase_				: Any=True					,						**UpperCamelCase_				: Dict					,						):
			lowerCAmelCase_   :       List[Any]    							=vocab_size
			lowerCAmelCase_   :       Any    							=max_position_embeddings
			lowerCAmelCase_   :       int    							=d_model
			lowerCAmelCase_   :       List[Any]    							=encoder_ffn_dim
			lowerCAmelCase_   :       Union[str, Any]    							=encoder_layers
			lowerCAmelCase_   :       Tuple    							=encoder_attention_heads
			lowerCAmelCase_   :       List[Any]    							=decoder_ffn_dim
			lowerCAmelCase_   :       List[Any]    							=decoder_layers
			lowerCAmelCase_   :       List[str]    							=decoder_attention_heads
			lowerCAmelCase_   :       str    							=dropout
			lowerCAmelCase_   :       Any    							=attention_dropout
			lowerCAmelCase_   :       Any    							=activation_dropout
			lowerCAmelCase_   :       Optional[int]    							=activation_function
			lowerCAmelCase_   :       List[Any]    							=init_std
			lowerCAmelCase_   :       List[str]    							=encoder_layerdrop
			lowerCAmelCase_   :       Optional[int]    							=decoder_layerdrop
			lowerCAmelCase_   :       int    							=use_cache
			lowerCAmelCase_   :       Union[str, Any]    							=encoder_layers
			lowerCAmelCase_   :       Tuple    							=scale_embedding  # scale factor will be sqrt(d_model) if True

			lowerCAmelCase_   :       Optional[int]    							=num_global_tokens
			lowerCAmelCase_   :       List[str]    							=block_size
			lowerCAmelCase_   :       Optional[int]    							=stagger_local_blocks

			super().__init__(
			    pad_token_id=UpperCamelCase_					,						eos_token_id=UpperCamelCase_					,						is_encoder_decoder=UpperCamelCase_					,						decoder_start_token_id=UpperCamelCase_					,						forced_eos_token_id=UpperCamelCase_					,						**UpperCamelCase_					,						)




	@property
	def 	__A      (						self				: List[str]      ):
			return self.encoder_attention_heads





	@property
	def 	__A      (						self				: int      ):
			return self.d_model






